March 2021: Top 10 Read Article in Computer Science & Information Technology

March 2021: Top 10 Read
Article in Computer Science
& Information Technology
International Journal of Computer Science and
Information Technology (IJCSIT)
Google Scholar Citation
ISSN: 0975-3826(online); 0975-4660 (Print)
http://airccse.org/journal/ijcsit.html

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
For More Details : https://aircconline.com/ijcsit/V10N2/10218ijcsit02.pdf
Volume Link : http://airccse.org/journal/ijcsit2018_curr.html

REFERENCES
[1] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A
vision, architectural elements, and future directions. Future generation computer systems,
29(7), 1645-1660.
[2] Li, S., Da Xu, L., & Zhao, S. (2015). The internet of things: a survey. Information Systems
Frontiers, 17(2), 243-259.
[3] Guo, B., Zhang, D., Wang, Z., Yu, Z., & Zhou, X. (2013). Opportunistic IoT: Exploring the
harmonious interaction between human and the internet of things. Journal of Network and
Computer Applications, 36(6), 1531-1539.
[4] Banafa, A. (2014). IoT Standardization and Implementation Challenges. IEEE. org
Newsletter.
[5] Banafa, A. (2015). „What is next for IoT and IIoT”. Enterprise Mobility Summit.
[6] Coetzee, L., & Eksteen, J. (2011, May). The Internet of Things-promise for the future? An
introduction. In IST-Africa Conference Proceedings, 2011 (pp. 1-9). IEEE.
[7] Cai, H., Da Xu, L., Xu, B., Xie, C., Qin, S., & Jiang, L. (2014). IoT-based configurable
information service platform for product lifecycle management. IEEE Transactions on
Industrial Informatics, 10(2), 1558-1567.
[8] Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012, December). Future internet: the internet
of things architecture, possible applications and key challenges. In Frontiers of Information
Technology (FIT), 2012 10th International Conference on (pp. 257-260). IEEE.
[9] Liu, Y., & Zhou, G. (2012, January). Key technologies and applications of internet of things.
In Intelligent Computation Technology and Automation (ICICTA), 2012 Fifth International
Conference on (pp. 197-200). IEEE.
[10] Sadeghi, A. R., Wachsmann, C., & Waidner, M. (2015, June). Security and privacy
challenges in industrial internet of things. In Proceedings of the 52nd annual design
automation conference (p. 54). ACM.
[11] Banafa, A. (2014). IoT and Blockchain Convergence: Benefits and Challenges. IEEE Internet
of Things.
[12] Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob,
I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges.
IEEE Access, 5, 5247-5261.
[13] Desai, P., Sheth, A., & Anantharam, P. (2015, June). Semantic gateway as a service
architecture for iot interoperability. In Mobile Services (MS), 2015 IEEE International
Conference on(pp. 313-319). IEEE.
[14] Koivu, A., Koivunen, L., Hosseinzadeh, S., Laurén, S., Hyrynsalmi, S., Rauti, S., &
Leppänen, V. (2016, December). Software Security Considerations for IoT. In Internet of
Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE
Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016
IEEE International Conference on (pp. 392-397). IEEE.

[15] Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and challenges for
realising the Internet of Things. Cluster of European Research Projects on the Internet of
Things, European Commision, 3(3), 34-36.
[16] Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., ... &
Doody, P. (2011). Internet of things strategic research roadmap. Internet of Things-Global
Technological and Societal Trends, 1(2011), 9-52.
[17] Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., & Leung, K. (2013). A survey on the
ietf protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE
Wireless Communications, 20(6), 91-98.
[18] Theoleyre, F., & Pang, A. C. (Eds.). (2013). Internet of Things and M2M Communications.
River Publishers.
[19] Coetzee, L., & Eksteen, J. (2011, May). The Internet of Things-promise for the future? An
introduction. In IST-Africa Conference Proceedings, 2011 (pp. 1-9). IEEE.
[20] Ji, Z., & Anwen, Q. (2010, November). The application of internet of things (IOT) in
emergency management system in China. In Technologies for Homeland Security (HST),
2010 IEEE International Conference on (pp. 139-142). IEEE.
[21] James Kirkland , “Internet of Things: insights from Red Hat” , Website:
https://developers.redhat.com/blog/2015/03/31/internet-of-things-insights-from-red-hat/ ,
Accesed : 2nd February 2018

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

REFERENCES
[1] Lu, H. P. and Su, Y. J. (2009). “Factors affecting purchase intention on mobile shopping web sites,”
Internet Research, Vol.19, No. 4, pp. 442-458.
[2] Zhang, R., Chen, J. Q., and Lee, C. J. (2013). “Mobile commerce and consumer privacy concerns,”
The Journal of Computer Information Systems, Vol. 53, No. 4, pp. 31-38.
[3] Kim, C., Mirusmonov, M., and Lee, I. (2010). “An empirical examination of factors influencing
the intention to use mobile payment,” Journal Computers in Human Behavior, Vol. 26, No. 3, pp.
310- 322.
[4] Nguyen, N., Cao, T. K., Dang, P. L. and Nguyen, H. A. (2016). “Predicting consumer intention to
use mobile payment services: empirical evidence from Vietnam,” International Journal of
Marketing Studies, Vol. 8, No. 1, pp. 117-124.
[5] Pousttchi, K. and Wiedemann, D. G. (2007). “What influences consumers’ intention to use mobile
payments?” Retrieved from: International Journal of Computer Science & Information Technology
(IJCSIT) Vol 9, No 3, June 2017 105
https://pdfs.semanticscholar.org/a4dd/ce87edaa6d2bf14d0bd362e1c9beb24b030b.pdf (March, 22,
2017).
[6] Ondrus, J. and Pigneur, Y. (2007). “Cross‐industry preferences for development of mobile
payments in Switzerland,” Electronic Markets, pp. 142-152.
7] Liébana-Cabanillas, F., Ramos de Luna, I., and Montoro-Ríos, F. (2017). “Intention to use new
mobile payment systems: a comparative analysis of SMS and NFC payments,” Economic
ResearchEkonomska Istraživanja, Vol. 30, No. 1, pp. 892-910.
[8] Dornan, A. (2001). “The essential guide to wireless communications applications: from cellular
systems to WAP and m-commerce,” Upper Saddle River, NJ, Prentice Hall.
[9] Heijden, H. (2002). “Factors affecting the successful introduction of mobile payment systems,”
15th Bled Electronic Commerce Conference eReality: Constructing the eEconomy, Bled, Slovenia,
June 17-19, pp. 430-443.
[10] Hahn I. and Kodó, K. (2017). “Mobile payment analysed from the aspects of Kano model.”
Retrieved from: http://www.diva-portal.org/smash/get/diva2:1079862/FULLTEXT01.pdf (March,
22, 2017).
[11] Dahlberg, T., Mallat, N., Ondrus, J., and Zmijewska, A. (2008). “Past, present and future of mobile
payments research: a literature review,” Electronic Commerce Research and Applications, Vol. 7,
No. 2, pp. 165-181.
[12] Varshney, U. (2002). “Mobile payment,” IEEE Computer, Vol. 35, No. 12, pp. 120-121.
[13] Ondrus, J. and Pigneur, Y. (2006). “Towards a holistic analysis of mobile payments: a multiple
perspectives approach,” Electronic Commerce Research and Applications, Vol. 5, No. 3, pp. 246–
257.
[14] Sang, R. J. and Murdock, K. (2013). “Consumer acceptance of mobile marketing communications
using the QR code,” Journal of Direct, Data and Digital Marketing Practice, Vol. 15, No. 2, pp. 11-
124.
[15] Davis, F. D. (1989). “Perceived usefulness, perceived ease of use, and user acceptance of

information technology,” MIS Quarterly, Vol. 13, No. 3, pp. 319-340.
[16] Fishbein, M. and Ajzen, I. (1975). “Belief, attitude, intention, and behavior: an introduction to
theory and research”. Reading, MA: Addison-Wesley.
[17] Taylor, S. and Todd, P. (1995). “Assessing IT usage: the role of prior experience,” MIS Quarterly,
Vol. 19, No. 4, pp. 561-570.
[18] Gefen, D., Karahanna, E., and Straub, D. W. (2003), “Trust and TAM in online shopping: an
integrated model,” MIS Quarterly, Vol. 27, No. 1, pp. 51-90.
[19] Moon, J.W. and Kim, Y.G. (2001). “Extending the TAM for a world-wide-web context,”
Information & Management, Vol. 38, pp. 217-230.
[20] Venkatesh, V. and Davis, F. (2000). “A theoretical extension of the technology acceptance model:
four longitudinal field studies,” Management Science, Vol. 46, No. 2, pp.186-204.
[21] Brown, L. G. (1989). “The strategic and tactical implications of convenience in consumer product
marketing,” The Journal of Consumer Marketing, Vol. 6, No. 3, pp. 13-19.
[22] Yoon, C. and Kim, S. (2007). “Convenience and TAM in a ubiquitous computing environment: the
case of wireless LAN,” Electronic Commerce Research & Applications, Vol. 6, No. 1, pp. 102-
112.
[23] Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
[24] Craig, V.S., France, B., and Christie, L.C. (2004), “Factors influencing the adoption of web based
shopping: the impact of trust,” ACM SIGMIS Database, Vol. 35, No. 2, pp. 32-49.
[25] Kim, E. and Tadisina, S. (2007). “A model of customers’ trust in e-businesses: Micro-level inter-
party trust formation,” Journal of Computer Information Systems, Vol. 48, No. 1, pp. 88-104. [26]
Kalakota, R. and Whinston, A. B. (1996). “Readings in Electronic Commerce” Addison-Wesley
Publishing, Reading, MA.
[27] Yoon, S. J. (2002). “The antecedents and consequences of trust in online-purchase decisions,”
Journal of Interactive Marketing, Vol. 16, No. 2, pp. 47-63.
[28] Dahlberg, T., Mallat, N., and Öörni, A. (2003). Trust enhanced technology acceptance
modelconsumer acceptance of mobile payment solutions: Tentative evidence, Stockholm Mobility
Roundtable, 22-23. [29] Nunnally, J. C. (1978). “Psychometric theory,” New York: McGraw-Hill.

EFFICIENT FIELD OF VISION ALGORITHMS FOR LARGE 2D GRIDS
Evan R.M. Debenham and Roberto Solis-Oba Department of Computer Science,
The University of Western Ontario, Canada
ABSTRACT
This paper presents new algorithms for Field of Vision (FOV) computation which improve on existing
work at high resolutions. FOV refers to the set of locations that are visible from a specific position in a
scene of a computer game. We review existing algorithms for FOV computation, describe their
limitations, and present new algorithms which aim to address these limitations. We first present an
algorithm which makes use of spatial data structures in a way which is new for FOV calculation. We then
present a novel technique which updates a previously calculated FOV, rather than re-calculating an FOV
from scratch. We compare our algorithms to existing FOV algorithms and show they provide substantial
improvements to running time. Our algorithms provide the largest improvement over existing FOV
algorithms at highresolutions, thus allowing the possibility of the creation of high resolution FOV-based
video games.
KEYWORDS
Field of Vision (FOV), Computer Games, Visibility Determination, Algorithms

REFERENCES
[1] League of Legends. [Online]. Riot Games, 2020. Available: https://na.leagueoflegends.com/en/
[2] Jung, J. (2016), “A story of fog and war”, [Online]. Available:
engineering.riotgames.com/news/story-fog-and-war [Accessed Sept. 12, 2020]
[3] Defense of the Ancients 2. [Online]. Valve Software, 2020. Available: http://blog.dota2.com
[4] Bergström, B. (2001), “FOV using recursive shadowcasting”, [Online].
Available:www.roguebasin.com/index.php?title=FOV_using_recursive_shadowcasting [Accessed
Sept. 12, 2020]
[5] Duerig, J. (2007), “Precise permissive field of view”, [Online]. Available:
http://www.roguebasin.com/index.php?title=Precise_Permissive_Field_of_View [Accessed Sept.
12, 2020]
[6] Scherzer, D., Wimmer, M., &Purgathofer, W. (2011) “A survey of real‐time hard shadow mapping
methods”, Computer graphics forum, Vol. 30, No. 1, 169-186.
[7] Liu, N., & Pang, M. Y. (2009) “A survey of shadow rendering algorithms: Projection shadows and
shadow volumes”, 2009 Second International Workshop on Computer Science and Engineering,
Vol. 1, 488-492.
[8] Moreau, P., Pharr, M., &Clarberg, P. (2019) “Dynamic many-light sampling for real-time ray
tracing”, High-Performance Graphics - Short Papers, 21-26.
[9] Theoharis, T., Papaioannou, G., &Karabassi, E. A. (2001) “The magic of the z-buffer: A survey”,
Ninth International Conference in Central Europe on Computer Graphics Visualization and
Computer Vision, 379-386.
[10] de Berg, M., Cheong, O., van Kreveld, M., & Overmars, M. (2008) “Binary space partitions: The
painter’s algorithm”, Computational Geometry: Algorithms and Applications, 259-281.
[11] Assarsson, U., & Moller, T. (2000) “Optimized view frustum culling algorithms for bounding
boxes”, Journal of Graphics Tools, 5(1), 9-22.
[12] Luebke, D., & Georges, C. (1995) “Portals and mirrors: Simple, fast evaluation of potentially
visible sets”, Proceedings of the 1995 Symposium on Interactive 3D graphics, 105-106.
[13] Jice. (2009), “Comparative study of field of view algorithms for 2D grid based worlds”, [Online].
Available:
http://www.roguebasin.com/index.php?title=Comparative_study_of_field_of_view_algorithms_f
or_2 D_grid_based_worlds [Accessed Sept. 12, 2020]
[14] Debenham, E., Solis-Oba, R. (2019) “New Algorithms for Computing Field of Vision over 2D
Grids” Electronic Thesis and Dissertation Repository. 6552. The University of Western Ontario.
Available: https://ir.lib.uwo.ca/etd/6552 [Accessed Sept. 12, 2020]
[15] Ohtsuki, T. (1982, May) “Minimum dissection of rectilinear regions”, IEEE International
Symposium on Circuits and Systems, 1210-1213.
[16] Samet, H. (1984). “The quadtree and related hierarchical data structures”, ACM Computing Surveys,
16(2), 187-260.

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.
For More Details : http://aircconline.com/ijcsit/V10N3/10318ijcsit03.pdf

REFERENCES
[1] Penpece D., & Elma O. E. (2014) “Predicting Sales Revenue by Using Artificial Neural Network
in Grocery Retailing Industry: A Case Study in Turkey”, International Journal of Trade Economics
and Finance, Vol. 5, No. 5, pp435-440.
[2] Thiesing F. M., & Vornberger, O. (1997) “Sales Forecasting Using Neural Networks”, IEEE
Proceedings ICNN’97, Houston, Texas, 9-12 June 1997, pp2125-2128.
[3] Zhang, G. P. (2003) “Time series forecasting using a hybrid ARIMA and neural network model”,
Neurocomputing, Vol. 50, pp159–175.
[4] Sharma, A., & Panigrahi, P. K. (2011) “Neural Network based Approach for Predicting Customer
Churn in Cellular Network Services”, International Journal of Computer Applications, Vol. 27,
No.11, pp0975–8887.
[5] Kamakura, W., Mela, C. F., Ansari A., & al. (2005) ” Choice Models and Customer Relationship
Management,” Marketing Letters, Vol. 16, No.3/4, pp279–291.
[6] Smith, K. A., & Gupta, J. N. D. (2000) “Neural Networks in Business: Techniques and Applications
for the Operations Researcher,” Computers & Operations Research, Vol. 27, No. 11–12, pp1023-
1044.
[7] Chattopadhyay, M., Dan, P. K., Majumdar, S., & Chakraborty, P. S. (2012) “Application of
Artificial Neural Network in Market Segmentation: A Review on Recent Trends,” Management
Science Letters, Vol. 2, pp425-438.
[8] Berry, J. A. M., & Linoff, G. S. (2004) “Data Mining Techniques For Marketing, Sales, and
Customer Relationship Management”, Wiley, Second Edition.
[9] Buttle, F. (2009) “Customer Relationship Management Concepts and Technologies”, Elsevier,
Second Edition.
[10] Thomassey, S. (2014) “Sales Forecasting in Apparel and Fashion Industry: A Review”, Springer,
chapter 2.
[11] Massaro, A. Barbuzzi, D., Vitti, V., Galiano, A., Aruci, M., Pirlo, G. (2016) “Predictive Sales
Analysis According to the Effect of Weather”, Proceeding of the 2nd International Conference on
Recent Trends and Applications in Computer Science and Information Technology, Tirana,
Albania, November 18 - 19, pp53-55.
[12] Parsons, A.G. (2001), “The Association between Daily Weather and Daily Shopping Patterns”,
Australasian Marketing Journal, Vol. 9, No. 2, pp78–84. [13] Steele, A.T., (1951) “Weather’s
Effect on the Sales of a Department Store”, Journal of Marketing Vol. 15, No. 4, pp436–443.
[14] Murray, K. B., Di Muro, F., Finn, A., & Leszczyc, P. P. (2010) “The Effect of Weather on
Consumer Spending”, Journal of Retailing and Consumer Services, Vol. 17, No.6, pp512-520.
[15] Massaro, A., Galiano, A., Barbuzzi, D., Pellicani, L., Birardi, G., Romagno, D. D., & Frulli, L.,
(2017) “Joint Activities of Market Basket Analysis and Product Facing for Business Intelligence
oriented on Global Distribution Market: examples of data mining applications,” International
Journal of Computer Science and Information Technologies, Vol. 8, No.2 , pp178-183.
[16] Aguinis, H., Forcum, L. E., & Joo, H. (2013) “Using Market Basket Analysis in Management
Research,” Journal of Management, Vol. 39, No. 7, pp1799-1824.

[17] Štulec, I, Petljak, K., & Kukor, A. (2016) “The Role of Store Layout and Visual Merchandising in
Food Retailing”, European Journal of Economics and Business Studies, Vol. 4, No. 1, pp139-152.
[18] Otha, M. & Higuci, Y. (2013) “Study on Design of Supermarket Store Layouts: the Principle of
“Sales Magnet””, World Academy of Science, Engieering and Technology, Vol. 7, No. 1, pp209-
212.
[19] Shallu, & Gupta, S. (2013) “Impact of Promotional Activities on Consumer Buying Behavior: A
Study of Cosmetic Industry”, International Journal of Commerce, Business and Management
(IJCBM), Vol. 2, No.6, pp379-385.
[20] Al Essa, A. & Bach, C. (2014)“ Data Mining and Knowledge Management for Marketing”,
International Journal of Innovation and Scientific Research, Vol. 2, No. 2, pp321-328.
[21] Kotu, V., & Deshpande B. (2015) “Predictive Analytics and Data Mining- Concepts and Practice
with RapidMiner” Elsevier.
[22] Wimmer, H., Powell, L. M. (2015) “A Comparison of Open Source Tools for Data Science”,
Proceedings of the Conference on Information Systems Applied Research. Wilmington, North
Carolina USA.
[23] Al-Khoder, A., Harmouch, H., “Evaluating Four Of The most Popular Open Source and Free Data
Mining Tools”, International Journal of Academic Scientific Research, Vol. 3, No. 1, pp13-23.
[24] Gulli, A., & Pal, S. (2017) “Deep Learning with Keras- Implement neural networks with Keras on
Theano and TensorFlow,” Birmingham -Mumbai Packt book, ISBN 978-1-78712-842-2.
[25] Kovalev, V., Kalinovsky, A., & Kovalev, S. (2016) “Deep Learning with Theano, Torch, Caffe,
TensorFlow, and deeplearning4j: which one is the best in speed and accuracy?” Proceeding of XIII
Int. Conf. on Pattern Recognition and Information Processing, 3-5 October, Minsk, Belarus State
University, pp99-103.
[26] “Walmart Recruiting - Store Sales Forecasting” 2018. [Online]. Available:
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
[27] Huang, H.-C. & Hou, C.-I.. (2017) “Tourism Demand Forecasting Model Using Neural Network”,
International Journal of Computer Science & Information Technology (IJCSIT), Vol. 9, No. 2,
pp19- 29.
[28] Kalyani, J., Bharathi, H. N., & Rao, J. (2016) “Stock Trend Prediction Using News Sentiment
Analysis”, International Journal of Computer Science & Information Technology (IJCSIT), Vol. 8,
No. 3, pp67-76.

APPLICATION OF A MULTILEVEL TECHNOLOGY ACCEPTANCE
MANAGEMENT MODEL FOR EFFECTIVE TECHNOLOGY
DEPLOYMENT
Gilbert Busolo, Lawrence Nderu and Kennedy Ogada
School of Computing and Information Technology, Department of Computing, Jomo
Kenyatta University of Agriculture and Technology, Nairobi, Kenya
ABSTRACT
Effective deployment of a technology in an environment is the desire of many system developers. Positive
uptake of a technology coupled with user acceptance is deemed as a key indicator towards technology
acceptance. Knowledge is weighed as a strategic resource for any successful data driven decision making
initiative. Institutions leverage on technological initiatives and tools to drive knowledge management
(KM) initiatives that enhance quality service delivery and prudent data management. These initiatives
provide the overall strategy for managing data resources. They make available knowledge organization
tools and techniques while enabling regular updates. Derived benefits of positive deployment of a
technological intervention are competency enhancement through gained knowledge, raised quality of
service and promotion of healthy development of e-commerce. Successful and timely adoption of
technological interventions through which knowledge management initiatives are deployed remains a key
challenge to many organizations. This paper proposes the application of a wholesome multilevel
technology acceptance management model towards effective technology deployment. The proposed
model takes into account human, technological and organizational variables, which exist in a deployment
environment. This model will be vital in driving early technology acceptance prediction and timely
deployment of mitigation measures to deploy technological interventions successfully.
KEYWORDS
Model, Technology Acceptance, Knowledge, Management, Multilevel

REFERENCES
[1] Uriarte F. A (2008). Introduction to Knowledge Management, Jakarta, Asean Foundation.
[2] Kwanya T. Kiplang’at J. & Wamukoya J (2017). Emerging Trends in Information and Knowledge
Management, Eldoret, Moi University Press.
[3] Fernandez, I.B & Sabherwal, R (2010). Knowledge Management systems and Processes, New
York, Armonk Publishers.
[4] Talisayon S. (2013). Knowledge Management for the Public Sector, Tokyo, Asian Productivity
Organization.
[5] Odinioha, G.J and Chukwuma, O.L (2013). The International Journal of Management, Volume 2,
Issue 3, Port Harcourt, Wiley-Blackwell Publishers.
[6] Maier R. (2007). Knowledge Management Systems, Third Edition, Innsbruck, Springer. DOI
10.1007/978-3-540-71408-8.
[7] Davis F., Bagozzi R. & Warshaw P. (1989). User Acceptance of Computer Technology: A
Comparison of Two Theoretical Models, Michigan, Ann Arbor, Institute of Management Science.
[8] Dwivendi Y. K, Rana P.N. Jeyaraj A., Clement M. & Williams M. D. (2019). Re-examining the
Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical
Model. 21, 719–734. DOI 10.1007/s10796-017-9774-y.
[9] Hwang, Y. Al-Arabiat M. & Shin, D.H (2016). Understanding Technology Acceptance in a
Mandatory Environment, UK, Sage Publishers.
[10] Scherer R., Siddiq F. & Tondeur J. (2018). The Technology Acceptance Model (TAM): A
Metaanalytic Structural Equation Modelling Approach to Explaining Teachers’ Adoption of Digital
Technology in Education: Norway, Elsevier.
[11] Aizstrauta D., Ginters E. & Eroles M. P (2015). Applying Theory of Diffussion of Innovations to
Evaluate Technology Acceptance and Sustainability. 43. 69-77. DOI 10.1016/j.procs.2014.12.010.
[12] Lowe B., Dwivedi Y. & D’Alessandro S. (2019). Consumers and Technology in a Changing World.
Retrieved 28 December, 2019 from https://kar.kent.ac.uk/73789/.
[13] Gucin N. O.& Berk O. S. (2015). Technology Acceptance in Health Care: An Integrative Review
of Predictive Factors and Intervention Programs. 195, 1698-1704. DOI
10.1016/j.sbspro.2015.06.263.
[14] Lai P. C (2017). The Literature Review of Technology Adoption Models and Theories for the
Novelty Technology, Electronic Journal of Information Systems and Technology Management,
14(1), 21-38, Retrieved 20 April, 2019, from http://www.jistem.fea.usp.br
[15] Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior.
Engelwood Cliffs, NJ, Prentice Hall.
[16] Greene K., (2009). Reasoned Action Theory Scope Components, New Jersey, Rutgers University
Press.
[17] Sutton, S., French, D.P., Hennings, S.J. et al. Curr Psychol (2003). 22: 234.
DOI.org/10.1007/s12144- 003-1019-1.

[18] Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to
Theory and Research. Reading, MA: Addison-Wesley.
[19] Taherdoost H. (2018). A review of Technology Acceptance and Adoption Models and Theories.
22. 960-967. DOI 10.1016/j.promfg.2018.08.137.
[20] Davis, F. D. (1993). User Acceptance of Information Technology: System Characteristics, User
Perceptions And Behavioral Impacts. International Journal of Man-Machine Studies, 38, 475-487.
DOI:10.1006/imms.1993.1022.
[21] Lim W. M (2018). Dialectic Antidotes to Critics of the Technology Acceptance Model: Conceptual,
Methodological and Replication Treatments for Behavioral Modelling in Technology-Mediated
Environments, Swinburne, Australasian Journal of Information Systems
[22] Goodhue D. L & Thompson R. L (1995). Task-Technology Fit and Individual Performance,
Management Information Systems Research Center, University of Minnesota. International Journal
of Computer Science & Information Technology (IJCSIT) Vol 13, No 1, February 2021 74
[23] Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of The Technology Acceptance
Model: Four Longitudinal Field Studies. Management Science. 46 ( 2) 186-204.
[24] Venkatesh V., Morris M. G., Davis G. B. & Davis F. D. (2003). User Acceptance of Information
Technology: Toward A Unified View, MIS Quarterly, Volume 27 (3),425-478.
[25] Taylor S & Todd P. A. (1995). Understanding Information Technology Usage: A Test of
Competing Models. The Institute for Operations Research and the Management Sciences,
DOI:10.1287/isre.6.2.144.
[26] Zhou L. L., Marfo J. O., Antwi H. A., Antwi M. O., Kachie A.D. & Wireko S. A. (2019).
Assessment of the social influence and dacilitating conditions that support nurses adoption of
hospital electronic information management systems (HEIMS) in Ghana using the unified theory
of acceptance and use of technology (UTAUT) model. Retrieved 20 January, 2020.
DOI.org/10.1186/s12911-019-0956-z.
[27] Hamzat S. & Mabawonku I. (2018). Influence of Performance Expectancy and Facilitating
Conditions on use of Digital Library by Engineering Lecturers in universities in South-west,
Nigeria. Library Philosophy and Practice (e-journal).1620. from
https://digitalcommons.unl.edu/libphilprac/1620
[28] Alam M. Z., Hu W., & Gani M. O. (2019). An Empirical Analysis of the Influencing Factors of
Adoption of Mobile Health Services in Bangladesh Based on Extended UTAUT Model. WHICEB
2019 Proceedings. 53 from https://aisel.aisel.org/whiceb2019/53
[29] Fayad R. & Paper D. (2015). The Technology Acceptance Model E-Commerce Extension: A
Conceptual Framework, Lebabon, Elsevier.
[30] Mahmood M. A., Gemoets L. A., & Jacquez C. (1999). Variables Affecting Information Technology
End-User Satisfaction. DOI:10.1006/ijhc.1999.0353

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

REFERENCES
[1] Alsayat, A., & El-Sayed, H. (2016). Efficient genetic K-Means clustering for health care knowledge
discovery. In Software Engineering Research, Management and Applications (SERA), 2016 IEEE
14th International Conference on (pp. 45-52). IEEE.
[2] Balasubramanian, T., & Umarani, R. (2012, March). An analysis on the impact of fluoride in human
health (dental) using clustering data mining technique. In Pattern Recognition, Informatics and
Medical Engineering (PRIME), 2012 International Conference on (pp. 370-375). IEEE.
[3] Banu G. Rasitha & Jamala J.H.Bousal (2015). Perdicting Heart Attack using Fuzzy C Means
Clustering Algorithm. International Journal of Latest Trends in Engineering and Technology
(IJLTET).
[4] Banu, M. N., & Gomathy, B. (2014). Disease forecasting system using data mining methods. In
Intelligent Computing Applications (ICICA), 2014 International Conference on (pp. 130-133).
IEEE.
[5] Belciug, S. (2009). Patients length of stay grouping using the hierarchical clustering algorithm.
Annals of the University of Craiova-Mathematics and Computer Science Series, 36(2), 79-84.
[6] Belciug, S., Salem, A. B., Gorunescu, F., & Gorunescu, M. (2010, November). Clustering-based
approach for detecting breast cancer recurrence. In Intelligent Systems Design and Applications
(ISDA), 2010 10th International Conference on (pp. 533-538). IEEE.
[7] Bruno, G., Cerquitelli, T., Chiusano, S., & Xiao, X. (2014). A clustering-based approach to analyse
examinations for diabetic patients. In Healthcare Informatics (ICHI), 2014 IEEE International
Conference on (pp. 45-50). IEEE.
[8] DeFreitas, K., & Bernard, M. (2015). Comparative performance analysis of clustering techniques
in educational data mining. IADIS International Journal on Computer Science & Information
Systems, 10(2).
[9] Escudero, J., Zajicek, J. P., & Ifeachor, E. (2011). Early detection and characterization of
Alzheimer's disease in clinical scenarios using Bioprofile concepts and K-means. In Engineering in
Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (pp.
6470-6473). IEEE.
[10] Han, J., Kamber, M., & Pei, J. (2012). Cluster Analysis-10: Basic Concepts and Methods.
[11] Ibrahim, N. H., Mustapha, A., Rosli, R., & Helmee, N. H. (2013). A hybrid model of hierarchical
clustering and decision tree for rule-based classification of diabetic patients. International Journal
of Engineering and Technology (IJET), 5(5), 3986-91.
[12] Jabel K. Merlin & Srividhya (2016). Performance analysis of clustering algorithms on heart dataset.
International Journal of Modern Computer Science, 5(4), 113-117.
[13] Kar Amit Kumar, Shailesh Kumar Patel & Rajkishor Yadav (2016). A Comparative Study &
Performance Evaluation of Different Clustering Techniques in Data Mining. ACEIT Conference
Proceeding.
[14] Lv, Y., Ma, T., Tang, M., Cao, J., Tian, Y., Al-Dhelaan, A., & Al-Rodhaan, M. (2016). An efficient
and scalable density-based clustering algorithm for datasets with complex structures.
Neurocomputing, 171, 9-22.

[15] Malli, S., Nagesh, H. R., & Joshi, H. G. (2014). A Study on Rural Health care Data sets using
Clustering Algorithms. International Journal of Engineering Research and Applications, 3(8), 517-
520.
[16] Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms
and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12),
1650-1654.
[17] Na, S., Xumin, L., & Yong, G. (2010, April). Research on k-means clustering algorithm: An
improved k-means clustering algorithm. In Intelligent Information Technology and Security
Informatics (IITSI), 2010 Third International Symposium on (pp. 63-67). IEEE.
[18] Paul, R., & Hoque, A. S. M. L. (2010, July). Clustering medical data to predict the likelihood of
diseases. In Digital Information Management (ICDIM), 2010 Fifth International Conference on (pp.
44-49). IEEE.
[19] Pham, D. T., Dimov, S. S., & Nguyen, C. D. (2005). Selection of K in K-means clustering.
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering
Science, 219(1), 103-119.
[20] R.Nithya & P.Manikandan & D.Ramyachitra (2015); Analysis of clustering technique for the
diabetes dataset using the training set parameter. International Journal of Advanced Research in
Computer and Communication Engineering Vol. 4, Issue 9.
[21] Sagar, H. K., & Sharma, V. (2014). Error Evaluation on K-Means and Hierarchical Clustering with
Effect of Distance Functions for Iris Dataset. International Journal of Computer Applications,
86(16).
[22] Shah, G. H., Bhensdadia, C. K., & Ganatra, A. P. (2012). An empirical evaluation of density-based
clustering techniques. International Journal of Soft Computing and Engineering (IJSCE) ISSN,
22312307, 216-223.
[23] Tan, P. N., Steinbach, M., & Kumar, V. (2013). Data mining cluster analysis: basic concepts and
algorithms. Introduction to data mining.
[24] Tomar, D., & Agarwal, S. (2013). A survey on Data Mining approaches for Healthcare.
International Journal of Bio-Science and Bio-Technology, 5(5), 241-266.
[25] Vijayarani, S., & Sudha, S. (2015). An efficient clustering algorithm for predicting diseases from
hemogram blood test samples. Indian Journal of Science and Technology, 8(17).
[26] Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction
using a hybrid of K-means and support vector machine algorithms. Expert Systems with
Applications, 41(4), 1476-1482.

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.
[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

ANALYTICAL STUDY TO REVIEW OF ARABIC LANGUAGE
LEARNING USING INTERNET WEBSITES
1
Samer Shorman and 2
Mohammad Al-Shoqran
1
Department of Computer Science, Applied Science University, Kingdom of Bahrain
2
Department of Mathematical Sciences, Ahlia University, Kingdom of Bahrain
ABSTRACT
Arabic language is one of the most commonly used language in the world. It plays a very important role
in educational operations around the world. It contains various parts such as poetry, poem, novel, and
stories, as well as linguistic and grammatical rules and movements of letters, which change the word
according to the movements accompanying each letter, for example, there are movements of lifting and
breaking and annexation and silence. In this paper, we will review the research papers that studied the
Arabic language learning websites, using the content analysis to determine strengths, weaknesses,
advantages, disadvantages, and limitations. This research paper concluded that there is still a shortage and
scarcity in the number of articles and websites on the internet that teach Arabic language. The suggestion
is to assign task of development to Arab world instituties and others by increasing their number of websites
on the internet and enriching their scientific content to improve it and increase its spread between the
learners.
KEYWORDS
Arabic language, learning, teaching, websites

REFERENCES
[1] Achour, H., &Abdesslam, W. B. (2012, July). An evaluation of Arabic language learning websites.
In International Conference on Education and E-Learning Innovations (pp. 1-6). IEEE.
[2] Azrien Mohamed Adnan, M., & Sariah Syed Hassan, S. (2015). PROMOTING INTERACTIONS
IN LEARNING ARABIC LANGUAGE VIA LEARNING MANAGEMENT SYSTEM: A
THEORETICAL FRAMEWORK. Education Sciences & Psychology, 34(2).
[3] Jeong-Bae Son (2005). « Exploring and evaluating language learning Web sites ». In J.-B. Son &
S. O'Neill (Eds.), Enhancing learning and teaching: Pedagogy, technology and language (pp. 215-
227). Flaxton: Post Pressed.
[4] Sahrir, M. S., Yahaya, M. F., & Nasir, M. S. (2013). EZ-Arabic for children: A virtual learning
resource tool for Malaysian primary schools. Procedia-Social and Behavioral Sciences, 90, 396-
404.
[5] Hasan, L. (2014). Evaluating the usability of educational websites based on students' preferences
of design characteristics. International Arab Journal of e-Technology, 3(3), 179-193.
[6] Bolliger, D. U. (2004). Key factors for determining student satisfaction in online
courses.International Journal on E-learning, 3(1), 61-67.
[7] Sahrir, M. S., & Alias, N. A. (2012). A study on Malaysian language learners’ perception towards
learning Arabic via online games. GEMA Online® Journal of Language Studies, 11(3).
[8] Sahrir, M. S., Alias, N. A., Ismail, Z., & Osman, N. (2012). Employing Design and Development
Research (DDR): Approaches in the Design and Development of Online Arabic Vocabulary
Learning Games Prototype. Turkish Online Journal of Educational Technology-TOJET, 11(2), 108-
119.
[9] Kaye, A. S. (2009). Arabic. In The world's major languages(pp. 573-590). Routledge.
[10] Ditter, E. (2006). Technologies for Arabic language teaching and learning. Handbook for Arabic
language teaching professionals in the 21st century, 239-252.
[11] Chejne, A. G. (1969). The Arabic language: Its role in history. U of Minnesota Press.
[12] Statista , https://www.statista.com/chart/14900/two-worlds_-languages-irl-and-online/
[13] Arabic-course, http://www.arabic-course.com/arabic-alphabet.html [14] Ritchie, J., Lewis, J.,
Nicholls, C. M., &Ormston, R. (Eds.). (2013). Qualitative research practice: A guide for social
science students and researchers. sage.
[15] Srivastava, A., & Thomson, S. B. (2009). Framework analysis: a qualitative methodology for
applied policy research.
[16] Answers, http://www.answers.com/topic/urdu.
[17] Aronoff, M., & Rees-Miller, J. (Eds.). (2017). The handbook of linguistics. John Wiley & Sons.
[18] Razzak, M. I., Belaïd, A., & Hussain, S. A. (2009, February). Effect of ghost character theory on

arabic script based languages character recognition. In WASE Global Conference on Image
Processing and Analysis-GCIA09.
[19] Habash, N. Y. (2010). Introduction to Arabic natural language processing. Synthesis Lectures on
Human Language Technologies, 3(1), 1-187.
[20] Abdelhadi, R., Hameed, L., Khaled, F., & Anderson, J. (2019). Creative interactions with art works:
an engaging approach to Arabic language-and-culture learning. Innovation in Language Learning
and Teaching, 1-17.

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

REFERENCES
[1] Konstantinou, I., Angelou, E., Boumpouka, C., Tsoumakos, D., & Koziris, N. (2011, October). On
the elasticity of nosql databases over cloud management platforms. In Proceedings of the 20th ACM
international conference on Information and knowledge management (pp. 2385-2388). ACM.
[2] Labrinidis, Alexandros, and Hosagrahar V. Jagadish. "Challenges and opportunities with big data."
Proceedings of the VLDB Endowment 5.12 (2012): 2032-2033.
[3] badi, D. J. (2009). Data management in the cloud: Limitations and opportunities. IEEE Data Eng.
Bull, 32(1), 3-12.
[4] Luhn, H. P. (1958). A business intelligence system. IBM Journal of Research and Development,
2(4), 314-319 International Journal of Computer Science & Information Technology (IJCSIT) Vol
11, No 4, August 2019 57 [5] Sivarajah, Uthayasankar, et al. "Critical analysis of Big Data
challenges and analytical methods." Journal of Business Research 70 (2017): 263-286.
[6] https://www.bmc.com/blogs/saas-vs-paas-vs-iaas-whats-the-difference-and-how-to-choose/
[7] Kavis, Michael J. Architecting the cloud: design decisions for cloud computing service models
(SaaS, PaaS, and IaaS). John Wiley & Sons, 2014. [8]
https://www.ripublication.com/ijaer17/ijaerv12n17_89.pdf
[9] Sakr, S. & Gaber, M.M., 2014. Large Scale and big data: Processing and Management Auerbach,
ed.
[10] Ji, Changqing, et al. "Big data processing in cloud computing environments." 2012 12th
international symposium on pervasive systems, algorithms and networks. IEEE, 2012.
[11] Han, J., Haihong, E., Le, G., & Du, J. (2011, October). Survey on nosql database. In Pervasive
Computing and Applications (ICPCA), 2011 6th International Conference on (pp. 363-366). IEEE.
[12] Zhang, L. et al., 2013. Moving big data to the cloud. INFOCOM, 2013 Proceedings IEEE, pp.405–
409
[13] Fernández, Alberto, et al. "Big Data with Cloud Computing: an insight on the computing
environment, MapReduce, and programming frameworks." Wiley Interdisciplinary Reviews: Data
Mining and Knowledge Discovery 4.5 (2014): 380-409.
[14] http://acme.able.cs.cmu.edu/pubs/uploads/pdf/IoTBD_2016_10.pdf
[15] Xiaofeng, Meng, and Chi Xiang. "Big data management: concepts, techniques and challenges [J]."
Journal of computer research and development 1.98 (2013): 146-169.
[16] Muniswamaiah, Manoj & Agerwala, Tilak & Tappert, Charles. (2019). Challenges of Big Data
Applications in Cloud Computing. 221-232. 10.5121/csit.2019.90918.

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

REFERENCES
[1] P. Bedi, V. Jindal, and A. Gautam, “Beginning with Big Data Simplified,” 2014.
[2] R. Kimball, M. Ross, W. Thorthwaite, B. Becker, and M. J, The Data Warehouse Lifecycle Toolkit,
2nd Edition. 2008.
[3] C. Todman, Designing A Data Warehouse: Supporting Customer Relationship Management. 2001.
[4] W. H. Inmon, Building the Data Warehouse, 4th Edition. 2005. [5] “Oracle Database 12c for Data
Warehousing and Big Data .” [Online]. Available:
http://www.oracle.com/technetwork/database/bi-datawarehousing/data-warehousing-wp-
12c1896097.pdf. [Accessed: 09-Sep-2015].
[6] M. Cox and D. Ellsworth, “Application-Controlled Demand Paging for Out-of-Core Visualization,”
1997. [Online]. Available: http://www.nas.nasa.gov/assets/pdf/techreports/1997/nas-97-010.pdf.
[Accessed: 09-Apr-2015].
[7] S. Chaudhuri and U. Dayal, “An overview of data warehousing and OLAP technology,” ACM
SIGMOD Rec., vol. 26, no. 1, pp. 65–74, 1997. [8] T. Maiorescu, “General Information on Business
Intelligence,” pp. 294–297, 2010. [9] “Data Warehouses and OLAP: Concepts, Architectures and
Solutions: 9781599043647: Library and Information Science Books | IGI Global.” .
[10] Y. Demchenko, C. De Laat, and P. Membrey, “Defining Architecture Components of the Big Data
Ecosystem,” Collab. Technol. Syst. (CTS), 2014 Int. Conf., pp. 104–112, 2014.
[11] G. NBD-PWG, “ISO/IEC JTC 1 Study Group on Big Data,” 2013. [Online]. Available:
http://bigdatawg.nist.gov/cochairs.php. [Accessed: 24-Oct-2015].
[12] D. L. W.H. Inmon, Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse
and Data Vault. Amsterdam,Boston: Elsevier, 2014.
[13] G. N. W.H. Inmon, Derek Strauss, DW 2.0: The Architecture for the Next Generation of Data
Warehousing (Morgan Kaufman Series in Data Management Systems) (): : Books. Burlington,
USA: Morgan Kaufmann Publishers Inc., 2008.
[14] R. Kimball, “The Evolving Role of the Enterprise Data Warehouse in the Era of Big Data
Analytics,” Kimball Gr., 2011.
[15] M. Muntean and T. Surcel, “Agile BI - The Future of BI,” Inform. Econ., vol. 17, no. 3, pp. 114–
124, 2013.
[16] D. Agrawal, “The Reality of Real-Time Business Intelligence,” in Business Intelligence for the
RealTime Enterprise, vol. 27 , M. Castellanos, U. Dayal, and T. Sellis, Eds. Springer Berlin
Heidelberg , 2009, pp. 75–88.
[17] R. Castillo, J. Morata, and L. del Arbol, “Operational Data Store (ODS) - 933.pdf,” Actas del III
taller nacional de minería de datos y aprendizaje, pp. 359–365, 2005.
[18] S. YiChuan and X. Yao, “Research of Real-time Data Warehouse Storage Strategy Based on
Multilevel Caches,” Phys. Procedia, vol. 25, no. 0, pp. 2315–2321, 2012.

[19] A. Ma. P. Díaz-zorita, “Evaluación de la herramienta de código libre Apache Hadoop,” Universidad
Carlos III de Madrid Escuela Politécnica Superior, 2011.
[20] R. Kimball, “Newly Emerging Best Practices for Big Data,” Kimball Group, p. 14, 2012.
[21] M. Maier, “Towards a Big Data Reference Architecture,” no. October, pp. 1–144, 2013.
[22] O. Corporation, “ORACLE ENTERPRISE ARCHITECTURE WHITE PAPER. An Enterprise
Architect ’ s Guide to Big Data,” no. February, 2015.
[23] F. Kramer, H. Muller, and K. Turowski, “Acceleration of Single Inserts for Columnar Databases --
An Experiment on Data Import Performance Using SAP HANA,” in Signal-Image Technology and
Internet-Based Systems (SITIS), 2014 Tenth International Conference on, 2014, pp. 672–676.
[24] M. R. Patil and F. Thia, Pentaho for Big Data Analytics, vol. 2013. PACKT PUBLISHING, 2013.
[25] S. G. Manikandan and S. Ravi, “Big Data Analysis Using Apache Hadoop,” in IT Convergence and
Security (ICITCS), 2014 International Conference on , 2014, pp. 1–4.
[26] J. Nandimath, E. Banerjee, A. Patil, P. Kakade, and S. Vaidya, “Big data analysis using Apache
Hadoop,” 2013 IEEE 14th Int. Conf. Inf. Reuse Integr., pp. 700–703, 2013.
[27] A. Katal, M. Wazid, and R. H. Goudar, “Big data: Issues, challenges, tools and Good practices,” in
Contemporary Computing (IC3), 2013 Sixth International Conference on , 2013, pp. 404–409.
[28] A. Pal and S. Agrawal, “An Experimental Approach Towards Big Data for Analyzing Memory
Utilization on a Hadoop cluster using HDFS and MapReduce .,” pp. 442–447, 2014.
[29] R. Zhang, D. Hildebrand, and R. Tewari, “In unity there is strength: Showcasing a unified big data
platform with MapReduce Over both object and file storage,” in Big Data (Big Data), 2014 IEEE
International Conference on , 2014, pp. 960–966.
[30] “Welcome to ApacheTM Hadoop®!” [Online]. Available: https://hadoop.apache.org/. [Accessed:
26- Mar-2015].
[31] “HDFS Architecture Guide.” [Online]. Available:
http://hadoop.apache.org/docs/r1.2.1/hdfs_design.html. [Accessed: 26-Mar-2015].
[32] S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” in
Computer Networks and ISDN Systems, 1998, pp. 107–117. [
[33] D. Garlasu, V. Sandulescu, I. Halcu, G. Neculoiu, O. Grigoriu, M. Marinescu, and V. Marinescu,
“A big data implementation based on Grid computing,” in Roedunet International Conference
(RoEduNet), 2013 11th, 2013, pp. 1–4.
[34] A. Jorgensen, C. Price, B. Mitchell, and J. Rowlan, Microsoft Big Data Solutions. John Wiley &
Sons, Inc., 2014.
[35] R. T. Kaushik, M. Bhandarkar, and K. Nahrstedt, “Evaluation and Analysis of GreenHDFS: A
SelfAdaptive, Energy-Conserving Variant of the Hadoop Distributed File System,” in Cloud
Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on,

2010, pp. 274–287.
[36] J. G. Shanahan and L. Dai, “Large Scale Distributed Data Science Using Apache Spark,” in
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining, 2015, pp. 2323–2324.
[37] R. S. Xin, J. Rosen, M. Zaharia, M. J. Franklin, S. Shenker, and I. Stoica, “Shark: SQL and Rich
Analytics at Scale,” in Proceedings of the 2013 ACM SIGMOD International Conference on
Management of Data, 2013, pp. 13–24.
[38] J. Li, J. Wu, X. Yang, and S. Zhong, “Optimizing MapReduce Based on Locality of K-V Pairs and
Overlap between Shuffle and Local Reduce,” in Parallel Processing (ICPP), 2015 44th International
Conference on, 2015, pp. 939–948.
[39] E. Brewer, “CAP Twelve Years Later: How the ‘Rules’ Have Changed,” InfoQ, 2012. [Online].
Available: http://www.infoq.com/articles/cap-twelve-years-later-how-the-rules-have-changed.
[Accessed: 26-Mar-2015].
[40] G. Vaish, Getting started with NoSQL. 2013.
[41] V. N. Gudivada, D. Rao, and V. V. Raghavan, “NoSQL Systems for Big Data Management,” 2014
IEEE World Congr. Serv., pp. 190–197, 2014.
[42] Cassandra, “The Apache Cassandra Project,” httpcassandraapacheorg, 2010. [Online]. Available:
http://cassandra.apache.org/.
[43] D. Borthakur, “Petabyte Scale Databases and Storage Systems at Facebook,” in Proceedings of the
2013 ACM SIGMOD International Conference on Management of Data, 2013, pp. 1267–1268.
[44] J. Huang, X. Ouyang, J. Jose, M. Wasi-ur-Rahman, H. Wang, M. Luo, H. Subramoni, C. Murthy,
and D. K. Panda, “High-Performance Design of HBase with RDMA over InfiniBand,” in Parallel
[45] G. Weintraub, “Dynamo and BigTable - Review and comparison,” Electr. Electron. Eng. Isr.
(IEEEI), 2014 IEEE 28th Conv., pp. 1–5, 2
[46] D. Pereira, P. Oliveira, and F. Rodrigues, “Data warehouses in MongoDB vs SQL Server: A
comparative analysis of the querie performance,” in Information Systems and Technologies
(CISTI), 2015 10th Iberian Conference on, 2015, pp. 1–7.
[47] K. Dehdouh, F. Bentayeb, O. Boussaid, and N. Kabachi, “Columnar NoSQL CUBE: Agregation
operator for columnar NoSQL data warehouse,” in Systems, Man and Cybernetics (SMC), 2014
IEEE International Conference on, 2014, pp. 3828–3833.
[48] Y. Liu and T. M. Vitolo, “Graph Data Warehouse: Steps to Integrating Graph Databases Into the
Traditional Conceptual Structure of a Data Warehouse,” in Big Data (BigData Congress), 2013
IEEE International Congress on, 2013, pp. 433–434.
[49] M. Chevalier, M. El Malki, A. Kopliku, O. Teste, and R. Tournier, “Benchmark for OLAP on
NoSQL technologies comparing NoSQL multidimensional data warehousing solutions,” in
Research Challenges in Information Science (RCIS), 2015 IEEE 9th International Conference on,
2015, pp. 480–485.

[50] F. Färber, S. K. Cha, J. Primsch, C. Bornhövd, S. Sigg, and W. Lehner, “SAP HANA Database:
Data Management for Modern Business Applications,” SIGMOD Rec., vol. 40, no. 4, pp. 45–51,
2012.
[51] K. M. A. Hasan, M. T. Omar, S. M. M. Ahsan, and N. Nahar, “Chunking implementation of
extendible array to handle address space overflow for large multidimensional data sets,” in
Electrical Information and Communication Technology (EICT), 2013 International Conference on,
2014, pp. 1– 6.
[52] S. Müller and H. Plattner, “An In-depth Analysis of Data Aggregation Cost Factors in a Columnar
Inmemory Database,” in Proceedings of the Fifteenth International Workshop on Data Warehousing
and OLAP, 2012, pp. 65–72.
[53] H. Plattner, “A Common Database Approach for OLTP and OLAP Using an In-memory Column
Database,” in Proceedings of the 2009 ACM SIGMOD International Conference on Management
of Data, 2009, pp. 1–2.
[54] J. Schaffner, A. Bog, J. Krüger, and A. Zeier, “A Hybrid Row-Column OLTP Database Architecture
for Operational Reporting,” in Business Intelligence for the Real-Time Enterprise SE - 5, vol. 27,
M. Castellanos, U. Dayal, and T. Sellis, Eds. Springer Berlin Heidelberg, 2009, pp. 61–74.
[55] V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe,
H. Shah, S. Seth, B. Saha, C. Curino, O. O’Malley, S. Radia, B. Reed, and E. Baldeschwieler,
“Apache Hadoop YARN: Yet Another Resource Negotiator,” in Proceedings of the 4th Annual
Symposium on Cloud Computing, 2013, pp. 5:1–5:16.
[56] “Apache Pig Philosophy.” [Online]. Available: http://pig.apache.org/philosophy.html. [Accessed:
26- Mar-2015].
[57] “Architecture - Apache Drill.” [Online]. Available: http://drill.apache.org/architecture/. [Accessed:
26-Mar-2015].
[58] “Storm, distributed and fault-tolerant realtime computation.” [Online]. Available:
https://storm.apache.org/. [Accessed: 26-Mar-2015].
[59] “Apache Hive TM.” [Online]. Available: https://hive.apache.org/. [Accessed: 26-Mar-2015]. [60]
“Sqoop -.” [Online]. Available: http://sqoop.apache.org/. [Accessed: 26-Mar-2015].
[61] “Impala.” [Online]. Available: http://www.cloudera.com/content/cloudera/en/products-
andservices/cdh/impala.html. [Accessed: 26-Mar-2015].
[62] “Apache Thrift - Home.” [Online]. Available: https://thrift.apache.org/. [Accessed: 26-Mar-2015].
[63] “Apache ZooKeeper - Home.” [Online]. Available: https://zookeeper.apache.org/. [Accessed: 26-
Mar-2015].
[64] D. Borthakur, J. Gray, J. Sen Sarma, K. Muthukkaruppan, N. Spiegelberg, H. Kuang, K.
Ranganathan, D. Molkov, A. Menon, S. Rash, R. Schmidt, and A. Aiyer, “Apache Hadoop Goes
Realtime at Facebook,” in Proceedings of the 2011 ACM SIGMOD International Conference on
Management of Data, 2011, pp. 1071–1080.
[65] B. Ghit, A. Iosup, and D. Epema, “Towards an Optimized Big Data Processing System,” in Cluster,

Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, 2013,
pp. 83–86.
[66] P. Agarwal, G. Shroff, and P. Malhotra, “Approximate Incremental Big-Data Harmonization,” in
Big Data (BigData Congress), 2013 IEEE International Congress on, 2013, pp. 118–125.
[67] Y. Elshater, P. Martin, D. Rope, M. McRoberts, and C. Statchuk, “A Study of Data Locality in
YARN,” 2015 IEEE Int. Congr. Big Data, pp. 174–181, 2015.
[68] A. H. B. James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles
Roxburgh, “Big data: The next frontier for innovation, competition, and productivity,” McKinsey
Glob. Inst., no. June, p. 156, 2011.
[69] J. S. Marron, “Big Data in context and robustness against heterogeneity,” Econom. Stat., vol. 2, pp.
73–80, 2017.
[70] L. Kugler, “What Happens When Big Data Blunders?,” Commun. ACM, vol. 59, no. 6, pp. 15–16,
2016.
[71] S. Sagiroglu, R. Terzi, Y. Canbay, and I. Colak, “Big data issues in smart grid systems,” in 2016
IEEE International Conference on Renewable Energy Research and Applications (ICRERA), 2016,
pp. 1007–1012.
[72] A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J.
Inf. Manage., vol. 35, no. 2, pp. 137–144, 2015.
[73] Jameela Al-Jaroodi, Brandon Hollein, Nader Mohamed, "Applying software engineering processes
for big data analytics applications development", Computing and Communication Workshop and
Conference (CCWC) 2017 IEEE 7th Annual, pp. 1-7, 2017

INTRUSION DETECTION SYSTEM CLASSIFICATION USING
DIFFERENT MACHINE LEARNING ALGORITHMS ON KDD-99 AND
NSL-KDD DATASETS - A REVIEW PAPER
Ravipati Rama Devi1
and Munther Abualkibash2
1
Department of Computer Science, Eastern Michigan University, Ypsilanti, Michigan,
USA
2
School of Information Security and Applied Computing, Eastern Michigan University,
Ypsilanti, Michigan, USA
ABSTRACT
Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting
malicious activities for the past couple of years. Anomaly detection is an intrusion detection system.
Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and
detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to
evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD
datasets. Results show which approach has performed better in term of accuracy, detection rate with
reasonable false alarm rate.
KEYWORDS
Intrusion Detection System, KDD-99 cup, NSL-KDD, Machine learning algorithms

REFERENCES
[1] “DARPA98 attack description and schedule,” https://www.ll.mit.edu/ideval/docs/attacks.html,
1998, retrieved December 15, 2016.
[2] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees,
1984.
[3] D. A. Cieslak and N. V. Chawla, “A framework for monitoring classifiers’ performance: when and
why failure occurs? “Knowledge and Information Systems, vol. 18, no. 1, pp. 83–108, 2009.
[4] M. Fugate and J. R. Gattiker, “Anomaly detection enhanced classification in computer intrusion
detection,” in Pattern Recognition with Support Vector Machines, vol. 2388, 2002, pp. 186–197.
[5] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99
data set,” in IEEE Symposium on Computational Intelligence for Security and Defense
Applications, CISDA 2009, 2009.
[6] J. McHugh, “Testing Intrusion detection systems: a critique of the 1998and 1999 DARPA intrusion
detection system evaluations as performed by Lincoln Laboratory,” ACM Transactions on
Information and System Security, vol. 3, no. 4, pp. 262–294, 2000.
[7] S. Sung, A.H. Mukkamala. “Identifying important features for intrusion detection using support
vector machines and neural networks”. In Proceedings of the Symposium on Applications and the
Internet (SAINT), pp. 209–216. IEEE.
[8] H. Kayacik, A. Zincir-Heywood and M. Heywood. “Selecting features for intrusion detection: A
feature relevance analysis on KDD 99 intrusion detection datasets”. In Proceedings of the Third
Annual Conference on Privacy, Security and Trust (PST). 2005.
[9] C. Lee, S. Shin and J. Chung. “Network intrusion detection through genetic feature selection”. In
Seventh ACIS International Conference on Software Engineering, Artificial Intelligence,
Networking, and Parallel/Distributed Computing (SNPD), pp. 109–114. IEEE Computer
Society,2006.
[10] H. Zhang and J.Su., “Naive Bayes for optimal ranking”, Journal of Experimental and Theoretical
Artificial Intelligence. 2008, 20: 79-93.
[11] Z. Muda, W. Yassin, M.N. Sulaiman, N.I. Udzir, “Intrusion Detection based on K-Means
Clustering and Naïve Bayes Classification” 2011 7thInternational Conference on IT in Asia
(CITA)”.
[12] Weiming Hu, Steve Maybank, “AdaBoost-Based Algorithm for Network Intrusion Detection”. In
IEEE transaction on systems, MAN, and CYBERNETICS, APRIL 2008.

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