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Handbook Of Research On Deep Learning Techniques For Cloudbased Industrial Iot Teamira P Swarnalatha Editor

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Handbook Of Research On Deep Learning Techniques For Cloudbased Industrial Iot Teamira P Swarnalatha Editor Handbook Of Research On Deep Learning Techniques For Cloudbased Industrial Iot Teamira P Swarnalatha Editor Handbook Of Research On Deep Learning Techniques For Cloudbased Industrial Iot Teamira P Swarnalatha Editor

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Handbook Of Research On Deep Learning Techniques For Cloudbased Industrial Iot Teamira P Swarnalatha Editor
Handbook of Research on Deep Learning Techniques for Cloud- Based Industrial IoT P. Swarnalatha Department of Information Security, School of Computer Science and Engi- neering, Vellore Institute of Technology, India S. Prabu Department Banking Technology, Pondicherry University, India A volume in the Advances in Computational Intelligence and Robotics (ACIR) Book Series
Published in the United States of America by IGI Global Engineering Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com Copyright © 2023 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: eresources@igi-global.com. Names: Swarnalatha, P. (Purushotham), 1977- editor. | Prabu, S., 1981- editor. Title: Handbook of research on deep learning techniques for cloud-based industrial IoT / edited by P. Swarnalatha, and S. Prabu. Description: Hershey, PA : Engineering Science Reference, [2023] | Includes bibliographical references and index. | Summary: “Deep Learning Techniques for Cloud-Based Industrial IoT aims to demonstrate how computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based Internet of Things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students”-- Provided by publisher. Identifiers: LCCN 2023000689 (print) | LCCN 2023000690 (ebook) | ISBN 9781668480984 (h/c) | ISBN 9781668480984 (eISBN) Subjects: LCSH: Internet of things--Industrial applications. | Deep learning (Machine learning) | Cloud computing. Classification: LCC TK5105.8857 .H337 2023 (print) | LCC TK5105.8857 (ebook) | DDC 004.67/82--dc23/eng/20230126 LC record available at https://lccn.loc.gov/2023000689 LC ebook record available at https://lccn.loc.gov/2023000690 This book is published in the IGI Global book series Advances in Computational Intelligence and Robotics (ACIR) (ISSN: 2327-0411; eISSN: 2327-042X)
Advances in Computational Intelligence and Robotics (ACIR) Book Series While intelligence is traditionally a term applied to humans and human cognition, technology has pro- gressed in such a way to allow for the development of intelligent systems able to simulate many human traits. With this new era of simulated and artificial intelligence, much research is needed in order to continue to advance the field and also to evaluate the ethical and societal concerns of the existence of artificial life and machine learning. The Advances in Computational Intelligence and Robotics (ACIR) Book Series encourages scholarly discourse on all topics pertaining to evolutionary computing, artificial life, computational intelligence, machine learning, and robotics. ACIR presents the latest research being conducted on di- verse topics in intelligence technologies with the goal of advancing knowledge and applications in this rapidly evolving field. Mission Ivan Giannoccaro University of Salento, Italy ISSN:2327-0411 EISSN:2327-042X • Evolutionary Computing • Neural Networks • Agent technologies • Computer Vision • Artificial Intelligence • Intelligent Control • Pattern Recognition • Adaptive and Complex Systems • Heuristics • Synthetic Emotions Coverage IGI Global is currently accepting manuscripts for publication within this series. To submit a pro- posal for a volume in this series, please contact our AcquisitionEditorsatAcquisitions@igi-global.com or visit: http://www.igi-global.com/publish/. The Advances in Computational Intelligence and Robotics (ACIR) Book Series (ISSN 2327-0411) is published by IGI Global, 701 E. Chocolate Avenue, Hershey, PA 17033-1240, USA, www.igi-global.com. This series is composed of titles available for purchase individually; each title is edited to be contextually exclusive from any other title within the series. For pricing and ordering information please visit http:// www.igi-global.com/book-series/advances-computational-intelligence-robotics/73674. Postmaster: Send all address changes to above address. Copyright © 2023 IGI Global. All rights, including translation in other languages reserved by the publisher. No part of this series may be reproduced or used in any form or by any means – graphics, electronic, or mechanical, including photocopying, recording, taping, or informa- tion and retrieval systems – without written permission from the publisher, except for non commercial, educational use, including classroom teaching purposes. The views expressed in this series are those of the authors, but not necessarily of IGI Global.
Titles in this Series For a list of additional titles in this series, please visit: www.igi-global.com/book-series Global Perspectives on Robotics and Autonomous Systems Development and Applications Maki K. Habib (The American University in Cairo, gypt) Engineering Science Reference • © 2023 • 310pp • H/C (ISBN: 9781668477915) • US $280.00 Scalable and Distributed Machine Learning and Deep Learning Patterns J. Joshua Thomas (UOW Malaysia KDU Penang University College, Malaysia) S. Harini (Vellore Institute of Technology, India) and V. Pattabiraman (Vellore Institute of Technology, ndia) Engineering Science Reference • © 2023 • 300pp • H/C (ISBN: 9781668498040) • US $270.00 Application and Adoption of Robotic Process Automation for Smart Cities R. K. Tailor (Manipal University Jaipur, India) Engineering Science Reference • © 2023 • 320pp • H/C (ISBN: 9781668471937) • US $270.00 Handbook of Research on Advancements in AI and IoT Convergence Technologies Jingyuan Zhao (University of Toronto, Canada) V. Vinoth Kumar (Jain University, India) Rajesh Natarajan (Uni- versity of Applied Science and Technology, Shinas, Oman) and T.R. Mahesh (Jain University, India) Engineering Science Reference • © 2023 • 415pp • H/C (ISBN: 9781668469712) • US $380.00 Stochastic Processes and Their Applications in Artificial Intelligence Christo Ananth (Samarkand State University, Uzbekistan) N. Anbazhagan (Alagappa University, India) and Mark Goh (National University of Singapore, Singapore) Engineering Science Reference • © 2023 • 325pp • H/C (ISBN: 9781668476796) • US $270.00 Applying AI-Based IoT Systems to Simulation-Based Information Retrieval Bhatia Madhulika (Amity University, India) Bhatia Surabhi (King Faisal University, Saudi Arabia) Poonam Tanwar (Manav Rachna International Institute of Research and Studies, India) and Kuljeet Kaur (Université du Québec, Canada) Engineering Science Reference • © 2023 • 229pp • H/C (ISBN: 9781668452554) • US $270.00 AI-Enabled Social Robotics in Human Care Services Sandeep Kautish (Lord Buddha Education Foundation, Nepal) Nirbhay Kumar Chaubey (Ganpat University, India) S.B. Goyal (City University, Malaysia) and Pawan Whig (Vivekananda Insitute of Professional Studies, India) Engineering Science Reference • © 2023 • 321pp • H/C (ISBN: 9781668481714) • US $270.00 701 East Chocolate Avenue, Hershey, PA 17033, USA Tel: 717-533-8845 x100 • Fax: 717-533-8661 E-Mail: cust@igi-global.com • www.igi-global.com
  List of Contributors  Aarthi, G. / B.S. Abdur Rahman Crescent Institute of Science and Technology, India....................106 Abinaya, V. / Hindusthan College of Arts and Sciences, India.........................................................255 Aisha Banu, W. / B.S. Abdur Rahman Crescent Institute of Science and Technology, India...........106 Appe, Seetharam Nagesh / Annamalai University, India .................................................................278 Arage, Pranav / Vellore Institute of Technology, India.....................................................................155 Arulselvi, G. / Annamalai University, India .....................................................................................278 Babu, C. V. Suresh / Hindustan Institute of Technolgy and Science, India ......................................322 Balaji, V. / Vardhaman College of Engineering, India .....................................................................201 Basha, Niha Kamal / Vellore Institute of Technology, India...............................................................61 Bhattacharyay, Rajarshi / School of Computer Science and Engineering, Vellore Institute of Technology, India .........................................................................................................................236 Bollipelly, Shiva Chaithanya Goud / Vellore Institute of Technology, India ...........................124, 354 Boopathi, Sampath / Muthayammal Engineering College, India............................................219, 290 C. R., Komala / HKBK College of Engineering, India.....................................................................219 C. S., Mohan Raj / Hindusthan College of Arts and Science, India .................................................134 Chaturvedi, Ankita / IIS University (Deemed), India......................................................................255 Chavan, Sahil Manoj / Department of Electrical Power System, Sandip University, India.............290 Dash, Samikshya / School of Computer Science and Engineering, VIT-AP University, India ........219 G. N., Balaji / Vellore Institute of Technology, India........................................................................278 Harshitha, Vemuri Lakshmi / Vellore Institute of Technology, India................................................61 Hema, N. / Department of Information Science and Engineering, RNS Institute of Technology, India..............................................................................................................................................290 Irawati, Indrarini Dyah / Telkom University, Indonesia ..................................................................366 Janapriyan, R. / Hindustan Institute of Technology and Science, India ..........................................322 Joshi, Aditya Deepak / School of Computer Science and Engineering, Vellore Institute of Technology, India .........................................................................................................................236 K. R., Jothi / Vellore Institute of Technology, India..........................................................................338 K., Suresh Joseph / Pondicherry University, India...........................................................................172 Kalyanaraman, P. / Vellore Institute of Technology, India ......................................................309, 338 Kaneswaran, Anantharajah / University of Jaffna, Sri Lanka..........................................................18 Katte, Pranav / Vellore Institute of Technology, India .....................................................................155 Kaur, Gaganpreet / Chitkara University, India ...............................................................134, 255, 366 Khalid, Saifullah / Civil Aviation Research Organisation, India.....................................................134 Koushik, Katakam / CVR College of Engineering, India..................................................................79
 Krishnamoorthy, N. / College of Science and Humanities, SRM Institute of Science and Technology, India .........................................................................................................................290 Krishnaveni, M. / Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India................................................................29 Kumar, A. V. Senthil / Hindusthan College of Arts and Sciences, India..................................134, 255 Kumar, A.V. Senthil / Hindusthan College of Arts and Sciences, India...........................................366 Kumar, N. M. G. / Sree Vidyanikethan Engineering College, Mohan Babu University, India.........290 Kuppusamy, Palanivel / Pondicherry University, India...................................................................172 Latip, Rohaya / Universiti Teknologi MARA, Malaysia ...................................................134, 255, 366 M., MohanaKrishnan / Hindusthan College of Arts and Sciences, India .......................................366 Meenakshi, S. / R.M.K. Engineering College, India ........................................................................219 Megala, G. / Vellore Institute of Technology, India ....................................................................18, 309 Mishra, Namita / ITS School of Management, India........................................................................134 Musirin, Ismail / Universiti Teknologi MARA, Malaysia.................................................................134 N. S., Akshayah / Hindustan Institute of Technology and Science, India.........................................322 Nadkarni, Satvik / Vellore Institute of Technology, India................................................................155 P., Maclin Vinola / Hindustan Institute of Technology and Science, India .......................................322 P., Uma Maheswari / CEG, Anna University, Chennai, India ..............................................................1 Parvaze Podili, Pariha / Vellore Institute of Technology, India.........................................................61 Prabakaran, N. / School of Computer Science and Engineering, Vellore Institute of Technology, India..............................................................................................................................................236 Prabu, S. / Pondicherry University, India...........................................................................................18 Pramila, P. V. / Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India..............................................................................................................................219 Raagav, Rithun / Vellore Institute of Technology, India...................................................................309 Rajasekaran, P. / School of Computing, SRM Institute of Science and Technology, India..............236 Sabarimuthu, M. / Kongu Engineering College, India....................................................................290 Sahai, Shivansh / Vellore Institute of Technology, India..................................................................201 Saleh, Omar S. / School of Computing, Universiti Utara Malaysia, Malaysia.................................366 Samuel, Jonathan Rufus / Vellore Institute of Technology, India ....................................................201 Sandhya, Karjala / Vellore Institute of Technology, India .................................................................61 Sanjay, V. / Vellore Institute of Technology, Vellore, India.................................................................92 Selvanambi, Ramani / Vellore Institute of Technology, India..........................................................155 Sevugan, Prabu / Pondicherry University, India................................................................79, 201, 354 Shanmugananthan, Suganthi / Annamalai University, India .........................................................172 Sharmila, L. / Agni College of Technology, India ............................................................................354 Sharon Priya, S. / B.S. Abdur Rahman Crescent Institute of Science and Technology, India..........106 Shiwakoti, Prarthana / Vellore Institute of Technology, India ........................................................338 Subashini, P. / Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India..........................................................................29 Suryanarayana, S. Venkata / CVR College of Engineering, India.....................................................79 Susan M. B., Jennyfer / Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India..........................................................29 Swarnalatha, P. / Vellore Institute of Technology, India ..............................................18, 92, 124, 201 Syamala, Maganti / Koneru Lakshmaiah Education Foundation, India..........................................219
 Talukdar, Veera / RNB Global University, India .....................................................................255, 366 V. R., Karishma / Anna University, Chennai, India.............................................................................1 Valliammai, V. / Vellore Institute of Technology, India......................................................................61 Vanishree, G. / IBS Hyderabad, India ..............................................................................................255 Venkatesan, R. / SASTRA University, India ...............................................................................18, 354 Vigneswaran, T. / SRM-TRB Engineering College, India....................................................................1
 Table of Contents  Foreword............................................................................................................................................xxiii Preface. ................................................................................................................................................ xxv Chapter 1 Artificial Intelligence in Video Surveillance........................................................................................... 1 Uma Maheswari P., CEG, Anna University, Chennai, India Karishma V. R., Anna University, Chennai, India T. Vigneswaran, SRM-TRB Engineering College, India Chapter 2 Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach................................................................................................................................................ 18 G. Megala, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India S. Prabu, Pondicherry University, India R. Venkatesan, SASTRA University, India Anantharajah Kaneswaran, University of Jaffna, Sri Lanka Chapter 3 Artificial Intelligence of Things for Smart Healthcare Development: An Experimental Review......... 29 Jennyfer Susan M. B., Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India P. Subashini, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India M. Krishnaveni, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India Chapter 4 Cloud-Based Intelligent Virtual Try-On Using Augmented Reality..................................................... 61 V. Valliammai, Vellore Institute of Technology, India Karjala Sandhya, Vellore Institute of Technology, India Vemuri Lakshmi Harshitha, Vellore Institute of Technology, India Pariha Parvaze Podili, Vellore Institute of Technology, India Niha Kamal Basha, Vellore Institute of Technology, India
 Chapter 5 Insulator Fault Detection From UAV Images Using YOLOv5.............................................................. 79 S. Venkata Suryanarayana, CVR College of Engineering, India Katakam Koushik, CVR College of Engineering, India Prabu Sevugan, Pondicherry University, India Chapter 6 Blockchain-Based Deep Learning Approach for Alzheimer’s Disease Classification.......................... 92 V. Sanjay, Vellore Institute of Technology, Vellore, India P. Swarnalatha, Vellore Institute of Technology, Vellore, India Chapter 7 Intrusion Detection on NF-BoT-IoT Dataset Using Artificial Intelligence Techniques. ...................... 106 G. Aarthi, B.S. Abdur Rahman Crescent Institute of Science and Technology, India S. Sharon Priya, B.S. Abdur Rahman Crescent Institute of Science and Technology, India W. Aisha Banu, B.S. Abdur Rahman Crescent Institute of Science and Technology, India Chapter 8 Real-Time Object Detection and Audio Output System for Blind Users: Using YOLOv3 Algorithm and 360 Degree Camera Sensor......................................................................................... 124 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India Chapter 9 Role of IoT Technologies in Agricultural Ecosystems........................................................................ 134 Mohan Raj C. S., Hindusthan College of Arts and Science, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Ismail Musirin, Universiti Teknologi MARA, Malaysia Saifullah Khalid, Civil Aviation Research Organisation, India Rohaya Latip, Universiti Teknologi MARA, Malaysia Namita Mishra, ITS School of Management, India Gaganpreet Kaur, Chitkara University, India Chapter 10 Applications of Deep Learning in Robotics. ........................................................................................ 155 Pranav Katte, Vellore Institute of Technology, India Pranav Arage, Vellore Institute of Technology, India Satvik Nadkarni, Vellore Institute of Technology, India Ramani Selvanambi, Vellore Institute of Technology, India Chapter 11 A Multicloud-Based Deep Learning Model for Smart Agricultural Applications.............................. 172 Palanivel Kuppusamy, Pondicherry University, India Suresh Joseph K., Pondicherry University, India Suganthi Shanmugananthan, Annamalai University, India
 Chapter 12 Automated MP3 Tag Editor via Data Mining: A Classification Software for Predicting MP3 Metadata. .............................................................................................................................................. 201 Jonathan Rufus Samuel, Vellore Institute of Technology, India Shivansh Sahai, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India V. Balaji, Vardhaman College of Engineering, India Chapter 13 Machine Learning-Integrated IoT-Based Smart Home Energy Management System. ........................ 219 Maganti Syamala, Koneru Lakshmaiah Education Foundation, India Komala C. R., HKBK College of Engineering, India P. V. Pramila, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India Samikshya Dash, School of Computer Science and Engineering, VIT-AP University, India S. Meenakshi, R.M.K. Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India Chapter 14 Generating Complex Animated Characters of Various Art Styles With Optimal Beauty Scores Using Deep Generative Adversarial Networks.................................................................................... 236 N. Prabakaran, School of Computer Science and Engineering, Vellore Institute of Technology, India Rajarshi Bhattacharyay, School of Computer Science and Engineering, Vellore Institute of Technology, India Aditya Deepak Joshi, School of Computer Science and Engineering, Vellore Institute of Technology, India P. Rajasekaran, School of Computing, SRM Institute of Science and Technology, India Chapter 15 Cloud-Based TPA Auditing With Risk Prevention. ............................................................................. 255 V. Abinaya, Hindusthan College of Arts and Sciences, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Rohaya Latip, Universiti Putra Malaysia, Malaysia Veera Talukdar, RNB Global University, India Ankita Chaturvedi, IIS University (Deemed), India G. Vanishree, IBS Hyderabad, India Gaganpreet Kaur, Chitkara University, India Chapter 16 Detection and Classification of Dense Tomato Fruits by Integrating Coordinate Attention Mechanism With YOLO Model. .......................................................................................................... 278 Seetharam Nagesh Appe, Annamalai University, India G. Arulselvi, Annamalai University, India Balaji G. N., Vellore Institute of Technology, India
 Chapter 17 A Study on an Internet of Things (IoT)-Enabled Smart Solar Grid System........................................ 290 N. Hema, Department of Information Science and Engineering, RNS Institute of Technology, India N. Krishnamoorthy, College of Science and Humanities, SRM Institute of Science and Technology, India Sahil Manoj Chavan, Department of Electrical Power System, Sandip University, India N. M. G. Kumar, Sree Vidyanikethan Engineering College, Mohan Babu University, India M. Sabarimuthu, Kongu Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India Chapter 18 Accuracy Determination: An Enhanced Intrusion Detection System Using Deep Learning Approach.............................................................................................................................................. 309 Rithun Raagav, Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India G. Megala, Vellore Institute of Technology, India Chapter 19 IoT-Based Smart Accident Detection and Alert System...................................................................... 322 C. V. Suresh Babu, Hindustan Institute of Technolgy and Science, India Akshayah N. S., Hindustan Institute of Technology and Science, India Maclin Vinola P., Hindustan Institute of Technology and Science, India R. Janapriyan, Hindustan Institute of Technology and Science, India Chapter 20 Revolutionizing the Farm-to-Table Journey: A Comprehensive Review of Blockchain Technology in Agriculture Supply Chain................................................................................................................ 338 Prarthana Shiwakoti, Vellore Institute of Technology, India Jothi K. R., Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India Chapter 21 Blockchain-Based Messaging System for Secure and Private Communication: Using Blockchain and Double AES Encryption. ............................................................................................................... 354 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India R. Venkatesan, SASTRA University, India L. Sharmila, Agni College of Technology, India
 Chapter 22 Artificial Intelligence in Cyber Security.............................................................................................. 366 MohanaKrishnan M., Hindusthan College of Arts and Sciences, India A.V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Veera Talukdar, RNB Global University, India Omar S. Saleh, School of Computing, Universiti Utara Malaysia, Malaysia Indrarini Dyah Irawati, Telkom University, Indonesia Rohaya Latip, Universiti Putra Malaysia, Malaysia Gaganpreet Kaur, Chitkara University Institute of Engineering and Technology, Chitkara University, India Compilation of References................................................................................................................ 386 About the Contributors..................................................................................................................... 423 Index.................................................................................................................................................... 430
  Detailed Table of Contents  Foreword............................................................................................................................................xxiii Preface. ................................................................................................................................................ xxv Chapter 1 Artificial Intelligence in Video Surveillance........................................................................................... 1 Uma Maheswari P., CEG, Anna University, Chennai, India Karishma V. R., Anna University, Chennai, India T. Vigneswaran, SRM-TRB Engineering College, India Surveillance is an essential component of security, and e-surveillance is one of the primary goals of the Indian Government’s Digital India development initiative. Video surveillance offers a wide range of applications to reduce ecological and economic losses and becomes one of the most effective means of ensuring security. This chapter addresses the problem of how artificial intelligence is powering video surveillance.Thereisasignificantresearchfocusonvideoanalyticsbutcomparativelylessefforthasbeen taken for surveillance videos. However, there is little evidence that researchers have approached the issue of intelligent video surveillance in terms of suspicious action detection, crime scene description, face detection, crowd counting, and the like. Most AI-powered surveillance is based on deep neural networks and deep learning techniques using analysis of video frames as images. Consequently, this chapter aims to provide an overview and significance of how artificial intelligence techniques are employed in video surveillance and image processing. Chapter 2 Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach................................................................................................................................................ 18 G. Megala, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India S. Prabu, Pondicherry University, India R. Venkatesan, SASTRA University, India Anantharajah Kaneswaran, University of Jaffna, Sri Lanka Content-basedvideoretrievalisaresearchfieldthataimstodevelopadvancedtechniquesforautomatically analyzingandretrievingvideocontent.Thisprocessinvolvesidentifyingandlocalizingspecificmoments in a video and retrieving videos with similar content. Deep bimodal fusion (DBF) is proposed that uses modified convolution neural networks (CNNs) to achieve considerable visual modality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities.
 By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method has higher precision, recall, F1-score, and accuracy in precise searching that retrieves relevant videos more quickly and effectively. Chapter 3 Artificial Intelligence of Things for Smart Healthcare Development: An Experimental Review......... 29 Jennyfer Susan M. B., Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India P. Subashini, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India M. Krishnaveni, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India Smart healthcare systems are the health services that use the technologies like wearable devices, internet of things (IoT), and mobile internet to access medical information dynamically. It connects people, materials, and institutions related to healthcare; actively manages; and automatically responds to medical ecosystem needs. It helps the traditional medical system in making healthcare more efficient, convenient, and personalized. This chapter proposed (1) a review of smart healthcare development using artificial intelligence, the internet of things, and smartphone Android apps; (2) an experimental approach using IoT-based smart monitoring systems, Android apps for data collection, and artificial algorithms to predict cervical cancer diseases; (3) the integration of IoT and AI algorithms. Artificial intelligence of things (AIoT) is proposed in this chapter as an experimental method for predicting cervical cancer from smart colposcopy images. The literature published in international journals and proceedings between 2010 and June 2022 is considered for the study. Chapter 4 Cloud-Based Intelligent Virtual Try-On Using Augmented Reality..................................................... 61 V. Valliammai, Vellore Institute of Technology, India Karjala Sandhya, Vellore Institute of Technology, India Vemuri Lakshmi Harshitha, Vellore Institute of Technology, India Pariha Parvaze Podili, Vellore Institute of Technology, India Niha Kamal Basha, Vellore Institute of Technology, India Advancement of technology had a significant impact on various industries, with innovative solutions like cloud computing, IoT, augmented reality (AR), and virtual reality (VR) changing the game in many ways. Here is a system known as “virtual try-ons” that leverages IoT devices like mobile cameras, cloud storage for data, and an intelligent interface for user interaction. Many people are opting for online shopping, and various challenges arise with this transition, one of which is the issue of “try-on.” VR solvesthischallengebyintroducing“virtualtry-on,”whichreplacestraditionaltry-onmethods.Itenables an individual to preview and virtually try on their desired products like clothes, watches, shoes, etc. from the comfort of their own homes, making the shopping experience easier and smoother. It also adds an element of fun and excitement to the shopping experience, increasing the hedonic value for consumers and allowing consumers to experiment and play with different products, styles, and colors in a way that is not possible with traditional shopping methods.
 Chapter 5 Insulator Fault Detection From UAV Images Using YOLOv5.............................................................. 79 S. Venkata Suryanarayana, CVR College of Engineering, India Katakam Koushik, CVR College of Engineering, India Prabu Sevugan, Pondicherry University, India Identification of insulator defects is one of the most important goals of an intelligent examination of high-voltage transmission lines. Because they provide mechanical support for electric transmission lines as well as electrical insulation, insulators are essential to the secure and reliable operation of power networks. A fresh dataset is first built by collecting aerial pictures in various scenes that have one or more defects. A feature pyramid network and an enhanced loss function are used by the CSPD-YOLO model to increase the precision of insulator failure detection. The insulator defective data set, which has two classes (insulator, defect), is used by the suggested technique to train and test the model using the YOLOv5 object detection algorithm. The authors evaluate how well the YOLOv3, YOLOv5, and related families perform when trained on the insulator defective dataset. Practitioners can use this information to choose the appropriate technique based on the insulator defective dataset. Chapter 6 Blockchain-Based Deep Learning Approach for Alzheimer’s Disease Classification.......................... 92 V. Sanjay, Vellore Institute of Technology, Vellore, India P. Swarnalatha, Vellore Institute of Technology, Vellore, India Blockchain is an emerging technology that is now being used to provide novel solutions in several industries, including healthcare. Deep learning (DL) algorithms have grown in popularity in medical image processing research. AD is diagnosed by magnetic resonance imaging (MRI) images. This study investigates the integration of blockchain technology with a DL model for Alzheimer’s disease prediction (AD). This proposed model was used to classify 3182 images from the ADNI collection. The edge-based segmentation algorithm has overcome the segmentation problem. During the investigation’s test stage, the DL-EfficientNetB0 model with blockchain earned the highest accuracy rate of 99.14%. The highest accuracy, sensitivity, and specificity scores were obtained utilizing the confusion matrix during the comparative assessment stage. According to the study’s results, EfficientNetB0 with blockchain model surpassed all other trained models in classification rate. This study will aid clinical research into the early detection and prevention of AD by identifying the sickness before it occurs. Chapter 7 Intrusion Detection on NF-BoT-IoT Dataset Using Artificial Intelligence Techniques. ...................... 106 G. Aarthi, B.S. Abdur Rahman Crescent Institute of Science and Technology, India S. Sharon Priya, B.S. Abdur Rahman Crescent Institute of Science and Technology, India W. Aisha Banu, B.S. Abdur Rahman Crescent Institute of Science and Technology, India The rapid development of internet of things (IoT) applications has created enormous possibilities, increased our productivity, and made our daily life easier. However, because of resource limitations and processing, IoT networks are open to number of threats. The network instruction detection system (NIDS) aims to provide a variety of methods for identifying the increasingly common cyberattacks (such as distributed denial of service [DDoS], denial of service [DoS], theft, etc.) and to prevent hazardous activities. In order to determine which algorithm is more effective in detecting network threats, multiple public datasets and different artificial intelligence (AI) techniques are evaluated. Some of the learning
 algorithms like logistic regression, random forest, decision tree, naive bayes, auto-encoder, and artificial neuralnetworkwereanalysedandconcludedontheNF-BoT-IoTdatasetusingvariousevaluationmetrics. In order to train the model for future anomaly detection prediction and analysis, the feature extraction and pre-processing data were then supplied into NIDS as data. Chapter 8 Real-Time Object Detection and Audio Output System for Blind Users: Using YOLOv3 Algorithm and 360 Degree Camera Sensor......................................................................................... 124 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India This chapter aims to create a real-time object detection and audio output system for blind users using the YOLOv3 algorithm and a 360-degree camera sensor. The system is designed to detect a wide range of objects, including people, vehicles, and other objects in the environment, and provide audio feedback to the user. The system architecture consists of a 360-degree camera sensor, a processing unit, and an audio output system. The camera sensor captures the environment, which is processed by the processing unit, which uses the YOLOv3 algorithm to detect and classify objects. The audio output system provides audio feedback to the user based on the objects detected by the system. The project has significant importance for blind users as it can help them navigate their environment and recognize objects in real time and can serve as a foundation for future research in the field of object detection systems for blind users. Chapter 9 Role of IoT Technologies in Agricultural Ecosystems........................................................................ 134 Mohan Raj C. S., Hindusthan College of Arts and Science, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Ismail Musirin, Universiti Teknologi MARA, Malaysia Saifullah Khalid, Civil Aviation Research Organisation, India Rohaya Latip, Universiti Teknologi MARA, Malaysia Namita Mishra, ITS School of Management, India Gaganpreet Kaur, Chitkara University, India Increasingdemandforfoodqualityandsizehasincreasedtheneedforindustrializationandintensification in the agricultural sector. The internet of things (IoT) is a promising technology that offers many innovative solutions to transform the agricultural sector. Research institutes and scientific groups are constantly working to provide solutions and products for different areas of agriculture using IoT. The main objective of this methodological study is to collect all relevant research results on agricultural IoT applications, sensors/devices, communication protocols, and network types. The authors also talk about the main problems and encounters encountered in the field of agriculture. An IoT agriculture framework is also available that contextualizes the view of various current farming solutions. National guidelines on IoT-based agriculture were also presented. Finally, open issues and challenges were presented, and researchers were highlighted as promising future directions in the field of IoT agriculture.
 Chapter 10 Applications of Deep Learning in Robotics. ........................................................................................ 155 Pranav Katte, Vellore Institute of Technology, India Pranav Arage, Vellore Institute of Technology, India Satvik Nadkarni, Vellore Institute of Technology, India Ramani Selvanambi, Vellore Institute of Technology, India Deep artificial neural network applications to robotic systems have seen a surge of study due to advancements in deep learning over the past 10 years. The ability of robots to explain the descriptions of their decisions and beliefs leads to a collaboration with the human race. The intensity of the challenges increases as robotics moves from lab to the real-world scenario. Existing robotic control algorithms find it extremely difficult to master the wide variety seen in real-world contexts. The robots have now been developed and advanced to such an extent that they can be useful in our day-to-day lives. All this has been possible because of improvisation of the algorithmic techniques and enhanced computation powers. The majority of traditional machine learning techniques call for parameterized models and functions that must be manually created, making them unsuitable for many robotic jobs. The pattern recognition paradigm may be switched from the combined learning of statistical representations, labelled classifiers, to the joint learning of manmade features and analytical classifiers. Chapter 11 A Multicloud-Based Deep Learning Model for Smart Agricultural Applications.............................. 172 Palanivel Kuppusamy, Pondicherry University, India Suresh Joseph K., Pondicherry University, India Suganthi Shanmugananthan, Annamalai University, India Modern agriculture primarily relies on smart agriculture to predict crop yields and make decisions. Crop productivity could suffer due to a lack of farmers, labor shortages in the agricultural sector, adverse weather, etc. Smart farming uses advanced technology to improve the productivity and efficiency of agriculture. Crop yield is increased with smart agriculture, which also keeps an eye on agricultural pests. Artificial intelligence is an innovative technology that uses sensor data to predict the future and make judgments for farmers. AI methods like machine learning and deep learning are the most clever way to boost agricultural productivity. Adopting AI can help with farming issues and promote increased food production. Deep learning is a modern method for processing images and analyzing big data, showing promise for producing superior results. The primary goals of this study are to examine the benefits of employing DL in smart agricultural applications and to suggest a multi-cloud DL architecture for such applications.
 Chapter 12 Automated MP3 Tag Editor via Data Mining: A Classification Software for Predicting MP3 Metadata. .............................................................................................................................................. 201 Jonathan Rufus Samuel, Vellore Institute of Technology, India Shivansh Sahai, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India V. Balaji, Vardhaman College of Engineering, India The music space in today’s world is ever evolving and expanding. With great improvements to today’s technology, we have been able to bring out music to the vast majority of today’s ever-growing and tech- savvy people. In today’s market, the biggest players for music streaming include behemoth corporations like Spotify, Gaana, Apple Music, YouTube Music, and so on and so forth. This also happens to be quite the shift from how music was once listened to. For songs downloaded out of old music databases without the song’s metadata in place, and other distribution sites, they oftentimes come without any known metadata, i.e., most of the details with regards to the songs are absent, such as the artist’s name, the year it was made, album art, etc. This chapter discusses how data mining, data scraping, and data classification are utilized to help add incomplete metadata to song files without the same, along with the design process, the software development, and research for the same. Chapter 13 Machine Learning-Integrated IoT-Based Smart Home Energy Management System. ........................ 219 Maganti Syamala, Koneru Lakshmaiah Education Foundation, India Komala C. R., HKBK College of Engineering, India P. V. Pramila, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India Samikshya Dash, School of Computer Science and Engineering, VIT-AP University, India S. Meenakshi, R.M.K. Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India The internet of things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi- dimensional data, and low computational cost. Prediction in energy consumption is essential for the enhancement of sustainable cities and urban planning, as buildings are the world’s largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting prediction performance,bestNwindowsize,andmodeluncertaintyestimation.Deeplearningmodelsforhousehold energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty.
 Chapter 14 Generating Complex Animated Characters of Various Art Styles With Optimal Beauty Scores Using Deep Generative Adversarial Networks.................................................................................... 236 N. Prabakaran, School of Computer Science and Engineering, Vellore Institute of Technology, India Rajarshi Bhattacharyay, School of Computer Science and Engineering, Vellore Institute of Technology, India Aditya Deepak Joshi, School of Computer Science and Engineering, Vellore Institute of Technology, India P. Rajasekaran, School of Computing, SRM Institute of Science and Technology, India A generative adversarial network (GAN) is a generative model that is able to generate fresh content by usingseveraldeeplearningtechniquestogether.Duetoitsfascinatingapplications,includingtheproduction of synthetic training data, the creation of art, style-transfer, image-to-image translation, etc., the topic has gained a lot of attraction in the machine learning community. GAN consists of two networks: the generator and the discriminator. The generator will make an effort to create phony samples in an effort to trick the discriminator into thinking they are real samples. In order to distinguish generated samples from both actual and fraudulent samples, the discriminator will strive to do so. The main motive of this chapter is to make use of several types of GANs like StyleGANs, cycle GANs, SRGANs, and conditional GANs to generate various animated characters of different art styles with optimal attractive scores, which can make a huge contribution in the entertainment and media sector. Chapter 15 Cloud-Based TPA Auditing With Risk Prevention. ............................................................................. 255 V. Abinaya, Hindusthan College of Arts and Sciences, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Rohaya Latip, Universiti Putra Malaysia, Malaysia Veera Talukdar, RNB Global University, India Ankita Chaturvedi, IIS University (Deemed), India G. Vanishree, IBS Hyderabad, India Gaganpreet Kaur, Chitkara University, India The chapter focuses on cloud security audit mechanisms and models. Here the third-party auditor (TPA) will be provided with the authority access scheme where the security of the auditing system will be enabled. The TPA will check out the auditing verification and shows a message about the data audited. The purpose of this work is to develop an auditing scheme that is secure, efficient to use, and possesses the capabilities such as privacy preserving, public auditing, maintaining the data integrity along with confidentiality. It consists of three entities: data owner, TPA, and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it, and generating a signature on it. TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them, and generates signature on it. Thus, the system frequently checks the security of the server-side resources.
 Chapter 16 Detection and Classification of Dense Tomato Fruits by Integrating Coordinate Attention Mechanism With YOLO Model. .......................................................................................................... 278 Seetharam Nagesh Appe, Annamalai University, India G. Arulselvi, Annamalai University, India Balaji G. N., Vellore Institute of Technology, India Real-time detection of objects is one of the important tasks of computer vision applications such as agriculture, surveillance, self-driving cars, etc. The fruit target detection rate based on traditional approaches is low due to the complex background, substantial texture interference, partial occlusion of fruits,etc.ThischapterproposesanimprovedYOLOv5modeltodetectandclassifythedensetomatoesby adding the coordinate attention mechanism and bidirectional pyramid network. The coordinate attention mechanism is used to detect and classify the dense tomatoes, and bidirectional pyramid network is used to detect the tomatoes at different scales. The proposed model produces good results in detecting the small dense tomatoes with an accuracy of 87.4%. Chapter 17 A Study on an Internet of Things (IoT)-Enabled Smart Solar Grid System........................................ 290 N. Hema, Department of Information Science and Engineering, RNS Institute of Technology, India N. Krishnamoorthy, College of Science and Humanities, SRM Institute of Science and Technology, India Sahil Manoj Chavan, Department of Electrical Power System, Sandip University, India N. M. G. Kumar, Sree Vidyanikethan Engineering College, Mohan Babu University, India M. Sabarimuthu, Kongu Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India Automation in the power consumption system could be applied to conserve a large amount of power. This chapter discusses the applications for the generation, transmission, distribution, and use of electricity that are IoT-enabled. It covers the physical layer implementation, used models, operating systems, standards, protocols, and architecture of the IoT-enabled SSG system. The configuration, design, solar power system, IoT device, and backend systems, workflow and procedures, implementation, test findings, and performance are discussed. The smart solar grid system’s real-time implementation is described, along with experimental findings and implementation challenges. Chapter 18 Accuracy Determination: An Enhanced Intrusion Detection System Using Deep Learning Approach.............................................................................................................................................. 309 Rithun Raagav, Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India G. Megala, Vellore Institute of Technology, India The internet of things (IoT) links several intelligent gadgets, providing consumers with a range of advantages. Utilizing an intrusion detection system (IDS) is crucial to resolving this issue and ensuring information security and reliable operations. Deep convolutional network (DCN), a specific IDS, has been developed, but it has significant limitations. It learns slowly and might not categorise correctly. These restrictions can be addressed with the aid of deep learning (DL) techniques, which are frequently
 utilised in secure data management, imaging, and signal processing. They provide capabilities including reuse, weak transfer learning, and module integration. The proposed method increases the effectiveness of training and the accuracy of detection. Utilising pertinent datasets, experimental investigations have been carried out to assess the proposed system. The outcomes show that the system’s performance is respectable and within the bounds of accepted practises. The system exhibits a 97.51% detection ability, a 96.28% reliability, and a 94.41% accuracy. Chapter 19 IoT-Based Smart Accident Detection and Alert System...................................................................... 322 C. V. Suresh Babu, Hindustan Institute of Technolgy and Science, India Akshayah N. S., Hindustan Institute of Technology and Science, India Maclin Vinola P., Hindustan Institute of Technology and Science, India R. Janapriyan, Hindustan Institute of Technology and Science, India The smart accident detection and alert system using IoT is a technical solution that detects accidents and alerts authorities and emergency services. The system mainly relies on sensors, GPS, and Arduino UNO to detect and collect information about the location and severity of the accident. The system then transmits this information in real time to the appropriate authorities using algorithms and protocols, enabling them to respond quickly and effectively, therefore increasing the possibility of saving lives and benefiting road users, emergency services, and transportation authorities in case of accidents. Chapter 20 Revolutionizing the Farm-to-Table Journey: A Comprehensive Review of Blockchain Technology in Agriculture Supply Chain................................................................................................................ 338 Prarthana Shiwakoti, Vellore Institute of Technology, India Jothi K. R., Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India In recent years, blockchain technology has gained a lot of attention for its various applications in various fields, with agriculture being one of the most promising. The use of blockchain in agriculture covers areas such as food security, information systems, agribusiness, finance, crop certification, and insurance. In developing countries, many farmers are struggling to earn a living, while in developed countries, the agriculture industry is thriving. This disparity is largely due to poor supply chain management, which can be improved using blockchain technology. Blockchain provides a permanent, sharable, and auditable record of products, improving product traceability, authenticity, and legality in a cost-effective manner. This chapter aims to compile all existing research on blockchain technology in agriculture and analyze the methodologies and contributions of different blockchain technologies to the agricultural sector. It also highlights the latest trends in blockchain research in agriculture and provides guidelines for future research.
 Chapter 21 Blockchain-Based Messaging System for Secure and Private Communication: Using Blockchain and Double AES Encryption. ............................................................................................................... 354 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India R. Venkatesan, SASTRA University, India L. Sharmila, Agni College of Technology, India In recent years, concerns about privacy and security in online communication have become increasingly prominent. To address these concerns, the authors propose a blockchain-based messaging system that provides secure and private communication using double AES encryption. The system utilizes the decentralized and tamper-resistant nature of the blockchain to ensure that messages are not modified or deleted by unauthorized parties. Additionally, they employ double AES encryption to ensure that the content of messages remains confidential even if the blockchain itself is compromised. They evaluate the performance of the system and show that it is scalable and efficient. The system provides a secure and private messaging solution that can be used by individuals and organizations alike. Chapter 22 Artificial Intelligence in Cyber Security.............................................................................................. 366 MohanaKrishnan M., Hindusthan College of Arts and Sciences, India A.V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Veera Talukdar, RNB Global University, India Omar S. Saleh, School of Computing, Universiti Utara Malaysia, Malaysia Indrarini Dyah Irawati, Telkom University, Indonesia Rohaya Latip, Universiti Putra Malaysia, Malaysia Gaganpreet Kaur, Chitkara University Institute of Engineering and Technology, Chitkara University, India In the digital age, cybersecurity has become an important issue. Data breaches, identity theft, captcha fracturing, and other similar designs abound, affecting millions of individuals and organizations. The challenges are always endless when it comes to inventing appropriate controls and procedures and implementing them as flawlessly as available to combat cyberattacks and crime. The risk of cyberattacks and crime has increased exponentially due to recent advances in artificial intelligence. It applies to almost all areas of the natural and engineering sciences. From healthcare to robotics, AI has revolutionized everything. In this chapter, the authors discuss certain encouraging artificial intelligence technologies. Theycovertheapplicationofthesetechniquesincybersecurity.Theyconcludetheirdiscussionbytalking about the future scope of artificial intelligence and cybersecurity. Compilation of References................................................................................................................ 386 About the Contributors..................................................................................................................... 423 Index.................................................................................................................................................... 430
  Foreword  I am happy to write a foreword for this book titled Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT. I felt delighted to note the different tools, technologies, key initiatives, and challenges in Deep Learning Techniques for Cloud-Based Industrial IoT. This book is a substantial compilation of 22 chapters encompassing an overview of Deep Learning, Cloud-Based Industrial IoT, Blockchain Based Deep Learning, critical theories, and concepts. In today’s fast-paced world, the Industrial Internet of Things (IIoT) has emerged as a transformative force in how industries operate, unlocking unprecedented opportunities for efficiency, productivity, and innovation. The convergence of cloud computing and IIoT has paved the way for a new era of intercon- nected intelligent systems, generating massive amounts of data that hold valuable insights for industrial processes. However, the sheer volume, velocity, and variety of data generated by IIoT pose significant chal- lenges in extracting meaningful information and making informed decisions. This is where the power of deep learning comes into play. Deep learning, a subset of artificial intelligence, offers remarkable capabilities in analyzing and interpreting complex patterns in vast amounts of data. Its ability to learn and adapt from data has made it a game-changer in numerous domains, and its potential in the industrial landscape is no exception. The Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT is a comprehensive guide that explores the symbiotic relationship between deep learning and cloud-based Industrial IoT. Authored by experts in the field, this book serves as a valuable resource for researchers, engineers, and industry professionals seeking to harness the full potential of deep learning techniques to optimize their industrial processes. The book begins by providing a solid foundation in IIoT, blockchain technology and cloud comput- ing fundamentals, ensuring readers have the necessary context to understand the subsequent chapters. It then delves into the core principles of deep learning, elucidating various architectures, algorithms, and techniques that have proven effective in analyzing industrial data. One of the key strengths of this book is its focus on practical implementation. The authors demonstrate how deep learning can be integrated into cloud-based IIoT systems and blockchain based deep learning to address specific challenges, such as predictive maintenance, anomaly detection, quality control, and energy optimization through real-world case studies and examples. The discussions not only highlight the benefits of employing deep learning techniques but also shed light on the potential pitfalls and con- siderations that must be considered. xxiii
Foreword Furthermore, the book addresses crucial aspects of deploying deep learning models in the blockchain, cloud including scalability, security, and privacy concerns. It examines the impact of cloud infrastructure on the performance and reliability of deep learning applications. It provides insights into optimizing model training and inference strategies in a cloud environment. As deep learning continues to evolve rapidly, this book goes beyond the present landscape and of- fers a glimpse into the future. It explores emerging trends and advancements in deep understanding for IIoT, such as federated learning, blockchain based deep learning and explainable AI. It presents readers with a forward-thinking perspective on the potential developments and their implications for industrial applications. Inconclusion,theHandbookofResearchonDeepLearningTechniquesforCloud-BasedIndustrialIoT is a comprehensive guide that combines deep learning and IIoT, offering readers a roadmap to unlocking the full potential of their industrial systems. Whether you are a researcher, an engineer, or an industry professional, this book will equip you with the knowledge and insights needed to navigate the complex landscape of cloud-based IIoT and leverage deep learning techniques to drive innovation and success. I’m delighted to greet the editors and authors on their accomplishments and inform readers that they are about to read a significant contribution to developing various models based on Deep Learning, the Internet of Things, Cloud Computing and Blockchain technology. I’m aware of your research interests and knowledge of the above domains. This publication would benefit significantly from adding new computational models for Smart Security Ecosystem. This book is an important step forward in devel- oping this discipline, and it will serve to challenge the academic, research, and scientific communities in various ways. Arun Kumar Sangaiah School of Computing Science and Engineering, Vellore Institute of Technology, India Arun Kumar Sangaiah is a Clarivate (WoS) Highly Cited Researcher (2021) and World Top 2% Scientists (Stanford). Dr. Sangaiah is currently a Professor at School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore-632014, Tamil Nadu, India. xxiv
  Preface  In recent years, the convergence of cloud computing and the Industrial Internet of Things (IIoT) has revolutionized how we interact with industrial systems and processes. The IIoT has ushered in a new era of connectivity and intelligence. At the heart of this technological transformation lies the power of data and the ability to extract valuable insights from the massive amounts of information generated by interconnected systems. Deeplearninghasemergedasaformidabletoolforanalyzingandinterpretingcomplexpatternswithin large volumes of data. The ability to learn from data, identify intricate relationships and make accurate predictions has made it an indispensable asset in various domains. In the realm of cloud-based IIoT, deep learning techniques have the potential to optimize industrial processes, enable predictive maintenance, enhance quality control, and drive intelligent decision-making. This book, Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT is a culmination of the knowledge, research, and experiences of experts in the field who have dedicated their efforts to exploring the synergies between deep learning and cloud-based IIoT. It aims to equip readers with the knowledge and skills required to harness the power of deep learning algorithms for analyzing, interpreting and making informed decisions based on the data generated by interconnected industrial devices. The book establishes a solid foundation, introducing the fundamental concepts and technologies underlying Industrial IoT and cloud computing. We delve into the key components of a cloud-based IIoT architecture, including data acquisition, storage, processing and analysis. By establishing this context, we ensure that readers clearly understand the environment in which deep learning techniques are employed. From there, we dive into the core principles of deep learning, explaining neural networks, activa- tion functions, optimization algorithms, and various deep learning architectures. We aim to demystify complex concepts through intuitive explanations and illustrative examples, allowing readers to grasp the underlying principles quickly. The book’s subsequent chapters focus on the practical implementation of deep learning techniques in cloud-based IIoT systems. We explore specific applications and use cases, shedding light on how deep learning can be leveraged to address challenges such as anomaly detection, predictive maintenance, energy optimization, and quality control. Through real-world case studies and examples, we highlight the effectiveness of deep learning techniques and discuss the considerations and trade-offs involved in their deployment. Additionally, the book addresses crucial aspects related to deploying deep learning models in the cloud. We delve into scalability, security, privacy concerns, and the impact of cloud infrastructure on the performance of deep learning applications. By providing insights into optimization strategies and best practices, we empower readers to overcome the challenges of deploying deep learning models in a cloud environment. xxv
Preface Finally, as deep learning and IIoT continue to evolve rapidly, we explore emerging trends and ad- vancements that hold promise for the future. We discuss topics such as federated learning, blockchain based deep learning and explainable AI, giving readers a glimpse into the potential developments and their implications for the industrial landscape. We hope this book serves as a valuable resource for researchers, engineers, and industry profes- sionals seeking to unlock the full potential of their cloud-based IIoT systems. We have endeavored to present the material in a comprehensive yet accessible manner, combining theoretical foundations with practical insights. We thank the contributors who have dedicated their time and expertise to make this book possible. Their valuable insights and expertise have enriched the content and provided readers with diverse per- spectives on the subject matter. We invite readers to embark on a journey through the pages of this book, exploring the intersection of deep learning, blockchain based deep learning and cloud-based IIoT. We hope this book will inspire you to explore the vast opportunities offered by deep learning in the context of cloud-based IIoT and help you navigate the exciting landscape of this rapidly evolving field. Organization of the book: Chapter1.SurveillanceisanessentialcomponentofsecurityandE-surveillanceisoneoftheprimary goals of the Indian Government’s Digital India development initiative. Video surveillance offers a wide range of applications to reduce ecological and economic losses and becomes one of the most effective means of ensuring security. This chapter addresses the problem of how Artificial Intelligence is power- ing video surveillance. There is a significant research focus on video analytics but comparatively less effort has been taken for surveillance videos. However, there is little evidence that researchers have ap- proached the issue of intelligent video surveillance in terms of suspicious action detection, crime scene description, face detection, crowd counting, and the like. Most AI-powered surveillance is based on Deep neural networks and deep learning techniques using analysis of video frames as images. Consequently, this chapter aims to provide an overview and significance of how Artificial Intelligence techniques are employed in video surveillance and image processing. Chapter 2. Content-based video retrieval is a research field that aims to develop advanced techniques for automatically analyzing and retrieving video content. This process involves identifying and localizing specific moments in a video and retrieving videos with similar content. Deep Bimodal Fusion (DBF) is proposedthatusesmodifiedconvolutionneuralnetworks(CNNs)toachieveconsiderablevisualmodality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities. By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method have higher precision, recall, F1-Score and accuracy in precise searching that retrieves relevant videos more quickly and effectively. Chapter 3. Smart healthcare systems are the health services that use the technologies like wearable devices, Internet of Things (IoT), and mobile internet to access medical information dynamically. It connects people, materials and institutions related to healthcare, actively manages and automatically responds to medical ecosystem needs. To transform the traditional medical system helps in making healthcare more efficient, convenient, and personalized. This chapter proposed: (1) A review of smart xxvi
Preface healthcare development using artificial intelligence, the Internet of Things, and Smartphone Android apps. (2) An experimental approach using IoT-based smart monitoring systems, Android apps for data collection, and artificial algorithms to predict cervical cancer diseases. (3) The integration of IoT and AI algorithms, or Artificial intelligence of things (AIoT), is proposed in this chapter as an experimental method for predicting cervical cancer from smart colposcopy images. The literature published in inter- national journals and proceedings between 2010 and June 2022 is considered for the study. Chapter 4. Advancement of technology had a significant impact on various industries, with innova- tive solutions like Cloud computing, IoT, Augmented reality (AR), and Virtual reality (VR) changing the game in many ways. Here is a system known as “Virtual Try-ons” which leverages IoT devices like mobile cameras, Cloud storage for data, and an intelligent interface for user interaction. Many people are opting for online shopping and various challenges arise with this transition, one of which is the issue of “Try-on.” VR solves this challenge by introducing “Virtual Try-on” which replaces traditional try- on methods. It enables an individual to preview and virtually try on their desired products like clothes, watches, shoes, etc., from the comfort of their own homes, making the shopping experience easier and smoother. It also adds an element of fun and excitement to the shopping experience, increasing the hedonic value for consumers, and allowing consumers to experiment and play with different products, styles, and colors in a way that is not possible with traditional shopping methods. Chapter 5. Identification of insulator defects is one of the most important goals of an intelligent examination of high-voltage transmission lines. Because they provide mechanical support for electric transmission lines as well as electrical insulation, insulators are essential to the secure and reliable op- eration of power networks. A fresh dataset is first built by collecting aerial pictures in various scenes that have one or more defects. A feature pyramid network and an enhanced loss function are used by the CSPD-YOLO model to increase the precision of insulator failure detection. The Insulator Defective data set, which has two classes (Insulator, Defect), is used by the suggested technique to train and test the model using the YOLOv5 Object Detection algorithm. We evaluate how well the YOLOv3, YOLOv5, and related families perform when trained on the Insulator Defective dataset. Practitioners can use this information to choose the appropriate technique based on the Insulator Defective dataset. Chapter 6. Blockchain is an emerging technology that is now being used to provide novel solutions in several industries, including healthcare. Deep learning (DL) algorithms have grown in popularity in medical image processing research. AD is diagnosed by magnetic resonance imaging (MRI) images. This study investigates the integration of blockchain technology with a DL model for Alzheimer’s disease prediction (AD). This proposed model was used to classify 3182 images from the ADNI collection. The Edge-basedSegmentationalgorithmhasovercometheSegmentationproblem.Duringtheinvestigation’s test stage, the DL-EfficientNetB0 model with blockchain earned the highest accuracy rate of 99.14%. The highest accuracy, sensitivity, and specificity scores were obtained utilizing the confusion matrix dur- ing the comparative assessment stage. According to the study’s results, EfficientNetB0 with blockchain model surpassed all other trained models in classification rate. This study will aid clinical research into the early detection and prevention of AD by identifying the sickness before it occurs. Chapter 7. The rapid development of Internet of Things (IoT) applications has created enormous possibilities, increased our productivity, and made our daily life easier. However, because of resource limitations and processing, IoT networks are open to number of threats.The Network Instruction De- tection System (NIDS) aims to provide a variety of methods for identifying the increasingly common cyberattacks (such as Distributed Denial of Service (DDoS), Denial of Service (DoS), Theft, etc.) and to prevent hazardous activities. In order to determine which algorithm is more effective in detecting xxvii
Preface network threats, multiple public datasets and different artificial intelligence (AI) techniques are evalu- ated. Some of the learning algorithms like Logistic Regression, Random Forest, Decision Tree, Navie Bayes, Auto-Encoder, and Artificial Neural Network, were analysed and concluded on the NF-BoT-IoT dataset using various evaluation metrics. In order to train the model for future anomaly detection predic- tion and analysis, the feature extraction and pre-processing data were then supplied into NIDS as data. Chapter 8. This project aims to create a real-time object detection and audio output system for blind users using the YOLOv3 algorithm and a 360-degree camera sensor. The system is designed to detect a wide range of objects, including people, vehicles, and other objects in the environment, and provide audio feedback to the user. The system architecture consists of a 360-degree camera sensor, a processing unit, and an audio output system. The camera sensor captures the environment, which is processed by the processing unit, which uses the YOLOv3 algorithm to detect and classify objects. The audio output system provides audio feedback to the user based on the objects detected by the system. The project has significant importance for blind users as it can help them navigate their environment and recognize objects in real-time, and can serve as a foundation for future research in the field of object detection systems for blind users. Chapter 9. Increasing demand for food quality and size has increased the need for industrialization and intensification in the agricultural sector. The Internet of Things (IoT) is a promising technology that offers many innovative solutions to transform the agricultural sector. Research institutes and scientific groups are constantly working to provide solutions and products for different areas of agriculture using IoT. The main objective of this methodological study is to collect all relevant research results on agri- cultural IoT applications, sensors/devices, communication protocols, and network types. We will also talk about the main problems and encounters encountered in the field of agriculture. An IoT agriculture framework is also available that contextualizes the view of various current farming solutions. National guidelines on IoT-based agriculture were also presented. Finally, open issues and challenges were pre- sented and researchers were highlighted as promising future directions in the field of IoT agriculture. Chapter 10. Deep artificial neural network applications to robotic systems have seen a surge of study due to advancements in deep learning over the past ten years. The ability of robots to explain the descriptions of its decisions and beliefs leads to an collaboration with human race. The intensity of the challenges increases as robotics moves from lab to the real-world scenario. Existing robotic control algorithms find it extremely difficult to master the wide variety seen in real-world contexts. The robots have now been developed and advanced to such an extent which can make them useful in our day-to-day lives, all this has been possible because of improvisation of the algorithmic techniques and enhanced computation powers. The majority of traditional machine learning techniques call for parameterized models and functions that must be manually created, making them unsuitable for many robotic jobs. he pattern recognition paradigm may be switched from the combined learning of statistical representations, labelled classifiers s to the joint learning of manmade features and analytical classifiers. Chapter 11. Modern agriculture primarily relies on smart agriculture to predict crop yields and make decisions. Crop productivity could suffer due to a lack of farmers, labor shortages in the agricultural sector, adverse weather, etc. Smart farming uses advanced technology to improve the productivity and efficiency of agriculture. Crop yield is increased with smart agriculture, which also keeps an eye on agricultural pests. Artificial intelligence is an innovative technology that uses sensor data to predict the future and make judgments for farmers. AI methods like machine learning and deep learning are the most clever ways to boost agricultural productivity. Adopting AI can help with farming issues and promote increased food production. Deep learning is a modern method for processing images and ana- xxviii
Preface lyzing Big Data, showing promise for producing superior results. The primary goals of this study are to examine the benefits of employing DL in smart agricultural applications and to suggest a multi-cloud DL architecture for such applications. Chapter 12. The Music space in today’s world is ever evolving and expanding. With great improve- ments to today’s technology, we have been able to bring out music to many today’s ever-growing and tech savvy people. In today’s market, the biggest players for Music Streaming include behemoth corpora- tions like Spotify, Gaana, Apple Music, YouTube Music and so on and so forth. This also happens to be quite the shift from how music was once listened to. For songs downloaded out of Old Music Databases without the song’s metadata in place, and other distribution sites, they oftentimes come without any known metadata. i.e., Most of the Details with regards to the songs are absent, such as the Artist’s name, the year it was made, Album Art, etc. This paper discusses how Data Mining, Data Scraping and Data Classification is utilized to help add incomplete metadata to song files without the same, along with the design process, the software development and research for the same. Chapter 13. The Internet of Things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi-dimensional data, and low computational cost. Prediction in energy consumption is es- sential for the enhancement of sustainable cities and urban planning, as buildings are the world’s largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting pre- diction performance, best N window size and model uncertainty estimation. Deep learning models for household energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty. Chapter 14. Generative Adversarial Network (GAN) is a generative model that can generate fresh content by using several deep learning techniques together. Due to its fascinating applications, including the production of synthetic training data, the creation of art, style-transfer, image-to-image translation, etc., the topic has gained a lot of attraction in the machine learning community. GAN consists of 2 net- works, the generator, and the discriminator. The generator will try to create phony samples in an effort to trick the discriminator into thinking they are real samples. In order to distinguish generated samples from both actual and fraudulent samples, the discriminator will strive to do so. The main motive of this paper is to make use of several types of GANs like StyleGANs, cycle GANs, SRGANs, and conditional GANs to generate various animated characters of different art styles with optimal attractive scores which can make a huge contribution in the entertainment and media sector. Chapter 15. The system proposes to focus on cloud security audit mechanisms and models. Here the Third-Party Auditor (TPA) will be provided with the authority access scheme where the security of the auditing system will be enabled. The TPA will check out the auditing verification and shows out a mes- sage about the data audited. The purpose of this work is to develop an auditing scheme which is secure, efficient to use and possesses the capabilities such as privacy preservation, public auditing, maintaining the data integrity along with confidentiality. It consists of three entities: data owner, TPA and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it, and generating a signature on it. TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them, and generating signature on it. Thus, the system fre- quently checks out the security of the server-side resources. xxix
Preface Chapter16.Real-timedetectionofobjectisoneoftheimportanttasksofComputervisionapplications such as agriculture, surveillance, self-driving cars etc. The fruit target detection rate based on traditional approaches is low due to the complex background, substantial texture interference, partial occlusion of fruits etc. This paper proposes an improved YOLOv5 model to detect and classify the dense tomatoes by adding the coordinate attention mechanism and bidirectional pyramid network. The Coordinate at- tention mechanism is used to detect and classify the dense tomatoes and bidirectional pyramid network is used to detect the tomatoes at different scales. The proposed model produces good results in detecting the small dense tomatoes with an accuracy of 87.4%. Chapter 17. Automation in the power consumption system could be applied to conserve the large amount of power. This Chapter discusses the applications for the generation, transmission, distribution, and use of electricity that are IoT-enabled. It covers the physical layer implementation, used models, op- erating systems, standards, protocols, and architecture of the IoT enabled SSG system. The configuration, design, solar power system, IoT device, and backend systems, workflow and procedures, implementation, test findings, and performance are discussed. The smart solar grid system’s real-time implementation is described, along with experimental findings and implementation challenges. Chapter 18. The Internet of Things (IoT) links several intelligent gadgets, providing consumers with a range of advantages. Utilizing an Intrusion Detection System (IDS) is crucial to resolving this issue and ensuring information security and reliable operations. Deep Convolutional Network (DCN), a specific IDS, has been developed, but it has significant limitations. It learns slowly and might not categorize correctly. These restrictions can be addressed with the aid of deep learning (DL) techniques, which are frequently utilized in secure data management, imaging, and signal processing. They provide capabili- ties including reuse, weak transfer learning, and module integration. The proposed method increases the effectiveness of training and the accuracy of detection. Utilizing pertinent datasets, experimental investigations have been carried out to assess the proposed system. The outcomes show that the system’s performance is respectable and within the bounds of accepted practices. The system exhibits a 97.51% detection ability, 96.28% reliability, and a 94.41% accuracy. Chapter 19. The Smart Accident Detection and Alert System using IoT is a technical solution that detects accidents and alerts authorities and emergency services. The system mainly relies on sensors, GPS, Arduino UNO to detect and collect information about the location and severity of the accident. The system then transmits this information in real-time to the appropriate authorities using algorithms and protocols, enabling them to respond quickly and effectively, therefore, increasing the possibility of saving lives and benefiting road users, emergency services, and transportation authorities in case of accidents. Chapter 20. In recent years, blockchain technology has gained a lot of attention for its various ap- plications in various fields, with agriculture being one of the most promising. The use of blockchain in agriculture covers areas such as food security, information systems, agribusiness, finance, crop certi- fication, and insurance. In developing countries, many farmers are struggling to earn a living, while in developed countries, the agriculture industry is thriving. This disparity is largely due to poor supply chain management, which can be improved using blockchain technology. Blockchain provides a permanent, sharable, and auditable record of products, improving product traceability, authenticity, and legality in a cost-effective manner. This survey paper aims to compile all existing research on blockchain technol- ogy in agriculture and analyze the methodologies and contributions of different blockchain technologies to the agricultural sector. It also highlights the latest trends in blockchain research in agriculture and provides guidelines for future research. xxx
Preface Chapter 21. In recent years, concerns about privacy and security in online communication have become increasingly prominent. To address these concerns, we propose a blockchain-based messaging systemthatprovidessecureandprivatecommunicationusingdoubleAESencryption.Oursystemutilizes the decentralized and tamper-resistant nature of the blockchain to ensure that messages are not modified or deleted by unauthorized parties. Additionally, we employ double AES encryption to ensure that the content of messages remains confidential even if the blockchain itself is compromised. We evaluate the performance of our system and show that it is scalable and efficient. Our system provides a secure and private messaging solution that can be used by individuals and organizations alike. Chapter 22. In the digital age, cybersecurity has become an important issue. Data breaches, iden- tity theft, captcha fracturing, and other similar designs abound, affecting millions of individuals and organizations. The challenges are always endless when it comes to inventing appropriate controls and procedures and implementing them as flawlessly as available to combat cyberattacks and crime. The risk of cyberattacks and crime has increased exponentially due to recent advances in artificial intelligence. It applies to almost all areas of the natural and engineering sciences. From healthcare to robotics, AI has revolutionizedeverything.Thisfireballputupnotbekeptawayfromcybercriminals,effectivea“normal” cyberattack within an “intelligent” cyberattack. In this chapter, the authors discuss certain encouraging artificial intelligence technologies. They cover the application of these techniques in cybersecurity. They conclude their discussion by talking about the future scope of artificial intelligence and cybersecurity. P. Swarnalatha Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India S. Prabu Department Banking Technology, Pondicherry University, India xxxi
1 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 1 DOI: 10.4018/978-1-6684-8098-4.ch001 ABSTRACT Surveillance is an essential component of security, and e-surveillance is one of the primary goals of the Indian Government’s Digital India development initiative. Video surveillance offers a wide range of applications to reduce ecological and economic losses and becomes one of the most effective means of ensuring security. This chapter addresses the problem of how artificial intelligence is powering video surveillance. There is a significant research focus on video analytics but comparatively less effort has been taken for surveillance videos. However, there is little evidence that researchers have approached the issueofintelligentvideosurveillanceintermsofsuspiciousactiondetection,crimescenedescription,face detection, crowd counting, and the like. Most AI-powered surveillance is based on deep neural networks and deep learning techniques using analysis of video frames as images. Consequently, this chapter aims to provide an overview and significance of how artificial intelligence techniques are employed in video surveillance and image processing. INTRODUCTION Today, security cameras have become an integral part of everyday life for the sake of safety and security. Surveillance camera installations in the private and public sectors have increased significantly to monitor public activities. Security experts focus significantly on video surveillance to combat crime and avoid unpleasant situations that harm human civilization. However, personal and corporate security cannot be achieved simply by installing a surveillance camera. The surveillance system should be sufficiently Artificial Intelligence in Video Surveillance Uma Maheswari P. CEG, Anna University, Chennai, India Karishma V. R. Anna University, Chennai, India T. Vigneswaran SRM-TRB Engineering College, India
2 Artificial Intelligence in Video Surveillance  assisted with Artificial intelligence to deliver security solutions that substantially prevent abnormali- ties. Artificial intelligence has significantly influenced society, whether it takes the shape of algorithms, machine learning models, robotics, or autonomous systems. Many marketed video surveillance systems have integrated Artificial Intelligence (AI)-powered video analytics technology as a method to make our lives smarter and safer, thanks to recent developments in deep learning technologies. Intelligent Visual Surveillance is a significant and hard area of image processing and computer vision research. As our society is rapidly evolving toward smart homes and smart cities, necessitating an increasing number of Internet of Things (IoT) device deployments. Background The application of artificial intelligence (AI) is becoming increasingly crucial in the quest for novel techniques and technologies. Clutter identification, target categorization, and target tracking are AI techniques for target surveillance with radar sensors. These are critical assets for effective target obser- vation. Because clutter (i.e., unwanted signal reflections) may significantly hamper target detection, its identification and subsequent suppression are critical. Furthermore, accurate target classification can aid in the successful prevention of possible threats, particularly in military circumstances. Finally, target tracking, the final link in the traditional chain of radar data processing, demands special attention since it provides the pivot point for sensor data fusion. Smart Home Surveillance System (Anthony et al., 2022;Koushik et al., 2022) The globe has seen a tremendous increase in the number of smart homes with the emergence of artificial intelligence (AI), the Internet of Things (IoT), and human-centric computing (HCC). But putting in place a reliable security system for SH’s citizens still seems impossible. The current smart houses include security features like biometric verification, activity tracking, and facial recognition. The lifespan costs of these systems increase with the integration of multi-sensor hardware, networking infrastructure, and data storage facilities. Important behavioral and purpose clues are sent through facial expressions, which can be employed as non-intrusive feedback for contextual threat assessments. For the protection of the occupants of the same residence, prompt mitigation of a hostile situation, such as a fight or attempted entrance, is essential. iSecureHome is a real-time facial emotion-based security system for smart homes that uses a CMOS camera and a passive infrared (PIR) motion sensor. Effects of chromatic and achro- matic characteristics on the identification of facial emotions (ER). Daily home invasions and house fires cause difficulties for the victims of these sad occurrences. Early identification of these circumstances enables quick responses and should always be a feature that all homeowners expect. The CCTV system, the Onboard processing, prediction, and decision logic, and the Alarm and remote alerting module are the three elements that make up the framework depicted in Figure 1. The house range and garden path are completely covered by the CCTV system’s numer- ous cameras. Each camera’s video feed is supplied into the model’s location’s onboard circuitry. Live CCTV systems may be used as inputs, or previously captured video may be used for offline analysis. The Onboard logic can record films played on the screen by any software to increase the suggested framework’s interoperability. The CNN model generates one of five events, which are then passed to the Decision logic for prediction because it is light enough to execute utilizing onboard processing as opposed to cloud resources.
3 Artificial Intelligence in Video Surveillance  According to the model’s results, matching Alarm messages with video feeds will be sent to notify the appropriate users. This model is intended to identify unusual situations in homes, especially when the owner is not there. This includes people, domestic animals, cars, fire, and smoke. An additional degree of security for the homeowner can be added when the model is included in home monitoring systems. Many of the things that the model tries to group are present in the image samples that were taken from open databases. The data is manually cleansed to ensure that the irrelevant objects did not affect training. The findings still offer accurate categorization even though the learning curve indicates that the model’s full potential has not yet been realized. When reading the images, monochrome (greyscale) images present a problem. When these photos are found, either delete them or expand them using the OpenCV channel to coordinate with RGB pictures. It will be beneficial for the model to train some features in grayscale so that it can learn and recognize these features when no color is presented, allowing the model to be useful in these systems. Some sur- veillance systems are going to record in grayscale, so it will benefit the model to train some features in grayscale to make the images usable. Since the input data for the neural network needs to be consistent, every image will be resized to 400 X 400. The model will have more pixels and data to deal with as the fixed size increases, ultimately extending training time. images bigger than this size can be cropped to ensure that the most important information can still be seen, or photos larger than this size can be re- duced to this size. Images less than this will be resized to fit this size. Therefore, just enlarge the image rather than cropping the features, which might have a detrimental effect on the model’s learning. 70% of the samples will be used to train the model, 10% to test it, and 20% to verify it over the full dataset. The classification performance of Accuracy, precision, Recall, and F1 score are noted for various classes of the dataset. The overall Accuracy is 82.31% Figure 1. Framework of alarming system
4 Artificial Intelligence in Video Surveillance  The framework has the following drawbacks: (a) The training samples were taken from public datasets rather than specific CCTV footage, which may have an impact on how well it performs on CCTV sys- tems; (b) There is no way to compare the performance of the framework to other baseline CNN models, and (c) The alarm and remote alerting cannot recognize complex situations. COVID-19 Surveillance in Public Places (Sreedhara et al., 2021; Hossain et al., 2020; Das et al., 2021; Suvarna et al., 2021) Artificial Intelligence (AI) based detection systems can be deployed at public places like airports, railway stations, etc. for continuous monitoring of potential infectious individuals and screening based on com- mon symptoms exhibited. The worldwide pandemic, COVID-19 has been caused by a newly discovered strain of coronavirus SARS-Cov-2. Its common symptoms are high fever, coughing, and shortness of breath. With the rising number of COVID-19 cases, manual detection of infectious individuals in public spaces is a hectic task. The BII Sneeze-Cough Human Action Video Dataset provided the sneeze-cough video dataset utilized for the research in this article (BIISC). A small real-time dataset is produced in addition to the sneeze cough dataset to test the classifier. A rate of 10 frames per second is used to extract images from films with various class labels. The retrieved pictures are used to feed a person detection algorithm, which employs the histogram of directed gradients as a basis for identifying human subjects inside a given image. Then, images are resized to 64 by 128 pixels. To minimize the pixel size, it is finally transformed from RGB to a grayscale picture. Features are retrieved from the pre-processed pictures to do categorization. Testing the Gabor Filter, Histogram of Oriented Gradients (HOG), and Spatial Pyramid Matching as three distinct feature extractors (SPM). To determine if the picture under review contains any certain frequency content, a Gabor filter analyses a constrained portion of the image in a particular direction. The pre-processed pictures are convolved with various filter masks using Gabor filters. Two distinct kinds of classifiers are tested to differentiate coughing activity from similar activities. K-Nearest Neighbor (KNN) is the first classifier, and a multi- class Support Vector Machine (SVM) employing various kernel operations. The dataset’s nonlinearity is the key justification for choosing kernel functions. A multi-class SVM classifier is used to test three distinct kernel function types: linear, polynomial, and radial basis functions. Figure 2 details the process used by our social distance monitoring tool. The algorithm engine is made up of five parts: alert creation, distance estimate, camera calibration, people tracking, and people detection. The application is implemented as a hybrid engagement of edge infrastructure-based model inferencing and cloud-based model training. Table 1. Classification performance Class No. of Truth No. of Classified Precision Recall F1 Score Vehicles 373 419 0.70 0.79 0.74 Pets 310 422 0.49 0.66 0.56 Default 224 216 0.58 0.57 0.58 Fire/Smoke 2390 2120 0.99 0.88 0.93 Human 105 227 0.31 0.68 0.43
5 Artificial Intelligence in Video Surveillance  Identifying people in a location and estimating their bounding box coordinates is the first step in tracking their movements. The YOLOv3-416 object detector, pre-trained on the MS-COCO dataset, and darknet-53 as the backbone are used for this. Only the detection results for the person class are extracted. The approach, as it is intended to be used with monocular cameras, calls for calibration to convert im- age pixel coordinates to geographic coordinates. The camera calibration module allows you to select between an automatic calibration and a tool-based calibration. The next stage is to estimate the pairwise distances between the persons to aid in monitoring compliance with social distancing. This is done once the camera calibration parameters and the bounding box coordinates of every person visible in an image frame have been collected. Each person’s position is determined by the midpoint coordinates at the base of each detection box. To determine if social distancing norms are being broken in tool-based calibra- tion, Euclidean distances between these locations in the aerial view are computed and compared to the reference distance. The number of different violations and the length of those violations, which may be calculated by frame-to-frame surveillance of people, determine the likelihood that a person would get infected. The solution includes Motpy, an online multiobject tracker framework that tracks persons in a scene by using a Kalman filter and IOU of bounding boxes between future frames. By following the earlier detections, the implementation of a tracking algorithm is also anticipated to reduce mistakes in distance computation caused by transient occlusions. The different parts of the application are separated into distinct modules that communicate with one another using message queues to simplify solution deployment and maintenance. To manage video in- puts from several IP (Internet Protocol) cameras within the program, it also utilized a multi-processing, multi-threading method, providing scalability. A separate thread on a multi-core edge device processes each video feed in turn, invoking the algorithm processing unit to process the accompanying picture frame before pushing the processed image and related metrics to a streaming application. Figure 2. Social distance surveillance application flow
6 Artificial Intelligence in Video Surveillance  (AIP*MAXAL)/SEF is an estimate of how many camera feeds can be handled on the edge device during deployment, where AIP stands for algorithm instance process rate and MAXAL is the maximum frame rate. MAXAL stands for maximum algorithm instance, where (the number of CPU cores, and GPU Memory) is the minimum. SEF: stream endpoint feed process rate 3 frames per second. For instance, if a machine has 12 CPU cores and 16 GB of graphics memory, MAXAL is at least (12,16) 12, supporting a total of (5*12)/3 = 20 cameras. By adopting the log-average miss rate as a performance indicator during testing on the Euro City Persons test dataset, it is further shown that YOLOv3 and FRCNN outperform SSD. The default object detector of the solution, YOLOv3, is deployed without further training after taking into account the trade- off between all of these measures. Numerous tests using internal CCTV footage under various lighting conditions, crowd density, gender, ethnicity, positions of persons, and occlusions produced accuracy and recall scores that fell between 68.8 and 75.6% and 75.5 to 85.1%, respectively. For crowd sizes up to 30 individuals, the pre-trained YOLOv3 detector exhibits the greatest detection performance, assuming that 80% of each person was visible. Maritime Surveillance System (Huang et al., 2022) Maritime surveillance systems are widely used in vessel traffic services. Cameras on the ground, at sea, or in the air can provide marine visual information. However, the collected visual data frequently suffer from blur effects as a result of unsteady imaging devices under harsh environments (e.g. wind, waves, and currents). To increase visual quality, picture-stabilizing technologies must be developed. Systems for maritime monitoring are crucial to enhancing maritime security. The efficiency and efficacy of maritime monitoring are considerably increased by intelligent marine surveillance systems, which use informa- tion technology to completely construct a new pattern. Technology primarily relies on the creation of surveillance resources to support the fusion and exchange of safety information. However, marine traffic monitoring is somewhat outdated when compared to sophisticated land traffic surveillance. The surveillance recordings are hazy because maritime surveillance systems are more vulnerable to erratic elements like wind, waves, and currents. The hazy photos make maritime surveillance more challenging and less effective. As the global economy and trade continue to grow, new video stabiliza- tion techniques are being suggested for maritime surveillance. Image deblurring, the foundation of video stabilization, is becoming more and more crucial in maritime surveillance. Similar to how land-based traffic surveillance systems operate, marine surveillance systems must provide the video and picture data they have gathered to the monitoring center. Finally, ships are coordinated and managed by ship traffic managers at the monitoring center once the data has been analyzed. Table 2. Object detection algorithm performance metrics Model mAP FPS Small Occlusion Heavy Occlusion YOLOv3-416 55.3 35 17.8 37.0 FPN FRCNN 59.1 6 16.6 52.0 SSD300 41.2 46 20.5 42.0
7 Artificial Intelligence in Video Surveillance  Due to the effect of wind, waves, and currents on maritime data acquisition, shore-borne, air-borne, and ship-borne acquisition systems are all prone to shaking, which makes the majority of marine movies unstable. In other words, the hazy images on the surveillance screen are frequently a result of the rela- tive distance between the visual aids and the supervised ships. The initial scene’s image pixel expands to its surroundings. These dispersed pixel light sources are referred to as blur kernels or point spread functions (PSFs) in the imaging profession. In marine surveillance, only uniform picture deblurring— which is geographically invariant—is taken into account for simplicity’s sake. Stabilizing the obtained marine surveillance videos is equivalent to deblurring the maritime pictures since the video is made up of a series of images called frames. Finding the latent crisp picture is the next stage. Blind picture deblurring becomes a non-blind deblur- ring challenge after determining the precise blur kernel. Numerous techniques have been developed for non-blind deblurring. There are primarily three categories: regularisation technique, iterative approach, and inverse filtering method. Due to its straightforward computation and quick processing time, inverse filtering is frequently used in picture restoration, however, it is vulnerable to noise. Then followed the restricted least squares approaches, the Kalman filter with the linear recursive minimum variance estimation as the criterion, and the Wiener filter with the minimum mean square error as the criterion for deblurring. However, each of the aforementioned techniques is a linear restoration technique. The Lucy-Richardson technique, a non-linear approach, was suggested to restore the blurred picture with a known PSF to precisely reconstruct the latent crisp image. Then, a ROF model using TV regularisation of the total variance. Visual sensors are used by the ship’s navigation system to understand the surroundings. However, the visual information gathered under tough circumstances is prone to blurring, making it challenging for ship auxiliary systems to precisely detect impediments in the area around the ship. It impairs ship navigation safety and leads to navigational mistakes. Video stabilization technology plays a crucial role in maritime transportation because of the unique navigational conditions of waterways. Image deblur- ring helps to increase the effectiveness of waterway transportation and ensure the safety of navigation. Our technique completely exploits the properties of the blur kernels and the natural pictures, enabling accurate blur kernel estimates and ensuring high-quality restoration outcomes. It is advantageous for accurately identifying the surrounding objects and maintaining navigation safety to eliminate the visual blur under challenging marine circumstances. Visual information technology-based marine surveillance systems have been extensively employed in a variety of nautical services in maritime engineering. However, inclement weather frequently makes the surveillance footage unsteady. It implies that pictures extracted from films occasionally include motion blur, noise, and other issues that drastically lower the visual quality. The restoration of fuzzy pictures in maritime engineering requires more focus. To conduct the comparison tests due to the absence of true maritime blurred datasets, should use artificial maritime-blurred photos with undetermined blur ker- nels. Blind deblurring techniques may be used to recover the obtained blurred pictures, and the visually improved films and photos can then be used to support our work in different nautical applications. One of the important marine applications is ship detection, and several ship detection techniques have been described. The accuracy of ship recognition will increase when the obtained maritime-blurred photos are deblurred by our hybrid regularisation approach, which is proven by YOLOv4.
8 Artificial Intelligence in Video Surveillance  Public Transport Surveillance (Santhosh et al., 2020; Rohit et al., 2020) Local transportation movement is also a worry in our nation, as is vehicle overspeeding, which generates a large number of road accidents. Integrating GPS tracking systems with automated safety systems can establish geofencing to regulate and monitor our country’s local buses. Through computer vision and visual surveillance, timely identification of traffic offenses and unusual pedestrian behavior in public spaces may be quite helpful for upholding traffic order in cities. Computer vision-based scene comprehension has become quite popular among the Computer Vision (CV) research community as a result of the pervasive usage of surveillance cameras in public spaces. Compared to other information sources like GPS, mobile location, radar signals, and so on, visual data includes extensive information. This means that in addition to gathering statistical data regarding the condition of road traffic, it may be extremely useful in identifying and forecasting traffic jams, accidents, andotherirregularities.Numerousresearchemployingcomputervisionhasbeencarriedout,concentrating on data collection, feature extraction, scene learning, activity learning, behavioral comprehension, etc. Scene analysis, video processing methods, anomaly detection strategies, vehicle detection and tracking, multi-camera techniques and challenges, activity recognition, traffic monitoring, human behavior analy- sis, emergency management, event detection, and so on are some of the aspects that can be considered. Asub-domainofbehaviorcomprehensionfromsurveillancesituationsisanomalydetection.Anomalies are often deviations from the norm of scene entities (such as automobiles, people, or the environment). There has been an increase in research outputs on video analysis and anomaly identification as a result of the accessibility of video feeds from public locations. Anomaly detection techniques often train on the norm to understand what is normal. Anything that dramatically deviates from typical behavior is consid- ered abnormal. Anomalies include things like automobiles on sidewalks, a quick dispersal of individuals in a crowd, someone falling abruptly while walking, jaywalking, signal bypassing at a traffic signal, or vehicles turning around at red lights. Typically, anomaly detection systems develop a normal profile by learning the typical data patterns. Once the typical patterns are understood, established methods may be used to identify abnormalities. The system can produce a label or score that indicates whether or not the data is anomalous, usually in the form of a metric. Anomalies are by nature contextual. It is impossible to apply the assumptions employed in anomaly detection to all traffic circumstances. The capabilities of anomaly detection techniques used in monitoring road traffic from a data perspective. It does this by classifying the methods according to how the scene is represented, the characteristics utilized, the mod- els used, and the approaches used. With numerous instances of the learning processes, used detection techniques, applied anomalous scenes, types of anomalies identified, and so on, the relevant technology provides an end-to-end perspective of the anomaly detection approach. In the current scenario, features are taken to be data and represented by feature descriptors. Depend- ing on the length of the feature descriptor, data generally take up a space in a multidimensional space. Data patterns that deviate from a well-established definition of typical behavior are known as anomalies. Anomalies have also been referred to as outliers and novelty in several application areas. Analyzing anomalies. Anomalies are often divided into three groups: point anomalies, contextual anomalies, and collective anomalies. If data deviate significantly from the expected distribution, they are considered to be a point anomaly. A point anomaly can be something like a stationary automobile on a busy road. Data that could be considered normal in one environment but not in another corresponds to contextual abnormalities. An abnormality, for instance, is if a cyclist in slow-moving traffic travels quicker than the
9 Artificial Intelligence in Video Surveillance  others. On a less congested route, though, that may be typical behavior. Even while each data instance may be normal on its own, a bunch of them together may result in an anomaly. A collective anomaly maybe something like a group of individuals dispersing quickly. Visual surveillance frequently reveals abnormalities that are categorized as local and global anomalies. Global abnormalities may be visible in a frame or a section of the video without a specific location being identified. Local abnormalities typically occur in a particular section of the scene, however, global anomaly detection systems might not pick them up. Some techniques can find abnormalities both locally and globally. Challenges and Study Scope Identifying a representative normal region, defining boundaries between the normal and anomalous regions that may not be clear or well defined, the notion of an anomaly differing depending on the application context, limited data available for training and validation, data that is frequently noisy due to inaccurate sensing, and the fact that normal behavior changes over time are the main challenges in anomaly detection. Learning typical behavior is important for many different use cases in addition to anomaly detection. Some of these include behavior analysis, categorization, pattern analysis, and prediction. There are four types of learning strategies: supervised, unsupervised, semi-supervised, and hybrid. The normal profile is created in supervised learning utilizing labeled data. It is frequently used in applications that are linked to classification and regression. In unsupervised learning, the connections between the components of the unlabeled dataset are used to structure the normal profile. With some guidance and a little quantity of labeled data for defining example classes that are already known a priori, semi-supervised learning predominantly uses unlabeled data. Active learning is defined as learning that occurs through the inter- active labeling of data as and when the label information is accessible. These techniques are employed when there are plenty of unlabeled data and human labeling is costly. To comprehend various aspects of thedata,hybridapproachescombinetheaforementionedtechniques.Objectidentification,classification, activity recognition, segmentation, anomaly detection, and other tasks also require learned models in addition to feature extraction. The formulation of the problem and the underlying characteristics determine the type of anomalies since anomalies often relate to deviations from normal behavior. The methods do not restrict the capacity to determine the kind of abnormality. The formulation of the problem and the underlying characteristics determine the type of anomalies since anomalies often relate to deviations from normal behavior. The methods do not restrict the capacity to determine the kind of abnormality. Based on a statistical model, it attempts to fit the data using a stochastic process while generally employing statistical approaches to learn the parameters of the model. The data points that were not produced by the expected stochastic model are known as anomalous samples. Both parametric and nonparametric models are available. The process of anomaly detection fundamentally involves using a particular approach to the derived feature. The fundamental data in visual surveillance, however, is a video, which is a collection of frames. Because these features serve as input to the particular approach utilized in anomaly identification, it is crucial to extract the pertinent features from the videos. The characteristics may be broadly divided into object-based and non-object-based categories. By extracting the objects or trajectories, anomalies may be found using object-based characteristics. The information used for anomaly detection is made up of objects or trajectories that are represented as feature descriptors. In the latter method, anomaly identifica- tion has been performed using low-level descriptors for pixel or pixel group characteristics, intensities, optical fluxes, or resulting features from spatiotemporal cubes (STCs). Some techniques employ hybrid characteristics to find anomalies.
10 Artificial Intelligence in Video Surveillance  Atypicalanomalydetectionframeworkdevelopmentprocesshastwostages.Inthefirststage,amodel is trained using the features from typical movies to learn the typical properties of the scenario. Later, the trained model is provided with features from the test videos. Test films are classified as normal or anomalous based on the chosen abnormality criteria. These approaches, however, use various precise detection strategies and anomaly definitions. Therefore, it is challenging to classify them just based on detection procedures. With the use of a CNN classifier, optical flow-based spatial-temporal volumes of interest (SVOI) are used to learn the classes for normal and pathological video. Based on the findings of the classifier, anomalies are found. With the use of deep features and optical flow data, Generative Adversarial Net- works (GANs) have been utilized to predict a future frame from a continuous collection of preceding frames. A frame is considered to be normal or abnormal based on the discrepancy between the anticipated future frame and the actual frame. Machine learning has undergone a paradigm change in the previous ten years, particularly in favor of DNN-based techniques. You may have noticed that deep learning techniques have already been used to tackle several anomaly detection issues. Studies using DNNs have shown success in extracting char- acteristics irrespective of light. Due to camera position and perspective, traditional ML frequently fails, especially when trying to recognize objects. Despite their increased processing cost, DNN-based systems like have proved quite accurate at detecting objects. Purely deep learning-based approaches have not been able to successfully track objects reliably, especially in dense settings, even though object tracking is a crucial step in many anomaly detection systems. To create the tracks, techniques like the Kalman filter and DNNs for object association and detec- tion are used. Although it employs YOLO, this too has to track issues that lead to shorter trajectories in crowded and obstructed settings. Access to powerful computational resources might be difficult when putting traffic anomaly detection systems utilizing DNNs into practice. Even though the majority of corporations provide free funding and access to cloud computing resources for university research, un- less hardware prices decrease, research dissemination may be constrained. AI-POWERED VIDEO SURVEILLANCE ISSUES However, there is no strong architecture with a suitable network model for commercial services that takes into account both high accuracy and cheap computing cost. Video monitoring with Closed-Circuit Tele- vision (CCTV) cameras has been studied for decades, but it has several drawbacks, including restricted area coverage, no location sharing, and tracking capabilities. Most video surveillance systems are fixed to infrastructure and typically particular to a site, but to construct a portable surveillance system, a highly accurate algorithm as well as a powerful computing and embedded device that can function with low power consumption is necessary. On the other hand, the vision sensors attached to drones are more scal- able and versatile, providing more extensive surveillance coverage but requiring big data computations. Deep Learning Solutions for AI-Based Video Surveillance Deep learning architectures can achieve more accuracy and operate better with huge datasets. The Deep Learning techniques addressed are Continuous learning, transfer learning, reinforcement learning, ensemble learning, and autoencoders. The Detection methodologies are classified under the learning
11 Artificial Intelligence in Video Surveillance  approachesasSupervised,Unsupervised,andSemi-Supervised.Unsupervisedclassificationiscomputer- controlled and does not require human intervention. Manual training and labeled data require supervised classification. Semi-supervised learning sits between unsupervised learning (no labeled training data) and supervised learning (with labeled training data). There are many available benchmarking datasets like UCSD (UCSD Anomaly Detection Dataset), The dataset was created using security cameras with 60 mm120 mm lenses from the Puri Rath Yatra event, CUHK, Avenue Dataset, Violent-flows, UCF50, Rodriguez’s and so on. However, the collection includes a variety of video genres, including surveil- lance and moving-camera recordings. As a result, it drives us to create a more realistic public difficult urban surveillance video collection to assess the effectiveness of various algorithms for object tracking and behavior analysis. A video is well-known to be a series of successive pictures known as frames. Each frame is treated as an image, and any image processing method can be applied to it. To recognize anomalous pictures or objects in a video sequence (Majeed et al., 2021; Fan et al., 2020), deep learning-based object detection models such as RCNN, Fast RCNN, Faster RCNN, and YOLOv5 are studied for their performance in competition with each other. Figure 3. Framework for abnormal behavior detection in video surveillance
12 Artificial Intelligence in Video Surveillance  Abnormal Behavior Detection (Mabrouk et al., 2018; Harrou et al., 2020) In recent years, significant progress has been made in the demanding problem of abnormal behavior identification in video surveillance. The early phases of low-level processing allow for detecting and describing moving objects in the scene. However, those actions do not enable figuring out what kind of activity the moving item is performing or if its behavior is typical. Finding appropriate characteristics that can withstand various transformations, such as changes to the backdrop and the look of the object, is the main problem in behavior representation. An intelligent video surveillance system’s goal is to effectively identify a noteworthy occurrence from a vast collection of films to head off harmful scenarios shown in Figure 3. This task often calls for two video processing tiers. There are two steps in the first one, low-level characteristics are retrieved to identify the scene’s interest zone. The interest region is then described by primitives that are produced based on low-level attributes. The second level establishes whether or not the behavior is normal by providing semantic information about human activity. Table 3 includes the most popular behavior representation feature. To identify and characterize an entity moving over time, many features are employed. Such features can be divided into local and global features. A predetermined area of the frame is where local features are found. A local location or an interest point may serve as the region’s representation. Motion through- out the whole frame is described using global features. Global motion data is frequently extracted using optical flow features. In recent years, significant progress has been made in the demanding problem of abnormal behavior detection in video surveillance. The early phases of low-level processing enable the recognition and description of moving objects in the scene. These actions do not, however, help us identify the sort of action taken by the moving item or establish whether or not its behavior is normal. There are several ways for recognizing abnormal behavior in video surveillance, including classification methods and Model- ing frameworks, scene density, and the interaction of moving objects. The three types of classification techniques are supervised, semi-supervised, and unsupervised techniques. Supervised approaches use labeled data to simulate both typical and atypical actions. Typically, they are made to identify particular deviant behaviors that were predefined during the training process, such as fighting, loitering, and falling. Thetwotypesofsemi-supervisedalgorithmsarerule-basedandmodel-based,andbothrequiresimply typical video data for training. The first group seeks to create a rule by leveraging common patterns. Any sample that deviates from this guideline is then regarded as an outlier. sparse coding in a rule-based approach to identify deviant actions. The examples that differ from the model’s representation of typical behavior are referred to as aberrant patterns in model-based techniques. The most popular models are the Markov Random Field (MRF), Gaussian Mixture Model (GMM), and Hidden Markov Model (HMM). Using statistical features derived from unlabeled data, unsupervised algorithms seek to identify normal and aberrant actions. Unsupervised learning is carried out using a framework based on a Dominant set and an unsupervised kernel framework for anomaly detection based on feature space and support vector data description. The number of people there is in the scene is reflected in how dense it is. The scene density has a direct impact on the strategies that are selected to define the behavior. Therefore, a single individual or a small group of people might be the moving item in the scene. The scene is distinguished as an uncrowded scene and a crowded scene. When one or a few people are visible in the camera’s field of view, the scene is said to be uncrowded. Three key anomalous behaviors are often taken into account when there is just one person present: falling detection, loitering, and being in the incorrect area. It is impossible to
13 Artificial Intelligence in Video Surveillance  observe and study each person’s conduct separately in a crowded environment that includes a group of people. Occlusion and the few pixels used to depict each individual in the frame are to blame for this. Therefore, it is preferable to model how individuals interact to spot unusual crowd behavior. A video surveillance system may be evaluated using several parameters. Equal Error Rate (EER) and Area Under Roc Curve (AUC) are the two most often utilized metrics. The Receiver Operating Charac- teristic Curve (ROC), which is widely used for performance comparison, is where the two criteria are formed. The EER point on the ROC curve is where the ratio of false positives to false negatives is equal. Motion Detection (Huang et al., 2019) Many computer vision applications, particularly video surveillance system analysis, rely heavily on motion detection. Its goal is to extract moving elements from a video clip one at a time. Motion analysis methods help to concentrate attention on the scene’s moving aspects. Three common methodologies are used for motion detection: time difference, background removal, and optical flow analysis. Approaches to temporal differencing invariably extract imperfect forms of moving objects. When employed in practical applications, optical flow techniques can make it difficult to achieve accurate motion detection because they either make the system more computationally demanding or more sensitive to noise. On the other hand,byusingareferencebackgroundmodelofpriorphotos,backgroundsubtractiontechniquesemployed in traffic monitoring systems can more thoroughly and precisely detect moving objects characterized by moderate temporal complexity. As a consequence, background subtraction techniques are more widely used, developed, and applied in several motion detection applications. The Gaussian Mixture Model (GMM) technique employs a specific distribution that may be used by separately modeling each pixel value. With this method, an incoming frame may be labeled as either having moving objects in it or not. Motion detection is accom- plished using the Sigma Difference Estimation (SDE) approach, which employs a filter technique. To derive the motion vector, this technique uses a pixel-based decision framework to estimate two orders of temporal statistics. However, because it is unable to account for complicated situations, using the filter alone frequently leads to inadequate detection. Multiple Temporal Difference (MTD) is a different background subtraction technique for motion detection that keeps track of multiple prior reference frames Table 3. Features for behavior detection Feature Types Description Features based on optical flow Using the statistical features extracted from the optical flow vector to characterize motion. Interest points Both spatial and temporal domains allow for the detection of salient locations. the depiction of large motion fluctuations correlating to erratic behaviors. Spatio-temporal volume, cube, blob, etc. The temporal dimension is obtained by assembling successive frames. Shape Describing the movement of the object’s form in a series of frames. Shape change detection correlates with abnormal conduct. Texture For each moving item included in a bounding box, rectangle, etc., local patterns are extracted. Object tracking and trajectory extraction Tracking a moving item using an optimization technique and its trajectory (coordinates in each frame).
14 Artificial Intelligence in Video Surveillance  to make it easier to locate presumed foreground items and close detection gaps for moving objects. The Simple Statistical Difference (SSD) technique, creates a straightforward background model based on the temporal average to identify areas of moving objects in traffic surveillance video streams. The majority of the most recent state-of-the-art background removal techniques can identify moving objects in video streams recorded by a stationary camera. These techniques may readily detect moving objects using their backdrop models in such a perfect setting. However, in the actual environment, power- ful winds or earth earthquakes may cause exterior cameras to vibrate. For these techniques, the accurate identification of moving objects in the Intelligent Transportation System (ITS) might be a challenge. To accurately identify moving objects in streaming video, the Gray Relational Analysis-based Motion Detection (GRAMD) technique is described. This approach consists of two crucial components: the Multi-sample Background Generation (MBG) module and the Moving Object Detection (MOD) mod- ule as shown in Figure 4. These modules allow for accurate and thorough detection as well as efficient adaptability to background changes. To detect moving objects in video streams with jitter backgrounds, the MBG module first builds a multi-sample background model using the grey relational analysis approach. Following MBG module construction. The MOD module then employs a multi-sample backdrop model to separate moving ob- jects in real-world scenes recorded by both jitter and static cameras. To prevent errors brought on by the camera jitter, a two-stage detection technique that consists of a rough detection procedure followed by a precise detection procedure is adopted. By doing this, both jitter and static cameras’ video streams can accurately detect moving things. Crowd counting (Sreenu et al., 2019) is used to determine the number of persons in a crowd of thou- sands. Deep learning models also enable face, action, and event detection in crowded environments. Attention mechanism-enabled deep learning was also explored to recognize activities and objects more accurately from surveillance videos. Crowd size is significant and changing in real-world situations, making crowd analysis challenging. It is difficult to distinguish between each entity and its actions. Traffic lights, major intersections, populated areas, gatherings that draw large crowds, and celebrations held by religious organizations, Among the aforementioned situations, crowd analysis inside offices is the most challenging. Identification of all acts, behaviors, and movements is necessary. In crowded scenarios, spatial-temporal convolutional neural networks for anomaly detection and localization reveal that the challenge of crowd analysis is difficult due to the following factors: a large Figure 4. GRAMD approach component framework diagram
15 Artificial Intelligence in Video Surveillance  number of people, Close closeness, a person’s appearance changing often, and frequent partial occlu- sions, crowd’s irregular movement pattern, dangerous behaviors include crowd fear, pixel, and frame level detection. The following steps involve a scene-independent technique that uses deep learning for scene-independent crowd analysis. Crowd counting, Crowd tracking, and division of the crowd Pedes- trian journey time estimate, crowd behavior analysis, crowd attributes recognition, and abnormality detection in a crowd. Methods like data-driven crowd analysis and density-aware tracking are described in the study of High-Density Crowds in films. The data-driven analysis uses a line-based method to understand the movement patterns of crowds from a vast collection of crowd footage. There are two steps to the solution. Both local and global crowd patch matching is used. Microscale and macroscopic crowd modeling, crowd behavior, crowd density analysis, and crowd behavior analysis datasets for crowd behavior analysis are all covered in crowd behavior analysis using fixed and moving cameras. Macroscopic methods are used to manage large crowds. Agents are managed in this case as a whole. In microscopic methods, each agent is dealt with separately. It is possible to gather motion data to depict the crowd using both stationary and moving cameras. For the investigation of crowd behavior, CNN- based techniques such as end-to-end deep CNN, Hydra-CNN architecture, switching CNN, cascade CNN architecture, 3D CNN, and spatiotemporal CNN are addressed. The chapter also includes descriptions of various datasets that are especially helpful for studying crowd behavior. MOTA (multiple-person tracker accuracy) and MOTP (multiple-person tracker precision) are the measures in use. These measures take into account the several targets that are frequently present in crowd situations. A Deep Spatiotemporal Perspective for Understanding Crowd Behavior combines long short-term memory with the convolution layer. Convolution layer-captured spatial data and temporal motion dynamics is constrained by LSTM. The approach predicts the path taken by pedestrians, calculates their destination, and then classifies their behavior based on how they move. According to Crowded Scene Understanding by Deeply Learned Volumetric Slices, a deep model and several fusion techniques should be used. Convolution layers, a global sum pooling layer, and fully linked layers make up the architecture. The architecture calls for weight-sharing and slice fusion techniques. It is anticipated that a new multitask learning deep model would successfully combine motion and appearance variables. As an input to the model, a novel idea of crowd motion channels is developed. In crowd videos, the motion channel examines the temporal pro- gression of the content. The temporal slices that clearly show how the contents of crowd recordings have changed over time agitate the motion channels. broad assessments using a variety of deep structures, data fusion techniques, and weight-sharing strategies to identify temporal aspects with activation functions like rectified linear unit and sigmoid function, the network is set up with a convolutional layer, pooling layer, and fully connected layer. To evaluate the efficacy of suggested input channels, three alternative slice fusion approaches are used. FUTURE TRENDS AND CONCLUSION This chapter elaborates on various models and techniques for AI-powered video surveillance systems that are hot areas in computer vision and video processing research. The comparison of these models in terms of various performance measures is to be discussed. The challenges involved in this system and the issues related are also addressed in detail. Understanding the socio-cognitive components of crowd behavior is a difficult but crucial issue, especially for human-computer interaction applications. This problem is critical to existing surveillance systems and future interactions between intelligent entities and
16 Artificial Intelligence in Video Surveillance  human crowds. Night video enhancement methods are commonly employed for recognizing suspicious actionsacquiredbynightvisualsurveillancesystems.However,artificiallightsourcesintheenvironment degrade the visual quality of the video shot at night. This non-uniform lighting impairs the capacity of a real-time visual surveillance system to identify and track objects. As a result, a uniform enhancement strategy is insufficient for dealing with such uneven lighting. Since Surveillance is a vast area, various case studies are encountered to gain domain knowledge. REFERENCES Ben Mabrouk, A., Zagrouba, E. (2018). Abnormal behavior recognition for intelligent video surveil- lancesystems:Areview.ExpertSystemswithApplications,91,480–491.doi:10.1016/j.eswa.2017.09.029 Das, S., Nag, A., Adhikary, D., Ram, R. J. (2021). Computer Vision-based Social Distancing Sur- veillance with Automated Camera Calibration for Large-scale Deployment. IEEE, 18th India Council International Conference, 1-6. 10.1109/INDICON52576.2021.9691485 Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M. D., Xiao, F. (2020). Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder. Computer Vision and Image Understanding, 195, 1–13. doi:10.1016/j.cviu.2020.102920 Harrou, F., Hittawe, M. M., Sun, Y., Beya, O. (2020). Malicious Attacks Detection in Crowded Areas Using Deep Learning-Based Approach. IEEE Instrumentation Measurement Magazine, 23(5), 57–62. doi:10.1109/MIM.2020.9153576 Hossain, M. S., Muhammad, G., Guizani, N. (2020). Explainable AI and Mass Surveillance System- Based Healthcare Framework to Combat COVID-19 Like Pandemics. IEEE Network, 34(4), 126–132. doi:10.1109/MNET.011.2000458 Huang, S.-C., Member, S. (2019). A Gray Relational Analysis-Based Motion Detection Algorithm for Real-World Surveillance Sensor Deployment. IEEE Sensors Journal, 19(3), 1019–1027. doi:10.1109/ JSEN.2018.2879187 Huang, Y., Liu, R. W., Liu, J. (2022). A two-step image stabilization method for promoting visual quality in vision-enabled maritime surveillance systems. IET Intelligent Transport Systems, 1-15. Kattiman Maidargi. (2021). An Automated Social Distance Monitoring Alarm System based on Human Structure Using Video Surveillance in CQVID-19 Pandemic by AI Techniques, A Review. IEEE International Conference on Electronics, Computing and Communication Technologies. Kaushik, H., Kumar, T., Bhalla, K. (2022). iSecureHome: A Deep fusion framework for surveillance of smart house using real-time emotion recognition. Applied Soft Computing, 122, 122. doi:10.1016/j. asoc.2022.108788 Majeed, F., Khan, F. Z., Iqbal, M. J., Nazir, M. (2021). Real-Time Surveillance System based on Facial Recognition using YOLOv5. IEEE, Mohammad Ali Jinnah University International Conference on Computing, 1-6. 10.1109/MAJICC53071.2021.9526254
17 Artificial Intelligence in Video Surveillance  Naguib, A., Cheng, Y., Chang, Y. (2022). AI Assistance for Home Surveillance. IEEE, 27th Interna- tional Conference on Automation and Computing. Rohit,M.H.(2020).AnIoT-basedsystemforpublicTransportSurveillanceusingreal-timeDataAnalysis and Computer Vision. IEEE, 3rd International Conference on Advance in Electronics, Computers, and Communications. 10.1109/ICAECC50550.2020.9339485 Santhosh, Dogra, Roy. (2020). Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey. ACM Computing Surveys, 53(6). Sreedhara, Raj, George, Ashok. (2021). A Novel Cough Detection Algorithm for COVID-19 Surveil- lance at Public Place. IEEE, 8th International Conference on Smart Computing and Communications (ICSCC), 119-123. Sreenu, G., Saleem Durai, M. A. (2019). Intelligent video surveillance: A review through deep learn- ing techniques for crowd analysis. Journal of Big Data, 6(48), 1–27. doi:10.118640537-019-0212-5
18 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 2 DOI: 10.4018/978-1-6684-8098-4.ch002 ABSTRACT Content-based video retrieval is a research field that aims to develop advanced techniques for automati- cally analyzing and retrieving video content. This process involves identifying and localizing specific moments in a video and retrieving videos with similar content. Deep bimodal fusion (DBF) is proposed that uses modified convolution neural networks (CNNs) to achieve considerable visual modality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities. By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method has higher precision, recall, F1-score, and accuracy in precise searching that retrieves relevant videos more quickly and effectively. Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach G. Megala https://orcid.org/0000-0002-8084-8292 Vellore Institute of Technology, India P. Swarnalatha Vellore Institute of Technology, India S. Prabu Pondicherry University, India R. Venkatesan https://orcid.org/0000-0002-4336-8628 SASTRA University, India Anantharajah Kaneswaran University of Jaffna, Sri Lanka
19 Content-Based Video Retrieval With Temporal Localization  INTRODUCTION Multimedia information systems are becoming more crucial due to the growth of internet access, big data, and high-speed networks as well as the increasing need for multimedia information with visualiza- tion. Multimedia data, however, needs a lot of processing (Megala et al., 2021) and storage (Megala Swarnalatha, 2022). Therefore, there is a requirement for effective extraction, archiving, indexing, and retrieval of video content from a huge multimedia database. The video has emerged as one of the most prevalent methods to share information because it is visual and powerful. Many people around the world have easy access to it. Media administrators find it hard to use video material for storage and search. Prominent web browsers today often skip searches that are heavy on content in facilitate subtitles that contain basic information regarding the videos being searched. As an alternative to traditional techniques of keyword search, users on online platforms desire to look up precise videos in almost real-time. Video moment localization and content-based video retrieval using deep bimodal fusion is an emerg- ing research field that aims to develop advanced techniques for analyzing and retrieving video content. With the proliferation of digital video content, the need for efficient and effective video retrieval systems has become increasingly important in a wide range of applications, including entertainment, education, and surveillance. The process of video moment localization involves identifying and localizing specific moments in a video, such as a particular scene or event. This can be a challenging task, as videos can contain a wide range of visual and auditory information, making it difficult to accurately identify specific moments of interest. Content-based video retrieval, on the other hand, involves retrieving videos that contain similar content to a given query video. Objects that occurred in the video or images are identified and are saved as a bag of visual features. Efficient object detection methods (Megala Swarnalatha, 2023) are used to perform depth prediction along spatial and temporal features. These bag of features are more helpful in the retrieval process. This process requires the development of accurate models for analyzing video content and identifying similarities between videos. To address these challenges, researchers have turned to deep learning techniques, particularly deep bimodal fusion. This approach involves integrating information from both visual and audio modalities to develop more accurate models for analyzing and retrieving video content. By combining information from both modalities, researchers can develop more robust and accurate models for identifying specific moments in videos and retrieving relevant videos based on content. In this work, we describe a deep bimodal fusion (DBF) method for recognizing a person’s obvious personality from movies, which addresses this issue and yields better results than previously published research. The DBF framework’s structure is depicted in Figure 1. Overall, the use of deep bimodal fusion techniques in video moment localization and content-based video retrieval has the potential to revolutionize the way we analyze and retrieve video content, making it easier and faster to find relevant videos in a wide range of applications. The structure of this chapter is as follows: related works on video retrieval followed by the proposed method, experimental analysis, and conclusion.
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The Project Gutenberg eBook of Aaron in the Wildwoods
This ebook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this ebook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook. Title: Aaron in the Wildwoods Author: Joel Chandler Harris Illustrator: Oliver Herford Release date: August 12, 2016 [eBook #52782] Most recently updated: October 23, 2024 Language: English Credits: Produced by David Edwards, Andrés V. Galia and the Online Distributed Proofreading Team at http://www.pgdp.net (This file was produced from images generously made available by The Internet Archive) *** START OF THE PROJECT GUTENBERG EBOOK AARON IN THE WILDWOODS ***
MR. COON INSISTED ON GADDING ABOUT. (Page 46)
Aaron in the Wildwoods BY JOEL CHANDLER HARRIS AUTHOR OF UNCLE REMUS, ETC. ILLUSTRATED BY OLIVER HERFORD BOSTON AND NEW YORK HOUGHTON, MIFFLIN AND COMPANY The Riverside Press, Cambridge 1897
COPYRIGHT, 1897 BY JOEL CHANDLER HARRIS AND HOUGHTON, MIFFLIN AND CO. ALL RIGHTS RESERVED
CONTENTS. CHAPTER Page Prelude 1 I. The Little Master 23 II. The Secrets of the Swamp 38 III. What Chunky Riley saw and heard 56 IV. Between Midnight and Dawn 74 V. The Hunt begins 92 VI. The Hunt ends 111 VII. Aaron sees the Signal 129 VIII. The Happenings of a Night 148 IX. The Upsetting of Mr. Gossett 166 X. Chunky Riley sees a Queer Sight 185 XI. The Problem that Timoleon presented 202 XII. What the Patrollers saw and heard 219 XIII. The Apparition the Fox Hunters saw 237 XIV. The Little Master says Good Night 253
LIST OF ILLUSTRATIONS Page Mr. Coon insisted on gadding about Frontispiece. It was a Swamp 8 That's Randall's Song 32 Mr. Red Fox meets Mr. Gray Fox 40 A-straddle of the Grunter's Back 48 The Horses were right at his Heels 72 The Goblin Pain 76 The Spring of Cool Refreshing Water 80 Brindle and Aaron 104 In the Swamp 124 Rambler's Fight with the Moccasin 132 He stood as still as a Statue 144 It was the White-Haired Master 160 They tore him all to Flinders 172 The Excited Horse plunged along 180 He edged away as far as he could 188 Aaron and Little Crotchet 212 Behind a Tree stood George Gossett 216 The Black Stallion 224 It was fine for Mr. Fox 238 The Phantom Horseman 242
Aaron and Timoleon 250 Big Sal holds the Little Master 262 The Death of the Little Master 268
Aaron in the Wildwoods.
Prelude. I. Once upon a time there lived on a large plantation in Middle Georgia a boy who was known as Little Crotchet. It was a very queer name, to be sure, but it seemed to fit the lad to a T. When he was a wee bit of a chap he fell seriously ill, and when, many weeks afterwards, the doctors said the worst was over, it was found that he had lost the use of his legs, and that he would never be able to run about and play as other children do. When he was told about this he laughed, and said he had known all along that he would never be able to run about on his feet again; but he had plans of his own, and he told his father that he wanted a pair of crutches made. But you can't use them, my son, said his father. Anyhow, I can try, insisted the lad. The doctors were told of his desire, and these wise men put their heads together. It is a crotchet, they declared, but it will be no harm for him to try. It is a little crotchet, said his mother, and he shall have the crutches. Thus it came about that the lad got both his name and his crutches, for his father insisted on calling him Little Crotchet after that, and he also insisted on sending all the way to Philadelphia for the crutches.
They seemed to be a long time in coming, for in those days they had to be brought to Charleston in a sailing vessel, and then sent by way of Augusta in a stage-coach; but when they came they were very welcome, for Little Crotchet had been inquiring for them every day in the week, and Sunday too. And yet when they came, strange to say, he seemed to have lost his interest in them. His mother brought them in joyously, but there was not even a glad smile on the lad's face. He looked at them gravely, weighed them in his hands, laid them across the foot of the bed, and then turned his head on his pillow, as if he wanted to go to sleep. His mother was surprised, and not a little hurt, as mothers will be when they do not understand their children; but she respected his wishes, darkened the room, kissed her boy, and closed the door gently. When everything was still, Little Crotchet sat up in bed, seized his crutches, and proceeded to try them. He did this every day for a week, and at the end of that time surprised everybody in the house, and on the place as well, by marching out on his crutches, and going from room to room without so much as touching his feet to the floor. It seemed to be a most wonderful feat to perform, and so it was; but Providence, in depriving the lad of the use of his legs, had correspondingly strengthened the muscles of his chest and arms, so that within a month he could use his crutches almost as nimbly and quite as safely as other boys use their feet. He could go upstairs and downstairs and walk about the place with as much ease, apparently, as those not afflicted, and it was not strange that the negroes regarded the performance with wonder akin to awe, declaring among themselves that their young master was upheld and supported by de sperits. And indeed it was a queer sight to see the frail lad going boldly about on crutches, his feet not touching the ground. The sight seemed to make the pet name of Little Crotchet more appropriate than ever. So his name stuck to him, even after he got his Gray Pony, and became a familiar figure in town and in country, as he went galloping about, his crutches strapped to the saddle, and dangling as gayly as the sword of some fine general. Thus it came to
pass that no one was surprised when Little Crotchet went cantering along, his Gray Pony snorting fiercely, and seeming never to tire. Early or late, whenever the neighbors heard the short, sharp snort of the Gray Pony and the rattling of the crutches, they would turn to one another and say, Little Crotchet! and that would be explanation enough. There seemed to be some sort of understanding between him and his Gray Pony. Anybody could ride the Gray Pony in the pasture or in the grove around the house, but when it came to going out by the big gate, that was another matter. He could neither be led nor driven beyond that boundary by any one except Little Crotchet. It was the same when it came to crossing water. The Gray Pony would not cross over the smallest running brook for any one but Little Crotchet; but with the lad on his back he would plunge into the deepest stream, and, if need be, swim across it. All this deepened and confirmed in the minds of the negroes the idea that Little Crotchet was upheld and protected by de sperits. They had heard him talking to the Gray Pony, and they had heard the Gray Pony whinny in reply. They had seen the Gray Pony with their little master on his back go gladly out at the big gate and rush with a snort through the plantation creek,— a bold and at times a dangerous stream. Seeing these things, and knowing the temper of the pony, they had no trouble in coming to the conclusion that something supernatural was behind it all. II. Thus it happened that Little Crotchet and his Gray Pony were pretty well known through all the country-side, for it seemed that he was never tired of riding, and that the pony was never tired of going. What was the rider's errand? Nobody knew. Why should he go skimming along the red road at day dawn? And why should he come whirling back at dusk,—a red cloud of dust rising beneath the Gray Pony's feet? Nobody could tell. This was almost as much of a puzzle to some of the whites as it was to the negroes; but this mystery, if it could be called such, was soon
eclipsed by a phenomenon that worried some of the wisest dwellers in that region. This phenomenon, apparently very simple, began to manifest itself in early fall, and continued all through that season and during the winter and on through the spring, until warm weather set in. It was in the shape of a thin column of blue smoke that could be seen on any clear morning or late afternoon rising from the centre of Spivey's Canebrake. This place was called a canebrake because a thick, almost impenetrable, growth of canes fringed the edge of a mile-wide basin lying between the bluffs of the Oconee River and the uplands beyond. Instead of being a canebrake it was a vast swamp, the site of cool but apparently stagnant ponds and of treacherous quagmires, in which cows, and even horses, had been known to disappear and perish. The cowitch grew there, and the yellow plumes of the poison-oak vine glittered like small torches. There, too, the thunder-wood tree exuded its poisonous milk, and long serpent-like vines wound themselves around and through the trees, and helped to shut out the sunlight. It was a swamp, and a very dismal one. The night birds gathered there to sleep during the day, and all sorts of creatures that shunned the sunlight or hated man found a refuge there. If the negroes had made paths through its recesses to enable them to avoid the patrol, nobody knew it but themselves. Why, then, should a thin but steady stream of blue smoke be constantly rising upwards from the centre of Spivey's Canebrake? It was a mystery to those who first discovered it, and it soon grew to be a neighborhood mystery. During the summer the smoke could not be seen, but in the fall and winter its small thin volume went curling upward continually. Little Crotchet often watched it from the brow of Turner's Hill, the highest part of the uplands. Early in the morning or late in the afternoon the vapor would rise from the Oconee; but the vapor was white and heavy, and was blown about by the wind, while the smoke in the swamp was blue and thin, and rose straight in the air above the tops of the trees in spite of the wayward winds. Once when Little Crotchet was sitting on his pony watching the blue smoke rise from the swamp he saw two of the neighbor farmers
coming along the highway. They stopped and shook hands with the lad, and then turned to watch the thin stream of blue smoke. The morning was clear and still, and the smoke rose straight in the air, until it seemed to mingle with the upper blue. The two farmers were father and son,—Jonathan Gadsby and his son Ben. They were both very well acquainted with Little Crotchet,—as, indeed, everybody in the county was,—and he was so bright and queer that they stood somewhat in awe of him. I reckin if I had a pony that wasn't afeard of nothin' I'd go right straight and find out where that fire is, and what it is, remarked Ben Gadsby. This stirred his father's ire apparently. Why, Benjamin! Why, what on the face of the earth do you mean? Ride into that swamp! Why, you must have lost what little sense you had when you was born! I remember, jest as well as if it was day before yesterday, when Uncle Jimmy Cosby's red steer got in that swamp, and we couldn't git him out. Git him out, did I say? We couldn't even git nigh him. We could hear him beller, but we never got where we could see ha'r nor hide of him. If I was thirty year younger I'd take my foot in my hand and wade in there and see where the smoke comes from.
IT WAS A SWAMP
Little Crotchet laughed. If I had two good legs, said he, I'd soon see what the trouble is. This awoke Ben Gadsby's ambition. I believe I'll go in there and see where the fire is. Fire! exclaimed old Mr. Gadsby, with some irritation. Who said anything about fire? What living and moving creetur could build a fire in that thicket? I'd like mighty well to lay my eyes on him. Well, said Ben Gadsby, where you see smoke there's obliged to be fire. I've heard you say that yourself. Me? exclaimed Mr. Jonathan Gadsby, with a show of alarm in the midst of his indignation. Did I say that? Well, it was when I wasn't so much as thinking that my two eyes were my own. What about foxfire? Suppose that some quagmire or other in that there swamp has gone and got up a ruction on its own hook? Smoke without fire? Why, I've seed it many a time. And maybe that smoke comes from an eruption in the ground. What then? Who's going to know where the fire is? Little Crotchet laughed, but Ben Gadsby put on a very bold front. Well, said he, I can find bee-trees, and I'll find where that fire is. Well, sir, remarked Mr. Jonathan Gadsby, looking at his son with an air of pride, find out where the smoke comes from, and we'll not expect you to see the fire. I wish I could go with you, said Little Crotchet. I don't need any company, replied Ben Gadsby. I've done made up my mind, and I a-going to show the folks around here that where there's so much smoke there's obliged to be some fire. The young man, knowing that he had some warm work before him, pulled off his coat, and tied the sleeves over his shoulder, sash fashion. Then he waved his hand to his father and to Little Crotchet, and went rapidly down the hill. He had undertaken the adventure in a spirit of bravado. He knew that a number of the neighbors had
tried to solve the mystery of the smoke in the swamp and had failed. He thought, too, that he would fail; and yet he was urged on by the belief that if he should happen to succeed, all the boys and all the girls in the neighborhood would regard him as a wonderful young man. He had the same ambition that animated the knight of old, but on a smaller scale. III. Now it chanced that Little Crotchet himself was on his way to the smoke in the swamp. He had been watching it, and wondering whether he should go to it by the path he knew, or whether he should go by the road that Aaron, the runaway, had told him of. Ben Gadsby interfered with his plans somewhat; for quite by accident, young Gadsby as he went down the hill struck into the path that Little Crotchet knew. There was a chance to gallop along the brow of the hill, turn to the left, plunge through a shallow lagoon, and strike into the path ahead of Gadsby, and this chance Little Crotchet took. He waved his hand to Mr. Jonathan Gadsby, gave the Gray Pony the rein, and went galloping through the underbrush, his crutches rattling, and the rings of the bridle-bit jingling. To Mr. Jonathan Gadsby it seemed that the lad was riding recklessly, and he groaned and shook his head as he turned and went on his way. But Little Crotchet rode on. Turning sharply to the left as soon as he got out of sight, he went plunging through the lagoon, and was soon going along the blind path a quarter of a mile ahead of Ben Gadsby. This is why young Gadsby was so much disturbed that he lost his way. He was bold enough when he started out, but by the time he had descended the hill and struck into what he thought was a cattle- path his courage began to fail him. The tall canes seemed to bend above him in a threatening manner. The silence oppressed him. Everything was so still that the echo of his own movements as he brushed along the narrow path seemed to develop into ominous whispers, as if all the goblins he had ever heard of had congregated in front of him to bar his way.
The silence, with its strange echoes, was bad enough, but when he heard the snorting of Little Crotchet's Gray Pony as it plunged through the lagoon, the rattle of the crutches and the jingling of the bridle-bit, he fell into a panic. What great beast could it be that went helter-skelter through this dark and silent swamp, swimming through the water and tearing through the quagmires? And yet, when Ben Gadsby would have turned back, the rank undergrowth and the trailing vines had quite obscured the track. The fear that impelled him to retrace his steps was equally powerful in impelling him to go forward. And this seemed the easiest plan. He felt that it would be just as safe to go on, having once made the venture, as to turn back. He had a presentiment that he would never find his way out anyhow, and the panic he was in nerved him to the point of desperation. So on he went, not always trying to follow the path, but plunging forward aimlessly. In half an hour he was calmer, and pretty soon he found the ground firm under his feet. His instincts as a bee-hunter came back to him. He had started in from the east side, and he paused to take his bearings. But it was hard to see the sun, and in the recesses of the swamp the mosses grew on all sides of the trees. And yet there was a difference, which Ben Gadsby did not fail to discover and take account of. They grew thicker and larger on the north side, and remembering this, he went forward with more confidence. He found that the middle of the swamp was comparatively dry. Huge poplar-trees stood ranged about, the largest he had ever seen. In the midst of a group of trees he found one that was hollow, and in this hollow he found the smouldering embers of a fire. But for the strange silence that surrounded him he would have given a whoop of triumph; but he restrained himself. Bee-hunter that he was, he took his coat from his shoulders and tied it around a small slim sapling standing near the big poplar where he had found the fire. It was his way when he found a bee-tree. It was a sort of guide. In returning he would take the general direction, and then hunt about
until he found his coat; and it was much easier to find a tree tagged with a coat than it was to find one not similarly marked. Thus, instead of whooping triumphantly, Ben Gadsby simply tied his coat about the nearest sapling, nodding his head significantly as he did so. He had unearthed the secret and unraveled the mystery, and now he would go and call in such of the neighbors as were near at hand and show them what a simple thing the great mystery was. He knew that he had found the hiding-place of Aaron, the runaway. So he fixed his landmark, and started out of the swamp with a lighter heart than he had when he came in. To make sure of his latitude and longitude, he turned in his tracks when he had gone a little distance and looked for the tree on which he had tied his coat. But it was not to be seen. He re-traced his steps, trying to find his coat. Looking about him cautiously, he saw the garment after a while, but it was in an entirely different direction from what he supposed it would be. It was tied to a sapling, and the sapling was near a big poplar. To satisfy himself, he returned to make a closer examination. Sure enough, there was the coat, but the poplar close by was not a hollow poplar, nor was it as large as the tree in which Ben Gadsby had found the smouldering embers of a fire. He sat on the trunk of a fallen tree and scratched his head, and discussed the matter in his mind the best he could. Finally he concluded that it would be a very easy matter, after he found his coat again, to find the hollow poplar. So he started home again. But he had not gone far when he turned around to take another view of his coat. It had disappeared. Ben Gadsby looked carefully around, and then a feeling of terror crept over his whole body—a feeling that nearly paralyzed his limbs. He tried to overcome this feeling, and did so to a certain degree. He plucked up sufficient courage to return and try to find his coat; but the task was indeed bewildering. He thought he had never seen so many large poplars with small slim saplings
standing near them, and then he began to wander around almost aimlessly. IV. Suddenly he heard a scream that almost paralyzed him—a scream that was followed by the sound of a struggle going on in the thick undergrowth close at hand. He could see the muddy water splash above the bushes, and he could hear fierce growlings and gruntings. Before he could make up his mind what to do, a gigantic mulatto, with torn clothes and staring eyes, rushed out of the swamp and came rushing by, closely pursued by a big white boar with open mouth and fierce cries. The white boar was right at the mulatto's heels, and his yellow tusks gleamed viciously as he ran with open mouth. Pursuer and pursued disappeared in the bushes with a splash and a crash, and then all was as still as before. In fact, the silence seemed profounder for this uncanny and appalling disturbance. It was so unnatural that half a minute after it happened Ben Gadsby was not certain whether it had occurred at all. He was a pretty bold youth, having been used to the woods and fields all his life, but he had now beheld a spectacle so out of the ordinary, and of so startling a character, that he made haste to get out of the swamp as fast as his legs, weakened by fear, would carry him. More than once, as he made his way out of the swamp, he paused to listen; and it seemed that each time he paused an owl, or some other bird of noiseless wing, made a sudden swoop at his head. Beyond the exclamation he made when this happened the silence was unbroken. This experience was unusual enough to hasten his steps, even if he had had no other motive for haste. When nearly out of the swamp, he came upon a large poplar, by the side of which a small slim sapling was growing. Tied around this sapling was his coat, which he thought he had left in the middle of the swamp. The sight almost took his breath away.
He examined the coat carefully, and found that the sleeves were tied around the tree just as he had tied them. He felt in the pockets. Everything was just as he had left it. He examined the poplar; it was hollow, and in the hollow was a pile of ashes. Well! exclaimed Ben Gadsby. I'm the biggest fool that ever walked the earth. If I ain't been asleep and dreamed all this, I'm crazy; and if I've been asleep, I'm a fool. His experience had been so queer and so confusing that he promised himself he'd never tell it where any of the older people could hear it, for he knew that they would not only treat his tale with scorn and contempt, but would make him the butt of ridicule among the younger folks. I know exactly what they'd say, he remarked to himself. They'd declare that a skeer'd hog run across my path, and that I was skeer'der than the hog. So Ben Gadsby took his coat from the sapling, and went trudging along his way toward the big road. When he reached that point he turned and looked toward the swamp. Much to his surprise, the stream of blue smoke was still flowing upward. He rubbed his eyes and looked again, but there was the smoke. His surprise was still greater when he saw Little Crotchet and the Gray Pony come ambling up the hill in the path he had just come over. What did you find? asked Little Crotchet, as he reined in the Gray Pony. Nothing—nothing at all, replied Ben Gadsby, determined not to commit himself. Nothing? cried Little Crotchet. Well, you ought to have been with me! Why, I saw sights! The birds flew in my face, and when I got in the middle of the swamp a big white hog came rushing out, and if this Gray Pony hadn't been the nimblest of his kind, you'd never have seen me any more. Is that so? asked Ben Gadsby, in a dazed way. Well, I declare! 'Twas all quiet with me. I just went in and come out again, and that's all there is to it.
I wish I'd been with you, said Little Crotchet, with a curious laugh. Good-by! With that he wheeled the Gray Pony and rode off home. Ben Gadsby watched Little Crotchet out of sight, and then, with a gesture of despair, surprise, or indignation, flung his coat on the ground, crying, Well, by jing! V. That night there was so much laughter in the top story of the Abercrombie house that the Colonel himself came to the foot of the stairs and called out to know what the matter was. It's nobody but me, replied Little Crotchet. I was just laughing. Colonel Abercrombie paused, as if waiting for some further explanation, but hearing none, said, Good-night, my son, and God bless you! Good-night, father dear, exclaimed the lad, flinging a kiss at the shadow his father's candle flung on the wall. Then he turned again into his own room, where Aaron the Arab (son of Ben Ali) sat leaning against the wall, as silent and as impassive as a block of tawny marble. Little Crotchet lay back in his bed, and the two were silent for a time. Finally Aaron said:— The White Grunter carried his play too far. He nipped a piece from my leg. I never saw anything like it, remarked little Crotchet. I thought the White Pig was angry. You did that to frighten Ben Gadsby. Yes, Little Master, responded Aaron, and I'm thinking the young man will never hunt for the smoke in the swamp any more. Little Crotchet laughed again, as he remembered how Ben Gadsby looked as Aaron and the White Pig went careening across the dry
place in the swamp. There was a silence again, and then Aaron said he must be going. And when are you going home to your master? Little Crotchet asked. Never! replied Aaron the runaway, with emphasis. Never! He is no master of mine. He is a bad man. Then he undressed Little Crotchet, tucked the cover about him,—for the nights were growing chill,—whispered good-night, and slipped from the window, letting down the sash gently as he went out. If any one had been watching, he would have seen the tall Arab steal along the roof until he came to the limb of an oak that touched the eaves. Along this he went nimbly, glided down the trunk to the ground, and disappeared in the darkness.
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Handbook Of Research On Deep Learning Techniques For Cloudbased Industrial Iot Teamira P Swarnalatha Editor

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  • 6. Published in the United States of America by IGI Global Engineering Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com Copyright © 2023 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: eresources@igi-global.com. Names: Swarnalatha, P. (Purushotham), 1977- editor. | Prabu, S., 1981- editor. Title: Handbook of research on deep learning techniques for cloud-based industrial IoT / edited by P. Swarnalatha, and S. Prabu. Description: Hershey, PA : Engineering Science Reference, [2023] | Includes bibliographical references and index. | Summary: “Deep Learning Techniques for Cloud-Based Industrial IoT aims to demonstrate how computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based Internet of Things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students”-- Provided by publisher. Identifiers: LCCN 2023000689 (print) | LCCN 2023000690 (ebook) | ISBN 9781668480984 (h/c) | ISBN 9781668480984 (eISBN) Subjects: LCSH: Internet of things--Industrial applications. | Deep learning (Machine learning) | Cloud computing. Classification: LCC TK5105.8857 .H337 2023 (print) | LCC TK5105.8857 (ebook) | DDC 004.67/82--dc23/eng/20230126 LC record available at https://lccn.loc.gov/2023000689 LC ebook record available at https://lccn.loc.gov/2023000690 This book is published in the IGI Global book series Advances in Computational Intelligence and Robotics (ACIR) (ISSN: 2327-0411; eISSN: 2327-042X)
  • 7. Advances in Computational Intelligence and Robotics (ACIR) Book Series While intelligence is traditionally a term applied to humans and human cognition, technology has pro- gressed in such a way to allow for the development of intelligent systems able to simulate many human traits. With this new era of simulated and artificial intelligence, much research is needed in order to continue to advance the field and also to evaluate the ethical and societal concerns of the existence of artificial life and machine learning. The Advances in Computational Intelligence and Robotics (ACIR) Book Series encourages scholarly discourse on all topics pertaining to evolutionary computing, artificial life, computational intelligence, machine learning, and robotics. ACIR presents the latest research being conducted on di- verse topics in intelligence technologies with the goal of advancing knowledge and applications in this rapidly evolving field. Mission Ivan Giannoccaro University of Salento, Italy ISSN:2327-0411 EISSN:2327-042X • Evolutionary Computing • Neural Networks • Agent technologies • Computer Vision • Artificial Intelligence • Intelligent Control • Pattern Recognition • Adaptive and Complex Systems • Heuristics • Synthetic Emotions Coverage IGI Global is currently accepting manuscripts for publication within this series. To submit a pro- posal for a volume in this series, please contact our AcquisitionEditorsatAcquisitions@igi-global.com or visit: http://www.igi-global.com/publish/. The Advances in Computational Intelligence and Robotics (ACIR) Book Series (ISSN 2327-0411) is published by IGI Global, 701 E. Chocolate Avenue, Hershey, PA 17033-1240, USA, www.igi-global.com. This series is composed of titles available for purchase individually; each title is edited to be contextually exclusive from any other title within the series. For pricing and ordering information please visit http:// www.igi-global.com/book-series/advances-computational-intelligence-robotics/73674. Postmaster: Send all address changes to above address. Copyright © 2023 IGI Global. All rights, including translation in other languages reserved by the publisher. No part of this series may be reproduced or used in any form or by any means – graphics, electronic, or mechanical, including photocopying, recording, taping, or informa- tion and retrieval systems – without written permission from the publisher, except for non commercial, educational use, including classroom teaching purposes. The views expressed in this series are those of the authors, but not necessarily of IGI Global.
  • 8. Titles in this Series For a list of additional titles in this series, please visit: www.igi-global.com/book-series Global Perspectives on Robotics and Autonomous Systems Development and Applications Maki K. Habib (The American University in Cairo, gypt) Engineering Science Reference • © 2023 • 310pp • H/C (ISBN: 9781668477915) • US $280.00 Scalable and Distributed Machine Learning and Deep Learning Patterns J. Joshua Thomas (UOW Malaysia KDU Penang University College, Malaysia) S. Harini (Vellore Institute of Technology, India) and V. Pattabiraman (Vellore Institute of Technology, ndia) Engineering Science Reference • © 2023 • 300pp • H/C (ISBN: 9781668498040) • US $270.00 Application and Adoption of Robotic Process Automation for Smart Cities R. K. Tailor (Manipal University Jaipur, India) Engineering Science Reference • © 2023 • 320pp • H/C (ISBN: 9781668471937) • US $270.00 Handbook of Research on Advancements in AI and IoT Convergence Technologies Jingyuan Zhao (University of Toronto, Canada) V. Vinoth Kumar (Jain University, India) Rajesh Natarajan (Uni- versity of Applied Science and Technology, Shinas, Oman) and T.R. Mahesh (Jain University, India) Engineering Science Reference • © 2023 • 415pp • H/C (ISBN: 9781668469712) • US $380.00 Stochastic Processes and Their Applications in Artificial Intelligence Christo Ananth (Samarkand State University, Uzbekistan) N. Anbazhagan (Alagappa University, India) and Mark Goh (National University of Singapore, Singapore) Engineering Science Reference • © 2023 • 325pp • H/C (ISBN: 9781668476796) • US $270.00 Applying AI-Based IoT Systems to Simulation-Based Information Retrieval Bhatia Madhulika (Amity University, India) Bhatia Surabhi (King Faisal University, Saudi Arabia) Poonam Tanwar (Manav Rachna International Institute of Research and Studies, India) and Kuljeet Kaur (Université du Québec, Canada) Engineering Science Reference • © 2023 • 229pp • H/C (ISBN: 9781668452554) • US $270.00 AI-Enabled Social Robotics in Human Care Services Sandeep Kautish (Lord Buddha Education Foundation, Nepal) Nirbhay Kumar Chaubey (Ganpat University, India) S.B. Goyal (City University, Malaysia) and Pawan Whig (Vivekananda Insitute of Professional Studies, India) Engineering Science Reference • © 2023 • 321pp • H/C (ISBN: 9781668481714) • US $270.00 701 East Chocolate Avenue, Hershey, PA 17033, USA Tel: 717-533-8845 x100 • Fax: 717-533-8661 E-Mail: cust@igi-global.com • www.igi-global.com
  • 9.   List of Contributors  Aarthi, G. / B.S. Abdur Rahman Crescent Institute of Science and Technology, India....................106 Abinaya, V. / Hindusthan College of Arts and Sciences, India.........................................................255 Aisha Banu, W. / B.S. Abdur Rahman Crescent Institute of Science and Technology, India...........106 Appe, Seetharam Nagesh / Annamalai University, India .................................................................278 Arage, Pranav / Vellore Institute of Technology, India.....................................................................155 Arulselvi, G. / Annamalai University, India .....................................................................................278 Babu, C. V. Suresh / Hindustan Institute of Technolgy and Science, India ......................................322 Balaji, V. / Vardhaman College of Engineering, India .....................................................................201 Basha, Niha Kamal / Vellore Institute of Technology, India...............................................................61 Bhattacharyay, Rajarshi / School of Computer Science and Engineering, Vellore Institute of Technology, India .........................................................................................................................236 Bollipelly, Shiva Chaithanya Goud / Vellore Institute of Technology, India ...........................124, 354 Boopathi, Sampath / Muthayammal Engineering College, India............................................219, 290 C. R., Komala / HKBK College of Engineering, India.....................................................................219 C. S., Mohan Raj / Hindusthan College of Arts and Science, India .................................................134 Chaturvedi, Ankita / IIS University (Deemed), India......................................................................255 Chavan, Sahil Manoj / Department of Electrical Power System, Sandip University, India.............290 Dash, Samikshya / School of Computer Science and Engineering, VIT-AP University, India ........219 G. N., Balaji / Vellore Institute of Technology, India........................................................................278 Harshitha, Vemuri Lakshmi / Vellore Institute of Technology, India................................................61 Hema, N. / Department of Information Science and Engineering, RNS Institute of Technology, India..............................................................................................................................................290 Irawati, Indrarini Dyah / Telkom University, Indonesia ..................................................................366 Janapriyan, R. / Hindustan Institute of Technology and Science, India ..........................................322 Joshi, Aditya Deepak / School of Computer Science and Engineering, Vellore Institute of Technology, India .........................................................................................................................236 K. R., Jothi / Vellore Institute of Technology, India..........................................................................338 K., Suresh Joseph / Pondicherry University, India...........................................................................172 Kalyanaraman, P. / Vellore Institute of Technology, India ......................................................309, 338 Kaneswaran, Anantharajah / University of Jaffna, Sri Lanka..........................................................18 Katte, Pranav / Vellore Institute of Technology, India .....................................................................155 Kaur, Gaganpreet / Chitkara University, India ...............................................................134, 255, 366 Khalid, Saifullah / Civil Aviation Research Organisation, India.....................................................134 Koushik, Katakam / CVR College of Engineering, India..................................................................79
  • 10.  Krishnamoorthy, N. / College of Science and Humanities, SRM Institute of Science and Technology, India .........................................................................................................................290 Krishnaveni, M. / Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India................................................................29 Kumar, A. V. Senthil / Hindusthan College of Arts and Sciences, India..................................134, 255 Kumar, A.V. Senthil / Hindusthan College of Arts and Sciences, India...........................................366 Kumar, N. M. G. / Sree Vidyanikethan Engineering College, Mohan Babu University, India.........290 Kuppusamy, Palanivel / Pondicherry University, India...................................................................172 Latip, Rohaya / Universiti Teknologi MARA, Malaysia ...................................................134, 255, 366 M., MohanaKrishnan / Hindusthan College of Arts and Sciences, India .......................................366 Meenakshi, S. / R.M.K. Engineering College, India ........................................................................219 Megala, G. / Vellore Institute of Technology, India ....................................................................18, 309 Mishra, Namita / ITS School of Management, India........................................................................134 Musirin, Ismail / Universiti Teknologi MARA, Malaysia.................................................................134 N. S., Akshayah / Hindustan Institute of Technology and Science, India.........................................322 Nadkarni, Satvik / Vellore Institute of Technology, India................................................................155 P., Maclin Vinola / Hindustan Institute of Technology and Science, India .......................................322 P., Uma Maheswari / CEG, Anna University, Chennai, India ..............................................................1 Parvaze Podili, Pariha / Vellore Institute of Technology, India.........................................................61 Prabakaran, N. / School of Computer Science and Engineering, Vellore Institute of Technology, India..............................................................................................................................................236 Prabu, S. / Pondicherry University, India...........................................................................................18 Pramila, P. V. / Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India..............................................................................................................................219 Raagav, Rithun / Vellore Institute of Technology, India...................................................................309 Rajasekaran, P. / School of Computing, SRM Institute of Science and Technology, India..............236 Sabarimuthu, M. / Kongu Engineering College, India....................................................................290 Sahai, Shivansh / Vellore Institute of Technology, India..................................................................201 Saleh, Omar S. / School of Computing, Universiti Utara Malaysia, Malaysia.................................366 Samuel, Jonathan Rufus / Vellore Institute of Technology, India ....................................................201 Sandhya, Karjala / Vellore Institute of Technology, India .................................................................61 Sanjay, V. / Vellore Institute of Technology, Vellore, India.................................................................92 Selvanambi, Ramani / Vellore Institute of Technology, India..........................................................155 Sevugan, Prabu / Pondicherry University, India................................................................79, 201, 354 Shanmugananthan, Suganthi / Annamalai University, India .........................................................172 Sharmila, L. / Agni College of Technology, India ............................................................................354 Sharon Priya, S. / B.S. Abdur Rahman Crescent Institute of Science and Technology, India..........106 Shiwakoti, Prarthana / Vellore Institute of Technology, India ........................................................338 Subashini, P. / Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India..........................................................................29 Suryanarayana, S. Venkata / CVR College of Engineering, India.....................................................79 Susan M. B., Jennyfer / Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India..........................................................29 Swarnalatha, P. / Vellore Institute of Technology, India ..............................................18, 92, 124, 201 Syamala, Maganti / Koneru Lakshmaiah Education Foundation, India..........................................219
  • 11.  Talukdar, Veera / RNB Global University, India .....................................................................255, 366 V. R., Karishma / Anna University, Chennai, India.............................................................................1 Valliammai, V. / Vellore Institute of Technology, India......................................................................61 Vanishree, G. / IBS Hyderabad, India ..............................................................................................255 Venkatesan, R. / SASTRA University, India ...............................................................................18, 354 Vigneswaran, T. / SRM-TRB Engineering College, India....................................................................1
  • 12.  Table of Contents  Foreword............................................................................................................................................xxiii Preface. ................................................................................................................................................ xxv Chapter 1 Artificial Intelligence in Video Surveillance........................................................................................... 1 Uma Maheswari P., CEG, Anna University, Chennai, India Karishma V. R., Anna University, Chennai, India T. Vigneswaran, SRM-TRB Engineering College, India Chapter 2 Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach................................................................................................................................................ 18 G. Megala, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India S. Prabu, Pondicherry University, India R. Venkatesan, SASTRA University, India Anantharajah Kaneswaran, University of Jaffna, Sri Lanka Chapter 3 Artificial Intelligence of Things for Smart Healthcare Development: An Experimental Review......... 29 Jennyfer Susan M. B., Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India P. Subashini, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India M. Krishnaveni, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India Chapter 4 Cloud-Based Intelligent Virtual Try-On Using Augmented Reality..................................................... 61 V. Valliammai, Vellore Institute of Technology, India Karjala Sandhya, Vellore Institute of Technology, India Vemuri Lakshmi Harshitha, Vellore Institute of Technology, India Pariha Parvaze Podili, Vellore Institute of Technology, India Niha Kamal Basha, Vellore Institute of Technology, India
  • 13.  Chapter 5 Insulator Fault Detection From UAV Images Using YOLOv5.............................................................. 79 S. Venkata Suryanarayana, CVR College of Engineering, India Katakam Koushik, CVR College of Engineering, India Prabu Sevugan, Pondicherry University, India Chapter 6 Blockchain-Based Deep Learning Approach for Alzheimer’s Disease Classification.......................... 92 V. Sanjay, Vellore Institute of Technology, Vellore, India P. Swarnalatha, Vellore Institute of Technology, Vellore, India Chapter 7 Intrusion Detection on NF-BoT-IoT Dataset Using Artificial Intelligence Techniques. ...................... 106 G. Aarthi, B.S. Abdur Rahman Crescent Institute of Science and Technology, India S. Sharon Priya, B.S. Abdur Rahman Crescent Institute of Science and Technology, India W. Aisha Banu, B.S. Abdur Rahman Crescent Institute of Science and Technology, India Chapter 8 Real-Time Object Detection and Audio Output System for Blind Users: Using YOLOv3 Algorithm and 360 Degree Camera Sensor......................................................................................... 124 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India Chapter 9 Role of IoT Technologies in Agricultural Ecosystems........................................................................ 134 Mohan Raj C. S., Hindusthan College of Arts and Science, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Ismail Musirin, Universiti Teknologi MARA, Malaysia Saifullah Khalid, Civil Aviation Research Organisation, India Rohaya Latip, Universiti Teknologi MARA, Malaysia Namita Mishra, ITS School of Management, India Gaganpreet Kaur, Chitkara University, India Chapter 10 Applications of Deep Learning in Robotics. ........................................................................................ 155 Pranav Katte, Vellore Institute of Technology, India Pranav Arage, Vellore Institute of Technology, India Satvik Nadkarni, Vellore Institute of Technology, India Ramani Selvanambi, Vellore Institute of Technology, India Chapter 11 A Multicloud-Based Deep Learning Model for Smart Agricultural Applications.............................. 172 Palanivel Kuppusamy, Pondicherry University, India Suresh Joseph K., Pondicherry University, India Suganthi Shanmugananthan, Annamalai University, India
  • 14.  Chapter 12 Automated MP3 Tag Editor via Data Mining: A Classification Software for Predicting MP3 Metadata. .............................................................................................................................................. 201 Jonathan Rufus Samuel, Vellore Institute of Technology, India Shivansh Sahai, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India V. Balaji, Vardhaman College of Engineering, India Chapter 13 Machine Learning-Integrated IoT-Based Smart Home Energy Management System. ........................ 219 Maganti Syamala, Koneru Lakshmaiah Education Foundation, India Komala C. R., HKBK College of Engineering, India P. V. Pramila, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India Samikshya Dash, School of Computer Science and Engineering, VIT-AP University, India S. Meenakshi, R.M.K. Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India Chapter 14 Generating Complex Animated Characters of Various Art Styles With Optimal Beauty Scores Using Deep Generative Adversarial Networks.................................................................................... 236 N. Prabakaran, School of Computer Science and Engineering, Vellore Institute of Technology, India Rajarshi Bhattacharyay, School of Computer Science and Engineering, Vellore Institute of Technology, India Aditya Deepak Joshi, School of Computer Science and Engineering, Vellore Institute of Technology, India P. Rajasekaran, School of Computing, SRM Institute of Science and Technology, India Chapter 15 Cloud-Based TPA Auditing With Risk Prevention. ............................................................................. 255 V. Abinaya, Hindusthan College of Arts and Sciences, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Rohaya Latip, Universiti Putra Malaysia, Malaysia Veera Talukdar, RNB Global University, India Ankita Chaturvedi, IIS University (Deemed), India G. Vanishree, IBS Hyderabad, India Gaganpreet Kaur, Chitkara University, India Chapter 16 Detection and Classification of Dense Tomato Fruits by Integrating Coordinate Attention Mechanism With YOLO Model. .......................................................................................................... 278 Seetharam Nagesh Appe, Annamalai University, India G. Arulselvi, Annamalai University, India Balaji G. N., Vellore Institute of Technology, India
  • 15.  Chapter 17 A Study on an Internet of Things (IoT)-Enabled Smart Solar Grid System........................................ 290 N. Hema, Department of Information Science and Engineering, RNS Institute of Technology, India N. Krishnamoorthy, College of Science and Humanities, SRM Institute of Science and Technology, India Sahil Manoj Chavan, Department of Electrical Power System, Sandip University, India N. M. G. Kumar, Sree Vidyanikethan Engineering College, Mohan Babu University, India M. Sabarimuthu, Kongu Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India Chapter 18 Accuracy Determination: An Enhanced Intrusion Detection System Using Deep Learning Approach.............................................................................................................................................. 309 Rithun Raagav, Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India G. Megala, Vellore Institute of Technology, India Chapter 19 IoT-Based Smart Accident Detection and Alert System...................................................................... 322 C. V. Suresh Babu, Hindustan Institute of Technolgy and Science, India Akshayah N. S., Hindustan Institute of Technology and Science, India Maclin Vinola P., Hindustan Institute of Technology and Science, India R. Janapriyan, Hindustan Institute of Technology and Science, India Chapter 20 Revolutionizing the Farm-to-Table Journey: A Comprehensive Review of Blockchain Technology in Agriculture Supply Chain................................................................................................................ 338 Prarthana Shiwakoti, Vellore Institute of Technology, India Jothi K. R., Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India Chapter 21 Blockchain-Based Messaging System for Secure and Private Communication: Using Blockchain and Double AES Encryption. ............................................................................................................... 354 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India R. Venkatesan, SASTRA University, India L. Sharmila, Agni College of Technology, India
  • 16.  Chapter 22 Artificial Intelligence in Cyber Security.............................................................................................. 366 MohanaKrishnan M., Hindusthan College of Arts and Sciences, India A.V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Veera Talukdar, RNB Global University, India Omar S. Saleh, School of Computing, Universiti Utara Malaysia, Malaysia Indrarini Dyah Irawati, Telkom University, Indonesia Rohaya Latip, Universiti Putra Malaysia, Malaysia Gaganpreet Kaur, Chitkara University Institute of Engineering and Technology, Chitkara University, India Compilation of References................................................................................................................ 386 About the Contributors..................................................................................................................... 423 Index.................................................................................................................................................... 430
  • 17.   Detailed Table of Contents  Foreword............................................................................................................................................xxiii Preface. ................................................................................................................................................ xxv Chapter 1 Artificial Intelligence in Video Surveillance........................................................................................... 1 Uma Maheswari P., CEG, Anna University, Chennai, India Karishma V. R., Anna University, Chennai, India T. Vigneswaran, SRM-TRB Engineering College, India Surveillance is an essential component of security, and e-surveillance is one of the primary goals of the Indian Government’s Digital India development initiative. Video surveillance offers a wide range of applications to reduce ecological and economic losses and becomes one of the most effective means of ensuring security. This chapter addresses the problem of how artificial intelligence is powering video surveillance.Thereisasignificantresearchfocusonvideoanalyticsbutcomparativelylessefforthasbeen taken for surveillance videos. However, there is little evidence that researchers have approached the issue of intelligent video surveillance in terms of suspicious action detection, crime scene description, face detection, crowd counting, and the like. Most AI-powered surveillance is based on deep neural networks and deep learning techniques using analysis of video frames as images. Consequently, this chapter aims to provide an overview and significance of how artificial intelligence techniques are employed in video surveillance and image processing. Chapter 2 Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach................................................................................................................................................ 18 G. Megala, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India S. Prabu, Pondicherry University, India R. Venkatesan, SASTRA University, India Anantharajah Kaneswaran, University of Jaffna, Sri Lanka Content-basedvideoretrievalisaresearchfieldthataimstodevelopadvancedtechniquesforautomatically analyzingandretrievingvideocontent.Thisprocessinvolvesidentifyingandlocalizingspecificmoments in a video and retrieving videos with similar content. Deep bimodal fusion (DBF) is proposed that uses modified convolution neural networks (CNNs) to achieve considerable visual modality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities.
  • 18.  By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method has higher precision, recall, F1-score, and accuracy in precise searching that retrieves relevant videos more quickly and effectively. Chapter 3 Artificial Intelligence of Things for Smart Healthcare Development: An Experimental Review......... 29 Jennyfer Susan M. B., Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India P. Subashini, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India M. Krishnaveni, Centre for Machine Learning and Intelligence, Avinashilingam Institute for Home Science and Higher Education for Women, India Smart healthcare systems are the health services that use the technologies like wearable devices, internet of things (IoT), and mobile internet to access medical information dynamically. It connects people, materials, and institutions related to healthcare; actively manages; and automatically responds to medical ecosystem needs. It helps the traditional medical system in making healthcare more efficient, convenient, and personalized. This chapter proposed (1) a review of smart healthcare development using artificial intelligence, the internet of things, and smartphone Android apps; (2) an experimental approach using IoT-based smart monitoring systems, Android apps for data collection, and artificial algorithms to predict cervical cancer diseases; (3) the integration of IoT and AI algorithms. Artificial intelligence of things (AIoT) is proposed in this chapter as an experimental method for predicting cervical cancer from smart colposcopy images. The literature published in international journals and proceedings between 2010 and June 2022 is considered for the study. Chapter 4 Cloud-Based Intelligent Virtual Try-On Using Augmented Reality..................................................... 61 V. Valliammai, Vellore Institute of Technology, India Karjala Sandhya, Vellore Institute of Technology, India Vemuri Lakshmi Harshitha, Vellore Institute of Technology, India Pariha Parvaze Podili, Vellore Institute of Technology, India Niha Kamal Basha, Vellore Institute of Technology, India Advancement of technology had a significant impact on various industries, with innovative solutions like cloud computing, IoT, augmented reality (AR), and virtual reality (VR) changing the game in many ways. Here is a system known as “virtual try-ons” that leverages IoT devices like mobile cameras, cloud storage for data, and an intelligent interface for user interaction. Many people are opting for online shopping, and various challenges arise with this transition, one of which is the issue of “try-on.” VR solvesthischallengebyintroducing“virtualtry-on,”whichreplacestraditionaltry-onmethods.Itenables an individual to preview and virtually try on their desired products like clothes, watches, shoes, etc. from the comfort of their own homes, making the shopping experience easier and smoother. It also adds an element of fun and excitement to the shopping experience, increasing the hedonic value for consumers and allowing consumers to experiment and play with different products, styles, and colors in a way that is not possible with traditional shopping methods.
  • 19.  Chapter 5 Insulator Fault Detection From UAV Images Using YOLOv5.............................................................. 79 S. Venkata Suryanarayana, CVR College of Engineering, India Katakam Koushik, CVR College of Engineering, India Prabu Sevugan, Pondicherry University, India Identification of insulator defects is one of the most important goals of an intelligent examination of high-voltage transmission lines. Because they provide mechanical support for electric transmission lines as well as electrical insulation, insulators are essential to the secure and reliable operation of power networks. A fresh dataset is first built by collecting aerial pictures in various scenes that have one or more defects. A feature pyramid network and an enhanced loss function are used by the CSPD-YOLO model to increase the precision of insulator failure detection. The insulator defective data set, which has two classes (insulator, defect), is used by the suggested technique to train and test the model using the YOLOv5 object detection algorithm. The authors evaluate how well the YOLOv3, YOLOv5, and related families perform when trained on the insulator defective dataset. Practitioners can use this information to choose the appropriate technique based on the insulator defective dataset. Chapter 6 Blockchain-Based Deep Learning Approach for Alzheimer’s Disease Classification.......................... 92 V. Sanjay, Vellore Institute of Technology, Vellore, India P. Swarnalatha, Vellore Institute of Technology, Vellore, India Blockchain is an emerging technology that is now being used to provide novel solutions in several industries, including healthcare. Deep learning (DL) algorithms have grown in popularity in medical image processing research. AD is diagnosed by magnetic resonance imaging (MRI) images. This study investigates the integration of blockchain technology with a DL model for Alzheimer’s disease prediction (AD). This proposed model was used to classify 3182 images from the ADNI collection. The edge-based segmentation algorithm has overcome the segmentation problem. During the investigation’s test stage, the DL-EfficientNetB0 model with blockchain earned the highest accuracy rate of 99.14%. The highest accuracy, sensitivity, and specificity scores were obtained utilizing the confusion matrix during the comparative assessment stage. According to the study’s results, EfficientNetB0 with blockchain model surpassed all other trained models in classification rate. This study will aid clinical research into the early detection and prevention of AD by identifying the sickness before it occurs. Chapter 7 Intrusion Detection on NF-BoT-IoT Dataset Using Artificial Intelligence Techniques. ...................... 106 G. Aarthi, B.S. Abdur Rahman Crescent Institute of Science and Technology, India S. Sharon Priya, B.S. Abdur Rahman Crescent Institute of Science and Technology, India W. Aisha Banu, B.S. Abdur Rahman Crescent Institute of Science and Technology, India The rapid development of internet of things (IoT) applications has created enormous possibilities, increased our productivity, and made our daily life easier. However, because of resource limitations and processing, IoT networks are open to number of threats. The network instruction detection system (NIDS) aims to provide a variety of methods for identifying the increasingly common cyberattacks (such as distributed denial of service [DDoS], denial of service [DoS], theft, etc.) and to prevent hazardous activities. In order to determine which algorithm is more effective in detecting network threats, multiple public datasets and different artificial intelligence (AI) techniques are evaluated. Some of the learning
  • 20.  algorithms like logistic regression, random forest, decision tree, naive bayes, auto-encoder, and artificial neuralnetworkwereanalysedandconcludedontheNF-BoT-IoTdatasetusingvariousevaluationmetrics. In order to train the model for future anomaly detection prediction and analysis, the feature extraction and pre-processing data were then supplied into NIDS as data. Chapter 8 Real-Time Object Detection and Audio Output System for Blind Users: Using YOLOv3 Algorithm and 360 Degree Camera Sensor......................................................................................... 124 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India This chapter aims to create a real-time object detection and audio output system for blind users using the YOLOv3 algorithm and a 360-degree camera sensor. The system is designed to detect a wide range of objects, including people, vehicles, and other objects in the environment, and provide audio feedback to the user. The system architecture consists of a 360-degree camera sensor, a processing unit, and an audio output system. The camera sensor captures the environment, which is processed by the processing unit, which uses the YOLOv3 algorithm to detect and classify objects. The audio output system provides audio feedback to the user based on the objects detected by the system. The project has significant importance for blind users as it can help them navigate their environment and recognize objects in real time and can serve as a foundation for future research in the field of object detection systems for blind users. Chapter 9 Role of IoT Technologies in Agricultural Ecosystems........................................................................ 134 Mohan Raj C. S., Hindusthan College of Arts and Science, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Ismail Musirin, Universiti Teknologi MARA, Malaysia Saifullah Khalid, Civil Aviation Research Organisation, India Rohaya Latip, Universiti Teknologi MARA, Malaysia Namita Mishra, ITS School of Management, India Gaganpreet Kaur, Chitkara University, India Increasingdemandforfoodqualityandsizehasincreasedtheneedforindustrializationandintensification in the agricultural sector. The internet of things (IoT) is a promising technology that offers many innovative solutions to transform the agricultural sector. Research institutes and scientific groups are constantly working to provide solutions and products for different areas of agriculture using IoT. The main objective of this methodological study is to collect all relevant research results on agricultural IoT applications, sensors/devices, communication protocols, and network types. The authors also talk about the main problems and encounters encountered in the field of agriculture. An IoT agriculture framework is also available that contextualizes the view of various current farming solutions. National guidelines on IoT-based agriculture were also presented. Finally, open issues and challenges were presented, and researchers were highlighted as promising future directions in the field of IoT agriculture.
  • 21.  Chapter 10 Applications of Deep Learning in Robotics. ........................................................................................ 155 Pranav Katte, Vellore Institute of Technology, India Pranav Arage, Vellore Institute of Technology, India Satvik Nadkarni, Vellore Institute of Technology, India Ramani Selvanambi, Vellore Institute of Technology, India Deep artificial neural network applications to robotic systems have seen a surge of study due to advancements in deep learning over the past 10 years. The ability of robots to explain the descriptions of their decisions and beliefs leads to a collaboration with the human race. The intensity of the challenges increases as robotics moves from lab to the real-world scenario. Existing robotic control algorithms find it extremely difficult to master the wide variety seen in real-world contexts. The robots have now been developed and advanced to such an extent that they can be useful in our day-to-day lives. All this has been possible because of improvisation of the algorithmic techniques and enhanced computation powers. The majority of traditional machine learning techniques call for parameterized models and functions that must be manually created, making them unsuitable for many robotic jobs. The pattern recognition paradigm may be switched from the combined learning of statistical representations, labelled classifiers, to the joint learning of manmade features and analytical classifiers. Chapter 11 A Multicloud-Based Deep Learning Model for Smart Agricultural Applications.............................. 172 Palanivel Kuppusamy, Pondicherry University, India Suresh Joseph K., Pondicherry University, India Suganthi Shanmugananthan, Annamalai University, India Modern agriculture primarily relies on smart agriculture to predict crop yields and make decisions. Crop productivity could suffer due to a lack of farmers, labor shortages in the agricultural sector, adverse weather, etc. Smart farming uses advanced technology to improve the productivity and efficiency of agriculture. Crop yield is increased with smart agriculture, which also keeps an eye on agricultural pests. Artificial intelligence is an innovative technology that uses sensor data to predict the future and make judgments for farmers. AI methods like machine learning and deep learning are the most clever way to boost agricultural productivity. Adopting AI can help with farming issues and promote increased food production. Deep learning is a modern method for processing images and analyzing big data, showing promise for producing superior results. The primary goals of this study are to examine the benefits of employing DL in smart agricultural applications and to suggest a multi-cloud DL architecture for such applications.
  • 22.  Chapter 12 Automated MP3 Tag Editor via Data Mining: A Classification Software for Predicting MP3 Metadata. .............................................................................................................................................. 201 Jonathan Rufus Samuel, Vellore Institute of Technology, India Shivansh Sahai, Vellore Institute of Technology, India P. Swarnalatha, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India V. Balaji, Vardhaman College of Engineering, India The music space in today’s world is ever evolving and expanding. With great improvements to today’s technology, we have been able to bring out music to the vast majority of today’s ever-growing and tech- savvy people. In today’s market, the biggest players for music streaming include behemoth corporations like Spotify, Gaana, Apple Music, YouTube Music, and so on and so forth. This also happens to be quite the shift from how music was once listened to. For songs downloaded out of old music databases without the song’s metadata in place, and other distribution sites, they oftentimes come without any known metadata, i.e., most of the details with regards to the songs are absent, such as the artist’s name, the year it was made, album art, etc. This chapter discusses how data mining, data scraping, and data classification are utilized to help add incomplete metadata to song files without the same, along with the design process, the software development, and research for the same. Chapter 13 Machine Learning-Integrated IoT-Based Smart Home Energy Management System. ........................ 219 Maganti Syamala, Koneru Lakshmaiah Education Foundation, India Komala C. R., HKBK College of Engineering, India P. V. Pramila, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India Samikshya Dash, School of Computer Science and Engineering, VIT-AP University, India S. Meenakshi, R.M.K. Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India The internet of things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi- dimensional data, and low computational cost. Prediction in energy consumption is essential for the enhancement of sustainable cities and urban planning, as buildings are the world’s largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting prediction performance,bestNwindowsize,andmodeluncertaintyestimation.Deeplearningmodelsforhousehold energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty.
  • 23.  Chapter 14 Generating Complex Animated Characters of Various Art Styles With Optimal Beauty Scores Using Deep Generative Adversarial Networks.................................................................................... 236 N. Prabakaran, School of Computer Science and Engineering, Vellore Institute of Technology, India Rajarshi Bhattacharyay, School of Computer Science and Engineering, Vellore Institute of Technology, India Aditya Deepak Joshi, School of Computer Science and Engineering, Vellore Institute of Technology, India P. Rajasekaran, School of Computing, SRM Institute of Science and Technology, India A generative adversarial network (GAN) is a generative model that is able to generate fresh content by usingseveraldeeplearningtechniquestogether.Duetoitsfascinatingapplications,includingtheproduction of synthetic training data, the creation of art, style-transfer, image-to-image translation, etc., the topic has gained a lot of attraction in the machine learning community. GAN consists of two networks: the generator and the discriminator. The generator will make an effort to create phony samples in an effort to trick the discriminator into thinking they are real samples. In order to distinguish generated samples from both actual and fraudulent samples, the discriminator will strive to do so. The main motive of this chapter is to make use of several types of GANs like StyleGANs, cycle GANs, SRGANs, and conditional GANs to generate various animated characters of different art styles with optimal attractive scores, which can make a huge contribution in the entertainment and media sector. Chapter 15 Cloud-Based TPA Auditing With Risk Prevention. ............................................................................. 255 V. Abinaya, Hindusthan College of Arts and Sciences, India A. V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Rohaya Latip, Universiti Putra Malaysia, Malaysia Veera Talukdar, RNB Global University, India Ankita Chaturvedi, IIS University (Deemed), India G. Vanishree, IBS Hyderabad, India Gaganpreet Kaur, Chitkara University, India The chapter focuses on cloud security audit mechanisms and models. Here the third-party auditor (TPA) will be provided with the authority access scheme where the security of the auditing system will be enabled. The TPA will check out the auditing verification and shows a message about the data audited. The purpose of this work is to develop an auditing scheme that is secure, efficient to use, and possesses the capabilities such as privacy preserving, public auditing, maintaining the data integrity along with confidentiality. It consists of three entities: data owner, TPA, and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it, and generating a signature on it. TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them, and generates signature on it. Thus, the system frequently checks the security of the server-side resources.
  • 24.  Chapter 16 Detection and Classification of Dense Tomato Fruits by Integrating Coordinate Attention Mechanism With YOLO Model. .......................................................................................................... 278 Seetharam Nagesh Appe, Annamalai University, India G. Arulselvi, Annamalai University, India Balaji G. N., Vellore Institute of Technology, India Real-time detection of objects is one of the important tasks of computer vision applications such as agriculture, surveillance, self-driving cars, etc. The fruit target detection rate based on traditional approaches is low due to the complex background, substantial texture interference, partial occlusion of fruits,etc.ThischapterproposesanimprovedYOLOv5modeltodetectandclassifythedensetomatoesby adding the coordinate attention mechanism and bidirectional pyramid network. The coordinate attention mechanism is used to detect and classify the dense tomatoes, and bidirectional pyramid network is used to detect the tomatoes at different scales. The proposed model produces good results in detecting the small dense tomatoes with an accuracy of 87.4%. Chapter 17 A Study on an Internet of Things (IoT)-Enabled Smart Solar Grid System........................................ 290 N. Hema, Department of Information Science and Engineering, RNS Institute of Technology, India N. Krishnamoorthy, College of Science and Humanities, SRM Institute of Science and Technology, India Sahil Manoj Chavan, Department of Electrical Power System, Sandip University, India N. M. G. Kumar, Sree Vidyanikethan Engineering College, Mohan Babu University, India M. Sabarimuthu, Kongu Engineering College, India Sampath Boopathi, Muthayammal Engineering College, India Automation in the power consumption system could be applied to conserve a large amount of power. This chapter discusses the applications for the generation, transmission, distribution, and use of electricity that are IoT-enabled. It covers the physical layer implementation, used models, operating systems, standards, protocols, and architecture of the IoT-enabled SSG system. The configuration, design, solar power system, IoT device, and backend systems, workflow and procedures, implementation, test findings, and performance are discussed. The smart solar grid system’s real-time implementation is described, along with experimental findings and implementation challenges. Chapter 18 Accuracy Determination: An Enhanced Intrusion Detection System Using Deep Learning Approach.............................................................................................................................................. 309 Rithun Raagav, Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India G. Megala, Vellore Institute of Technology, India The internet of things (IoT) links several intelligent gadgets, providing consumers with a range of advantages. Utilizing an intrusion detection system (IDS) is crucial to resolving this issue and ensuring information security and reliable operations. Deep convolutional network (DCN), a specific IDS, has been developed, but it has significant limitations. It learns slowly and might not categorise correctly. These restrictions can be addressed with the aid of deep learning (DL) techniques, which are frequently
  • 25.  utilised in secure data management, imaging, and signal processing. They provide capabilities including reuse, weak transfer learning, and module integration. The proposed method increases the effectiveness of training and the accuracy of detection. Utilising pertinent datasets, experimental investigations have been carried out to assess the proposed system. The outcomes show that the system’s performance is respectable and within the bounds of accepted practises. The system exhibits a 97.51% detection ability, a 96.28% reliability, and a 94.41% accuracy. Chapter 19 IoT-Based Smart Accident Detection and Alert System...................................................................... 322 C. V. Suresh Babu, Hindustan Institute of Technolgy and Science, India Akshayah N. S., Hindustan Institute of Technology and Science, India Maclin Vinola P., Hindustan Institute of Technology and Science, India R. Janapriyan, Hindustan Institute of Technology and Science, India The smart accident detection and alert system using IoT is a technical solution that detects accidents and alerts authorities and emergency services. The system mainly relies on sensors, GPS, and Arduino UNO to detect and collect information about the location and severity of the accident. The system then transmits this information in real time to the appropriate authorities using algorithms and protocols, enabling them to respond quickly and effectively, therefore increasing the possibility of saving lives and benefiting road users, emergency services, and transportation authorities in case of accidents. Chapter 20 Revolutionizing the Farm-to-Table Journey: A Comprehensive Review of Blockchain Technology in Agriculture Supply Chain................................................................................................................ 338 Prarthana Shiwakoti, Vellore Institute of Technology, India Jothi K. R., Vellore Institute of Technology, India P. Kalyanaraman, Vellore Institute of Technology, India In recent years, blockchain technology has gained a lot of attention for its various applications in various fields, with agriculture being one of the most promising. The use of blockchain in agriculture covers areas such as food security, information systems, agribusiness, finance, crop certification, and insurance. In developing countries, many farmers are struggling to earn a living, while in developed countries, the agriculture industry is thriving. This disparity is largely due to poor supply chain management, which can be improved using blockchain technology. Blockchain provides a permanent, sharable, and auditable record of products, improving product traceability, authenticity, and legality in a cost-effective manner. This chapter aims to compile all existing research on blockchain technology in agriculture and analyze the methodologies and contributions of different blockchain technologies to the agricultural sector. It also highlights the latest trends in blockchain research in agriculture and provides guidelines for future research.
  • 26.  Chapter 21 Blockchain-Based Messaging System for Secure and Private Communication: Using Blockchain and Double AES Encryption. ............................................................................................................... 354 Shiva Chaithanya Goud Bollipelly, Vellore Institute of Technology, India Prabu Sevugan, Pondicherry University, India R. Venkatesan, SASTRA University, India L. Sharmila, Agni College of Technology, India In recent years, concerns about privacy and security in online communication have become increasingly prominent. To address these concerns, the authors propose a blockchain-based messaging system that provides secure and private communication using double AES encryption. The system utilizes the decentralized and tamper-resistant nature of the blockchain to ensure that messages are not modified or deleted by unauthorized parties. Additionally, they employ double AES encryption to ensure that the content of messages remains confidential even if the blockchain itself is compromised. They evaluate the performance of the system and show that it is scalable and efficient. The system provides a secure and private messaging solution that can be used by individuals and organizations alike. Chapter 22 Artificial Intelligence in Cyber Security.............................................................................................. 366 MohanaKrishnan M., Hindusthan College of Arts and Sciences, India A.V. Senthil Kumar, Hindusthan College of Arts and Sciences, India Veera Talukdar, RNB Global University, India Omar S. Saleh, School of Computing, Universiti Utara Malaysia, Malaysia Indrarini Dyah Irawati, Telkom University, Indonesia Rohaya Latip, Universiti Putra Malaysia, Malaysia Gaganpreet Kaur, Chitkara University Institute of Engineering and Technology, Chitkara University, India In the digital age, cybersecurity has become an important issue. Data breaches, identity theft, captcha fracturing, and other similar designs abound, affecting millions of individuals and organizations. The challenges are always endless when it comes to inventing appropriate controls and procedures and implementing them as flawlessly as available to combat cyberattacks and crime. The risk of cyberattacks and crime has increased exponentially due to recent advances in artificial intelligence. It applies to almost all areas of the natural and engineering sciences. From healthcare to robotics, AI has revolutionized everything. In this chapter, the authors discuss certain encouraging artificial intelligence technologies. Theycovertheapplicationofthesetechniquesincybersecurity.Theyconcludetheirdiscussionbytalking about the future scope of artificial intelligence and cybersecurity. Compilation of References................................................................................................................ 386 About the Contributors..................................................................................................................... 423 Index.................................................................................................................................................... 430
  • 27.   Foreword  I am happy to write a foreword for this book titled Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT. I felt delighted to note the different tools, technologies, key initiatives, and challenges in Deep Learning Techniques for Cloud-Based Industrial IoT. This book is a substantial compilation of 22 chapters encompassing an overview of Deep Learning, Cloud-Based Industrial IoT, Blockchain Based Deep Learning, critical theories, and concepts. In today’s fast-paced world, the Industrial Internet of Things (IIoT) has emerged as a transformative force in how industries operate, unlocking unprecedented opportunities for efficiency, productivity, and innovation. The convergence of cloud computing and IIoT has paved the way for a new era of intercon- nected intelligent systems, generating massive amounts of data that hold valuable insights for industrial processes. However, the sheer volume, velocity, and variety of data generated by IIoT pose significant chal- lenges in extracting meaningful information and making informed decisions. This is where the power of deep learning comes into play. Deep learning, a subset of artificial intelligence, offers remarkable capabilities in analyzing and interpreting complex patterns in vast amounts of data. Its ability to learn and adapt from data has made it a game-changer in numerous domains, and its potential in the industrial landscape is no exception. The Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT is a comprehensive guide that explores the symbiotic relationship between deep learning and cloud-based Industrial IoT. Authored by experts in the field, this book serves as a valuable resource for researchers, engineers, and industry professionals seeking to harness the full potential of deep learning techniques to optimize their industrial processes. The book begins by providing a solid foundation in IIoT, blockchain technology and cloud comput- ing fundamentals, ensuring readers have the necessary context to understand the subsequent chapters. It then delves into the core principles of deep learning, elucidating various architectures, algorithms, and techniques that have proven effective in analyzing industrial data. One of the key strengths of this book is its focus on practical implementation. The authors demonstrate how deep learning can be integrated into cloud-based IIoT systems and blockchain based deep learning to address specific challenges, such as predictive maintenance, anomaly detection, quality control, and energy optimization through real-world case studies and examples. The discussions not only highlight the benefits of employing deep learning techniques but also shed light on the potential pitfalls and con- siderations that must be considered. xxiii
  • 28. Foreword Furthermore, the book addresses crucial aspects of deploying deep learning models in the blockchain, cloud including scalability, security, and privacy concerns. It examines the impact of cloud infrastructure on the performance and reliability of deep learning applications. It provides insights into optimizing model training and inference strategies in a cloud environment. As deep learning continues to evolve rapidly, this book goes beyond the present landscape and of- fers a glimpse into the future. It explores emerging trends and advancements in deep understanding for IIoT, such as federated learning, blockchain based deep learning and explainable AI. It presents readers with a forward-thinking perspective on the potential developments and their implications for industrial applications. Inconclusion,theHandbookofResearchonDeepLearningTechniquesforCloud-BasedIndustrialIoT is a comprehensive guide that combines deep learning and IIoT, offering readers a roadmap to unlocking the full potential of their industrial systems. Whether you are a researcher, an engineer, or an industry professional, this book will equip you with the knowledge and insights needed to navigate the complex landscape of cloud-based IIoT and leverage deep learning techniques to drive innovation and success. I’m delighted to greet the editors and authors on their accomplishments and inform readers that they are about to read a significant contribution to developing various models based on Deep Learning, the Internet of Things, Cloud Computing and Blockchain technology. I’m aware of your research interests and knowledge of the above domains. This publication would benefit significantly from adding new computational models for Smart Security Ecosystem. This book is an important step forward in devel- oping this discipline, and it will serve to challenge the academic, research, and scientific communities in various ways. Arun Kumar Sangaiah School of Computing Science and Engineering, Vellore Institute of Technology, India Arun Kumar Sangaiah is a Clarivate (WoS) Highly Cited Researcher (2021) and World Top 2% Scientists (Stanford). Dr. Sangaiah is currently a Professor at School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore-632014, Tamil Nadu, India. xxiv
  • 29.   Preface  In recent years, the convergence of cloud computing and the Industrial Internet of Things (IIoT) has revolutionized how we interact with industrial systems and processes. The IIoT has ushered in a new era of connectivity and intelligence. At the heart of this technological transformation lies the power of data and the ability to extract valuable insights from the massive amounts of information generated by interconnected systems. Deeplearninghasemergedasaformidabletoolforanalyzingandinterpretingcomplexpatternswithin large volumes of data. The ability to learn from data, identify intricate relationships and make accurate predictions has made it an indispensable asset in various domains. In the realm of cloud-based IIoT, deep learning techniques have the potential to optimize industrial processes, enable predictive maintenance, enhance quality control, and drive intelligent decision-making. This book, Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT is a culmination of the knowledge, research, and experiences of experts in the field who have dedicated their efforts to exploring the synergies between deep learning and cloud-based IIoT. It aims to equip readers with the knowledge and skills required to harness the power of deep learning algorithms for analyzing, interpreting and making informed decisions based on the data generated by interconnected industrial devices. The book establishes a solid foundation, introducing the fundamental concepts and technologies underlying Industrial IoT and cloud computing. We delve into the key components of a cloud-based IIoT architecture, including data acquisition, storage, processing and analysis. By establishing this context, we ensure that readers clearly understand the environment in which deep learning techniques are employed. From there, we dive into the core principles of deep learning, explaining neural networks, activa- tion functions, optimization algorithms, and various deep learning architectures. We aim to demystify complex concepts through intuitive explanations and illustrative examples, allowing readers to grasp the underlying principles quickly. The book’s subsequent chapters focus on the practical implementation of deep learning techniques in cloud-based IIoT systems. We explore specific applications and use cases, shedding light on how deep learning can be leveraged to address challenges such as anomaly detection, predictive maintenance, energy optimization, and quality control. Through real-world case studies and examples, we highlight the effectiveness of deep learning techniques and discuss the considerations and trade-offs involved in their deployment. Additionally, the book addresses crucial aspects related to deploying deep learning models in the cloud. We delve into scalability, security, privacy concerns, and the impact of cloud infrastructure on the performance of deep learning applications. By providing insights into optimization strategies and best practices, we empower readers to overcome the challenges of deploying deep learning models in a cloud environment. xxv
  • 30. Preface Finally, as deep learning and IIoT continue to evolve rapidly, we explore emerging trends and ad- vancements that hold promise for the future. We discuss topics such as federated learning, blockchain based deep learning and explainable AI, giving readers a glimpse into the potential developments and their implications for the industrial landscape. We hope this book serves as a valuable resource for researchers, engineers, and industry profes- sionals seeking to unlock the full potential of their cloud-based IIoT systems. We have endeavored to present the material in a comprehensive yet accessible manner, combining theoretical foundations with practical insights. We thank the contributors who have dedicated their time and expertise to make this book possible. Their valuable insights and expertise have enriched the content and provided readers with diverse per- spectives on the subject matter. We invite readers to embark on a journey through the pages of this book, exploring the intersection of deep learning, blockchain based deep learning and cloud-based IIoT. We hope this book will inspire you to explore the vast opportunities offered by deep learning in the context of cloud-based IIoT and help you navigate the exciting landscape of this rapidly evolving field. Organization of the book: Chapter1.SurveillanceisanessentialcomponentofsecurityandE-surveillanceisoneoftheprimary goals of the Indian Government’s Digital India development initiative. Video surveillance offers a wide range of applications to reduce ecological and economic losses and becomes one of the most effective means of ensuring security. This chapter addresses the problem of how Artificial Intelligence is power- ing video surveillance. There is a significant research focus on video analytics but comparatively less effort has been taken for surveillance videos. However, there is little evidence that researchers have ap- proached the issue of intelligent video surveillance in terms of suspicious action detection, crime scene description, face detection, crowd counting, and the like. Most AI-powered surveillance is based on Deep neural networks and deep learning techniques using analysis of video frames as images. Consequently, this chapter aims to provide an overview and significance of how Artificial Intelligence techniques are employed in video surveillance and image processing. Chapter 2. Content-based video retrieval is a research field that aims to develop advanced techniques for automatically analyzing and retrieving video content. This process involves identifying and localizing specific moments in a video and retrieving videos with similar content. Deep Bimodal Fusion (DBF) is proposedthatusesmodifiedconvolutionneuralnetworks(CNNs)toachieveconsiderablevisualmodality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities. By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method have higher precision, recall, F1-Score and accuracy in precise searching that retrieves relevant videos more quickly and effectively. Chapter 3. Smart healthcare systems are the health services that use the technologies like wearable devices, Internet of Things (IoT), and mobile internet to access medical information dynamically. It connects people, materials and institutions related to healthcare, actively manages and automatically responds to medical ecosystem needs. To transform the traditional medical system helps in making healthcare more efficient, convenient, and personalized. This chapter proposed: (1) A review of smart xxvi
  • 31. Preface healthcare development using artificial intelligence, the Internet of Things, and Smartphone Android apps. (2) An experimental approach using IoT-based smart monitoring systems, Android apps for data collection, and artificial algorithms to predict cervical cancer diseases. (3) The integration of IoT and AI algorithms, or Artificial intelligence of things (AIoT), is proposed in this chapter as an experimental method for predicting cervical cancer from smart colposcopy images. The literature published in inter- national journals and proceedings between 2010 and June 2022 is considered for the study. Chapter 4. Advancement of technology had a significant impact on various industries, with innova- tive solutions like Cloud computing, IoT, Augmented reality (AR), and Virtual reality (VR) changing the game in many ways. Here is a system known as “Virtual Try-ons” which leverages IoT devices like mobile cameras, Cloud storage for data, and an intelligent interface for user interaction. Many people are opting for online shopping and various challenges arise with this transition, one of which is the issue of “Try-on.” VR solves this challenge by introducing “Virtual Try-on” which replaces traditional try- on methods. It enables an individual to preview and virtually try on their desired products like clothes, watches, shoes, etc., from the comfort of their own homes, making the shopping experience easier and smoother. It also adds an element of fun and excitement to the shopping experience, increasing the hedonic value for consumers, and allowing consumers to experiment and play with different products, styles, and colors in a way that is not possible with traditional shopping methods. Chapter 5. Identification of insulator defects is one of the most important goals of an intelligent examination of high-voltage transmission lines. Because they provide mechanical support for electric transmission lines as well as electrical insulation, insulators are essential to the secure and reliable op- eration of power networks. A fresh dataset is first built by collecting aerial pictures in various scenes that have one or more defects. A feature pyramid network and an enhanced loss function are used by the CSPD-YOLO model to increase the precision of insulator failure detection. The Insulator Defective data set, which has two classes (Insulator, Defect), is used by the suggested technique to train and test the model using the YOLOv5 Object Detection algorithm. We evaluate how well the YOLOv3, YOLOv5, and related families perform when trained on the Insulator Defective dataset. Practitioners can use this information to choose the appropriate technique based on the Insulator Defective dataset. Chapter 6. Blockchain is an emerging technology that is now being used to provide novel solutions in several industries, including healthcare. Deep learning (DL) algorithms have grown in popularity in medical image processing research. AD is diagnosed by magnetic resonance imaging (MRI) images. This study investigates the integration of blockchain technology with a DL model for Alzheimer’s disease prediction (AD). This proposed model was used to classify 3182 images from the ADNI collection. The Edge-basedSegmentationalgorithmhasovercometheSegmentationproblem.Duringtheinvestigation’s test stage, the DL-EfficientNetB0 model with blockchain earned the highest accuracy rate of 99.14%. The highest accuracy, sensitivity, and specificity scores were obtained utilizing the confusion matrix dur- ing the comparative assessment stage. According to the study’s results, EfficientNetB0 with blockchain model surpassed all other trained models in classification rate. This study will aid clinical research into the early detection and prevention of AD by identifying the sickness before it occurs. Chapter 7. The rapid development of Internet of Things (IoT) applications has created enormous possibilities, increased our productivity, and made our daily life easier. However, because of resource limitations and processing, IoT networks are open to number of threats.The Network Instruction De- tection System (NIDS) aims to provide a variety of methods for identifying the increasingly common cyberattacks (such as Distributed Denial of Service (DDoS), Denial of Service (DoS), Theft, etc.) and to prevent hazardous activities. In order to determine which algorithm is more effective in detecting xxvii
  • 32. Preface network threats, multiple public datasets and different artificial intelligence (AI) techniques are evalu- ated. Some of the learning algorithms like Logistic Regression, Random Forest, Decision Tree, Navie Bayes, Auto-Encoder, and Artificial Neural Network, were analysed and concluded on the NF-BoT-IoT dataset using various evaluation metrics. In order to train the model for future anomaly detection predic- tion and analysis, the feature extraction and pre-processing data were then supplied into NIDS as data. Chapter 8. This project aims to create a real-time object detection and audio output system for blind users using the YOLOv3 algorithm and a 360-degree camera sensor. The system is designed to detect a wide range of objects, including people, vehicles, and other objects in the environment, and provide audio feedback to the user. The system architecture consists of a 360-degree camera sensor, a processing unit, and an audio output system. The camera sensor captures the environment, which is processed by the processing unit, which uses the YOLOv3 algorithm to detect and classify objects. The audio output system provides audio feedback to the user based on the objects detected by the system. The project has significant importance for blind users as it can help them navigate their environment and recognize objects in real-time, and can serve as a foundation for future research in the field of object detection systems for blind users. Chapter 9. Increasing demand for food quality and size has increased the need for industrialization and intensification in the agricultural sector. The Internet of Things (IoT) is a promising technology that offers many innovative solutions to transform the agricultural sector. Research institutes and scientific groups are constantly working to provide solutions and products for different areas of agriculture using IoT. The main objective of this methodological study is to collect all relevant research results on agri- cultural IoT applications, sensors/devices, communication protocols, and network types. We will also talk about the main problems and encounters encountered in the field of agriculture. An IoT agriculture framework is also available that contextualizes the view of various current farming solutions. National guidelines on IoT-based agriculture were also presented. Finally, open issues and challenges were pre- sented and researchers were highlighted as promising future directions in the field of IoT agriculture. Chapter 10. Deep artificial neural network applications to robotic systems have seen a surge of study due to advancements in deep learning over the past ten years. The ability of robots to explain the descriptions of its decisions and beliefs leads to an collaboration with human race. The intensity of the challenges increases as robotics moves from lab to the real-world scenario. Existing robotic control algorithms find it extremely difficult to master the wide variety seen in real-world contexts. The robots have now been developed and advanced to such an extent which can make them useful in our day-to-day lives, all this has been possible because of improvisation of the algorithmic techniques and enhanced computation powers. The majority of traditional machine learning techniques call for parameterized models and functions that must be manually created, making them unsuitable for many robotic jobs. he pattern recognition paradigm may be switched from the combined learning of statistical representations, labelled classifiers s to the joint learning of manmade features and analytical classifiers. Chapter 11. Modern agriculture primarily relies on smart agriculture to predict crop yields and make decisions. Crop productivity could suffer due to a lack of farmers, labor shortages in the agricultural sector, adverse weather, etc. Smart farming uses advanced technology to improve the productivity and efficiency of agriculture. Crop yield is increased with smart agriculture, which also keeps an eye on agricultural pests. Artificial intelligence is an innovative technology that uses sensor data to predict the future and make judgments for farmers. AI methods like machine learning and deep learning are the most clever ways to boost agricultural productivity. Adopting AI can help with farming issues and promote increased food production. Deep learning is a modern method for processing images and ana- xxviii
  • 33. Preface lyzing Big Data, showing promise for producing superior results. The primary goals of this study are to examine the benefits of employing DL in smart agricultural applications and to suggest a multi-cloud DL architecture for such applications. Chapter 12. The Music space in today’s world is ever evolving and expanding. With great improve- ments to today’s technology, we have been able to bring out music to many today’s ever-growing and tech savvy people. In today’s market, the biggest players for Music Streaming include behemoth corpora- tions like Spotify, Gaana, Apple Music, YouTube Music and so on and so forth. This also happens to be quite the shift from how music was once listened to. For songs downloaded out of Old Music Databases without the song’s metadata in place, and other distribution sites, they oftentimes come without any known metadata. i.e., Most of the Details with regards to the songs are absent, such as the Artist’s name, the year it was made, Album Art, etc. This paper discusses how Data Mining, Data Scraping and Data Classification is utilized to help add incomplete metadata to song files without the same, along with the design process, the software development and research for the same. Chapter 13. The Internet of Things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi-dimensional data, and low computational cost. Prediction in energy consumption is es- sential for the enhancement of sustainable cities and urban planning, as buildings are the world’s largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting pre- diction performance, best N window size and model uncertainty estimation. Deep learning models for household energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty. Chapter 14. Generative Adversarial Network (GAN) is a generative model that can generate fresh content by using several deep learning techniques together. Due to its fascinating applications, including the production of synthetic training data, the creation of art, style-transfer, image-to-image translation, etc., the topic has gained a lot of attraction in the machine learning community. GAN consists of 2 net- works, the generator, and the discriminator. The generator will try to create phony samples in an effort to trick the discriminator into thinking they are real samples. In order to distinguish generated samples from both actual and fraudulent samples, the discriminator will strive to do so. The main motive of this paper is to make use of several types of GANs like StyleGANs, cycle GANs, SRGANs, and conditional GANs to generate various animated characters of different art styles with optimal attractive scores which can make a huge contribution in the entertainment and media sector. Chapter 15. The system proposes to focus on cloud security audit mechanisms and models. Here the Third-Party Auditor (TPA) will be provided with the authority access scheme where the security of the auditing system will be enabled. The TPA will check out the auditing verification and shows out a mes- sage about the data audited. The purpose of this work is to develop an auditing scheme which is secure, efficient to use and possesses the capabilities such as privacy preservation, public auditing, maintaining the data integrity along with confidentiality. It consists of three entities: data owner, TPA and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it, and generating a signature on it. TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them, and generating signature on it. Thus, the system fre- quently checks out the security of the server-side resources. xxix
  • 34. Preface Chapter16.Real-timedetectionofobjectisoneoftheimportanttasksofComputervisionapplications such as agriculture, surveillance, self-driving cars etc. The fruit target detection rate based on traditional approaches is low due to the complex background, substantial texture interference, partial occlusion of fruits etc. This paper proposes an improved YOLOv5 model to detect and classify the dense tomatoes by adding the coordinate attention mechanism and bidirectional pyramid network. The Coordinate at- tention mechanism is used to detect and classify the dense tomatoes and bidirectional pyramid network is used to detect the tomatoes at different scales. The proposed model produces good results in detecting the small dense tomatoes with an accuracy of 87.4%. Chapter 17. Automation in the power consumption system could be applied to conserve the large amount of power. This Chapter discusses the applications for the generation, transmission, distribution, and use of electricity that are IoT-enabled. It covers the physical layer implementation, used models, op- erating systems, standards, protocols, and architecture of the IoT enabled SSG system. The configuration, design, solar power system, IoT device, and backend systems, workflow and procedures, implementation, test findings, and performance are discussed. The smart solar grid system’s real-time implementation is described, along with experimental findings and implementation challenges. Chapter 18. The Internet of Things (IoT) links several intelligent gadgets, providing consumers with a range of advantages. Utilizing an Intrusion Detection System (IDS) is crucial to resolving this issue and ensuring information security and reliable operations. Deep Convolutional Network (DCN), a specific IDS, has been developed, but it has significant limitations. It learns slowly and might not categorize correctly. These restrictions can be addressed with the aid of deep learning (DL) techniques, which are frequently utilized in secure data management, imaging, and signal processing. They provide capabili- ties including reuse, weak transfer learning, and module integration. The proposed method increases the effectiveness of training and the accuracy of detection. Utilizing pertinent datasets, experimental investigations have been carried out to assess the proposed system. The outcomes show that the system’s performance is respectable and within the bounds of accepted practices. The system exhibits a 97.51% detection ability, 96.28% reliability, and a 94.41% accuracy. Chapter 19. The Smart Accident Detection and Alert System using IoT is a technical solution that detects accidents and alerts authorities and emergency services. The system mainly relies on sensors, GPS, Arduino UNO to detect and collect information about the location and severity of the accident. The system then transmits this information in real-time to the appropriate authorities using algorithms and protocols, enabling them to respond quickly and effectively, therefore, increasing the possibility of saving lives and benefiting road users, emergency services, and transportation authorities in case of accidents. Chapter 20. In recent years, blockchain technology has gained a lot of attention for its various ap- plications in various fields, with agriculture being one of the most promising. The use of blockchain in agriculture covers areas such as food security, information systems, agribusiness, finance, crop certi- fication, and insurance. In developing countries, many farmers are struggling to earn a living, while in developed countries, the agriculture industry is thriving. This disparity is largely due to poor supply chain management, which can be improved using blockchain technology. Blockchain provides a permanent, sharable, and auditable record of products, improving product traceability, authenticity, and legality in a cost-effective manner. This survey paper aims to compile all existing research on blockchain technol- ogy in agriculture and analyze the methodologies and contributions of different blockchain technologies to the agricultural sector. It also highlights the latest trends in blockchain research in agriculture and provides guidelines for future research. xxx
  • 35. Preface Chapter 21. In recent years, concerns about privacy and security in online communication have become increasingly prominent. To address these concerns, we propose a blockchain-based messaging systemthatprovidessecureandprivatecommunicationusingdoubleAESencryption.Oursystemutilizes the decentralized and tamper-resistant nature of the blockchain to ensure that messages are not modified or deleted by unauthorized parties. Additionally, we employ double AES encryption to ensure that the content of messages remains confidential even if the blockchain itself is compromised. We evaluate the performance of our system and show that it is scalable and efficient. Our system provides a secure and private messaging solution that can be used by individuals and organizations alike. Chapter 22. In the digital age, cybersecurity has become an important issue. Data breaches, iden- tity theft, captcha fracturing, and other similar designs abound, affecting millions of individuals and organizations. The challenges are always endless when it comes to inventing appropriate controls and procedures and implementing them as flawlessly as available to combat cyberattacks and crime. The risk of cyberattacks and crime has increased exponentially due to recent advances in artificial intelligence. It applies to almost all areas of the natural and engineering sciences. From healthcare to robotics, AI has revolutionizedeverything.Thisfireballputupnotbekeptawayfromcybercriminals,effectivea“normal” cyberattack within an “intelligent” cyberattack. In this chapter, the authors discuss certain encouraging artificial intelligence technologies. They cover the application of these techniques in cybersecurity. They conclude their discussion by talking about the future scope of artificial intelligence and cybersecurity. P. Swarnalatha Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India S. Prabu Department Banking Technology, Pondicherry University, India xxxi
  • 36. 1 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 1 DOI: 10.4018/978-1-6684-8098-4.ch001 ABSTRACT Surveillance is an essential component of security, and e-surveillance is one of the primary goals of the Indian Government’s Digital India development initiative. Video surveillance offers a wide range of applications to reduce ecological and economic losses and becomes one of the most effective means of ensuring security. This chapter addresses the problem of how artificial intelligence is powering video surveillance. There is a significant research focus on video analytics but comparatively less effort has been taken for surveillance videos. However, there is little evidence that researchers have approached the issueofintelligentvideosurveillanceintermsofsuspiciousactiondetection,crimescenedescription,face detection, crowd counting, and the like. Most AI-powered surveillance is based on deep neural networks and deep learning techniques using analysis of video frames as images. Consequently, this chapter aims to provide an overview and significance of how artificial intelligence techniques are employed in video surveillance and image processing. INTRODUCTION Today, security cameras have become an integral part of everyday life for the sake of safety and security. Surveillance camera installations in the private and public sectors have increased significantly to monitor public activities. Security experts focus significantly on video surveillance to combat crime and avoid unpleasant situations that harm human civilization. However, personal and corporate security cannot be achieved simply by installing a surveillance camera. The surveillance system should be sufficiently Artificial Intelligence in Video Surveillance Uma Maheswari P. CEG, Anna University, Chennai, India Karishma V. R. Anna University, Chennai, India T. Vigneswaran SRM-TRB Engineering College, India
  • 37. 2 Artificial Intelligence in Video Surveillance  assisted with Artificial intelligence to deliver security solutions that substantially prevent abnormali- ties. Artificial intelligence has significantly influenced society, whether it takes the shape of algorithms, machine learning models, robotics, or autonomous systems. Many marketed video surveillance systems have integrated Artificial Intelligence (AI)-powered video analytics technology as a method to make our lives smarter and safer, thanks to recent developments in deep learning technologies. Intelligent Visual Surveillance is a significant and hard area of image processing and computer vision research. As our society is rapidly evolving toward smart homes and smart cities, necessitating an increasing number of Internet of Things (IoT) device deployments. Background The application of artificial intelligence (AI) is becoming increasingly crucial in the quest for novel techniques and technologies. Clutter identification, target categorization, and target tracking are AI techniques for target surveillance with radar sensors. These are critical assets for effective target obser- vation. Because clutter (i.e., unwanted signal reflections) may significantly hamper target detection, its identification and subsequent suppression are critical. Furthermore, accurate target classification can aid in the successful prevention of possible threats, particularly in military circumstances. Finally, target tracking, the final link in the traditional chain of radar data processing, demands special attention since it provides the pivot point for sensor data fusion. Smart Home Surveillance System (Anthony et al., 2022;Koushik et al., 2022) The globe has seen a tremendous increase in the number of smart homes with the emergence of artificial intelligence (AI), the Internet of Things (IoT), and human-centric computing (HCC). But putting in place a reliable security system for SH’s citizens still seems impossible. The current smart houses include security features like biometric verification, activity tracking, and facial recognition. The lifespan costs of these systems increase with the integration of multi-sensor hardware, networking infrastructure, and data storage facilities. Important behavioral and purpose clues are sent through facial expressions, which can be employed as non-intrusive feedback for contextual threat assessments. For the protection of the occupants of the same residence, prompt mitigation of a hostile situation, such as a fight or attempted entrance, is essential. iSecureHome is a real-time facial emotion-based security system for smart homes that uses a CMOS camera and a passive infrared (PIR) motion sensor. Effects of chromatic and achro- matic characteristics on the identification of facial emotions (ER). Daily home invasions and house fires cause difficulties for the victims of these sad occurrences. Early identification of these circumstances enables quick responses and should always be a feature that all homeowners expect. The CCTV system, the Onboard processing, prediction, and decision logic, and the Alarm and remote alerting module are the three elements that make up the framework depicted in Figure 1. The house range and garden path are completely covered by the CCTV system’s numer- ous cameras. Each camera’s video feed is supplied into the model’s location’s onboard circuitry. Live CCTV systems may be used as inputs, or previously captured video may be used for offline analysis. The Onboard logic can record films played on the screen by any software to increase the suggested framework’s interoperability. The CNN model generates one of five events, which are then passed to the Decision logic for prediction because it is light enough to execute utilizing onboard processing as opposed to cloud resources.
  • 38. 3 Artificial Intelligence in Video Surveillance  According to the model’s results, matching Alarm messages with video feeds will be sent to notify the appropriate users. This model is intended to identify unusual situations in homes, especially when the owner is not there. This includes people, domestic animals, cars, fire, and smoke. An additional degree of security for the homeowner can be added when the model is included in home monitoring systems. Many of the things that the model tries to group are present in the image samples that were taken from open databases. The data is manually cleansed to ensure that the irrelevant objects did not affect training. The findings still offer accurate categorization even though the learning curve indicates that the model’s full potential has not yet been realized. When reading the images, monochrome (greyscale) images present a problem. When these photos are found, either delete them or expand them using the OpenCV channel to coordinate with RGB pictures. It will be beneficial for the model to train some features in grayscale so that it can learn and recognize these features when no color is presented, allowing the model to be useful in these systems. Some sur- veillance systems are going to record in grayscale, so it will benefit the model to train some features in grayscale to make the images usable. Since the input data for the neural network needs to be consistent, every image will be resized to 400 X 400. The model will have more pixels and data to deal with as the fixed size increases, ultimately extending training time. images bigger than this size can be cropped to ensure that the most important information can still be seen, or photos larger than this size can be re- duced to this size. Images less than this will be resized to fit this size. Therefore, just enlarge the image rather than cropping the features, which might have a detrimental effect on the model’s learning. 70% of the samples will be used to train the model, 10% to test it, and 20% to verify it over the full dataset. The classification performance of Accuracy, precision, Recall, and F1 score are noted for various classes of the dataset. The overall Accuracy is 82.31% Figure 1. Framework of alarming system
  • 39. 4 Artificial Intelligence in Video Surveillance  The framework has the following drawbacks: (a) The training samples were taken from public datasets rather than specific CCTV footage, which may have an impact on how well it performs on CCTV sys- tems; (b) There is no way to compare the performance of the framework to other baseline CNN models, and (c) The alarm and remote alerting cannot recognize complex situations. COVID-19 Surveillance in Public Places (Sreedhara et al., 2021; Hossain et al., 2020; Das et al., 2021; Suvarna et al., 2021) Artificial Intelligence (AI) based detection systems can be deployed at public places like airports, railway stations, etc. for continuous monitoring of potential infectious individuals and screening based on com- mon symptoms exhibited. The worldwide pandemic, COVID-19 has been caused by a newly discovered strain of coronavirus SARS-Cov-2. Its common symptoms are high fever, coughing, and shortness of breath. With the rising number of COVID-19 cases, manual detection of infectious individuals in public spaces is a hectic task. The BII Sneeze-Cough Human Action Video Dataset provided the sneeze-cough video dataset utilized for the research in this article (BIISC). A small real-time dataset is produced in addition to the sneeze cough dataset to test the classifier. A rate of 10 frames per second is used to extract images from films with various class labels. The retrieved pictures are used to feed a person detection algorithm, which employs the histogram of directed gradients as a basis for identifying human subjects inside a given image. Then, images are resized to 64 by 128 pixels. To minimize the pixel size, it is finally transformed from RGB to a grayscale picture. Features are retrieved from the pre-processed pictures to do categorization. Testing the Gabor Filter, Histogram of Oriented Gradients (HOG), and Spatial Pyramid Matching as three distinct feature extractors (SPM). To determine if the picture under review contains any certain frequency content, a Gabor filter analyses a constrained portion of the image in a particular direction. The pre-processed pictures are convolved with various filter masks using Gabor filters. Two distinct kinds of classifiers are tested to differentiate coughing activity from similar activities. K-Nearest Neighbor (KNN) is the first classifier, and a multi- class Support Vector Machine (SVM) employing various kernel operations. The dataset’s nonlinearity is the key justification for choosing kernel functions. A multi-class SVM classifier is used to test three distinct kernel function types: linear, polynomial, and radial basis functions. Figure 2 details the process used by our social distance monitoring tool. The algorithm engine is made up of five parts: alert creation, distance estimate, camera calibration, people tracking, and people detection. The application is implemented as a hybrid engagement of edge infrastructure-based model inferencing and cloud-based model training. Table 1. Classification performance Class No. of Truth No. of Classified Precision Recall F1 Score Vehicles 373 419 0.70 0.79 0.74 Pets 310 422 0.49 0.66 0.56 Default 224 216 0.58 0.57 0.58 Fire/Smoke 2390 2120 0.99 0.88 0.93 Human 105 227 0.31 0.68 0.43
  • 40. 5 Artificial Intelligence in Video Surveillance  Identifying people in a location and estimating their bounding box coordinates is the first step in tracking their movements. The YOLOv3-416 object detector, pre-trained on the MS-COCO dataset, and darknet-53 as the backbone are used for this. Only the detection results for the person class are extracted. The approach, as it is intended to be used with monocular cameras, calls for calibration to convert im- age pixel coordinates to geographic coordinates. The camera calibration module allows you to select between an automatic calibration and a tool-based calibration. The next stage is to estimate the pairwise distances between the persons to aid in monitoring compliance with social distancing. This is done once the camera calibration parameters and the bounding box coordinates of every person visible in an image frame have been collected. Each person’s position is determined by the midpoint coordinates at the base of each detection box. To determine if social distancing norms are being broken in tool-based calibra- tion, Euclidean distances between these locations in the aerial view are computed and compared to the reference distance. The number of different violations and the length of those violations, which may be calculated by frame-to-frame surveillance of people, determine the likelihood that a person would get infected. The solution includes Motpy, an online multiobject tracker framework that tracks persons in a scene by using a Kalman filter and IOU of bounding boxes between future frames. By following the earlier detections, the implementation of a tracking algorithm is also anticipated to reduce mistakes in distance computation caused by transient occlusions. The different parts of the application are separated into distinct modules that communicate with one another using message queues to simplify solution deployment and maintenance. To manage video in- puts from several IP (Internet Protocol) cameras within the program, it also utilized a multi-processing, multi-threading method, providing scalability. A separate thread on a multi-core edge device processes each video feed in turn, invoking the algorithm processing unit to process the accompanying picture frame before pushing the processed image and related metrics to a streaming application. Figure 2. Social distance surveillance application flow
  • 41. 6 Artificial Intelligence in Video Surveillance  (AIP*MAXAL)/SEF is an estimate of how many camera feeds can be handled on the edge device during deployment, where AIP stands for algorithm instance process rate and MAXAL is the maximum frame rate. MAXAL stands for maximum algorithm instance, where (the number of CPU cores, and GPU Memory) is the minimum. SEF: stream endpoint feed process rate 3 frames per second. For instance, if a machine has 12 CPU cores and 16 GB of graphics memory, MAXAL is at least (12,16) 12, supporting a total of (5*12)/3 = 20 cameras. By adopting the log-average miss rate as a performance indicator during testing on the Euro City Persons test dataset, it is further shown that YOLOv3 and FRCNN outperform SSD. The default object detector of the solution, YOLOv3, is deployed without further training after taking into account the trade- off between all of these measures. Numerous tests using internal CCTV footage under various lighting conditions, crowd density, gender, ethnicity, positions of persons, and occlusions produced accuracy and recall scores that fell between 68.8 and 75.6% and 75.5 to 85.1%, respectively. For crowd sizes up to 30 individuals, the pre-trained YOLOv3 detector exhibits the greatest detection performance, assuming that 80% of each person was visible. Maritime Surveillance System (Huang et al., 2022) Maritime surveillance systems are widely used in vessel traffic services. Cameras on the ground, at sea, or in the air can provide marine visual information. However, the collected visual data frequently suffer from blur effects as a result of unsteady imaging devices under harsh environments (e.g. wind, waves, and currents). To increase visual quality, picture-stabilizing technologies must be developed. Systems for maritime monitoring are crucial to enhancing maritime security. The efficiency and efficacy of maritime monitoring are considerably increased by intelligent marine surveillance systems, which use informa- tion technology to completely construct a new pattern. Technology primarily relies on the creation of surveillance resources to support the fusion and exchange of safety information. However, marine traffic monitoring is somewhat outdated when compared to sophisticated land traffic surveillance. The surveillance recordings are hazy because maritime surveillance systems are more vulnerable to erratic elements like wind, waves, and currents. The hazy photos make maritime surveillance more challenging and less effective. As the global economy and trade continue to grow, new video stabiliza- tion techniques are being suggested for maritime surveillance. Image deblurring, the foundation of video stabilization, is becoming more and more crucial in maritime surveillance. Similar to how land-based traffic surveillance systems operate, marine surveillance systems must provide the video and picture data they have gathered to the monitoring center. Finally, ships are coordinated and managed by ship traffic managers at the monitoring center once the data has been analyzed. Table 2. Object detection algorithm performance metrics Model mAP FPS Small Occlusion Heavy Occlusion YOLOv3-416 55.3 35 17.8 37.0 FPN FRCNN 59.1 6 16.6 52.0 SSD300 41.2 46 20.5 42.0
  • 42. 7 Artificial Intelligence in Video Surveillance  Due to the effect of wind, waves, and currents on maritime data acquisition, shore-borne, air-borne, and ship-borne acquisition systems are all prone to shaking, which makes the majority of marine movies unstable. In other words, the hazy images on the surveillance screen are frequently a result of the rela- tive distance between the visual aids and the supervised ships. The initial scene’s image pixel expands to its surroundings. These dispersed pixel light sources are referred to as blur kernels or point spread functions (PSFs) in the imaging profession. In marine surveillance, only uniform picture deblurring— which is geographically invariant—is taken into account for simplicity’s sake. Stabilizing the obtained marine surveillance videos is equivalent to deblurring the maritime pictures since the video is made up of a series of images called frames. Finding the latent crisp picture is the next stage. Blind picture deblurring becomes a non-blind deblur- ring challenge after determining the precise blur kernel. Numerous techniques have been developed for non-blind deblurring. There are primarily three categories: regularisation technique, iterative approach, and inverse filtering method. Due to its straightforward computation and quick processing time, inverse filtering is frequently used in picture restoration, however, it is vulnerable to noise. Then followed the restricted least squares approaches, the Kalman filter with the linear recursive minimum variance estimation as the criterion, and the Wiener filter with the minimum mean square error as the criterion for deblurring. However, each of the aforementioned techniques is a linear restoration technique. The Lucy-Richardson technique, a non-linear approach, was suggested to restore the blurred picture with a known PSF to precisely reconstruct the latent crisp image. Then, a ROF model using TV regularisation of the total variance. Visual sensors are used by the ship’s navigation system to understand the surroundings. However, the visual information gathered under tough circumstances is prone to blurring, making it challenging for ship auxiliary systems to precisely detect impediments in the area around the ship. It impairs ship navigation safety and leads to navigational mistakes. Video stabilization technology plays a crucial role in maritime transportation because of the unique navigational conditions of waterways. Image deblur- ring helps to increase the effectiveness of waterway transportation and ensure the safety of navigation. Our technique completely exploits the properties of the blur kernels and the natural pictures, enabling accurate blur kernel estimates and ensuring high-quality restoration outcomes. It is advantageous for accurately identifying the surrounding objects and maintaining navigation safety to eliminate the visual blur under challenging marine circumstances. Visual information technology-based marine surveillance systems have been extensively employed in a variety of nautical services in maritime engineering. However, inclement weather frequently makes the surveillance footage unsteady. It implies that pictures extracted from films occasionally include motion blur, noise, and other issues that drastically lower the visual quality. The restoration of fuzzy pictures in maritime engineering requires more focus. To conduct the comparison tests due to the absence of true maritime blurred datasets, should use artificial maritime-blurred photos with undetermined blur ker- nels. Blind deblurring techniques may be used to recover the obtained blurred pictures, and the visually improved films and photos can then be used to support our work in different nautical applications. One of the important marine applications is ship detection, and several ship detection techniques have been described. The accuracy of ship recognition will increase when the obtained maritime-blurred photos are deblurred by our hybrid regularisation approach, which is proven by YOLOv4.
  • 43. 8 Artificial Intelligence in Video Surveillance  Public Transport Surveillance (Santhosh et al., 2020; Rohit et al., 2020) Local transportation movement is also a worry in our nation, as is vehicle overspeeding, which generates a large number of road accidents. Integrating GPS tracking systems with automated safety systems can establish geofencing to regulate and monitor our country’s local buses. Through computer vision and visual surveillance, timely identification of traffic offenses and unusual pedestrian behavior in public spaces may be quite helpful for upholding traffic order in cities. Computer vision-based scene comprehension has become quite popular among the Computer Vision (CV) research community as a result of the pervasive usage of surveillance cameras in public spaces. Compared to other information sources like GPS, mobile location, radar signals, and so on, visual data includes extensive information. This means that in addition to gathering statistical data regarding the condition of road traffic, it may be extremely useful in identifying and forecasting traffic jams, accidents, andotherirregularities.Numerousresearchemployingcomputervisionhasbeencarriedout,concentrating on data collection, feature extraction, scene learning, activity learning, behavioral comprehension, etc. Scene analysis, video processing methods, anomaly detection strategies, vehicle detection and tracking, multi-camera techniques and challenges, activity recognition, traffic monitoring, human behavior analy- sis, emergency management, event detection, and so on are some of the aspects that can be considered. Asub-domainofbehaviorcomprehensionfromsurveillancesituationsisanomalydetection.Anomalies are often deviations from the norm of scene entities (such as automobiles, people, or the environment). There has been an increase in research outputs on video analysis and anomaly identification as a result of the accessibility of video feeds from public locations. Anomaly detection techniques often train on the norm to understand what is normal. Anything that dramatically deviates from typical behavior is consid- ered abnormal. Anomalies include things like automobiles on sidewalks, a quick dispersal of individuals in a crowd, someone falling abruptly while walking, jaywalking, signal bypassing at a traffic signal, or vehicles turning around at red lights. Typically, anomaly detection systems develop a normal profile by learning the typical data patterns. Once the typical patterns are understood, established methods may be used to identify abnormalities. The system can produce a label or score that indicates whether or not the data is anomalous, usually in the form of a metric. Anomalies are by nature contextual. It is impossible to apply the assumptions employed in anomaly detection to all traffic circumstances. The capabilities of anomaly detection techniques used in monitoring road traffic from a data perspective. It does this by classifying the methods according to how the scene is represented, the characteristics utilized, the mod- els used, and the approaches used. With numerous instances of the learning processes, used detection techniques, applied anomalous scenes, types of anomalies identified, and so on, the relevant technology provides an end-to-end perspective of the anomaly detection approach. In the current scenario, features are taken to be data and represented by feature descriptors. Depend- ing on the length of the feature descriptor, data generally take up a space in a multidimensional space. Data patterns that deviate from a well-established definition of typical behavior are known as anomalies. Anomalies have also been referred to as outliers and novelty in several application areas. Analyzing anomalies. Anomalies are often divided into three groups: point anomalies, contextual anomalies, and collective anomalies. If data deviate significantly from the expected distribution, they are considered to be a point anomaly. A point anomaly can be something like a stationary automobile on a busy road. Data that could be considered normal in one environment but not in another corresponds to contextual abnormalities. An abnormality, for instance, is if a cyclist in slow-moving traffic travels quicker than the
  • 44. 9 Artificial Intelligence in Video Surveillance  others. On a less congested route, though, that may be typical behavior. Even while each data instance may be normal on its own, a bunch of them together may result in an anomaly. A collective anomaly maybe something like a group of individuals dispersing quickly. Visual surveillance frequently reveals abnormalities that are categorized as local and global anomalies. Global abnormalities may be visible in a frame or a section of the video without a specific location being identified. Local abnormalities typically occur in a particular section of the scene, however, global anomaly detection systems might not pick them up. Some techniques can find abnormalities both locally and globally. Challenges and Study Scope Identifying a representative normal region, defining boundaries between the normal and anomalous regions that may not be clear or well defined, the notion of an anomaly differing depending on the application context, limited data available for training and validation, data that is frequently noisy due to inaccurate sensing, and the fact that normal behavior changes over time are the main challenges in anomaly detection. Learning typical behavior is important for many different use cases in addition to anomaly detection. Some of these include behavior analysis, categorization, pattern analysis, and prediction. There are four types of learning strategies: supervised, unsupervised, semi-supervised, and hybrid. The normal profile is created in supervised learning utilizing labeled data. It is frequently used in applications that are linked to classification and regression. In unsupervised learning, the connections between the components of the unlabeled dataset are used to structure the normal profile. With some guidance and a little quantity of labeled data for defining example classes that are already known a priori, semi-supervised learning predominantly uses unlabeled data. Active learning is defined as learning that occurs through the inter- active labeling of data as and when the label information is accessible. These techniques are employed when there are plenty of unlabeled data and human labeling is costly. To comprehend various aspects of thedata,hybridapproachescombinetheaforementionedtechniques.Objectidentification,classification, activity recognition, segmentation, anomaly detection, and other tasks also require learned models in addition to feature extraction. The formulation of the problem and the underlying characteristics determine the type of anomalies since anomalies often relate to deviations from normal behavior. The methods do not restrict the capacity to determine the kind of abnormality. The formulation of the problem and the underlying characteristics determine the type of anomalies since anomalies often relate to deviations from normal behavior. The methods do not restrict the capacity to determine the kind of abnormality. Based on a statistical model, it attempts to fit the data using a stochastic process while generally employing statistical approaches to learn the parameters of the model. The data points that were not produced by the expected stochastic model are known as anomalous samples. Both parametric and nonparametric models are available. The process of anomaly detection fundamentally involves using a particular approach to the derived feature. The fundamental data in visual surveillance, however, is a video, which is a collection of frames. Because these features serve as input to the particular approach utilized in anomaly identification, it is crucial to extract the pertinent features from the videos. The characteristics may be broadly divided into object-based and non-object-based categories. By extracting the objects or trajectories, anomalies may be found using object-based characteristics. The information used for anomaly detection is made up of objects or trajectories that are represented as feature descriptors. In the latter method, anomaly identifica- tion has been performed using low-level descriptors for pixel or pixel group characteristics, intensities, optical fluxes, or resulting features from spatiotemporal cubes (STCs). Some techniques employ hybrid characteristics to find anomalies.
  • 45. 10 Artificial Intelligence in Video Surveillance  Atypicalanomalydetectionframeworkdevelopmentprocesshastwostages.Inthefirststage,amodel is trained using the features from typical movies to learn the typical properties of the scenario. Later, the trained model is provided with features from the test videos. Test films are classified as normal or anomalous based on the chosen abnormality criteria. These approaches, however, use various precise detection strategies and anomaly definitions. Therefore, it is challenging to classify them just based on detection procedures. With the use of a CNN classifier, optical flow-based spatial-temporal volumes of interest (SVOI) are used to learn the classes for normal and pathological video. Based on the findings of the classifier, anomalies are found. With the use of deep features and optical flow data, Generative Adversarial Net- works (GANs) have been utilized to predict a future frame from a continuous collection of preceding frames. A frame is considered to be normal or abnormal based on the discrepancy between the anticipated future frame and the actual frame. Machine learning has undergone a paradigm change in the previous ten years, particularly in favor of DNN-based techniques. You may have noticed that deep learning techniques have already been used to tackle several anomaly detection issues. Studies using DNNs have shown success in extracting char- acteristics irrespective of light. Due to camera position and perspective, traditional ML frequently fails, especially when trying to recognize objects. Despite their increased processing cost, DNN-based systems like have proved quite accurate at detecting objects. Purely deep learning-based approaches have not been able to successfully track objects reliably, especially in dense settings, even though object tracking is a crucial step in many anomaly detection systems. To create the tracks, techniques like the Kalman filter and DNNs for object association and detec- tion are used. Although it employs YOLO, this too has to track issues that lead to shorter trajectories in crowded and obstructed settings. Access to powerful computational resources might be difficult when putting traffic anomaly detection systems utilizing DNNs into practice. Even though the majority of corporations provide free funding and access to cloud computing resources for university research, un- less hardware prices decrease, research dissemination may be constrained. AI-POWERED VIDEO SURVEILLANCE ISSUES However, there is no strong architecture with a suitable network model for commercial services that takes into account both high accuracy and cheap computing cost. Video monitoring with Closed-Circuit Tele- vision (CCTV) cameras has been studied for decades, but it has several drawbacks, including restricted area coverage, no location sharing, and tracking capabilities. Most video surveillance systems are fixed to infrastructure and typically particular to a site, but to construct a portable surveillance system, a highly accurate algorithm as well as a powerful computing and embedded device that can function with low power consumption is necessary. On the other hand, the vision sensors attached to drones are more scal- able and versatile, providing more extensive surveillance coverage but requiring big data computations. Deep Learning Solutions for AI-Based Video Surveillance Deep learning architectures can achieve more accuracy and operate better with huge datasets. The Deep Learning techniques addressed are Continuous learning, transfer learning, reinforcement learning, ensemble learning, and autoencoders. The Detection methodologies are classified under the learning
  • 46. 11 Artificial Intelligence in Video Surveillance  approachesasSupervised,Unsupervised,andSemi-Supervised.Unsupervisedclassificationiscomputer- controlled and does not require human intervention. Manual training and labeled data require supervised classification. Semi-supervised learning sits between unsupervised learning (no labeled training data) and supervised learning (with labeled training data). There are many available benchmarking datasets like UCSD (UCSD Anomaly Detection Dataset), The dataset was created using security cameras with 60 mm120 mm lenses from the Puri Rath Yatra event, CUHK, Avenue Dataset, Violent-flows, UCF50, Rodriguez’s and so on. However, the collection includes a variety of video genres, including surveil- lance and moving-camera recordings. As a result, it drives us to create a more realistic public difficult urban surveillance video collection to assess the effectiveness of various algorithms for object tracking and behavior analysis. A video is well-known to be a series of successive pictures known as frames. Each frame is treated as an image, and any image processing method can be applied to it. To recognize anomalous pictures or objects in a video sequence (Majeed et al., 2021; Fan et al., 2020), deep learning-based object detection models such as RCNN, Fast RCNN, Faster RCNN, and YOLOv5 are studied for their performance in competition with each other. Figure 3. Framework for abnormal behavior detection in video surveillance
  • 47. 12 Artificial Intelligence in Video Surveillance  Abnormal Behavior Detection (Mabrouk et al., 2018; Harrou et al., 2020) In recent years, significant progress has been made in the demanding problem of abnormal behavior identification in video surveillance. The early phases of low-level processing allow for detecting and describing moving objects in the scene. However, those actions do not enable figuring out what kind of activity the moving item is performing or if its behavior is typical. Finding appropriate characteristics that can withstand various transformations, such as changes to the backdrop and the look of the object, is the main problem in behavior representation. An intelligent video surveillance system’s goal is to effectively identify a noteworthy occurrence from a vast collection of films to head off harmful scenarios shown in Figure 3. This task often calls for two video processing tiers. There are two steps in the first one, low-level characteristics are retrieved to identify the scene’s interest zone. The interest region is then described by primitives that are produced based on low-level attributes. The second level establishes whether or not the behavior is normal by providing semantic information about human activity. Table 3 includes the most popular behavior representation feature. To identify and characterize an entity moving over time, many features are employed. Such features can be divided into local and global features. A predetermined area of the frame is where local features are found. A local location or an interest point may serve as the region’s representation. Motion through- out the whole frame is described using global features. Global motion data is frequently extracted using optical flow features. In recent years, significant progress has been made in the demanding problem of abnormal behavior detection in video surveillance. The early phases of low-level processing enable the recognition and description of moving objects in the scene. These actions do not, however, help us identify the sort of action taken by the moving item or establish whether or not its behavior is normal. There are several ways for recognizing abnormal behavior in video surveillance, including classification methods and Model- ing frameworks, scene density, and the interaction of moving objects. The three types of classification techniques are supervised, semi-supervised, and unsupervised techniques. Supervised approaches use labeled data to simulate both typical and atypical actions. Typically, they are made to identify particular deviant behaviors that were predefined during the training process, such as fighting, loitering, and falling. Thetwotypesofsemi-supervisedalgorithmsarerule-basedandmodel-based,andbothrequiresimply typical video data for training. The first group seeks to create a rule by leveraging common patterns. Any sample that deviates from this guideline is then regarded as an outlier. sparse coding in a rule-based approach to identify deviant actions. The examples that differ from the model’s representation of typical behavior are referred to as aberrant patterns in model-based techniques. The most popular models are the Markov Random Field (MRF), Gaussian Mixture Model (GMM), and Hidden Markov Model (HMM). Using statistical features derived from unlabeled data, unsupervised algorithms seek to identify normal and aberrant actions. Unsupervised learning is carried out using a framework based on a Dominant set and an unsupervised kernel framework for anomaly detection based on feature space and support vector data description. The number of people there is in the scene is reflected in how dense it is. The scene density has a direct impact on the strategies that are selected to define the behavior. Therefore, a single individual or a small group of people might be the moving item in the scene. The scene is distinguished as an uncrowded scene and a crowded scene. When one or a few people are visible in the camera’s field of view, the scene is said to be uncrowded. Three key anomalous behaviors are often taken into account when there is just one person present: falling detection, loitering, and being in the incorrect area. It is impossible to
  • 48. 13 Artificial Intelligence in Video Surveillance  observe and study each person’s conduct separately in a crowded environment that includes a group of people. Occlusion and the few pixels used to depict each individual in the frame are to blame for this. Therefore, it is preferable to model how individuals interact to spot unusual crowd behavior. A video surveillance system may be evaluated using several parameters. Equal Error Rate (EER) and Area Under Roc Curve (AUC) are the two most often utilized metrics. The Receiver Operating Charac- teristic Curve (ROC), which is widely used for performance comparison, is where the two criteria are formed. The EER point on the ROC curve is where the ratio of false positives to false negatives is equal. Motion Detection (Huang et al., 2019) Many computer vision applications, particularly video surveillance system analysis, rely heavily on motion detection. Its goal is to extract moving elements from a video clip one at a time. Motion analysis methods help to concentrate attention on the scene’s moving aspects. Three common methodologies are used for motion detection: time difference, background removal, and optical flow analysis. Approaches to temporal differencing invariably extract imperfect forms of moving objects. When employed in practical applications, optical flow techniques can make it difficult to achieve accurate motion detection because they either make the system more computationally demanding or more sensitive to noise. On the other hand,byusingareferencebackgroundmodelofpriorphotos,backgroundsubtractiontechniquesemployed in traffic monitoring systems can more thoroughly and precisely detect moving objects characterized by moderate temporal complexity. As a consequence, background subtraction techniques are more widely used, developed, and applied in several motion detection applications. The Gaussian Mixture Model (GMM) technique employs a specific distribution that may be used by separately modeling each pixel value. With this method, an incoming frame may be labeled as either having moving objects in it or not. Motion detection is accom- plished using the Sigma Difference Estimation (SDE) approach, which employs a filter technique. To derive the motion vector, this technique uses a pixel-based decision framework to estimate two orders of temporal statistics. However, because it is unable to account for complicated situations, using the filter alone frequently leads to inadequate detection. Multiple Temporal Difference (MTD) is a different background subtraction technique for motion detection that keeps track of multiple prior reference frames Table 3. Features for behavior detection Feature Types Description Features based on optical flow Using the statistical features extracted from the optical flow vector to characterize motion. Interest points Both spatial and temporal domains allow for the detection of salient locations. the depiction of large motion fluctuations correlating to erratic behaviors. Spatio-temporal volume, cube, blob, etc. The temporal dimension is obtained by assembling successive frames. Shape Describing the movement of the object’s form in a series of frames. Shape change detection correlates with abnormal conduct. Texture For each moving item included in a bounding box, rectangle, etc., local patterns are extracted. Object tracking and trajectory extraction Tracking a moving item using an optimization technique and its trajectory (coordinates in each frame).
  • 49. 14 Artificial Intelligence in Video Surveillance  to make it easier to locate presumed foreground items and close detection gaps for moving objects. The Simple Statistical Difference (SSD) technique, creates a straightforward background model based on the temporal average to identify areas of moving objects in traffic surveillance video streams. The majority of the most recent state-of-the-art background removal techniques can identify moving objects in video streams recorded by a stationary camera. These techniques may readily detect moving objects using their backdrop models in such a perfect setting. However, in the actual environment, power- ful winds or earth earthquakes may cause exterior cameras to vibrate. For these techniques, the accurate identification of moving objects in the Intelligent Transportation System (ITS) might be a challenge. To accurately identify moving objects in streaming video, the Gray Relational Analysis-based Motion Detection (GRAMD) technique is described. This approach consists of two crucial components: the Multi-sample Background Generation (MBG) module and the Moving Object Detection (MOD) mod- ule as shown in Figure 4. These modules allow for accurate and thorough detection as well as efficient adaptability to background changes. To detect moving objects in video streams with jitter backgrounds, the MBG module first builds a multi-sample background model using the grey relational analysis approach. Following MBG module construction. The MOD module then employs a multi-sample backdrop model to separate moving ob- jects in real-world scenes recorded by both jitter and static cameras. To prevent errors brought on by the camera jitter, a two-stage detection technique that consists of a rough detection procedure followed by a precise detection procedure is adopted. By doing this, both jitter and static cameras’ video streams can accurately detect moving things. Crowd counting (Sreenu et al., 2019) is used to determine the number of persons in a crowd of thou- sands. Deep learning models also enable face, action, and event detection in crowded environments. Attention mechanism-enabled deep learning was also explored to recognize activities and objects more accurately from surveillance videos. Crowd size is significant and changing in real-world situations, making crowd analysis challenging. It is difficult to distinguish between each entity and its actions. Traffic lights, major intersections, populated areas, gatherings that draw large crowds, and celebrations held by religious organizations, Among the aforementioned situations, crowd analysis inside offices is the most challenging. Identification of all acts, behaviors, and movements is necessary. In crowded scenarios, spatial-temporal convolutional neural networks for anomaly detection and localization reveal that the challenge of crowd analysis is difficult due to the following factors: a large Figure 4. GRAMD approach component framework diagram
  • 50. 15 Artificial Intelligence in Video Surveillance  number of people, Close closeness, a person’s appearance changing often, and frequent partial occlu- sions, crowd’s irregular movement pattern, dangerous behaviors include crowd fear, pixel, and frame level detection. The following steps involve a scene-independent technique that uses deep learning for scene-independent crowd analysis. Crowd counting, Crowd tracking, and division of the crowd Pedes- trian journey time estimate, crowd behavior analysis, crowd attributes recognition, and abnormality detection in a crowd. Methods like data-driven crowd analysis and density-aware tracking are described in the study of High-Density Crowds in films. The data-driven analysis uses a line-based method to understand the movement patterns of crowds from a vast collection of crowd footage. There are two steps to the solution. Both local and global crowd patch matching is used. Microscale and macroscopic crowd modeling, crowd behavior, crowd density analysis, and crowd behavior analysis datasets for crowd behavior analysis are all covered in crowd behavior analysis using fixed and moving cameras. Macroscopic methods are used to manage large crowds. Agents are managed in this case as a whole. In microscopic methods, each agent is dealt with separately. It is possible to gather motion data to depict the crowd using both stationary and moving cameras. For the investigation of crowd behavior, CNN- based techniques such as end-to-end deep CNN, Hydra-CNN architecture, switching CNN, cascade CNN architecture, 3D CNN, and spatiotemporal CNN are addressed. The chapter also includes descriptions of various datasets that are especially helpful for studying crowd behavior. MOTA (multiple-person tracker accuracy) and MOTP (multiple-person tracker precision) are the measures in use. These measures take into account the several targets that are frequently present in crowd situations. A Deep Spatiotemporal Perspective for Understanding Crowd Behavior combines long short-term memory with the convolution layer. Convolution layer-captured spatial data and temporal motion dynamics is constrained by LSTM. The approach predicts the path taken by pedestrians, calculates their destination, and then classifies their behavior based on how they move. According to Crowded Scene Understanding by Deeply Learned Volumetric Slices, a deep model and several fusion techniques should be used. Convolution layers, a global sum pooling layer, and fully linked layers make up the architecture. The architecture calls for weight-sharing and slice fusion techniques. It is anticipated that a new multitask learning deep model would successfully combine motion and appearance variables. As an input to the model, a novel idea of crowd motion channels is developed. In crowd videos, the motion channel examines the temporal pro- gression of the content. The temporal slices that clearly show how the contents of crowd recordings have changed over time agitate the motion channels. broad assessments using a variety of deep structures, data fusion techniques, and weight-sharing strategies to identify temporal aspects with activation functions like rectified linear unit and sigmoid function, the network is set up with a convolutional layer, pooling layer, and fully connected layer. To evaluate the efficacy of suggested input channels, three alternative slice fusion approaches are used. FUTURE TRENDS AND CONCLUSION This chapter elaborates on various models and techniques for AI-powered video surveillance systems that are hot areas in computer vision and video processing research. The comparison of these models in terms of various performance measures is to be discussed. The challenges involved in this system and the issues related are also addressed in detail. Understanding the socio-cognitive components of crowd behavior is a difficult but crucial issue, especially for human-computer interaction applications. This problem is critical to existing surveillance systems and future interactions between intelligent entities and
  • 51. 16 Artificial Intelligence in Video Surveillance  human crowds. Night video enhancement methods are commonly employed for recognizing suspicious actionsacquiredbynightvisualsurveillancesystems.However,artificiallightsourcesintheenvironment degrade the visual quality of the video shot at night. This non-uniform lighting impairs the capacity of a real-time visual surveillance system to identify and track objects. As a result, a uniform enhancement strategy is insufficient for dealing with such uneven lighting. Since Surveillance is a vast area, various case studies are encountered to gain domain knowledge. REFERENCES Ben Mabrouk, A., Zagrouba, E. (2018). Abnormal behavior recognition for intelligent video surveil- lancesystems:Areview.ExpertSystemswithApplications,91,480–491.doi:10.1016/j.eswa.2017.09.029 Das, S., Nag, A., Adhikary, D., Ram, R. J. (2021). Computer Vision-based Social Distancing Sur- veillance with Automated Camera Calibration for Large-scale Deployment. IEEE, 18th India Council International Conference, 1-6. 10.1109/INDICON52576.2021.9691485 Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M. D., Xiao, F. (2020). Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder. Computer Vision and Image Understanding, 195, 1–13. doi:10.1016/j.cviu.2020.102920 Harrou, F., Hittawe, M. M., Sun, Y., Beya, O. (2020). Malicious Attacks Detection in Crowded Areas Using Deep Learning-Based Approach. IEEE Instrumentation Measurement Magazine, 23(5), 57–62. doi:10.1109/MIM.2020.9153576 Hossain, M. S., Muhammad, G., Guizani, N. (2020). Explainable AI and Mass Surveillance System- Based Healthcare Framework to Combat COVID-19 Like Pandemics. IEEE Network, 34(4), 126–132. doi:10.1109/MNET.011.2000458 Huang, S.-C., Member, S. (2019). A Gray Relational Analysis-Based Motion Detection Algorithm for Real-World Surveillance Sensor Deployment. IEEE Sensors Journal, 19(3), 1019–1027. doi:10.1109/ JSEN.2018.2879187 Huang, Y., Liu, R. W., Liu, J. (2022). A two-step image stabilization method for promoting visual quality in vision-enabled maritime surveillance systems. IET Intelligent Transport Systems, 1-15. Kattiman Maidargi. (2021). An Automated Social Distance Monitoring Alarm System based on Human Structure Using Video Surveillance in CQVID-19 Pandemic by AI Techniques, A Review. IEEE International Conference on Electronics, Computing and Communication Technologies. Kaushik, H., Kumar, T., Bhalla, K. (2022). iSecureHome: A Deep fusion framework for surveillance of smart house using real-time emotion recognition. Applied Soft Computing, 122, 122. doi:10.1016/j. asoc.2022.108788 Majeed, F., Khan, F. Z., Iqbal, M. J., Nazir, M. (2021). Real-Time Surveillance System based on Facial Recognition using YOLOv5. IEEE, Mohammad Ali Jinnah University International Conference on Computing, 1-6. 10.1109/MAJICC53071.2021.9526254
  • 52. 17 Artificial Intelligence in Video Surveillance  Naguib, A., Cheng, Y., Chang, Y. (2022). AI Assistance for Home Surveillance. IEEE, 27th Interna- tional Conference on Automation and Computing. Rohit,M.H.(2020).AnIoT-basedsystemforpublicTransportSurveillanceusingreal-timeDataAnalysis and Computer Vision. IEEE, 3rd International Conference on Advance in Electronics, Computers, and Communications. 10.1109/ICAECC50550.2020.9339485 Santhosh, Dogra, Roy. (2020). Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey. ACM Computing Surveys, 53(6). Sreedhara, Raj, George, Ashok. (2021). A Novel Cough Detection Algorithm for COVID-19 Surveil- lance at Public Place. IEEE, 8th International Conference on Smart Computing and Communications (ICSCC), 119-123. Sreenu, G., Saleem Durai, M. A. (2019). Intelligent video surveillance: A review through deep learn- ing techniques for crowd analysis. Journal of Big Data, 6(48), 1–27. doi:10.118640537-019-0212-5
  • 53. 18 Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 2 DOI: 10.4018/978-1-6684-8098-4.ch002 ABSTRACT Content-based video retrieval is a research field that aims to develop advanced techniques for automati- cally analyzing and retrieving video content. This process involves identifying and localizing specific moments in a video and retrieving videos with similar content. Deep bimodal fusion (DBF) is proposed that uses modified convolution neural networks (CNNs) to achieve considerable visual modality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities. By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method has higher precision, recall, F1-score, and accuracy in precise searching that retrieves relevant videos more quickly and effectively. Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach G. Megala https://orcid.org/0000-0002-8084-8292 Vellore Institute of Technology, India P. Swarnalatha Vellore Institute of Technology, India S. Prabu Pondicherry University, India R. Venkatesan https://orcid.org/0000-0002-4336-8628 SASTRA University, India Anantharajah Kaneswaran University of Jaffna, Sri Lanka
  • 54. 19 Content-Based Video Retrieval With Temporal Localization  INTRODUCTION Multimedia information systems are becoming more crucial due to the growth of internet access, big data, and high-speed networks as well as the increasing need for multimedia information with visualiza- tion. Multimedia data, however, needs a lot of processing (Megala et al., 2021) and storage (Megala Swarnalatha, 2022). Therefore, there is a requirement for effective extraction, archiving, indexing, and retrieval of video content from a huge multimedia database. The video has emerged as one of the most prevalent methods to share information because it is visual and powerful. Many people around the world have easy access to it. Media administrators find it hard to use video material for storage and search. Prominent web browsers today often skip searches that are heavy on content in facilitate subtitles that contain basic information regarding the videos being searched. As an alternative to traditional techniques of keyword search, users on online platforms desire to look up precise videos in almost real-time. Video moment localization and content-based video retrieval using deep bimodal fusion is an emerg- ing research field that aims to develop advanced techniques for analyzing and retrieving video content. With the proliferation of digital video content, the need for efficient and effective video retrieval systems has become increasingly important in a wide range of applications, including entertainment, education, and surveillance. The process of video moment localization involves identifying and localizing specific moments in a video, such as a particular scene or event. This can be a challenging task, as videos can contain a wide range of visual and auditory information, making it difficult to accurately identify specific moments of interest. Content-based video retrieval, on the other hand, involves retrieving videos that contain similar content to a given query video. Objects that occurred in the video or images are identified and are saved as a bag of visual features. Efficient object detection methods (Megala Swarnalatha, 2023) are used to perform depth prediction along spatial and temporal features. These bag of features are more helpful in the retrieval process. This process requires the development of accurate models for analyzing video content and identifying similarities between videos. To address these challenges, researchers have turned to deep learning techniques, particularly deep bimodal fusion. This approach involves integrating information from both visual and audio modalities to develop more accurate models for analyzing and retrieving video content. By combining information from both modalities, researchers can develop more robust and accurate models for identifying specific moments in videos and retrieving relevant videos based on content. In this work, we describe a deep bimodal fusion (DBF) method for recognizing a person’s obvious personality from movies, which addresses this issue and yields better results than previously published research. The DBF framework’s structure is depicted in Figure 1. Overall, the use of deep bimodal fusion techniques in video moment localization and content-based video retrieval has the potential to revolutionize the way we analyze and retrieve video content, making it easier and faster to find relevant videos in a wide range of applications. The structure of this chapter is as follows: related works on video retrieval followed by the proposed method, experimental analysis, and conclusion.
  • 55. Random documents with unrelated content Scribd suggests to you:
  • 59. The Project Gutenberg eBook of Aaron in the Wildwoods
  • 60. This ebook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this ebook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook. Title: Aaron in the Wildwoods Author: Joel Chandler Harris Illustrator: Oliver Herford Release date: August 12, 2016 [eBook #52782] Most recently updated: October 23, 2024 Language: English Credits: Produced by David Edwards, Andrés V. Galia and the Online Distributed Proofreading Team at http://www.pgdp.net (This file was produced from images generously made available by The Internet Archive) *** START OF THE PROJECT GUTENBERG EBOOK AARON IN THE WILDWOODS ***
  • 61. MR. COON INSISTED ON GADDING ABOUT. (Page 46)
  • 62. Aaron in the Wildwoods BY JOEL CHANDLER HARRIS AUTHOR OF UNCLE REMUS, ETC. ILLUSTRATED BY OLIVER HERFORD BOSTON AND NEW YORK HOUGHTON, MIFFLIN AND COMPANY The Riverside Press, Cambridge 1897
  • 63. COPYRIGHT, 1897 BY JOEL CHANDLER HARRIS AND HOUGHTON, MIFFLIN AND CO. ALL RIGHTS RESERVED
  • 64. CONTENTS. CHAPTER Page Prelude 1 I. The Little Master 23 II. The Secrets of the Swamp 38 III. What Chunky Riley saw and heard 56 IV. Between Midnight and Dawn 74 V. The Hunt begins 92 VI. The Hunt ends 111 VII. Aaron sees the Signal 129 VIII. The Happenings of a Night 148 IX. The Upsetting of Mr. Gossett 166 X. Chunky Riley sees a Queer Sight 185 XI. The Problem that Timoleon presented 202 XII. What the Patrollers saw and heard 219 XIII. The Apparition the Fox Hunters saw 237 XIV. The Little Master says Good Night 253
  • 65. LIST OF ILLUSTRATIONS Page Mr. Coon insisted on gadding about Frontispiece. It was a Swamp 8 That's Randall's Song 32 Mr. Red Fox meets Mr. Gray Fox 40 A-straddle of the Grunter's Back 48 The Horses were right at his Heels 72 The Goblin Pain 76 The Spring of Cool Refreshing Water 80 Brindle and Aaron 104 In the Swamp 124 Rambler's Fight with the Moccasin 132 He stood as still as a Statue 144 It was the White-Haired Master 160 They tore him all to Flinders 172 The Excited Horse plunged along 180 He edged away as far as he could 188 Aaron and Little Crotchet 212 Behind a Tree stood George Gossett 216 The Black Stallion 224 It was fine for Mr. Fox 238 The Phantom Horseman 242
  • 66. Aaron and Timoleon 250 Big Sal holds the Little Master 262 The Death of the Little Master 268
  • 67. Aaron in the Wildwoods.
  • 68. Prelude. I. Once upon a time there lived on a large plantation in Middle Georgia a boy who was known as Little Crotchet. It was a very queer name, to be sure, but it seemed to fit the lad to a T. When he was a wee bit of a chap he fell seriously ill, and when, many weeks afterwards, the doctors said the worst was over, it was found that he had lost the use of his legs, and that he would never be able to run about and play as other children do. When he was told about this he laughed, and said he had known all along that he would never be able to run about on his feet again; but he had plans of his own, and he told his father that he wanted a pair of crutches made. But you can't use them, my son, said his father. Anyhow, I can try, insisted the lad. The doctors were told of his desire, and these wise men put their heads together. It is a crotchet, they declared, but it will be no harm for him to try. It is a little crotchet, said his mother, and he shall have the crutches. Thus it came about that the lad got both his name and his crutches, for his father insisted on calling him Little Crotchet after that, and he also insisted on sending all the way to Philadelphia for the crutches.
  • 69. They seemed to be a long time in coming, for in those days they had to be brought to Charleston in a sailing vessel, and then sent by way of Augusta in a stage-coach; but when they came they were very welcome, for Little Crotchet had been inquiring for them every day in the week, and Sunday too. And yet when they came, strange to say, he seemed to have lost his interest in them. His mother brought them in joyously, but there was not even a glad smile on the lad's face. He looked at them gravely, weighed them in his hands, laid them across the foot of the bed, and then turned his head on his pillow, as if he wanted to go to sleep. His mother was surprised, and not a little hurt, as mothers will be when they do not understand their children; but she respected his wishes, darkened the room, kissed her boy, and closed the door gently. When everything was still, Little Crotchet sat up in bed, seized his crutches, and proceeded to try them. He did this every day for a week, and at the end of that time surprised everybody in the house, and on the place as well, by marching out on his crutches, and going from room to room without so much as touching his feet to the floor. It seemed to be a most wonderful feat to perform, and so it was; but Providence, in depriving the lad of the use of his legs, had correspondingly strengthened the muscles of his chest and arms, so that within a month he could use his crutches almost as nimbly and quite as safely as other boys use their feet. He could go upstairs and downstairs and walk about the place with as much ease, apparently, as those not afflicted, and it was not strange that the negroes regarded the performance with wonder akin to awe, declaring among themselves that their young master was upheld and supported by de sperits. And indeed it was a queer sight to see the frail lad going boldly about on crutches, his feet not touching the ground. The sight seemed to make the pet name of Little Crotchet more appropriate than ever. So his name stuck to him, even after he got his Gray Pony, and became a familiar figure in town and in country, as he went galloping about, his crutches strapped to the saddle, and dangling as gayly as the sword of some fine general. Thus it came to
  • 70. pass that no one was surprised when Little Crotchet went cantering along, his Gray Pony snorting fiercely, and seeming never to tire. Early or late, whenever the neighbors heard the short, sharp snort of the Gray Pony and the rattling of the crutches, they would turn to one another and say, Little Crotchet! and that would be explanation enough. There seemed to be some sort of understanding between him and his Gray Pony. Anybody could ride the Gray Pony in the pasture or in the grove around the house, but when it came to going out by the big gate, that was another matter. He could neither be led nor driven beyond that boundary by any one except Little Crotchet. It was the same when it came to crossing water. The Gray Pony would not cross over the smallest running brook for any one but Little Crotchet; but with the lad on his back he would plunge into the deepest stream, and, if need be, swim across it. All this deepened and confirmed in the minds of the negroes the idea that Little Crotchet was upheld and protected by de sperits. They had heard him talking to the Gray Pony, and they had heard the Gray Pony whinny in reply. They had seen the Gray Pony with their little master on his back go gladly out at the big gate and rush with a snort through the plantation creek,— a bold and at times a dangerous stream. Seeing these things, and knowing the temper of the pony, they had no trouble in coming to the conclusion that something supernatural was behind it all. II. Thus it happened that Little Crotchet and his Gray Pony were pretty well known through all the country-side, for it seemed that he was never tired of riding, and that the pony was never tired of going. What was the rider's errand? Nobody knew. Why should he go skimming along the red road at day dawn? And why should he come whirling back at dusk,—a red cloud of dust rising beneath the Gray Pony's feet? Nobody could tell. This was almost as much of a puzzle to some of the whites as it was to the negroes; but this mystery, if it could be called such, was soon
  • 71. eclipsed by a phenomenon that worried some of the wisest dwellers in that region. This phenomenon, apparently very simple, began to manifest itself in early fall, and continued all through that season and during the winter and on through the spring, until warm weather set in. It was in the shape of a thin column of blue smoke that could be seen on any clear morning or late afternoon rising from the centre of Spivey's Canebrake. This place was called a canebrake because a thick, almost impenetrable, growth of canes fringed the edge of a mile-wide basin lying between the bluffs of the Oconee River and the uplands beyond. Instead of being a canebrake it was a vast swamp, the site of cool but apparently stagnant ponds and of treacherous quagmires, in which cows, and even horses, had been known to disappear and perish. The cowitch grew there, and the yellow plumes of the poison-oak vine glittered like small torches. There, too, the thunder-wood tree exuded its poisonous milk, and long serpent-like vines wound themselves around and through the trees, and helped to shut out the sunlight. It was a swamp, and a very dismal one. The night birds gathered there to sleep during the day, and all sorts of creatures that shunned the sunlight or hated man found a refuge there. If the negroes had made paths through its recesses to enable them to avoid the patrol, nobody knew it but themselves. Why, then, should a thin but steady stream of blue smoke be constantly rising upwards from the centre of Spivey's Canebrake? It was a mystery to those who first discovered it, and it soon grew to be a neighborhood mystery. During the summer the smoke could not be seen, but in the fall and winter its small thin volume went curling upward continually. Little Crotchet often watched it from the brow of Turner's Hill, the highest part of the uplands. Early in the morning or late in the afternoon the vapor would rise from the Oconee; but the vapor was white and heavy, and was blown about by the wind, while the smoke in the swamp was blue and thin, and rose straight in the air above the tops of the trees in spite of the wayward winds. Once when Little Crotchet was sitting on his pony watching the blue smoke rise from the swamp he saw two of the neighbor farmers
  • 72. coming along the highway. They stopped and shook hands with the lad, and then turned to watch the thin stream of blue smoke. The morning was clear and still, and the smoke rose straight in the air, until it seemed to mingle with the upper blue. The two farmers were father and son,—Jonathan Gadsby and his son Ben. They were both very well acquainted with Little Crotchet,—as, indeed, everybody in the county was,—and he was so bright and queer that they stood somewhat in awe of him. I reckin if I had a pony that wasn't afeard of nothin' I'd go right straight and find out where that fire is, and what it is, remarked Ben Gadsby. This stirred his father's ire apparently. Why, Benjamin! Why, what on the face of the earth do you mean? Ride into that swamp! Why, you must have lost what little sense you had when you was born! I remember, jest as well as if it was day before yesterday, when Uncle Jimmy Cosby's red steer got in that swamp, and we couldn't git him out. Git him out, did I say? We couldn't even git nigh him. We could hear him beller, but we never got where we could see ha'r nor hide of him. If I was thirty year younger I'd take my foot in my hand and wade in there and see where the smoke comes from.
  • 73. IT WAS A SWAMP
  • 74. Little Crotchet laughed. If I had two good legs, said he, I'd soon see what the trouble is. This awoke Ben Gadsby's ambition. I believe I'll go in there and see where the fire is. Fire! exclaimed old Mr. Gadsby, with some irritation. Who said anything about fire? What living and moving creetur could build a fire in that thicket? I'd like mighty well to lay my eyes on him. Well, said Ben Gadsby, where you see smoke there's obliged to be fire. I've heard you say that yourself. Me? exclaimed Mr. Jonathan Gadsby, with a show of alarm in the midst of his indignation. Did I say that? Well, it was when I wasn't so much as thinking that my two eyes were my own. What about foxfire? Suppose that some quagmire or other in that there swamp has gone and got up a ruction on its own hook? Smoke without fire? Why, I've seed it many a time. And maybe that smoke comes from an eruption in the ground. What then? Who's going to know where the fire is? Little Crotchet laughed, but Ben Gadsby put on a very bold front. Well, said he, I can find bee-trees, and I'll find where that fire is. Well, sir, remarked Mr. Jonathan Gadsby, looking at his son with an air of pride, find out where the smoke comes from, and we'll not expect you to see the fire. I wish I could go with you, said Little Crotchet. I don't need any company, replied Ben Gadsby. I've done made up my mind, and I a-going to show the folks around here that where there's so much smoke there's obliged to be some fire. The young man, knowing that he had some warm work before him, pulled off his coat, and tied the sleeves over his shoulder, sash fashion. Then he waved his hand to his father and to Little Crotchet, and went rapidly down the hill. He had undertaken the adventure in a spirit of bravado. He knew that a number of the neighbors had
  • 75. tried to solve the mystery of the smoke in the swamp and had failed. He thought, too, that he would fail; and yet he was urged on by the belief that if he should happen to succeed, all the boys and all the girls in the neighborhood would regard him as a wonderful young man. He had the same ambition that animated the knight of old, but on a smaller scale. III. Now it chanced that Little Crotchet himself was on his way to the smoke in the swamp. He had been watching it, and wondering whether he should go to it by the path he knew, or whether he should go by the road that Aaron, the runaway, had told him of. Ben Gadsby interfered with his plans somewhat; for quite by accident, young Gadsby as he went down the hill struck into the path that Little Crotchet knew. There was a chance to gallop along the brow of the hill, turn to the left, plunge through a shallow lagoon, and strike into the path ahead of Gadsby, and this chance Little Crotchet took. He waved his hand to Mr. Jonathan Gadsby, gave the Gray Pony the rein, and went galloping through the underbrush, his crutches rattling, and the rings of the bridle-bit jingling. To Mr. Jonathan Gadsby it seemed that the lad was riding recklessly, and he groaned and shook his head as he turned and went on his way. But Little Crotchet rode on. Turning sharply to the left as soon as he got out of sight, he went plunging through the lagoon, and was soon going along the blind path a quarter of a mile ahead of Ben Gadsby. This is why young Gadsby was so much disturbed that he lost his way. He was bold enough when he started out, but by the time he had descended the hill and struck into what he thought was a cattle- path his courage began to fail him. The tall canes seemed to bend above him in a threatening manner. The silence oppressed him. Everything was so still that the echo of his own movements as he brushed along the narrow path seemed to develop into ominous whispers, as if all the goblins he had ever heard of had congregated in front of him to bar his way.
  • 76. The silence, with its strange echoes, was bad enough, but when he heard the snorting of Little Crotchet's Gray Pony as it plunged through the lagoon, the rattle of the crutches and the jingling of the bridle-bit, he fell into a panic. What great beast could it be that went helter-skelter through this dark and silent swamp, swimming through the water and tearing through the quagmires? And yet, when Ben Gadsby would have turned back, the rank undergrowth and the trailing vines had quite obscured the track. The fear that impelled him to retrace his steps was equally powerful in impelling him to go forward. And this seemed the easiest plan. He felt that it would be just as safe to go on, having once made the venture, as to turn back. He had a presentiment that he would never find his way out anyhow, and the panic he was in nerved him to the point of desperation. So on he went, not always trying to follow the path, but plunging forward aimlessly. In half an hour he was calmer, and pretty soon he found the ground firm under his feet. His instincts as a bee-hunter came back to him. He had started in from the east side, and he paused to take his bearings. But it was hard to see the sun, and in the recesses of the swamp the mosses grew on all sides of the trees. And yet there was a difference, which Ben Gadsby did not fail to discover and take account of. They grew thicker and larger on the north side, and remembering this, he went forward with more confidence. He found that the middle of the swamp was comparatively dry. Huge poplar-trees stood ranged about, the largest he had ever seen. In the midst of a group of trees he found one that was hollow, and in this hollow he found the smouldering embers of a fire. But for the strange silence that surrounded him he would have given a whoop of triumph; but he restrained himself. Bee-hunter that he was, he took his coat from his shoulders and tied it around a small slim sapling standing near the big poplar where he had found the fire. It was his way when he found a bee-tree. It was a sort of guide. In returning he would take the general direction, and then hunt about
  • 77. until he found his coat; and it was much easier to find a tree tagged with a coat than it was to find one not similarly marked. Thus, instead of whooping triumphantly, Ben Gadsby simply tied his coat about the nearest sapling, nodding his head significantly as he did so. He had unearthed the secret and unraveled the mystery, and now he would go and call in such of the neighbors as were near at hand and show them what a simple thing the great mystery was. He knew that he had found the hiding-place of Aaron, the runaway. So he fixed his landmark, and started out of the swamp with a lighter heart than he had when he came in. To make sure of his latitude and longitude, he turned in his tracks when he had gone a little distance and looked for the tree on which he had tied his coat. But it was not to be seen. He re-traced his steps, trying to find his coat. Looking about him cautiously, he saw the garment after a while, but it was in an entirely different direction from what he supposed it would be. It was tied to a sapling, and the sapling was near a big poplar. To satisfy himself, he returned to make a closer examination. Sure enough, there was the coat, but the poplar close by was not a hollow poplar, nor was it as large as the tree in which Ben Gadsby had found the smouldering embers of a fire. He sat on the trunk of a fallen tree and scratched his head, and discussed the matter in his mind the best he could. Finally he concluded that it would be a very easy matter, after he found his coat again, to find the hollow poplar. So he started home again. But he had not gone far when he turned around to take another view of his coat. It had disappeared. Ben Gadsby looked carefully around, and then a feeling of terror crept over his whole body—a feeling that nearly paralyzed his limbs. He tried to overcome this feeling, and did so to a certain degree. He plucked up sufficient courage to return and try to find his coat; but the task was indeed bewildering. He thought he had never seen so many large poplars with small slim saplings
  • 78. standing near them, and then he began to wander around almost aimlessly. IV. Suddenly he heard a scream that almost paralyzed him—a scream that was followed by the sound of a struggle going on in the thick undergrowth close at hand. He could see the muddy water splash above the bushes, and he could hear fierce growlings and gruntings. Before he could make up his mind what to do, a gigantic mulatto, with torn clothes and staring eyes, rushed out of the swamp and came rushing by, closely pursued by a big white boar with open mouth and fierce cries. The white boar was right at the mulatto's heels, and his yellow tusks gleamed viciously as he ran with open mouth. Pursuer and pursued disappeared in the bushes with a splash and a crash, and then all was as still as before. In fact, the silence seemed profounder for this uncanny and appalling disturbance. It was so unnatural that half a minute after it happened Ben Gadsby was not certain whether it had occurred at all. He was a pretty bold youth, having been used to the woods and fields all his life, but he had now beheld a spectacle so out of the ordinary, and of so startling a character, that he made haste to get out of the swamp as fast as his legs, weakened by fear, would carry him. More than once, as he made his way out of the swamp, he paused to listen; and it seemed that each time he paused an owl, or some other bird of noiseless wing, made a sudden swoop at his head. Beyond the exclamation he made when this happened the silence was unbroken. This experience was unusual enough to hasten his steps, even if he had had no other motive for haste. When nearly out of the swamp, he came upon a large poplar, by the side of which a small slim sapling was growing. Tied around this sapling was his coat, which he thought he had left in the middle of the swamp. The sight almost took his breath away.
  • 79. He examined the coat carefully, and found that the sleeves were tied around the tree just as he had tied them. He felt in the pockets. Everything was just as he had left it. He examined the poplar; it was hollow, and in the hollow was a pile of ashes. Well! exclaimed Ben Gadsby. I'm the biggest fool that ever walked the earth. If I ain't been asleep and dreamed all this, I'm crazy; and if I've been asleep, I'm a fool. His experience had been so queer and so confusing that he promised himself he'd never tell it where any of the older people could hear it, for he knew that they would not only treat his tale with scorn and contempt, but would make him the butt of ridicule among the younger folks. I know exactly what they'd say, he remarked to himself. They'd declare that a skeer'd hog run across my path, and that I was skeer'der than the hog. So Ben Gadsby took his coat from the sapling, and went trudging along his way toward the big road. When he reached that point he turned and looked toward the swamp. Much to his surprise, the stream of blue smoke was still flowing upward. He rubbed his eyes and looked again, but there was the smoke. His surprise was still greater when he saw Little Crotchet and the Gray Pony come ambling up the hill in the path he had just come over. What did you find? asked Little Crotchet, as he reined in the Gray Pony. Nothing—nothing at all, replied Ben Gadsby, determined not to commit himself. Nothing? cried Little Crotchet. Well, you ought to have been with me! Why, I saw sights! The birds flew in my face, and when I got in the middle of the swamp a big white hog came rushing out, and if this Gray Pony hadn't been the nimblest of his kind, you'd never have seen me any more. Is that so? asked Ben Gadsby, in a dazed way. Well, I declare! 'Twas all quiet with me. I just went in and come out again, and that's all there is to it.
  • 80. I wish I'd been with you, said Little Crotchet, with a curious laugh. Good-by! With that he wheeled the Gray Pony and rode off home. Ben Gadsby watched Little Crotchet out of sight, and then, with a gesture of despair, surprise, or indignation, flung his coat on the ground, crying, Well, by jing! V. That night there was so much laughter in the top story of the Abercrombie house that the Colonel himself came to the foot of the stairs and called out to know what the matter was. It's nobody but me, replied Little Crotchet. I was just laughing. Colonel Abercrombie paused, as if waiting for some further explanation, but hearing none, said, Good-night, my son, and God bless you! Good-night, father dear, exclaimed the lad, flinging a kiss at the shadow his father's candle flung on the wall. Then he turned again into his own room, where Aaron the Arab (son of Ben Ali) sat leaning against the wall, as silent and as impassive as a block of tawny marble. Little Crotchet lay back in his bed, and the two were silent for a time. Finally Aaron said:— The White Grunter carried his play too far. He nipped a piece from my leg. I never saw anything like it, remarked little Crotchet. I thought the White Pig was angry. You did that to frighten Ben Gadsby. Yes, Little Master, responded Aaron, and I'm thinking the young man will never hunt for the smoke in the swamp any more. Little Crotchet laughed again, as he remembered how Ben Gadsby looked as Aaron and the White Pig went careening across the dry
  • 81. place in the swamp. There was a silence again, and then Aaron said he must be going. And when are you going home to your master? Little Crotchet asked. Never! replied Aaron the runaway, with emphasis. Never! He is no master of mine. He is a bad man. Then he undressed Little Crotchet, tucked the cover about him,—for the nights were growing chill,—whispered good-night, and slipped from the window, letting down the sash gently as he went out. If any one had been watching, he would have seen the tall Arab steal along the roof until he came to the limb of an oak that touched the eaves. Along this he went nimbly, glided down the trunk to the ground, and disappeared in the darkness.
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