New research articles_---_2019_april_issue

International Journal of Computer Science and
Information Technology (IJCSIT)
ISSN: 0975-3826(online); 0975-4660 (Print)
http://airccse.org/journal/ijcsit.html
Current Issue: April 2019, Volume 11,
Number 2 --- Table of Contents
http://airccse.org/journal/ijcsit2019_curr.html

Paper -01
TOWARDS AN APPROACH FOR INTEGRATING
BUSINESS CONTINUITY MANAGEMENT INTO
ENTERPRISE ARCHITECTURE
Hanane Anir, MouniaFredj and Meryem Kassou
AlQualsadi team, ENSIAS, Mohammed V University, Rabat, Morocco
ABSTRACT
In today’s global and complex business environment, security is a major issue for any
organization. All organizations should have the capability to plan and respond to
incidents and business disruptions. Business continuity management is part of
information security management and the process of Business continuity management
(BCM) can meet these needs. Indeed, Business Continuity refers to the ability of a
business to continue its operations even if some sort of failure or disaster occurs.
Business continuity management (BCM) requires a holistic approach that considers
technological and organizational aspects. Besides, Enterprise architecture (EA) is a
comprehensive view of organizational architecture, business, and technology architecture
and their relationships. EA is also considered by several studies as a foundation for BC
and security management. Our research aims at studying how BCM aspect can be
embedded into the enterprise architecture. In this sense, this paper proposes a metamodel
and an implementation method that considers BC in the design and implementation of
EA..
.
KEYWORDS
Business Continuity Management, Enterprise Architecture, Security Management,
Enterprise Risk Management, Meta Modeling
For More Details: http://aircconline.com/abstract/ijcsit/v11n2/11219ijcsit01.html
Volume Link: http://airccse.org/journal/ijcsit2019_curr.html

REFERENCES
[1] N. Bajgoric et Y. B. Moon, « Enhancing systems integration by incorporating
business continuity drivers », Ind. Manag. Data Syst., vol. 109, no 1, p. 74‑97, janv.
2009.
[2] P. Gomes, G. Cadete, et M. M. da Silva, « Using Enterprise Architecture to Assist
Business Continuity Planning in Large Public Organizations », 2017, p. 70‑78.
[3] N. Banaeianjahromi et K. Smolander, « What do we know about the role of enterprise
architecture in enterprise integration? A systematic mapping study », J. Enterp. Inf.
Manag., vol. 29, no 1, p.140‑164, févr. 2016.
[4] N. Mayer, E. Grandry, C. Feltus, et E. Goettelmann, « Towards the ENTRI Framework:
Security Risk Management Enhanced by the Use of Enterprise Architectures », in
Advanced Information Systems Engineering Workshops, vol. 215, A. Persson et J. Stirna,
Éd. Cham: Springer International Publishing, 2015, p. 459‑469.
[5] R. Winter et R. Fischer, « Essential layers, artifacts, and dependencies of enterprise
architecture », in 2006 10th IEEE International Enterprise Distributed Object Computing
Conference Workshops (EDOCW’06), 2006, p. 30–30.
[6] T. Bucher, R. Fischer, S. Kurpjuweit, et R. Winter, « Analysis and application scenarios
of enterprise architecture: An exploratory study », in Enterprise Distributed Object
Computing Conference Workshops, 2006. EDOCW’06. 10th IEEE International, 2006, p.
28–28.
[7] Andrew Hiles, Definitive Handbook of Business Continuity Management. John Wiley
& Sons, 2011.
[8] S. Snedaker et C. Rima, Business continuity and disaster recovery planning for IT
professionals, 2. ed. Waltham, Mass: Elsevier, Syngress, 2014.
[9] S. Bernard, An Introduction To Enterprise Architecture: Second Edition 2nd Edition.
2012.
[10] S. Bente, U. Bombosch, et S. Langade, Collaborative Enterprise Architecture:
Enriching EA with lean, agile, and enterprise 2.0 practices. Newnes, 2012.
[11] Charles Tupper, Data architecture: from zen to reality. Elsevier, 2011.
[12] K. D. Niemann, From enterprise architecture to IT governance: elements of effective IT
management, 1. ed. Wiesbaden: Vieweg, 2006.
[13] B. Scholtz, A. Calitz, et A. Connolley, « An analysis of the adoption and usage of
enterprise architecture », in Enterprise Systems Conference (ES), 2013, 2013, p. 1–9.
[14] J. Zachman, « The zachman framework for enterprise architecture », Zachman Int., p. 79,
2002.

[15] R. V. McCarthy, « Toward a unified enterprise architecture framework: An
analytical evaluation »,Issues Inf. Syst., vol. 7, no 2, p. 14–17, 2006.
[16] J. Ralyté, S. España, et Ó. Pastor, Éd., The Practice of Enterprise Modeling, vol. 235.
Cham: Springer International Publishing, 2015.
[17] M. S. Beasley, B. V.Handcock, et B. C.Branson, « Strengthening Enterprise Risk
Management for Strategic Advantage ». Coso, 2009.
[18] H. Anir, M. Kassou, et M. Fredj, « Systematic Literature Review of Security and
Enterprise Architecture », présenté à 4th International Workshop on Advanced
Information Systems for Enterprises (IWAISE’16), Rabat Morocco, 2016.
[19] M. E. Zadeh, G. Millar, et E. Lewis, « Mapping the Enterprise Architecture Principles in
TOGAF to the Cybernetic Concepts--An Exploratory Study », 2012, p. 4270‑4276.
[20] . Tovstukha, « Management of Security Risks in the Enterprise Architecture using
ArchiMate and Mal-activities », p. 53, 2014.
[21] F. Innerhofer-Oberperfler et R. Breu, « Using an Enterprise Architecture for IT Risk
Management. », in ISSA, 2006, p. 1–12.
[22] O. Rejeb, R. Bastide, E. Lamine, F. Marmier, et H. Pingaud, « A model driven
engineering approach for business continuity management in e-Health systems », in
Digital Ecosystems Technologies (DEST), 2012 6th IEEE International Conference on,
2012, p. 1–7.
[23] N. Mayer, J. Aubert, E. Grandry, C. Feltus, E. Goettelmann, et R. Wieringa, « An
integrated conceptual model for information system security risk management
supported by enterprise architecture management », Softw. Syst. Model., févr. 2018.
[24] J. Brás et S. Guerreiro, « DEMO Business Processes Design to Improve the Enterprise
Business Continuity Plans », in Advances in Enterprise Engineering XI, vol. 284, D.
Aveiro, R. Pergl, G. Guizzardi, J. P. Almeida, R. Magalhães, et H. Lekkerkerk, Éd.
Cham: Springer International Publishing, 2017, p. 99‑107.
[25] K. Peffers, T. Tuunanen, M. A. Rothenberger, et S. Chatterjee, « A Design Science
Research Methodology for Information Systems Research », J. Manag. Inf. Syst., vol.
24, no 3, p. 45‑77, déc. 2007.
[26] C. M. Pereira et P. Sousa, « A method to define an Enterprise Architecture using the
Zachman Framework », in Proceedings of the 2004 ACM symposium on Applied
computing, 2004, p. 1366–1371.
[27] A. Role et D. Role, « The DoDAF Architecture Framework Version 2.0 », 2011.
[28] The Open Group, TOGAF® Version 9.1. Van Haren Publishing, ZaltBommel, 2011.

[29] S. Aier, C. Fischer, et R. Winter, « Construction and evaluation of a meta-model for
enterprise architecture design principles », 2011.
[30] F. J. Armour, S. H. Kaisler, et S. Y. Liu, « Building an enterprise architecture step by
step », IT Prof., vol. 1, no 4, p. 31–39, 1999.
[31] J. Hoogervorst, « Enterprise architecture: Enabling integration, agility and change »,
Int. J. Coop. Inf. Syst., vol. 13, no 03, p. 213–233, 2004.
[32] F. Innerhofer et R. Breu, « USING AN ENTERPRISE ARCHITECTURE FOR IT
RISK MANAGEMENT », p. 12.
[33] R. Winter et J. Schelp, « Enterprise architecture governance: the need for a business-
to-IT approach », in Proceedings of the 2008 ACM symposium on Applied computing,
2008, p. 548–552.
[34] L. B. FBCI, « Dictionary of Business Continuity Management Terms », 2011.
[35] M. Lankhorst, Enterprise Architecture at Work. Berlin, Heidelberg: Springer Berlin
Heidelberg, 2009.
[36] « ISO/Guide 73:2009 ». 1, nov-2009.
[37] P. Kirvan, « The Importance of Performance Metrics in Business Continuity », 2014.
[38] RIMS, « Exploring Risk Appetite and Risk Tolerance ». RIMS, 2012.
[39] E. Zambon, D. Bolzoni, S. Etalle, et M. Salvato, « A model supporting business
continuity auditing and planning in information systems », in Internet Monitoring and
Protection, 2007. ICIMP 2007. Second International Conference on, 2007, p. 33–33.
[40] ISO, « ISO 22313 ». 2012.
[41] A. Singh, « CoBIT 5: Managing Continuity Aspects With A Practical Approach », p. 25,
2015.
[42] M. Swanson, P. Bowen, A. W. Phillips, D. Gallup, et D. Lynes, « Contingency planning
guide for federal information systems », National Institute of Standards and Technology,
Gaithersburg, MD, NIST SP 800-34r1, 2010.
[43] M. Swanson, A. Wohl, L. Pope, T. Grance, J. Hash, et R. Thomas, « Contingency
planning guide for information technology systems :: recommendations of the National
Institute of Standards and Technology », National Institute of Standards and Technology,
Gaithersburg, MD, NIST SP 800-34, 2002.
[44] ISO, « ISO 22301 ». 2012

Paper -02
A SURVEY OF CLUSTERING ALGORITHMS IN
ASSOCIATION RULES MINING
Wael Ahmad AlZoubi
Applied Science Department, Ajloun University College, Balqa Applied
University.
ABSTRACT
The main goal of cluster analysis is to classify elements into groupsbased on their
similarity. Clustering has many applications such as astronomy, bioinformatics,
bibliography, and pattern recognition. In this paper, a survey of clustering methods and
techniques and identification of advantages and disadvantages of these methods are
presented to give a solid background to choose the best method to extract strong
association rules.

REFERENCES
1). Dhillon, I. S., Guan, Y. and Kulis, B. Kernel k-means: spectral clustering and
normalized cuts. Proceeding of KDD '04 Proceedings of the tenth ACM SIGKDD
international conference on Knowledge discovery and data mining. Seattle, WA,
USA — August 22 - 25, 2004.
2) Berkhin, P. A Survey of Clustering Data Mining Techniques. United States,
North America: Springer, 2006. PP. 25 – 71.
3) AlZoubi, W. A. An Improved Clustered Based Technique for Frequent Items
Generation from Transaction Datasets. CCIT 2018.
4) Moreira, A. Density-based clustering algorithms – DBSCAN and SNN. Version
1.0, 25.07.2005, University of Minho – Portugal.
5) Han, J., Cheng, H., Xin, D., & Yan, X. 2007. Frequent pattern mining: current
status and future directions. Data Mining Knowledge Disc (2007), pp. 55–86.
6) Astashyn, A. 2004. Deterministic Data Reduction Methods for Transactional
Datasets. Master Thesis. Polytechnic University.
http://photon.poly.edu/~hbr/publi/alex_msthesis.pdf.
7) Pal N. R., Pal K., Keller J. M., and Bezdec J. C.2006. A possibilistic fuzzy c-means
clustering algorithm. IEEE Transactions on Fuzzy Systems. Issue 4, Volume 13,
August2005, pp. 517 – 530.
8) Alfred R. &Dimitar, K. 2007. A Clustering Approach to Generalized Pattern
Identification Based on Multi-instanced Objects with DARA. In Local
Proceedings of ADBIS. Varna. pp. 38 – 49.
9) Khan S. and Ahmad A. Cluster center initialization algorithm for K-means
clustering. Pattern Recognition Letters. Volume 25, Issue 11, August 2004, pp.
1293 – 1302.
10) Fraley, C. Algorithms for Model-Based Gaussian Hierarchical Clustering. SIAM
Journal on Scientific Computing, 1998, Vol. 20, No. 1. pp. 270-281.
11) Eyal Salman, H., Hammad, M., Seriai, A. and Al-Sbou, A. Semantic Clustering of
Functional Requirements Using Agglomerative Hierarchical Clustering.
Information 2018, 9, 222; doi:10.3390/info9090222.
www.mdpi.com/journal/information.

12) Heyer L, Kruglyak S, Yooseph S (1999) Exploring expression data: identification
and analysis of coexpressed genes. Genome Res 9:1106–1115.
13) Rodriguez A, Laio A. Clustering by fast search and find of density peaks.
Science 27 Jun 2014: Vol. 344, Issue 6191, pp. 1492-1496 DOI:
10.1126/science.1242072.
14) Song M, Christian W. Günther, Wil M. P. van der Aalst. Trace Clustering in
Process Mining. International conference on Business Process Management (BPM
2008): Business Process Management Workshops pp 109-120.
15) T. Asano, B. Bhattacharya, M. Keil, and F. Yao. Clustering algorithms based on
minimum and maximum spanning trees. In Proceedings of the 4th Annual
Symposium on Computational Geometry, pages 252-257, 1988.
16) Tsay, Y.-J. & Chiang, J.-Y. 2005. CBAR: an efficient method for mining
association rules. Knowledge-Based Systems 18 (2005), pp. 99–105.
17) Tsay, Y.-J. &Chien.-C, Y.-W. 2004. An efficient cluster and decomposition
algorithm for mining association rules. Information Sciences 160 (2004) 161–
171.
18) Hanirex, K &Rangaswamy, D. 2011. Efficient algorithm for miningfrequent
itemsets using clustering techniques.International Journal on Computer Science
and Engineering (IJCSE), Vol. 3 No. 3 Mar 2011, pp. 1028 - 1032.

Paper - 03
COMPROMISING SYSTEMS: MPLEMENTING
HACKING PHASES
Marlon intal tayag1
and Maria emmalyn asuncion de vigal capuno2
1
College of Information and Communications Technology,Holy Angel University,
Angeles, Philippines
2
Faculty of Information Technology, Future University, Khartoum, Sudan
ABSTRACT
In the cyber world more and more cyber-attacks are being perpetrated. Hackers have now
become the warriors of the internet. They attack and do harmful things to compromised
system. This paper will show the methodology use by hackers to gained access to system
and the different tools used by them and how they are group based on their skills. It will
identify exploits that can be used to attack a system and find mitigation to those exploits.
In addition, the paper discusses the actual implementation of the hacking phases with the
virtual machines use in the process. The virtual machines specification is also listed. it
will also provide means and insights on how to protect one system from being
compromised..
KEYWORDS
compromised systems, hacking, penetration testing, exploit, vulnerability

REFERENCES
[1] S. Begum and S. Kumar, “IJESRT INTERNATIONAL JOURNAL OF ENGINEERING
SCIENCES & RESEARCH TECHNOLOGY A COMPREHENSIVE STUDY ON
ETHICAL HACKING,” vol. 5, no. 8, pp. 214–219, 2016.
[2] “Role of Ethical Hacking in System,” no. May, 2018.
[3] “What is white hat? - Definition from WhatIs.com.” [Online]. Available:
https://searchsecurity.techtarget.com/definition/white-hat. [Accessed: 14-Mar-2019].
[4] “What is ethical hacker? - Definition from WhatIs.com.” [Online]. Available:
https://searchsecurity.techtarget.com/definition/ethical-hacker.[Accessed: 14-Apr-
2019].
[5] “Types of Hackers and What They Do: White, Black, and Grey | EC-Council Official Blog.”
[Online].Available: https://blog.eccouncil.org/types-of-hackers-and-what-they-do-
white-black-and-grey/.[Accessed: 14-Mar-2019].
[6] “What is the Difference Between Black, White and Grey Hat Hackers?” [Online].
Available:https://us.norton.com/internetsecurity-emerging-threats-what-is-the-
difference-between-black-whiteand-grey-hat-hackers.html. [Accessed: 14-Mar-2019].
[7] S. Satapathy and D. Ranjan Patra, “Ethical Hacking,” Int. J. Sci. Res. Publ., vol. 5, no. 6,
pp. 2250–3153, 2015.
[8] C. C. Palmer, “Ethical hacking,” vol. 40, no. 3, pp. 769–780, 2001.
[9] I.-C. MIHAI, “Penetration Tests on Virtual Environment,” Int. J. Inf. Secur. Cybercrime,
vol. 1, no.1, pp. 37–45, 2016.
[10] B. Sahare, A. Naik, and S. Khandey, “Study Of Ethical Hacking,” vol. 2, no. 4, pp. 6–10,
2014.
[11] D. Hafele, “Information Security Reading Room Three Different Shades of Ethical
Hacking : Black , White and Gray In tu ll r igh,” 2019.
[12] “Exploitable vulnerabilities #1 (MS08-067).” [Online]. Available:
https://blog.rapid7.com/2014/02/03/new-ms08-067/. [Accessed: 14-Mar-2019].
[13] “ Microsoft Security Bulletin MS08-067 - Critical | Microsoft Docs.” [Online].
Available:https://docs.microsoft.com/en-us/security-
Updates/securitybulletins/2008/ms08-067. [Accessed: 21-Mar-2019].

Paper - 04
ANALYTICAL STUDYTO REVIEW OFARABIC
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 theArabic 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.

Paper -05
ENSEMBLE LEARNING MODEL FOR SCREENING
AUTISM IN CHILDREN
Mofleh Al Diabat1
and Najah Al-Shanableh2
1,2
Department of Computer Science, Al Albayt University, Al Mafraq- Jordan
ABSTRACT
Autistic Spectrum Disorder (ASD) is a neurological condition associated with
communication, repetitive, and social challenges. ASD screening is the process of
detecting potential autistic traits in individuals using tests conducted by a medical
professional, a caregiver, or a parent. These tests often contain large numbers of items to
be covered by the user and they generate a score based on scoring functions designed by
psychologists and behavioural scientists. Potential technologies that may improve the
reliability and accuracy of ASD tests are Artificial Intelligence and Machine Learning.
This paper presents a new framework for ASD screening based on Ensembles Learning
called Ensemble Classification for Autism Screening (ECAS). ECAS employs a powerful
learning method that considers constructing multiple classifiers from historical cases and
controls and then utilizes these classifiers to predict autistic traits in test instances. ECAS
performance has been measured on a real dataset related to cases and controls of children
and using different Machine Learning techniques. The results revealed that ECAS was
able to generate better classifiers from the children dataset than the other Machine
Learning methods considered in regard to levels of sensitivity, specificity, and accuracy.
KEYWORDS
Artificial Neural Network, Autism Screening, Classification, Ensemble Learners, Predictive
Models, Machine Learning

REFERENCES
[1] Pennington, M. L., Cullinan, D., & Southern, L. B. (2014). Defining autism:
variability in state education agency definitions of and evaluations for Autism
Spectrum Disorders. Autism Research and Treatment, 1-8.
[2] Thabtah, F. (2018A) Machine learning in autistic spectrum disorder behavioral
research: A review and ways forward. Informatics for Health and Social Care 43
(2), 1-20.
[3] Chu, K. C., Huang, H. J., & Huang, Y. S. (2016). Machine learning approach for
distinction of ADHD and OSA. In Advances in Social Networks Analysis and
Mining (ASONAM), 2016 IEEE/ACM International Conference on (pp. 1044-
1049). IEEE.
[4] Lopez Marcano, J. L. (2016). Classification of ADHD and non-ADHD Using AR
Models and Machine Learning Algorithms (Doctoral dissertation, Virginia Tech).
[5] Duda M., Ma R., Haber N., Wall D.P. (2016). Use of machine learning for
behavioral distinction of autism and ADHD. Translational Psychiatry (9(6), 732.
[6] Bone, D., Goodwin, M. S., Black, M. P., Lee, C.-C., Audhkhasi, K., & Narayanan,
S. (2016). Applying machine learning to facilitate autism diagnostics: pitfalls
and promises. Journal of Autism and Developmental Disorders, 1121–1136.
[7] Thabtah F., Kamalov F., Rajab K. (2018) A new computational intelligence
approach to detect autistic features for autism screening. International Journal
of Medical Informatics, Volume 117, pp. 112-124.
[8] Abbas, H., Garberson, F., Glover, E., & Wall, D. P. (2018). Machine learning
approach for early detection of autism by combining questionnaire and home
video screening. Journal of the American Medical Informatics Association, 25(8),
1000-1007. doi:10.1093/jamia/ocy039
[9] Altay, O., &Ulas, M. (2018). Prediction of the Autism Spectrum Disorder
Diagnosis with Linear Discriminant Analysis Classifier and K-Nearest Neighbor in
Children. 2018 6th International Symposium on Digital Forensic and Security
(ISDFS). Antalya, Turkey: IEEE. doi:10.1109/ISDFS.2018.8355354
[10] Ravindranath, V., & Ra, S. (2018). A machine learning based approach to classify
Autism with optimum behaviour sets. International Journal of Engineering and
Technology. doi:10.14419/ijet.v7i3.18.14907
[11] Thabtah F., Peebles D. (2019) A new machine learning model based on induction
of rules for autism detection. Health Informatics Journal, 1460458218824711.

[12] R. M. Mohammad, F. Thabtah and L. McCluskey, “Predicting Phishing Websites
using Neural Network trained with Back-Propagation,” in ICAI, Las Vigas,
2013-C.
[13] R. M. Mohammad, F. Thabtah and L. McCluskey, “Predicting phishing websites
based on self-structuring neural network,” Neural Computing and Applications,
vol. 25, no. 2, pp. 443-458, 2013-B.
[14] S. Madhusmita, S. K. Dash, S. Dash and A. Mohapatra, “An approach for iris
plant classification using neural network,” International Journal on Soft
Computing , vol. 3, no. 1, 2012.
[15] F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl and J. Havel,
“Artificial neural networks in medical diagnosis,” Journal of Applied
Biomedicine, vol. 11, no. 2, p. 47–58, 2013.
[16] M. Riley, J. Karl and T. Chris, “A Study of Early Stopping, Ensembling, and
Patchworking for Cascade Correlation Neural Networks,” IAENG International
Journal of Applied Mathematics, vol. 40, no. 4, pp. 307-316, 2010.
[17] Thabtah, F. (2018). An accessible and efficient autism screening method for
behavioural data and predictive analyses. Health Informatics Journal, 1-17.
doi:10.1177/1460458218796636
[18] Thabtah F., ASDTests. A mobile App for ASD Screening, (2017) (Accessed 14
March 2019), www.asdtests.com
[19] Ventola, P., Kleinman, J., Pandey, J., Barton, M., Allen, S., Green, J., . . . Fein, D.
(2006). Agreement among four diagnostic instruments for autism spectrum
disorders in toddlers. Journal of Autism and Developmental Disorders, 839-47.
[20] Vllasaliu, L., Jensen, K., Hoss, S., Landenberger, M., Menze, M., Schutz, M., . . .
Freitag, C. M. (2016). Diagnostic instruments for autism spectrum disorder
(ASD). Cochrane Database of Systematic Reviews, 1-27.
[21] Thabtah F. (2017) Autism Spectrum Disorder Tools: Machin Learning Adaptation
and DSM-5 Fulfillment: An Investigative Study. Proceedings of the2017
International Conference on Medical and Health Informatics (ICMHI 2017), pp. 1-6.
Taichung, Taiwan. ACM.
[22] Baron-Cohen, S. (2001). Take the AQ test. Journal of Autism and developmental
disorders, 5-17.

[23] Allison, C., Baron-Cohen, S., Charman, T., Wheelwright, S., Richler, J., Pasco, G.,
&Brayne, C. (2008). The Q-CHAT (quantitative checklist for autism in
toddlers): a normally distributed quantitative measure of autistic traits at 18–
24 months of age: preliminary report. Journal of Autism and Developmental
Disorders, 1414–1425.
[24] Witten, I. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools
and Techniques.
[25] Freund, Y. and Schapire, R.E., (1997) A Decision-Theoretic Generalization of
On-Line Learning and an Application to Boosting. Journal of Computer and
System Sciences, 55(1), p.119–139.
[26] Quinlan, J. (1986). Induction of Decision Trees. Mach. Learn. 1(1): 81-106.
[27] Fusaroli, R., Lambrechts, A., Bang, D., Bowler, D. M., & Gaigg, S. B. (2017,
March). “Is voice a marker for Autism spectrum disorder? A systematic review and
meta‐analysis”. Autism Research, 10, 384-407. doi:https://doi.org/10.1002/aur.1678
[28] Tariq, Q., Daniels, J., Schwartz, J. N., Washington, P., Kalantarian, H., & Wall, D.
P. (2018, November 27). Mobile detection of autism through machine learning on
home video: A development and prospective validation study. PLoS Med, 15(11).
doi:https://doi.org/10.1371/journal.pmed.1002705
[29] Satu, S., Sathi, F. F., Arifen, S., & Ali, H. (January 2019). Early Detection of
Autism by Extracting Features:A Case Study in Bangladesh. International
Conference on Robotics, Electrical and Signal Processing Techniques. Dhaka.
Retrievedfrom
https://www.researchgate.net/publication/330383730_Early_Detection_of_Auti
sm_by_Extracting_Features_A_Case_Study_in_Bangladesh
[30] Wong , V., Hui , L., Lee , W., Leung , L., Ho , P., Lau, W., . . . Chung, B. (2004).
A modified screening tool for autism (Checklist for Autism in Toddlers [CHAT-
23]) for Chinese children. Pediatrics, 166-76.
[31] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H.
(2009). The WEKA data mining software: an update. ACM SIGKDD explorations
newsletter, 11(1), 10-18.
[32] Quinlan, J. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan
Kaufmann.
[33] BreimanL. (2001) Random forests. Mach. Learning, 45(1):5-32, 2001. 1300
[34] Friedman, N., Geiger, D. and Goldszmidt, M. (1997) Bayesian Network Classifiers.

Machine Learning - Special issue on learning with probabilistic representations,
29(2-3), pp.131-63.
[35] Bi, X.-a., Wang, Y., Shu, Q., Sun, Q., & Xu, Q. (2018). Classification of Autism
Spectrum Disorder Using Random Support Vector Machine Cluster. Frontiers
in genetics, 9(18). doi:10.3389/fgene.2018.00018
[36] Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., &
Pickles, A. (2000). The Autism diagnostic observation schedule-generic: a
standard measure of social and communication deficits associated with the
spectrum of autism. Journal of Autism and Developmental Disorders, 205-223.
[37] Schopler, E., Van Bourgondien, M. E., Wellman, J., & Love, S. R. (1980). Toward
objective classification of childhood autism: childhood autism rating scale
(cars). Autism DevDisord, 91–103.
[38] Allison, C., Auyeung, B., & Baron-Cohen, S. (2012). Toward brief “red flags”
for autism screening: the short autism spectrum quotient and the short
quantitative checklist in 1,000 cases and 3,000 controls. Journal of the American
Academy of Child & Adolescent Psychiatry, 51(2), 202-212.
[39] Frank, E., and, Witten, I. (1998) Generating accurate rule sets without global
optimisation. Proceedings of the Fifteenth International Conference on Machine
Learning, (p. . 144–151). Madison, Wisconsin.
[40] Cohen, W. W. (1995). Fast effective rule induction. In Machine Learning
Proceedings 1995 (pp. 115-123). Morgan Kaufmann.
[41] Freund, Y., &Schapire, R. E. (1999). Large margin classification using the
perceptron algorithm. Machine learning, 37(3), 277-296.
[42] Abdelhamid, N., Thabtah, F.,and Abdel-jaber, H. (2017). Phishing detection: A
recent intelligent machine learning comparison based on models content and
features. 2017 IEEE International Conference on Intelligence and Security
Informatics (ISI), pp. 72-77. 2017/7/22, Beijing, China.
[43] Abdelhamid N., Ayesh A., Thabtah F. (2013) Classification. Proceedings of the
International conference on AI ‘2013, pp. 687-695. LV, USA. Associative
Classification Mining for Website Phishing
[44] Thabtah F., Hadi W., Abdelhamid N., Issa A. (2011) Prediction Phase in
Associative Classification. Journal of Knowledge Engineering and Software
Engineering. Volume: 21, Issue: 6(2011) pp. 855-876. WorldScinet.

[45] Thabtah F., Mahmood Q., McCluskey L., Abdel-jaber H (2010). A new
Classification based on Association Algorithm. Journal of Information and
Knowledge Management, Vol 9, No. 1, pp. 55-64. World Scientific.
[46] Thabtah F., Cowling P., and Peng Y. (2006): Multiple Label Classification Rules
Approach. Journal of Knowledge and Information System. Volume 9:109-129.
Springer.

Paper -06
A NOVEL PROTOTYPE MODEL FOR SWARM MOBILE
ROBOT NAVIGATION BASED FUZZY LOGIC
CONTROLLER
Sherif Kamel Hussein1
, Mashael Amer Al-Mutairi2
1
Associate Professor – Department of Communications and Computer Engineering
October University for Modern Sciences and Arts ,Giza- Egypt
1
Head of Computer Science Department, Arab East Colleges for Graduate Studies,
Riyadh, KSA
2
Co- Author: Master of Computer Science,Arab East Colleges for Graduate
Studies,- Riyadh, KSA
ABSTRACT
Autonomous mobile robots have been used to carry out different tasks without
continuous human guidance. To achieve the tasks, they must be able to navigate and
avoid different kinds of obstacles that faced them. Navigation means that the robot can
move through the environment to reach a destination. Obstacles avoidance considers a
challenge which robot must overcome. In this work, the authors propose an efficient
technique for obstacles avoidance through navigation of swarm mobile robot in an
unstructured environment. All robots cooperate with each other to avoid obstacles. The
robots detect the obstacles position around them and store their positions in shared
memory. By accessing the shared memory, the other robots of the swarm can avoid the
detected obstacles when they face them. To implement this idea, the Authors used a
MATLAB® and V-REP® (Virtual Robot Experimentation Platform).
..
KEYWORDS
mobile robot, swarm robot, navigation, obstacle avoidance, fuzzy logic controller

REFERENCES
[1] Murphy, R. 2000. Introduction to AI robotics. MIT press.
[2] Niku, S. 2010. Introduction to robotics. John Wiley & Sons.
[3] Nehmzow, Ulrich. 2012. Mobile robotics : A practical introduction. London:
Praxis.
[4] Alves S. F., Rosario J. M., Ferasoli Filho H., Rincon L. K., & Yamasaki R. A.
2011. Conceptual bases of robot navigation modeling, control and applications.
In Advances in Robot Navigation. InTech.
[5] Siegwart, Roland, Illah Reza Nourbakhsh, and Davide Scaramuzza. 2014;2011;.
Introduction to autonomous mobile robots. 2nd ed. Cambridge: MIT Press.
[6] Goris, K. 2005. Autonomous mobile robot mechanical design. Vrije Universite it
Brussel, Engineering Degree Thesis, Brussels, Belgium.
[7] Özkil A. G. 2009. Technical Report on Autonomous Mobile Robot Navigation.
[8] I brahim, M. Y., and A. Fernandes. 2004. Study on mobile robot navigation
techniques.
[9] Nirmala, G., Dr S. Geetha, and Dr S. Selvakumar. 2017. Mobile robot localization
and navigation in artificial intelligence: Survey. Computational Methods in
Social Sciences 4 (2): 12-22.
[10] Beni, Gerardo. 2005. From swarm intelligence to swarm robotics. In . Vol. 3342, 1-
9. Berlin, Heidelberg: Springer Berlin Heidelberg.
[11] Şahin, Erol. 2005. Swarm robotics: From sources of inspiration to domains of
application. In . Vol. 3342, 10-20. Berlin, Heidelberg: Springer Berlin Heidelberg.
[12] Brutschy, A. 2009. Task allocation in swarm robotics. Towards a method for
selforganized allocation to complex tasks. University Libre de Brux-elles, 1050
Bruxelles, Belgium, Technical Report TRlIRIDIA12009–007, 52009.
[13] Rashid, Razif, Irraivan Elamvazuthi, Mumtaj Begam, and M. Arrofiq. 2010.
Differential drive wheeled mobile robot (WMR) control using fuzzy logic
techniques.
[14] Rekik, Chokri, Mohamed Jallouli, and Nabil Derbel. 2009. Integrated genetic
algorithms and fuzzy control approach for optimization mobile robot navigation.

[15] Faisal, Mohammed, Ramdane Hedjar, Mansour Al Sulaiman, and Khalid Al-Mutib.
2013. Fuzzy logic navigation and obstacle avoidance by a mobile robot in an
unknown dynamic environment. International Journal of Advanced Robotic
Systems 10 (1): 37.
[16] Narvydas, G., R. Simutis, and V. Raudonis. 2007. Autonomous mobile robot
control using fuzzy logic and genetic algorithm.
[17] Güzel, Mehmet Serdar, Mehmet Kara, and Mehmet Sıtkı Beyazkılıç. 2017. An
adaptive framework for mobile robot navigation. Adaptive Behavior 25 (1): 30-9.
[18] Hayes, A. T., and P. Dormiani-Tabatabaei. 2002. Self-organized flocking with
agent failure: Off-line optimization and demonstration with real robots.
[19] Nga Le Thi Thuy, and Thang Nguyen Trong. 2017. The multitasking system of
swarm robot based on null-space-behavioral control combined with fuzzy
logic. Micromachines 8 (12): 357.
[20] Ducatelle, Frederick, Gianni A. Di Caro, Carlo Pinciroli, Francesco Mondada, and
Luca Gambardella. 2011. Communication assisted navigation in robotic swarms:
Self-organization and cooperation.
[21] Sugawara, K., T. Kazama, and T. Watanabe. 2004. Foraging behavior of interacting
robots with virtual pheromone.
[22] Batalin, Maxim A., and Gaurav S. Sukhatme. 2004. Coverage, exploration and
deployment by a mobile robot and communication network.
Telecommunication Systems 26 (2): 181-96.
[23] Houcque, David. "Introduction to Matlab for engineering students."
Northwestern University (2005): 1-64.
[24]“Math Works”, [Online]. Available: https://www.mathworks.com. Accessed:
September 2018.
[25] “Coppelia Robotics”, [Online]. Available: http://www.coppeliarobotics.com.
Accessed: September 2018.
[26] Mondada, F., Franzi, E., & Guignard, A.1999. The development of khepera. In
Experiments with the Mini-Robot Khepera, Proceedings of the First
International Khepera Workshop No. LSRO-CONF-2006-060: 7-14.
[27] “K Team ”, [Online]. Available: https://www.k-team.com. Accessed: March 2018.

AUTHORS
Sherif Kamel Hussein Hassan Ratib: Graduated from the faculty of engineering in
1989 Communications and Electronics Department,Helwan University. He received
his Diploma,MSc,and Doctorate in Computer Science-2007, Major Information
Technology and Networking. He has been working in many private and
governmental universities inside and outside Egypt for almost 15 years.He shared in
the development of many industrial courses. His research interest is GSM Based
Control and Macro mobility based Mobile IP. He is an Associate Professor and
Faculty Member at Communications and Computer Engineering department in
October University for Modern Sciences and Arts - Egypt. Now he is working as
head of Computer Science department in Arab East Colleges for Postgraduate
Studies in Riyadh- KSA.

Paper -07
WEB CRAWLER FOR SOCIAL NETWORK USER DATA
PREDICTION USING SOFT COMPUTING METHODS
José L. V. Sobrinho1
, Gélson da Cruz Júnior2
and Cássio Dener Noronha
Vinhal3
1,2,3
Faculty of Electrical and Computing Engineering, Federal University of Goiás, Brazil
ABSTRACT
This paper addresses how elementary data from a public user profile in Instagram can be
scraped and loaded into a database without any consent. Furthermore, discusses how soft
computing methods such neural networks can be used to determine the popularity of a
user’s post. Conclusively, raises questions about user’s privacy and how tools like this
can be used for better or for worse.
.
KEYWORDS
Social Network, Instagram, Business Intelligence, Soft Computing, Neural Network,
User Privacy, ETL, Database, Node.js, Data Analysis

REFERENCES
[1] C. Olston and M. Najork, Web Crawling, in Foundations and Trends in Information
Retrieval Vol. 4, No. 3 (2010) 175–246.
[2] GraphQL – Get started with a query language for your API [Web page]. Retrieved
September 21, 2018, from https://graphql.org/
[3] Instagram API [Web page]. Retrieved September 21, 2018, from
https://developers.facebook.com/docs/instagram-api/overview/
[4] What it is ETL [Web page]. Retrieved September 21, 2018, from
https://www.sas.com/en_us/insights/data-management/what-is-etl.html
[5] The Face API Service [Web page]. Retrieved September 21, 2018, from
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
[6] What is Power BI [Web page]. Retrieved September 21, 2018, from
https://powerbi.microsoft.com/en-us/what-is-power-bi/
[7] Ulag, A. M. (2018, February 27). Gartner recognizes Microsoft as a leader in BI
[Web blog post]. Retrieved September 21, 2018, from
https://powerbi.microsoft.com/pt-br/blog/gartner-recognizes-microsoft-as-a-
leader-in-analytics-and-bi-platforms-for-11-consecutive-years.
[8] Instagram Terms of Use [Web page]. Retrieved September 21, 2018, from
https://help.instagram.com/581066165581870/
[9] Instagram Data Policy [Web page]. Retrieved September 21, 2018, from
https://help.instagram.com/519522125107875/
[10] K. Michael and M. G. Michael, Computing Ethics – No Limits to Watching, in
Com. of the ACM Magazine Vol. 56, No. 11 (2013) 25-28.
[11] C. J. Qian, J. D. Tang, M. A. Penza, C. M. Ferri, in Instagram Popularity Prediction
via Neural Networks and Regression Analysis [Web page]. Retrieved September 21,
2018, from http://cjqian.github.io/docs/instagram_paper.pdf

AUTHORS
José Luís Vieira Sobrinho, Computer Engineer graduated from the Federal
University of Goiás (Brazil) and former exchange student at the State
University of New York at Oswego (United States) pursuing a Master’s
Degree.
Gélson da Cruz Júnior, He holds a bachelor's degree in Electrical
Engineering from Universidade Estadual Paulista Júlio de Mesquita Filho
(1990), a Master's degree in Electrical Engineering from the State University
of Campinas (1994) and a PhD in Electrical Engineering from the State
University of Campinas (1998). He holds a post-doctorate from INESC-Porto
and is currently a full professor of the postgraduate course in Electrical and
Computer Engineering at the School of Electrical, Mechanical and Computing
Engineering of the Federal University of Goiás.
Cássio Dener Noronha Vinhal, He holds a degree in Electrical Engineering
by the Federal University of Uberlândia (1990), Master's Degree in Electrical
Engineering by UNICAMP (1994) and PhD in Electrical Engineering by
UNICAMP (1998). He is currently a Full Professor at the School of
Electrical, Mechanical and Computer Engineering at the Federal University of
Goiás. He was postdoctoral fellow at the Institute of Systems and Computer
Engineering, University of Porto, Portugal (2006-2007).

Paper -08
REDUCTIONOFMONITORINGREGISTERSON
SOFTWARE DEFINED NETWORKS
Luz Angela Aristizábal Q.1 and Nicolás Toro G.2
1
Department of Computation, Faculty of Management, Universidad
Nacional de Colombia.
2
Department of Electrical and Electronic Engineering, Universidad
Nacional de Colombia.
ABSTRACT
Characterization of data network monitoring registers allows for reductions in the number
of data, which is essential when the information flow is high, and implementation of
processes with short response times, such as interchange of control information between
devices and anomaly detection is required. The present investigation applied wavelet
transforms, so as to characterize the statistic monitoring register of a software-defined
network. Its main contribution lies in the obtention of a record that, although reduced,
retains detailed, essential information for the correct application of anomaly detectors.
KEYWORDS
Characterization, wavelet transform, network monitoring, anomaly detectors, Software-
defined Networking (SDN).

REFERENCES
[1] Ibidunmoye, Olumuyiwa, Hernandez R Francisco, Elmroth Erick .(2015)
“Performance Anomaly Detection and Bottleneck Identification”. ACM
Computing Surveys, Vol. 48, No. 1, Article 4.
[2] M, Jammal, T. Singh, A. Shami, R.l Asal, Y. Li, (2014) "Software defined
networking: State of the art and research challenges", Computer Networks.
[3] Dabbagh, B.Hamdaoui, M. Guizani, and A. Rayes,(2015) "Software-Defined
Networking Security: Pros and Cons", IEEE Communications Magazine.
[4] N, S. Bailey, Deepak Bansal, Linda Dunbar, Dave Hood, Zoltán Lajos Kis, (2012)
"SDN Architecture Overview”. Open Network Foundation.
(https://www.opennetworking.org/images/stories/downloads/sdn-
resources/technical- reports/SDN-architecture-overview-1.0.pdf)
[5] K. Giotis, C. Argyropoulos, G. Androulidakis, D. Kalogeras, V. Maglaris, (2014)
"Combining OpenFlow and sFlow for an effective and scalable anomaly detection
and mitigation mechanism on SDN environments”, Computer pag. 122–136
[6] L. Jose, M. Yu, and J. Rexford, (2011) “Online measurement of large traffic
aggregates on commodity switches”, in Proc. of the USENIX workshop.
[7] Stéphane. Mallat, (2008) “A wavelet tour of signal processing”. Academic Press,
USA.
[8] L. Kalinichemko, I. Shanin, I. Taraban,(2014) "Methods for Anormaly Detection:
a Survey", Advanced Methodos and Technologies, digital collections. Pag. 20-25.
[9] J. Uthayakumar, T.Vengattaraman, P. Dhavachelvan (2018) “A survey on data
compression techniques: From the perspective of data quality, coding schemes,
data type and applications” Journal of King Saud University – Computer
and Information Sciences. Pags. 1-22
[10] L.A. Aristizábal (2009). “Paralell implementation for Generalized Method 1-D
Biosignal Compession” World Academic of Science, Engineering and
Technology.
[11] L.A. Aristizábal, C. Cortes, R, Flórez (2008).”ECG signal noise reduction and
compression for remote diagnosis systems”. INTER-NOISE Congress
Proceedings. Pags 4130-4135.

[12] K. Kyriakopoulos, D.J. Parish (2009). “Using Wavelets for Compression and
Detecting Events in Anomalous Network Traffic”. Fourth International
Conference on Systems and Networks Communications. IEEE Xplore.
[13] A. Al-Jawad, P. Shah, O. Gemikonakli, R. Trestian.(2016). “Compression-based
technique for SDN using sparse-representation dictionary”. IEEE/IFIF Network
Operations and Management Symposium.
[14 ]A. Cortes. (2018) “Simulation of Software Define Networks with Open
Network Operating System and Mininet”. International Journal of Computer
Science and Information Technology. (IJCSIT). Vol 10. Nro. 5. Pags 21-32.
[15] M. Dabagh, B. Handaoul, M. Guizani, A. Rayes, (2015) "Software-Defined
Networking Security: Pro and Cons”, IEEE Communications Magazine. pags.
73-79.
[16] L. Seunghyeon, K. Jinwoo, S. Seungwon, P. Porras, (2017). “Athena: A
framework for scalable Anormaly Detection in Software Defined
Networks”,47th Annual IEEE/IFIP International Conference on Dependable
Systems and Networks. Pags 249-260.
AUTHOR
Luz A. Aristizábal Q. is a professor in the Department of Computing in the
Faculty of Management at the Universidad Nacional de Colombia. She
received her Meng. in Physical Instrumentation from the Universidad
Tecnológica de Pereira in 2009, her degree in Data Network Specialization
from la Universidad del Valle in 1991, and her degree in Engineering Systems
from la Universidad Autónoma in 1989. Her research focuses on aspects of
computer and data networks, including network simulators, signal processing,
and network paradigms.
Nicolás Toro G. is a professor in the Department of Electricity, Electronics,
and Computing. He received his PhD in Engineering-Automation and a
bachelor’s degree in Electrical Engineering from the Universidad Nacional de
Colombia in 2013 and 1983, respectively, and his master’s degree in
Automated Production Systems from the Universidad Tecnológica de Pereira
in 1992. His research focuses on many aspects of industrial automation,
including network design, measurement, and analysis.

Paper -08
DATA-DRIVEN MODEL FOR NON-FUNCTIONAL
REQUIREMENTS IN MOBILE APPLICATION
DEVELOPMENT
Salisu Garba1
Babangida Isyaku2
and Mujahid Abdullahi3
1,2,3
Department of Mathematics & Computer Science, Sule Lamido
University, Kafin Hausa. Jigawa State.
ABSTRACT
The incredible development in the utilization of smartphones has driven the development
of billions of software applications famously known as ‘apps’ to accomplish roles outside
phone call and SMS messages in the day-to-day lives of users. Current assessments show
that there are a huge number of applications developed at a meteor pace to give clients a
rich and quick client experience. Mobile apps users are more concerned about stability
and quality now more than ever despite the increase in the scale and size of apps. As
such, mobile apps have to be designed, built, and produced for less money
(maintainability, portability, and reusability), with greater performance, reliable security
and fewer resources (efficiency) than ever before. This paper aimed at providing support
for mobile application developers in dealing with the ever-eluding non-functional
requirements by proposing a data-driven model that simplifies the non-functional
requirements (NFR) p in the development of an application for mobile devices. The study
tries to find out if NFR can be treated the same way as functional requirements in mobile
application development. Finally, this paper shows the experimental evaluation of the
proposed data-driven model of dealing for non-functional requirements in the
development of mobile apps and the results obtained from the application of the model
are also discussed.
KEYWORDS
Non-Functional Requirements, Mobile Application Development, Data-Driven Requirement
Engineering, Requirement Modelling

REFERENCES
[1] Carreño, L. V. G., & Winbladh, K. (2013, May). Analysis of user comments: an
approach for software requirements evolution. In Software Engineering (ICSE),
2013 35th International Conference on (pp. 582-591). IEEE.
[2] Chen, N., Lin, J., Hoi, S. C., Xiao, X., & Zhang, B. (2014, May). AR-miner:
mining informative reviews for developers from mobile app marketplace. In
Proceedings of the 36th International Conference on Software Engineering (pp.
767-778). ACM.
[3] Eckhardt, J., Vogelsang, A., & Fernández, D. M. (2016, May). Are" Non-
functional" Requirements really Non-functional? An Investigation of Non-
functional Requirements in Practice. In Software Engineering (ICSE), 2016
IEEE/ACM 38th International Conference on (pp. 832-842). IEEE.
[4] Finkelstein, A., Harman, M., Jia, Y., Sarro, F., & Zhang, Y. (2013). Mining app
stores: Extracting technical, business and customer rating information for
analysis and prediction. RN, 13, 21.
[5] Guzman, E., & Maalej, W. (2014, August). How do users like this feature? a fine-
grained sentiment analysis of app reviews. In Requirements Engineering
Conference (RE), 2014 IEEE 22nd International (pp. 153-162). IEEE.
[6] Grønli, T. M., & Ghinea, G. (2016, January). Meeting quality standards for
mobile application development in businesses: A framework for cross-platform
testing. In System Sciences (HICSS), 2016 49th Hawaii International Conference
on (pp. 5711-5720). IEEE.
[7] Huang, J., Xu, Q., Tiwana, B., Mao, Z. M., Zhang, M., & Bahl, P. (2010, June).
Anatomizing application performance differences on smartphones. In
Proceedings of the 8th international conference on Mobile systems, applications,
and services (pp. 165-178). ACM.
[8] Iacob, C., & Harrison, R. (2013, May). Retrieving and analyzing mobile apps
feature requests from online reviews. In Mining Software Repositories (MSR),
2013 10th IEEE Working Conference on (pp. 41-44). IEEE.
[9] MacCall, J. A., & Matsumoto, M. T. (1980). Software quality measurement
manual. National Technical Information Service.
[10] Malkawi, M. I. (2013). The art of software systems development: Reliability,
Availability, Maintainability, Performance (RAMP). Human-Centric
Computing and Information Sciences, 3(1), 22.

[11] Mushtaq, Z., Kirmani, M., & Saif, S. M. (2016). Mobile Application
Development: Issues and Challenges. International Research Journal of
Engineering and Technology on (pp. 1096-1099). IRJET
[12] Nagappan, M., & Shihab, E. (2016, March). Future trends in software engineering
research for mobile apps. In Software Analysis, Evolution, and Reengineering
(SANER), 2016 IEEE 23rd International Conference on (Vol. 5, pp. 21-32).
IEEE.
[13] Niemelä, E., & Latvakoski, J. (2004, October). Survey of requirements and
solutions for ubiquitous software. In Proceedings of the 3rd international
conference on Mobile and ubiquitous multimedia (pp. 71-78). ACM.
[14] Rein, A. D., & Münch, J. (2013). Feature prioritization based on mock-
purchase: A mobile case study. In Lean Enterprise Software and Systems (pp.
165-179). Springer, Berlin, Heidelberg.
[15] Romano, B. L., Gomes, P. M. E., Fernandes, H. C., Montini, D. Á., Dias, L. A.
V., & da Cunha, A. M. (2011). A Model-Driven Method for Documentation
and Analysis of Software-Intensive Systems Requirements.
[16] Rosen, C., & Shihab, E. (2016). What are mobile developers asking about? a
large-scale study using stack overflow. Empirical Software Engineering, 21(3),
1192-1223.
[17] Sarro, F., Al-Subaihin, A. A., Harman, M., Jia, Y., Martin, W., & Zhang, Y.
(2015, August). Feature lifecycles as they spread, migrate, remain, and die in app
stores. In Requirements Engineering Conference (RE), 2015 IEEE 23rd
International (pp. 76-85). IEEE.
[18] Spriestersbach, A., & Springer, T. (2004, April). Quality attributes in mobile
web application development. In International Conference on Product Focused
Software Process Improvement (pp. 120-130). Springer Berlin Heidelberg.
[19] Van Vliet, H. (2000). Software Engineering: Principles and Practice. New York:
Wiley.
[20] Zhao, H., Qiu, M., Gai, K., Li, J., & He, X. (2015, November). Maintainable
mobile model using pre-cache technology for high performance android system.
In Cyber Security and Cloud Computing (CSCloud), 2015 IEEE 2nd International
Conference on (pp. 175-180). IEEE.
[21] Zou, J., Xu, L., Yang, M., Zhang, X., & Yang, D. (2017). Towards
comprehending the non-functional requirements through Developers’ eyes:
An exploration of Stack Overflow using topic analysis. Information and
Software Technology, 84, 19-32.

New research articles_---_2019_april_issue

Recommended

Recommended

More Related Content

What's hot

What's hot (13)

Similar to New research articles_---_2019_april_issue

Similar to New research articles_---_2019_april_issue (20)

More from AIRCC Publishing Corporation

More from AIRCC Publishing Corporation (20)

Recently uploaded

Recently uploaded (20)

New research articles_---_2019_april_issue