May 2022: Most Downloaded Articles in Computer Science &Information Technology

May 2022: Most Downloaded
Articles in Computer Science
&Information Technology
International Journal of Computer Science and Information
Technology (IJCSIT)
ISPEC,WJCI Indexed
ISSN: 0975-3826(online); 0975-4660 (Print)
https://airccse.org/journal/ijcsit.html

SECURITY THREATS ON CLOUD COMPUTING VULNERABILITIES
Te-Shun Chou
Department of Technology Systems, East Carolina University, Greenville, NC, U.S.A.
ABSTRACT
Clouds provide a powerful computing platform that enables individuals and organizations to
perform variety levels of tasks such as: use of online storage space, adoption of business
applications, development of customized computer software, and creation of a ―realistic‖
network environment. In previous years, the number of people using cloud services has
dramatically increased and lots of data has been stored in cloud computing environments. In the
meantime, data breaches to cloud services are also increasing every year due to hackers who are
always trying to exploit the security vulnerabilities of the architecture of cloud. In this paper,
three cloud service models were compared; cloud security risks and threats were investigated
based on the nature of the cloud service models. Real world cloud attacks were included to
demonstrate the techniques that hackers used against cloud computing systems. In addition,
counter measures to cloud security breaches are presented.
KEYWORDS
Cloud computing, cloud security threats and countermeasures, cloud service models
Volume URL : http://airccse.org/journal/ijcsit2013_curr.html
Source URL : http://airccse.org/journal/jcsit/5313ijcsit06.pdf

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Revisited,‖ IEEE International Conference on Web Services, Los Angeles, 2009.
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DATA WAREHOUSE AND BIG DATA INTEGRATION
Sonia Ordoñez Salinas and Alba Consuelo Nieto Lemus
Faculty of Engineering, Distrial F.J.C University, Bogotá, Colombia
ABSTRACT
Big Data triggered furthered an influx of research and prospective on concepts and processes
pertaining previously to the Data Warehouse field. Some conclude that Data Warehouse as such
will disappear; others present Big Data as the natural Data Warehouse evolution (perhaps
without identifying a clear division between the two); and finally, some others pose a future of
convergence, partially exploring the possible integration of both. In this paper, we revise the
underlying technological features of Big Data and Data Warehouse, highlighting their
differences and areas of convergence. Even when some differences exist, both technologies
could (and should) be integrated because they both aim at the same purpose: data exploration
and decision making support. We explore some convergence strategies, based on the common
elements in both technologies. We present a revision of the state-of-the-art in integration
proposals from the point of view of the purpose, methodology, architecture and underlying
technology, highlighting the common elements that support both technologies that may serve as
a starting point for full integration and we propose a proposal of integration between the two
technologies.
KEYWORDS
Big Data, Data Warehouse, Integration, Hadoop, NoSql, MapReduce, 7V‘s, 3C‘s, M&G
Source URL : https://aircconline.com/ijcsit/V9N2/9217ijcsit01.pdf

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CLUSTERING ALGORITHM FOR A HEALTHCARE DATASET USING
SILHOUETTE SCORE VALUE
Godwin Ogbuabor1
and Ugwoke, F. N2
1
School of Computer Science, University of Lincoln, United Kingdom
2
Department of Computer Science, Michael Okpara University of Agriculture Umudike, Abia
State, Nigeria
ABSTRACT
The huge amount of healthcare data, coupled with the need for data analysis tools has made
data mining interesting research areas. Data mining tools and techniques help to discover and
understand hidden patterns in a dataset which may not be possible by mainly visualization of
the data. Selecting appropriate clustering method and optimal number of clusters in healthcare
data can be confusing and difficult most times. Presently, a large number of clustering
algorithms are available for clustering healthcare data, but it is very difficult for people with
little knowledge of data mining to choose suitable clustering algorithms. This paper aims to
analyze clustering techniques using healthcare dataset, in order to determine suitable algorithms
which can bring the optimized group clusters. Performances of two clustering algorithms
(Kmeans and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-
means algorithm using different number of clusters (K) and different distance metrics.
Secondly, we analyzed DBSCAN algorithm using different minimum number of points
required to form a cluster (minPts) and different distance metrics. The experimental result
indicates that both K-means and DBSCAN algorithms have strong intra-cluster cohesion and
inter-cluster separation. Based on the analysis, K-means algorithm performed better compare to
DBSCAN algorithm in terms of clustering accuracy and execution time.
KEYWORDS
Dataset, Clustering, Healthcare data, Silhouette score value, K-means, DBSCAN
Volume URL : https://airccse.org/journal/ijcsit2018_curr.html

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FUTURE AND CHALLENGES OF INTERNET OF THINGS
Falguni Jindal1
, Rishabh Jamar2
, Prathamesh Churi3
1,2
Bachelors of Technology in Computer Engineering SVKM’s NMIMS Mukesh Patel School of
Technology Management and Engineering, Mumbai, India
3
Assistant Professor (Computer Engineering) SVKM’s NMIMS Mukesh Patel School of
Technology Management and Engineering, Mumbai, India
ABSTRACT
The world is moving forward at a fast pace, and the credit goes to ever growing technology.
One such concept is IOT (Internet of things) with which automation is no longer a virtual
reality. IOT connects various non-living objects through the internet and enables them to share
information with their community network to automate processes for humans and makes their
lives easier. The paper presents the future challenges of IoT , such as the technical (connectivity
, compatibility and longevity , standards , intelligent analysis and actions , security), business (
investment , modest revenue model etc. ), societal (changing demands , new devices, expense,
customer confidence etc. ) and legal challenges ( laws, regulations, procedures, policies etc. ). A
section also discusses the various myths that might hamper the progress of IOT, security of data
being the most critical factor of all. An optimistic approach to people in adopting the unfolding
changes brought by IOT will also help in its growth.
KEYWORDS
IoT, Internet of Things, Security, Sensors.

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Computer Science & Information Technology (IJCSIT) Vol 10, No 2, April 2018 25
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February 2018.

DATA MINING MODEL PERFORMANCE OF SALES PREDICTIVE
ALGORITHMS BASED ON RAPIDMINER WORKFLOWS
Alessandro Massaro, Vincenzo Maritati
Angelo Galiano Dyrecta Lab, IT research
Laboratory,via Vescovo Simplicio, 45, 70014 Conversano (BA), Italy
ABSTRACT
By applying RapidMiner workflows has been processed a dataset originated from different data files,
and containing information about the sales over three years of a large chain of retail stores.
Subsequently, has been constructed a Deep Learning model performing a predictive algorithm suitable
for sales forecasting. This model is based on artificial neural network –ANN- algorithm able to learn the
model starting from sales historical data and by pre-processing the data. The best built model uses a
multilayer neural network together with an ―optimized operator‖ able to find automatically the best
parameter setting of the implemented algorithm. In order to prove the best performing predictive model,
other machine learning algorithms have been tested. The performance comparison has been performed
between Support Vector Machine –SVM-, k-Nearest Neighbor k-NN-,Gradient Boosted Trees, Decision
Trees, and Deep Learning algorithms. The comparison of the degree of correlation between real and
predicted values, the average absolute error and the relative average error proved that ANN exhibited the
best performance. The Gradient Boosted Trees approach represents an alternative approach having the
second best performance. The case of study has been developed within the framework of an industry
project oriented on the integration of high performance data mining models able to predict sales using–
ERP- and customer relationship management –CRM- tools.
KEYWORDS
RapidMiner, Neural Network, Deep Learning, Gradient Boosted Trees, Data Mining Performance, Sales
Prediction.
Source URL : http://aircconline.com/ijcsit/V10N3/10318ijcsit03.pdf

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AN EXPLORATION OF THE FACTORS AFFECTING USERS’
SATISFACTION WITH MOBILE PAYMENTS
Lisa Y. Chen and Wan-Ning Wu
Department of Information Management,
I-Shou University, Taiwan
ABSTRACT
Mobile payment allows consumers to make more flexible payments through convenient mobile devices.
While mobile payment is easy and time save, the operation and security of mobile payment must ensure
that the payment is fast, convenient, reliable and safety in order to increase the users‘ satisfaction.
Therefore, this study based on technology acceptance model to explore the impact of external variables
through perceived usefulness and perceived ease of use on users‘ satisfaction. The data analysis methods
used in this study are descriptive statistical analysis, reliability and validity analysis, Pearson correlation
analysis and regression analysis to verify the hypotheses. The results show that all hypotheses are
supported. However, mobile payment is still subject to many restrictions on development and there are
limited related researches. The results of this study provided insight into the factors that affect the users‘
satisfaction for mobile payment. Related services development of mobile payment and future research
suggestions are also offered.
KEYWORDS
Mobile Payment, Technology Acceptance Model, Users‘ satisfaction

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BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIES
Manoj Muniswamaiah, Tilak Agerwala and Charles Tappert
Seidenberg School of CSIS, Pace University, White Plains, New York
ABSTRACT
Big Data is used in decision making process to gain useful insights hidden in the data for
business and engineering. At the same time it presents challenges in processing, cloud computing
has helped in advancement of big data by providing computational, networking and storage
capacity. This paper presents the review, opportunities and challenges of transforming big data
using cloud computing resources.
KEYWORDS
Big data; cloud computing; analytics; database; data warehouse

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Applications in Cloud Computing. 221-232. 10.5121/csit.2019.90918.

AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN
MEDICAL DIAGNOSIS
Shakuntala Jatav1
and Vivek Sharma2
1
M.Tech Scholar, Department of CSE, TIT College, Bhopal
2
Professor, Department of CSE, TIT College, Bhopal
ABSTRACT
The Healthcare industry contains big and complex data that may be required in order to discover
fascinating pattern of diseases & makes effective decisions with the help of different machine
learning techniques. Advanced data mining techniques are used to discover knowledge in
database and for medical research. This paper has analyzed prediction systems for Diabetes,
Kidney and Liver disease using more number of input attributes. The data mining classification
techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on
Diabetes, Kidney and Liver disease database. The performance of these techniques is compared,
based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed
algorithm is designed using SVM and RF algorithm and the experimental result shows the
accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
KEYWORDS
Data Mining, Clinical Decision Support System, Disease Prediction, Classification, SVM, RF.

REFERENCES
[1] Nidhi Bhatla, Kiran Jyoti, ―An Analysis of Heart Disease Prediction using Different Data Mining
Techniques‖,IJERT,Vol 1, Issue 8, 2012.
[2] Syed Umar Amin, Kavita Agarwal, Rizwan Beg, ―Genetic Neural Network based Data Mining in
Prediction of Heart Disease using Risk Factors‖, IEEE, 2013.
[3] A H Chen, S Y Huang, P S Hong, C H Cheng, E J Lin, ―HDPS: Heart Disease Prediction System‖,
IEEE, 2011.
[4] M. Akhil Jabbar, B. L Deekshatulu, Priti Chandra, ―Heart Disease Prediction using Lazy Associative
Classification‖, IEEE, 2013.
[5] Chaitrali S. Dangare, Sulabha S. Apte, ―Improved Study of Heart Disease Prediction System using
Data Mining Classification Techniques‖, IJCA, Volume 47– No.10, June 2012. [6] P. Bhandari, S. Yadav,
S. Mote, D.Rankhambe, ―Predictive System for Medical Diagnosis with Expertise Analysis‖, IJESC, Vol.
6, pp. 4652-4656, 2016.
[7] Nishara Banu, Gomathy, ―Disease Forecasting System using Data Mining Methods‖, IEEE
Transaction on Intelligent Computing Applications, 2014.
[8] A. Iyer, S. Jeyalatha and R. Sumbaly, ―Diagnosis of Diabetes using Classification Mining
Techniques‖, IJDKP, Vol. 5, pp. 1-14, 2015.
[9] Sadiyah Noor Novita Alfisahrin and Teddy Mantoro, ―Data Mining Techniques for Optimatization of
Liver Disease Classification‖, International Conference on Advanced Computer Science Applications and
Technologies, IEEE, pp. 379-384, 2013.
[10] A. Naik and L. Samant, ―Correlation Review of Classification Algorithm using Data Mining Tool:
WEKA, Rapidminer , Tanagra ,Orange and Knime‖, ELSEVIER, Vol. 85, pp. 662-668, 2016.
[11] Uma Ojha and Savita Goel, ―A study on prediction of breast cancer recurrence using data mining
techniques‖, International Conference on Cloud Computing, Data Science & Engineering, IEEE, 2017.
[12] Naganna Chetty, Kunwar Singh Vaisla, Nagamma Patil, ―An Improved Method for Disease
Prediction using Fuzzy Approach‖, International Conference on Advances in Computing and
Communication Engineering, IEEE, pp. 568-572, 2015.
[13] Kumari Deepika and Dr. S. Seema, ―Predictive Analytics to Prevent and Control Chronic Diseases‖,
International Conference on Applied and Theoretical Computing and Communication Technology, IEEE,
pp. 381-386, 2016.
[14] Emrana Kabir Hashi, Md. Shahid Uz Zaman and Md. Rokibul Hasan, ―An Expert Clinical Decision
Support System to Predict Disease Using Classification Techniques‖, IEEE, 2017.

Monitoring Student Attendance Using A Smart System at Taif University
Saleh Alghamdi
Department of Information Technology, Taif University, Al-taif, Saudi Arabia
ABSTRACT
The university system in the Kingdom of Saudi Arabia is concerned with student attendance for
lectures, and it is the responsibility of lecturers to monitor student attendance for each lecture. By
the end of the semester, students get an attendance register indicating which lectures the student
has attended and it reports the calculated percentage for each student‘s attendance in each course.
Universities have regulated the mechanisms and the acceptable percentages of student absence.
The process for a lecturer to manually check student attendance consumes a lot of time and
effort, either during the lecture or when in the process of emptying absenteeism and inserting it
into the university‘s electronic system. Therefore, Saudi universities compete to find modern
methods of checking student attendance that will avoid the disadvantages of manually taking
attendance. For this reason, they have produced electronic attendance systems, for example,
using a student's fingerprint, an eye recognition system, or a mobile phone system to read a QR
code designed for the same purpose. All of these systems have the disadvantage that they
consume a lot of time, as all students have to line up at the fingerprint reader or the eye detector
for identification. Therefore, the problem of the consumption of lecture time is still present, even
with these modern systems. Therefore, the aim of this research is to propose a smart mobile
application that is able to check the attendance of students without having to consume lecture
time or require any effort from the lecturer. The system automatically recognizes the attendance
of students through their university ID cards. Each lecturer would use his/ her own mobile phone
to use the proposed system to check the attendance of students instead of using manual method to
register the attendance of students and the students‘ ID cards that are detected by coming within
range of the lecturer reader would represent present students, and missing student ID cards
represent absent students
KEYWORDS
Context Awareness, RFID, Monitoring Student Attendance.

REFERENCES
[1] A. N. Yumang, D. Padilla, M. Sejera, A. C. U. Pajarillo, G. V. L. B. Palmiano and M. M. F. Racho,
"Attendance checker for students of Mapúa University," 2017IEEE 9th International Conference on
Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and
Management (HNICEM), Manila, 2017, pp. 1-3.
[2] M. M. Islam, M. K. Hasan, M. M. Billah and M. M. Uddin, "Development of smartphone-based
student attendance system," 2017 IEEE Region 10 Humanitarian Technology Conference (R10- HTC),
Dhaka, 2017, pp. 230-233.
[3] M. I. Moksin and N. M. Yasin, "The Implementation of Wireless Student Attendance System in an
Examination Procedure," 2009 International Association of Computer Science and Information
Technology - Spring Conference, Singapore, 2009, pp. 174-177.
[4] L. Ma, J. Pan, L. Cao and J. Shen, "The Design of Wireless Students Management System Based on
Fingerprint Sensor," 2010 International Conference on E-Business and E-Government, Guangzhou, 2010,
pp. 2153-2155.
[5] A. Purohit, K. Gaurav, C. Bhati and A. Oak, "Smart attendance," 2017 International conference of
Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2017, pp. 415- 419.
[6] J. A. Rubella, M. Suganya, K. Senathipathi, B. S. Kumar, K. R. Gowdham and M. Ranjithkumar,
"Fingerprint based license checking for auto-mobiles," 2012 Fourth International Conference on
Advanced Computing (ICoAC), Chennai, 2012, pp. 1-8.
[7] Han Xi, Niu Wenliang and Li Zheying, "Application of fingerprint recognition on the laboratory
management," 2008 9th International Conference on Signal Processing, Beijing, 2008, pp. 2960- 2963.
[8] N. Wenliang, H. Xi, L. Zheying and W. Shuying, "An Open Laboratory Management System Based
on Fingerprints Recognition," 2009 Second International Symposium on Computational Intelligence and
Design, Changsha, 2009, pp. 510-512.
[9] R. Samet and M. Tanriverdi, "Face Recognition-Based Mobile Automatic Classroom Attendance
Management System," 2017 International Conference on Cyberworlds (CW), Chester, 2017, pp. 253-256.
[10] Y. Mao, H. Li and Z. Yin, "Who missed the class? — Unifying multi-face detection, tracking and
recognition in videos," 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu,
2014, pp. 1-6.
[11] S. M. Čisar, R. Pinter, V. Vojnić, V. Tumbas and P. Čisar, "Smartphone application for tracking
students' class attendance," 2016 IEEE 14th International Symposium on Intelligent Systems and
Informatics (SISY), Subotica, 2016, pp. 227-232.
[12] S. M. A. Rahman and O. N. A. Hatali, "Trends of the Academic Staff to Use Electronic Attendance
in the College of Applied Science, Ibri," 2015 Fifth International Conference on eLearning (econf),
Manama, 2015, pp. 225-230.
[13] J. Park, K. An, D. Kim and J. Choi, "Multiple human tracking using multiple kinects for an
attendance check system of a smart class," 2013 10th International Conference on Ubiquitous Robots and
Ambient Intelligence (URAI), Jeju, 2013, pp. 130-130. International Journal of Computer Science &

Information Technology (IJCSIT) Vol 11, No 1, February 2019 115
[14] G. J. Saxena et al., "Three stage robust attendance recording and monitoring using social media
platform, image processing and cloud computing," 2017 4th International Conference on Signal
Processing, Computing and Control (ISPCC), Solan, 2017, pp. 194-199.
[15] U. E. Peter, C. K. A. Joe-Uzuegbu, L. Uzoechi and F. K. Opara, "Biometric-based attendance system
with remote real-time monitoring for tertiary institutions in developing countries," 2013 IEEE
International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing
Society (NIGERCON), Owerri, 2013, pp. 1-8.
[16] Xiao, Sheng & Liang, Wenlong & Tang, Yishan. (2018). Classroom Attention Restoration Using
Computer Game Rewarding Mechanism. pp. 1-6.
[17] J. He, A. Atabekov and H. M. Haddad, "Internet-of-Things Based Smart Resource Management
System: A Case Study Intelligent Chair System," 2016 25th International Conference on Computer
Communication and Networks (ICCCN), Waikoloa, HI, 2016, pp. 1-6
AUTHOR
Saleh Ahmed Alghamdi, Assistant Professor of College of Computers and
Information Technology, department of Information Technology, Taif University,
Taif, Saudi Arabia. Saleh completed Bachelor of Education degree in the
department of Computer Science, Teachers‘ college, Riyadh, Saudi Arabia, GPA
4.72 out of 5 With the second honor degree, 2004. Then he got Master of
Information Technology, from Latrobe University, Melbourne, Australia. 2008-
2010. After that Saleh got Doctor of Philosophy (Computer Science), Royal
Melbourne Institute of Technology (RMIT) University, Melbourne, Australia.
2010- 2014, thesis title ―A Context-aware Navigational Autonomy Aid for the Blind‖. Now the main area
of Interest in research is: Context Awareness, Positioning and Navigation and Visually Impaired
Assistance.

ENVIRONMENTAL MONITORING AND CONTROLLING SYSTEM FOR
MUSHROOM FARM WITH ONLINE INTERFACE
Arjuna Marzuki and Soh Yan Ying
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang,
Malaysia
ABSTRACT
Agriculture sensors play an important role in modern agriculture. The use of sensors in various agriculture
sectors minimizes the environmental impact on crops, helps in increasing yield and saving cost of
operation. Among all agriculture industries in Malaysia, the mushroom industry is a comparatively new
and small. As most of the mushroom farms in Malaysia are small-scaled, their production capability is
limited by inadequate environmental control system and the lack of financial resources to upgrade the
systems. This paper presents an environmental monitoring and controlling system to monitor and control
the environmental conditions in a mushroom farm. It enables user to monitor temperature, humidity,
carbon dioxide concentration and light intensity in a mushroom farm on an android device by using
ThingSpeak online platform. The control algorithm is able to control devices in a mushroom farm
automatically based on feedback from the sensors to maintain the environment in an optimum condition
for mushroom growth. The measured percentage error of temperature, humidity, carbon dioxide and the
light using the developed system was as low as 0.4%, 1.5%, 2.2% and 1.34% respectively
KEYWORDS
Agriculture, Interface Circuit, Internet of Things, Monitoring and Control, Sensor, Wireless.

REFERENCES
[1] Unit Pengurusan Prestasi dan Pelaksanaan (2010) Economic Transformation Programme:
A Roadmap for Malaysia (1 Malaysia). Performance Management and Delivery Unit,
Jabatan Perdana Menteri.
[2] Istikoma Qurat-ul-Ain., & Dahlan A. R. A, (2015) ―The Transformation of Agriculture
Based Economy to an Industrial Sector through Crowd Sourcing In Malaysia‖, Int. J.
Comput. Sci. Inf. Technol. Res., Vol. 3, No. 1, pp.34–41.
[3] Bakar B.B., (2009) ―The Malaysian Agricultural Industry in the New Millennium – Issues
and Challenges,‖ pp. 337–356.
[4] Rosmiza M., Davies W., Aznie R. C., Jabil M., & Mazdi M, (2016) ―Prospects for
Increasing Commercial Mushroom Production in Malaysia: Challenges and
Opportunities‖, Mediterr. J. Soc. Sci., Vol. 7, No. 1, pp. 406–415.
[5] Haimid M. T., Rahim H., & Dardak R. A, (2013) ―Understanding the mushroom industry
and its marketing strategies for fresh produce in Malaysia‖, Econ. Technol. Manag. Rev.,
Vol. 8, pp. 27–37.
[6] Mat Amin M. Z., & Harun A, (2015) ―Competitiveness of the Mushroom Industry in
Malaysia‖ [Online]. Available: http://ap.fftc.agnet.org/ap_db.php?id=481&print=1.
[Accessed: 18-Oct-2016].
[7] Australian Mushroom Growers Association, ―Introduction to Mushroom Growing,‖
AMGA, pp. 1-16.
[8] Van Nieuwenhuijzen, Bram., & Oei, P (2005) Small-scale mushroom cultivation oyster,
shiitake and wood ear mushrooms, Agrodok;40. Agromisa/CTA, Wageningen, The
Netherlands.
[9] Stamets P., & Chilton, J. S, (1983) ―The Mushroom Cultivator: A Practical Guide to
Growing Mushrooms at Home‖, S. Cal. L. Rev., p. 416.
[10] Grant, J.J (2002) An investigation of the airflow in mushroom growing structures, the
development of an improved, three-dimensional solution technique for fluid flow and its
evaluation for the modelling of mushroom growing structures. PhD thesis, Dublin City
University.
[11] Kwon H., & Kim, B. S (2004) Mushroom Grow. Handb. 1, pp. 192–196.
[12] Tisdale T. E (2004) Cultivation of the Oyster Mushroom (Pleurotussp.) on Wood
Substrates in Hawaii. MSc thesis, University of Hawai‘i.

[13] Wang X., (2014) ―Temperature and Humidity Monitoring System Based on GSM Module‖,
International Journal of Computer, Consumer and Control., Vol. 3, No. 1, pp. 41–49.
[14] Rahali A., Guerbaoui M., Ed-dahhak A., El Afou Y., Tannouche A., Lachhab A., &
Bouchikhi, B, (2011) ―Development of a data acquisition and greenhouse control system
based on GSM‖, Int. J. Eng. Sci. Technol., Vol. 3, No. 8, pp. 297–306.
[15] Kalinin Y. S., Velikov E. K., & Markova, V. I, (2015) ―Design of Indoor Environment
Monitoring
System Using Arduino‖, Int. J. Innov. Sci. Mod. Eng., Vol. 3, No. 7, pp. 46–49, 20.
[16] Lokesh Krishna K., Madhuri J., & Anuradha K, (2016) ―A ZigBee based Energy Efficient
Environmental Monitoring Alerting and Controlling System‖, in International Conference
On Information Communication And Embedded Systems (ICICES2016).

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