APRIL 2020 TOP READ ARTICLES IN ARTIFICIAL INTELLIGENCE - International Journal of Artificial Intelligence & Applications (IJAIA)

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International Journal of Artificial Intelligence
& Applications (IJAIA)
ISSN: 0975-900X (Online); 0976-2191 (Print)
http://www.airccse.org/journal/ijaia/ijaia.html

TEXT CLASSIFICATION AND CLASSIFIERS: A
SURVEY
Vandana Korde
Sardar Vallabhbhai National Institute of Technology, Surat
Department of Computer Science&IT, Dr.B.A.M.U Aurangabad
ABSTRACT
As most information (over 80%) is stored as text, text mining is believed to have a high
commercial potential value. knowledge may be discovered from many sources of information;
yet, unstructured texts remain the largest readily available source of knowledge .Text
classification which classifies the documents according to predefined categories .In this paper we
are tried to give the introduction of text classification, process of text classification as well as the
overview of the classifiers and tried to compare the some existing classifier on basis of few
criteria like time complexity, principal and performance.
KEYWORDS
Text classification, Text Representation, Classifiers
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Volume Link: http://airccse.org/journal/ijaia/current2012.html

REFERENCES
[1] F. Sebastiani, “Text categorization”, Alessandro Zanasi (ed.) Text Mining and its Applications, WIT
Press, Southampton, UK, pp. 109-129, 2005.
[2] A. Dasgupta, P. Drineas, B. Harb, “Feature Selection Methods for Text Classification”, KDD’07,
ACM, 2007.
[3] A. Khan, B. Baharudin, L. H. Lee, K. Khan, “A Review of Machine Learning Algorithms for
TextDocuments Classification”, Journal of Advances Information Technology, vol. 1, 2010.
[4] F. Sebastiani, “Machine Learning in Automated Text Categorization”, ACM 2002.
[5] Y. Y. X. Liu, “A re-examination of Text categorization Methods” IGIR-99, 1999.
[6] Hein Ragas Cornelis H.A. Koster, “Four text classification algorithms compared on a Dutch corpus”
SIGIR 1998: 369-370 1998.
[7] Susan Dumais John Platt David Heckerman, “Inductive Learning Algorithms and Representations
for Text Categorization”, Published by ACM, 1998.
[8] Michael Pazzani Daniel Billsus “Learning and Revising User Profiles: The Identification of
Interesting Web Sites”, Machine Learning, 313–331 1997
[9] Gongde Guo, Hui Wang, David Bell, Yaxin Bi and Kieran Greer, “KNN Model-Based Approach in
Classification”, Proc. ODBASE pp- 986 – 996, 2003
[10] Eiji Aramaki and Kengo Miyo, “Patient status classification by using rule based sentence
extraction and bm25-knn based classifier”, Proc. of i2b2 AMIA workshop, 2006.
[11] Muhammed Miah, “Improved k-NN Algorithm for Text Classification”, Department of Computer
Science and Engineering University of Texas at Arlington, TX, USA.
[12] Fang Lu Qingyuan Bai, “A Refined Weighted K-Nearest Neighbours Algorithm for Text
Categorization”, IEEE 2010.
[13] Kwangcheol Shin, Ajith Abraham, and Sang Yong Han, “Improving kNN Text Categorization by
Removing Outliers from Training Set”, Springer-Verlag Berlin Heidelberg 2006.
[14] Methods Ali Danesh Behzad Moshiri “Improve text classification accuracy based on classifier
fusion methods”. 10th International Conference on Information Fusion, 1-6 2007.
[15] SHI Yong-feng, ZHAO, “Comparison of text categorization algorithm”, Wuhan university Journal
of natural sciences. 2004.
[16] D. Lewis, “Naive Bayes at Forty: The Independence Assumption in Information Retrieval”,
Proc. ECML-98, 10th European Conf. Machine 1998.

[17] Vidhya. K.A G.Aghila, “A Survey of Naïve Bayes Machine Learning approach in Text Document
Classification”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7,
2010.
[18] McCallum, A. and Nigam K., "A Comparison of Event Models for Naive Bayes Text
Classification". AAAI/ ICML -98 Workshop on Learning for Text Categorization
[19] Sang- Bum Kim, et al, “Some Effective Techniques for Naive Bayes Text Classification “IEEE
Transactions on Knowledge and Data Engineering, Vol. 18, November 2006.
[20] Yirong Shen and Jing Jiang” Improving the Performance of Naive Bayes for Text
Classification”CS224N Spring 2003
[21] Michael J. Pazzani “Searching for dependencies in Bayesian classifiers” Proceedings of the Fifth
Int. workshop on AI and, Statistics. Pearl, 1988
[22] Dino Isa “Text Document Pre-Processing Using the Bayes Formula for Classification Based on
the Vector Space Mode”, Computer and Information Science November, 2008
[23] Bayes Jingnian Chen a, b, Houkuan Huang a, Shengfeng Tian a, Youli Qua a “Feature selection for
text classification with Naïve”, China Expert Systems with Applications 36 5432–54352009
[24] Mnish Mehta, Rakesh agrwal” SLIQ: A Fast Scalable Classifier for Data Mining” 1996.
[25] Peerapon Vateekul and Miroslav Kubat, “Fast Induction of Multiple Decision Trees in Text
Categorization From Large Scale,Imbalanced, and Multi-label Data”, IEEE International Conference
on Data MiningWorkshops 2009
[26] D. E. Johnson F. J. Oles T. Zhang T. Goetz, “A decision-tree-based symbolic rule induction system
for text Categorization”, by IBM SYSTEMS JOURNAL, VOL 41, NO 3, 2002
[27] David D. Lewis and Marc Ringuette, “A comparison of two learning algorithms for text
categorization”, Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and
Information Retrieval, Las Vegas, US 1994.
[28] HAO CHEN, YAN ZHAN, YAN LI, “The Application Of Decision Tree In Chinese Email
Classification”, Proceedings of the Ninth International Conference on Machine Learning and
Cybernetics, Qingdao, 11-14 July 2010
[29] C.Apte, F. Damerau, and S.M. Weiss “Automated Learning of Decision Rules for Text
Categorization”, ACM Transactions on Information Systems, 1994
[30] Sholom M. Weiss Nitin Indurkhya, “Rule-based Machine Learning Methods for Functional
Prediction”, Journal of Artificial Intelligence Research 3 383-403 1995
[31] Chih-Hung Wu “Behavior-based spam detection using a hybrid method of rule-based Techniques and
neural networks”, Expert Systems with Applications 36 4321– 4330 2009
[32] Joachims, T. “Text categorization with support vector machines: learning with many relevant
features”. In Proceedings of ECML-98, 10th European Conference on Machine Learning (Chemnitz,
DE), pp. 137–142 1998.
[33] Loubes, J. M. and van de Geer, S “Support vector machines and the Bayes rule in classification”,
Data mining knowledge and discovery 6 259-275.2002
[34] Chen donghui Liu zhijing, “A new text categorization method based on HMM and SVM”, IEEE2010

[35] Yu-ping Qin Xiu-kun Wang, “Study on Multi-label Text Classification Based on SVM” Sixth
International Conference on Fuzzy Systems and Knowledge Discovery 2009
[36] Dagan, I., Karov, Y., and Roth, D. “Mistake-Driven Learning in Text Categorization.” In
Proceedings of CoRR. 1997
[37] MIgual E .Ruiz, Padmini Srinivasn, “Automatic Text Categorization Using Neural networks”,
Advaces in Classification Research, Volume VIII.
[38] Cheng Hua Li , Soon Choel Park “An efficient document classification model using an improved
back propagation neural network and singular value decomposition”, Expert Systems with
Applications, 3208–3215, 2009
[39] Hwee TOU Ng Wei Boon Goh Kok Leong Low, “Feature Selection, Perception Learning, and a
Usability Case Study for Text Categorization”, SIGIR 97 Philadelphia PA,
[40] Amy J.C. Trappey a, Fu-Chiang Hsu a, Charles V. Trappey b, Chia-I. Lin “Development of a patent
document classification and search platform using a back-propagation network”, Expert
Systems with Applications 31 755–765 2006
[41] Yiming Yang And Christopher G. Chute Mayo Cllnic “An Example-Based Mapping Method For
Text Categorization And Retrieval” ACM Transactions On Information Systems, Vol. 12, No 3,
Pages 252-277, July 1994
[42] Yiming Yang Christopher G. Chute “A Linear Least Squares Fit Mapping Method For Information
Retrieval From Natural Language Texts” Acres De Coling-92 Nantes, 23-28 AOUT 1992
[43] Li, Y. H. and Jain, A. K. “Classification of text documents”. The Computer Journal, 537–546. 1998.
[44] Larkey, L. S. and Croft, W. B. “Combining classifiers in text categorization”. In Proceedings of
SIGIR-96, 19th ACM International Conference on Research and Development in Information
Retrieval (Zurich, CH, 1996), pp. 289–297 1996
[45] O. Zaiane, and M. Antonie, “Text Document Categorization by Term Associaton”, Proceedings of
ICDM 2002, IEEE, , pp.19-26 2002
[46] Supaporn Buddeewong1 and Worapoj Kreesuradej” A New Association Rule-Based Text Classifier
Algorithm”, Proceedings of the 17th IEEE International Conference on Tools with Artificial
Intelligence, 2005
[47] S. M. Kamruzzaman, Chowdhury Mofizur Rahman: “Text Categorization using Association Rule
and Naive Bayes Classifier” CoRR, 2010
[48] Mohammad Masud Hasan and Chowdhury Mofizur Rahman,” Text Categorization Using
Association Rule Based Decision Tree”, Proceeding of the 6th International Conference on
Computer and Information Technology (ICCIT), pp 453-456, Bangladesh, 2003
[49] Sholom M. Weiss, Chidanand Apte, Fred J. Damerau, David E. Johnson, Frank J. Oles, Thilo Goetz,
and Thomas Hampp, IBM T.J. Watson Research Center “Maximizing Text-Mining Performance”
1094-7167/99 IEEE INTELLIGENT SYSTEMS. 1999
[50] Songbo Tan ”An improved centroid classifier for text categorization” Expert Systems with
Applications xxx 2007

[51] Eui-Hong (Sam) Han and George Karypis “Centroid-Based Document Classification: Analysis &
Experimental Results” PKDD '00 Proceedings of the 4th European Conference on Principles of Data
Mining and Knowledge Discovery Springer-Verlag London, UK ©2000.
[52] B S Harish, D S Guru, S Manjunath ” Representation and Classification of Text Documents: A Brief
Review” IJCA Special Issue on “Recent Trends in Image Processing and Pattern
Recognition”RTIPPR, 2010.
[53] Shi Yong-Feng, Zhao Yan-Ping in Wuhan “ Comparison of Text Categorization Algorithms ”
University Journal of Natural Sciences 2004
[54] Yiming Yang “An Evolution of statistical Approaches to Text Categorization” Information
Retrieval 1, 69-90 1999.
[55] Kjersti Aas and Line Eikvil “Text Categorization: A Survey” Report No. 941. ISBN 82-539-0425-8.
,June, 1999.
[56] B S Harish, D S Guru, S Manjunath “Representation and Classification of Text Documents: A Brief
Review” IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition”
RTIPPR, 2010.
[57] Su-Jeong Ko and Jung-Hyun Lee “Feature Selection Using Association Word Mining for
Classification “H.C. Mayr et al. (Eds.): DEXA 2001, LNCS 2113, pp. 211–220, 2001.
[58] Anirban Dasgupta “Feature Selection Methods for Text Classification “KDD’07, August 12–15,
2007.
[59] Wei Zhao “A New Feature Selection Algorithm in Text Categorization “International Symposium on
Computer, Communication, Control and Automation 2010.

A HYBRID ALGORITHM BASED ON INVASIVE WEED
OPTIMIZATION ALGORITHM AND GREY WOLF
OPTIMIZATION ALGORITHM
Wisam Abdulelah Qasim1
and Ban Ahmed Mitras2
1
M.sc. Student, Department of Mathematics, College of Computer Science &
Mathematics, Mosul, Iraq.
2
Department of Mathematics, College of Computer Sciences & Mathematics,
Mosul, Iraq
ABSTRACT
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is
algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm
and the second algorithm representing the grey wolves optimization. This algorithm is one of the
algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed
optimization is inspired by nature as the weeds have colonial behavior and were introduced by
Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of
their adaptability and are a threat to the overall planting process. The behavior of these weeds has
been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is
considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best
solution. The algorithm was designed by Seyedali Mirijalili in 2014 and taking advantage of the
intelligence of the squadrons is to avoid falling into local solutions so the new hybridization
process between the previous algorithms GWO and IWO and we will symbolize the new
algorithm IWOGWO. Comparing the suggested hybrid algorithm with the original algorithms it
results were excellent. The optimum solution was found in most of test functions.
KEYWORDS
Invasive weeds optimization algorithm , grey wolves optimization algorithm , hybrid algorithms,
optimization.

REFERENCES
[1] K. E. Parsopoulos and M. N. Vrahatis, “Particle swarm optimization and intelligence: advances
and applications,” 2010.
[2] H. T. Yaseen, B. A. Mitras, and A. S. M. Khidhir, “Hybrid Invasive Weed Optimization Algorithm
with Chicken Swarm Optimization Algorithm to solve Global Optimization Problems,” Int. J.
Comput. Networks Commun. Secur., vol. 6, no. 8, pp. 173–181, 2018.
[3] X.-S. Yang, Engineering optimization: an introduction with metaheuristic applications. John Wiley &
Sons, 2010.
[4] X.-S. Yang, Nature-inspired metaheuristic algorithms. Luniver press, 2010.
[5] C. Blum, A. Roli, and M. Sampels, Hybrid metaheuristics: an emerging approach to optimization, vol.
114. Springer, 2008.
[6] A. R. Mehrabian and C. Lucas, “A novel numerical optimization algorithm inspired from weed
colonization,” Ecol. Inform., vol. 1, no. 4, pp. 355–366, 2006.
[7] Y. Zhao, L. Leng, Z. Qian, and W. Wang, “A discrete hybrid invasive weed optimization algorithm
for the capacitated vehicle routing problem,” Procedia Comput. Sci., vol. 91, pp. 978–987, 2016.
[8] C. Liu and H. Wu, “Synthesis of thinned array with side lobe levels reduction using improved
binary invasive weed optimization,” Prog. Electromagn. Res., vol. 37, pp. 21–30, 2014.
[9] L. Korayem, M. Khorsid, and S. S. Kassem, “Using grey wolf algorithm to solve the capacitated
vehicle routing problem,” in IOP conference series: materials science and engineering, 2015, vol. 83,
no. 1, p. 12014.
[10] Y. Ren, T. Ye, M. Huang, and S. Feng, “Gray Wolf Optimization Algorithm for Multi-Constraints
Second-Order Stochastic Dominance Portfolio Optimization,” Algorithms, vol. 11, no. 5, p. 72, 2018.
[11] M. Karimi and S. M. Babamir, “QoS-aware web service composition using Gray Wolf Optimizer,”
Int. J. Inf. Commun. Technol. Res., vol. 9, no. 1, pp. 9–16, 2017.
[12] N. Singh and S. B. Singh, “Hybrid algorithm of particle swarm optimization and grey wolf optimizer
for improving convergence performance,” J. Appl. Math., vol. 2017, 2017.
[13] H. Faris, I. Aljarah, M. A. Al-Betar, and S. Mirjalili, “Grey wolf optimizer: a review of recent
variants and applications,” Neural Comput. Appl., vol. 30, no. 2, pp. 413–435, 2018.
[14] H. Turabieh, “A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease,” Am. J. Oper.
Res., vol. 6, no. 02, p. 136, 2016.
[15] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–
61, 2014.

AN ONTOLOGICAL ANALYSIS AND NATURAL
LANGUAGE PROCESSING OF FIGURES OF SPEECH
Christiana Panayiotou
Department of Communications and Internet Studies,
Technological University of Cyprus
ABSTRACT
The purpose of the current paper is to present an ontological analysis to the identification of a
particular type of prepositional figures of speech via the identification of inconsistencies in
ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to
describe real world events and activities. However, one aspect that makes a prepositional noun
phrase poetical is that the latter suggests a semantic relationship between concepts that does not
exist in the real world. The current paper shows that a set of rules based on WordNet classes and
an ontology representing human behaviour and properties, can be used to identify figures of
speech due to the discrepancies in the semantic relations of the concepts involved. Based on this
realization, the paper describes a method for determining poetic vs. non-poetic prepositional
figures of speech, using WordNet class hierarchies. The paper also addresses the problem of
inconsistency resulting from the assertion of figures of speech in ontological knowledge bases,
identifying the problems involved in their representation. Finally, it discusses how a
contextualized approach might help to resolve this problem.
KEYWORDS
Ontologies, NLP, Linguistic creativity

REFERENCES
[1] Literary Devices Editors, (2013) “Figure of Speech” [Online]. Available:
https://literarydevices.net/figure-of-speech/
[2] Poetry.org. (2005) “what is poetry” [Online]. Available: http://www.poetry.org/whatis.htm.
[3] Hugo Concalo Oliveira. (2009) “Automatic generation of poetry: an overview”, CISUC, DEI,
University of Coimbra, Tech. Rep. 1.
[4] Abrams, M.H and Geoffrey G. Harpham. (1999) “A Glossary of Literal terms, Boston”, MA:
Thomson, Wadsworth.
[5] Project Gutenberg. Retrieved February 21, 2016, [Online]. Available: www.gutenberg.org.
[6] Miller, George A. (1995) “WordNet: A Lexical Database for English”, Communications of the
ACM, Vol. 38, No. 11.
[7] Literary Devices Editors (2013) “Personification” [Online]. Available:
https://literarydevices.net/personification.
[8] Blake, W., and Blake, W. (1992) “Songs of innocence; and songs of experience”. New York: Dover.
[9] Bailey, R.W. (1974) “Computer Assisted Poetry: The writing machine is for everybody”, Computers
in Humanities, Cambridge University Press, pp283-295.
[10] Toivanen, J.M., Toivonen, H. and Valitutti, A. (2013) “Harnessing constraint Programming for poetry
composition”, In Proceedings of the Fourth International Conference on Computational Creativity,
pp160-167.
[11] Manurung, H.M. (1999) “Chart Generation of Rythm-Patterned Text”, Proceedings of the First
International Workshop on Literature in Cognition and Computers.
[12] Amitava Das and Bjorn Gamback. (2014) “Poetic Machine: Computational Creativity for
Automatic Poetry Generation in Bengali”, ICCC.
[13] Manurung, H. (2004) “An evolutionary algorithm approach to poetry generation”, PhD thesis,
University of Edinburgh.
[14] LitCharts Editors (2015) “Literary devices and terms” [Online]. Available:
https://www.litcharts.com/literary-devices-and-terms.
[15] Loper, Edward and Bird, Steven (2002) “NLTK: The Natural Language Toolkit”, Proceedings of the
ACL-20 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing

and Computational Linguistics, Association of Computational Linguistics, ETMTNLP, Vol. 1, pp63-
70.
[16] De Smedt, T. and Daelemans, W. (2012) “Pattern for Python”, Journal of Machine Learning
Research, Vol. 13, Issue 1, pp2063-2067.
[17] Flouris, G., Huang, Z., Pan, J., Plexoudakis, D. and Wache, H. (2006) “Inconsistencies, Negations
and Changes in Ontologies”, Proceedings of AAAI Conference on Artificial Intelligence.
[18] Hayes, Patrick and Patel-Schneider Peter Editors (2014) “RDF 1.1 Semantics” [Online]. Available:
https://www.w3.org/TR/rdf11-mt.
[19] Brickley, Dan and Guha, R.V. (2004) “RDF Vocabulary Description Language 1.0:RDF Schema”
[Online]. Available: https://www.w3.org/TR/2004/REC-rdf-schema-20040210.
[20] Wood, D., Lanthaler, M and Cyganiak, R. (2014) “RDF 1.1 Concepts and Abstract Syntax” [Online].
Available: https://www.w3.org/TR/2014/REC-rdf11-concepts-20140225.
[21] Hayes, P. and Welty, C. (2006) “Defining N-ary Relations on the Semantic Web” [Online].
Available: https://www.w3.org/TR/swbp-n-aryRelations.
[22] Nguyen, V., Bodenraider, O. and Sheth, A. (2014) “Don’t like RDF Reification? Making Statements
about Statements Using Singleton Property, Proceedings of the International World Wide Web
Conference, pp759-770.
[23] Carrol, J., Bizer, C., Hayes, P. and Stickler, P. (2005) “Named Graphs, Provenance and Trust”,
Proceedings of the 14th International Conference on World Wide Web, pp613-622.
[24] Boettiger, C. (2018) “rdflib: A high level wrapper around the redland package for common rdf
publications, Zenodo Publisher.
[25] Bouquet, P., Giunchiglia, F., Harmelen, F., Serafini, L. and Stuckenschmidt, H. (2003) “C-OWL:
Contextualizing Ontologies”, Proceedings of the second International Semantic Web Conference,
Lecture Notes in Artificial Intelligence, No. 2870, pp164-179.
[26] Ghidini, C. and Giunchiglia, F. (2001) “Local Model Semantics, or Contextual Reasoning = Locality
+ Compatibility”, Journal of Artificial Intelligence, Vol. 127, No. 2, pp221-259.
[27] Leite, M.A., and Ricarte, I.L. (2008) “Using Multiple Related Ontologies in a Fuzzy Information
Retrieval Model” WONTO.
[28] Attia, Z.E., Gadallah, A.M., and Hefny, H.A. (2014) “Semantic Information Retrieval Model: Fuzzy
Ontology Approach”.
[29] Lau, R.Y., Song, D., Li, Y., Cheung, C., and Hao, J. (2009) “Toward a Fuzzy Domain Ontology
Extraction Method for Adaptive e-Learning”, IEEE Transactions on Knowledge and Data
Engineering, Vol. 21, pp800-813.
[30] Versaci, M., Foresta F.L., Morabito F.C. and Angiulli, G. (2018) “A fuzzy divergence approach for
solving electrostatic identification problems for NDT applications”, International Journal of Applied
Electromagnetics and Mechanics. Vol. 57. pp1-14.

[31] Cacciola, M., Calcagno, S., Megali, G., Pellicanò, D., Versaci, M., and Morabito, F.C. (2010)
“Wavelet Coherence and Fuzzy Subtractive Clustering for Defect Classification in Aeronautic
CFRP”, International Conference on Complex, Intelligent and Software Intensive Systems, pp101-
107.
[32] Bezdek, J.C. (1993) “Fuzzy models—What are they, and why?” [Editorial], IEEE Transactions on
Fuzzy Systems, Vol. 1.
[33] Quan, T.T Hui, S.C and Cao, T.H. (2004) “FOGA: A Fuzzy Ontology Generation Framework for
Scholarly Semantic Web”.
[34] Arab, A.M, Gadallah, A.M., and Salah, A. (2017) “Training-less Multi-Domain Classification
Approach using Ontology and Fuzzy sets”, International Journal of Computer Science and
Information Security. Vol 15. No.1. pp282-288.
[35] Panayiotou, C. (2019), "An Ontological Approach to the Extraction of Figures of Speech", The 5th
International Conference on Artificial Intelligence and Applications, Dubai.
AUTHORS
Professor at the Cyprus University of Dr. Christiana Panayiotou is an Assistant
Technology.

Hand Gesture Recognition: A Literature Review
1
Rafiqul Zaman Khan and 2
Noor Adnan Ibraheem
1,2
Department of Computer Science, A.M.U. Aligarh, India
ABSTRACT
Hand gesture recognition system received great attention in the recent few years because of its
manifoldness applications and the ability to interact with machine efficiently through human
computer interaction. In this paper a survey of recent hand gesture recognition systems is
presented. Key issues of hand gesture recognition system are presented with challenges of gesture
system. Review methods of recent postures and gestures recognition system presented as well.
Summary of research results of hand gesture methods, databases, and comparison between main
gesture recognition phases are also given. Advantages and drawbacks of the discussed systems
are explained finally.
KEYWORDS
Hand Posture, Hand Gesture, Human Computer Interaction (HCI), Segmentation, Feature
Extraction, Classification Tools, Neural Networks.

REFERENCES
[1] G. R. S. Murthy, R. S. Jadon. (2009). “A Review of Vision Based Hand Gestures
Recognition,” International Journal of Information Technology and Knowledge Management, vol.
2(2), pp. 405- 410.
[2] P. Garg, N. Aggarwal and S. Sofat. (2009). “Vision Based Hand Gesture Recognition,” World
Academy of Science, Engineering and Technology, Vol. 49, pp. 972-977.
[3] Fakhreddine Karray, Milad Alemzadeh, Jamil Abou Saleh, Mo Nours Arab, (2008)
.“HumanComputer Interaction: Overview on State of the Art”, International Journal on Smart Sensing
and Intelligent Systems, Vol. 1(1).
[4] Wikipedia Website.
[5] Mokhtar M. Hasan, Pramoud K. Misra, (2011). “Brightness Factor Matching For Gesture
Recognition System Using Scaled Normalization”, International Journal of Computer Science
& Information Technology (IJCSIT), Vol. 3(2).
[6] Xingyan Li. (2003). “Gesture Recognition Based on Fuzzy C-Means Clustering Algorithm”,
Department of Computer Science. The University of Tennessee Knoxville.
[7] S. Mitra, and T. Acharya. (2007). “Gesture Recognition: A Survey” IEEE Transactions on systems,
Man and Cybernetics, Part C: Applications and reviews, vol. 37 (3), pp. 311- 324, doi:
10.1109/TSMCC.2007.893280.
[8] Simei G. Wysoski, Marcus V. Lamar, Susumu Kuroyanagi, Akira Iwata, (2002). “A Rotation
Invariant Approach On Static-Gesture Recognition Using Boundary Histograms And Neural
Networks,” IEEE Proceedings of the 9th International Conference on Neural Information Processing,
Singapura.
[9] Joseph J. LaViola Jr., (1999). “A Survey of Hand Posture and Gesture Recognition
Techniques and Technology”, Master Thesis, Science and Technology Center for Computer
Graphics and Scientific Visualization, USA.
[10] Rafiqul Z. Khan, Noor A. Ibraheem, (2012). “Survey on Gesture Recognition for Hand Image
Postures”, International Journal of Computer And Information Science, Vol. 5(3), Doi:
10.5539/cis.v5n3p110
[11] Thomas B. Moeslund and Erik Granum, (2001). “A Survey of Computer Vision-Based Human
Motion Capture,” Elsevier, Computer Vision and Image Understanding, Vol. 81, pp. 231–268.
[12] N. Ibraheem, M. Hasan, R. Khan, P. Mishra, (2012). “comparative study of skin color based
segmentation techniques”, Aligarh Muslim University, A.M.U., Aligarh, India.
[13] Mahmoud E., Ayoub A., J¨org A., and Bernd M., (2008). “Hidden Markov Model-Based Isolated and
Meaningful Hand Gesture Recognition”, World Academy of Science, Engineering and Technology
41.

[14] E. Stergiopoulou, N. Papamarkos. (2009). “Hand gesture recognition using a neural network shape
fitting technique,” Elsevier Engineering Applications of Artificial Intelligence, vol. 22(8), pp. 1141–
1158, doi: 10.1016/j.engappai.2009.03.008
[15] M. M. Hasan, P. K. Mishra, (2011). “HSV Brightness Factor Matching for Gesture
Recognition System”, International Journal of Image Processing (IJIP), Vol. 4(5).
[16] Malima, A., Özgür, E., Çetin, M. (2006). “A Fast Algorithm for Vision-Based Hand Gesture
Recognition For Robot Control”, IEEE 14th conference on Signal Processing and Communications
Applications, pp. 1-4. doi: 10.1109/SIU.2006.1659822
[17] Mokhar M. Hasan, Pramod K. Mishra, (2012) “Features Fitting using Multivariate Gaussian
Distribution for Hand Gesture Recognition”, International Journal of Computer Science & Emerging
Technologies IJCSET, Vol. 3(2).
[18] Mokhar M. Hasan, Pramod K. Mishra, (2012). “Robust Gesture Recognition Using Gaussian
Distribution for Features Fitting’, International Journal of Machine Learning and Computing,
Vol. 2(3).
[19] W. T. Freeman and Michal R., (1995) “Orientation Histograms for Hand Gesture Recognition”, IEEE
International Workshop on Automatic Face and Gesture Recognition.
[20] Min B., Yoon, H., Soh, J., Yangc, Y., & Ejima, T. (1997). “Hand Gesture Recognition Using Hidden
Markov Models”. IEEE International Conference on computational cybernetics and simulation. Vol.
5, Doi: 10.1109/ICSMC.1997.637364
[21] Verma, R., Dev A. (2009).”Vision based hand gesture recognition using finite state machines and
fuzzy logic”. IEEE International Conference on Ultra-Modern Telecommunications & Workshops
(ICUMT '09), pp. 1-6. doi: 10.1109/ICUMT.2009.5345425
[22] Luigi Lamberti, Francesco Camastra, (2011). “Real-Time Hand Gesture Recognition Using a Color
Glove”, Springer Proceedings of the 16th international conference on Image analysis and processing:
Part I ICIAP.
[23] Minghai Y., Xinyu Q., Qinlong G., Taotao R., Zhongwang L., (2010). “Online PCA with Adaptive
Subspace Method for Real-Time Hand Gesture Learning and Recognition”, journal World Scientific
and Engineering Academy and SocietWSEAN, Vol. 9(6).
[24] N. A. Ibraheem., R. Z. Khan, (2012). “Vision Based Gesture Recognition Using Neural
Networks Approaches: A Review”, International Journal of Human Computer Interaction
(IJHCI), Malaysia, Vol. 3(1).
[25] Manar Maraqa, Raed Abu-Zaiter. (2008). “Recognition of Arabic Sign Language (ArSL) Using
Recurrent Neural Networks,” IEEE First International Conference on the Applications of Digital
Information and Web Technologies, (ICADIWT), pp. 478-48. doi: 10.1109/ICADIWT.2008.4664396
[26] Tin Hninn H. Maung. (2009).“Real-Time Hand Tracking and Gesture Recognition System Using
Neural Networks,” World Academy of Science, Engineering and Technology 50, pp. 466- 470.
[27] Cheng-Chang L. and Chung-Lin H., (1999).“The Model-Based Dynamic Hand Posture Identification
Using Genetic Algorithm”, Springer, Machine Vision and Applications Vol. 11.
[28] Kouichi M., Hitomi T. (1999) “Gesture Recognition using Recurrent Neural Networks” ACM
conference on Human factors in computing systems: Reaching through technology (CHI '91), pp.237-
242. doi: 10.1145/108844.108900

[29] Guan, Y., Zheng, .M. (2008). “Real-time 3D pointing gesture recognition for natural HCI. IEEE
Proceedings of the 7th World Congress on Intelligent Control and Automation WCICA 2008, doi:
10.1109/WCICA.2008.4593304
[30] Freeman, W. T., Weissman, C. D. (1995). ” Television Control by Hand Gestures”. IEEE
International Workshop on Automatic Face and Gesture Recognition.
[31] V. S. Kulkarni, S.D.Lokhande, (2010) “Appearance Based Recognition of American Sign Language
Using Gesture Segmentation”, International Journal on Computer Science and Engineering (IJCSE),
Vol. 2(3), pp. 560-565.
[32] Shuying Zhao, Wenjun Tan, Shiguang Wen, and Yuanyuan Liu, (2008). “An Improved Algorithm of
Hand Gesture Recognition under Intricate Background”, Springer the First International Conference
on Intelligent Robotics and Applications (ICIRA 2008),: Part I. pp. 786–794, 2008. Doi:10.1007/978-
3-540-88513-9_85
Authors
Dr. Rafiqul Zama Khan obtained his B.Sc degree from M.J.P Rohilkhand
University, Bareilly, M.Sc and M.C.A from Aligarh Muslim University, Aligarh,
and his Ph.D. from Jamia Hamdard University, New Delhi. He has 18 years of
rich teaching experience of various reputed National (Pune University, Jamia
Hamdard University) & International Universities (King Fhad University of
Petroleum & Minerals, Dharan, K.S.A; Ittihad University, U.A.E). Presently he is
working as an Associate Professor in Department of Computer Science, Aligarh
Muslim University, Aligarh (U.P), India. He worked as a Head of the Department
of Computer Science at Poona College, University of Pune. He also worked as a
Chairman of the Department of Computer Science, at Aligarh Muslim University, Aligarh, India. He is also
working as a PhD guide of several students. He has published more than 25 research papers in
International/National Journals. He is the member of Editorial Board of number of International Journals.
Noor Adnan Ibraheem: Received her B.Sc. and M.Sc. in computer science from
BGU in 2001 and 2005 respectively, she is currently a Ph.D. student at Aligarh
Muslim University, Aligarh, Uttar Pradesh, India. Her research interests include
computer vision, image processing, and artificial intelligent.

USING SEMI-SUPERVISED CLASSIFIER TO
FORECAST EXTREME CPU UTILIZATION
Nitin Khosla1
and Dharmendra Sharma2
1
Assistant Director- Performance Engineering, ICTCAPM, Dept. of Home Affairs, Canberra,
Australia
2
Professor – Computer Science, University of Canberra, Australia
ABSTRACT
A semi-supervised classifier is used in this paper is to investigate a model for forecasting
unpredictable load on the IT systems and to predict extreme CPU utilization in a complex
enterprise environment with large number of applications running concurrently. This proposed
model forecasts the likelihood of a scenario where extreme load of web traffic impacts the IT
systems and this model predicts the CPU utilization under extreme stress conditions. The
enterprise IT environment consists of a large number of applications running in a real time
system. Load features are extracted while analysing an envelope of the patterns of work-load
traffic which are hidden in the transactional data of these applications. This method simulates and
generates synthetic workload demand patterns, run use-case high priority scenarios in a test
environment and use our model to predict the excessive CPU utilization under peak load
conditions for validation. Expectation Maximization classifier with forced-learning, attempts to
extract and analyse the parameters that can maximize the chances of the model after subsiding the
unknown labels. As a result of this model, likelihood of an excessive CPU utilization can be
predicted in short duration as compared to few days in a complex enterprise environment.
Workload demand prediction and profiling has enormous potential in optimizing usages of IT
resources with minimal risk
KEYWORDS
Semi-Supervised Learning, Performance Engineering, Load And Stress Testing, Machine
Learning.

REFERENCES
[1] Daniel Gmach, Jerry Rolia, Ludmila Cherkasova, Alfons Kemper, (2007) “Workload Analysis And
Demand Prediction Of Enterprise Data Center Applications”, IEEE 10th International Symposium On
Workload Characterization, Boston, USA.
[2] Jia Li, Andrew W. Moore, (2008) “Forecasting Web Page Views: Methods And Observations”,
Journal Of Machine Learning Research.
[3] Adams, R. P. And Ghahramani, Z. (2009) “Archipelago: Nonparametric Bayesian Semi-
Supervised Learning”, In Proceedings Of The International Conference On Machine Learning
(ICML).
[4] H. Zhao, N. Ansari, (2012) “Wavelet Transform Based Network Traffic Prediction: A Fast
Online Approach”, Journal Of Computing And Information Technology, 20(1).
[5] Yuzong Liu, Katrin Krichhoff, (2013), “Graph Based Semi-Supervised Learning For Phone And
Segment Classification”, France.
[6] Danilo J Rezende, Shakir Mohamed, Daan Wierstra, (2014) “Stochastic Backpropagation And
Approximate Inference In Deep Generative Models”, Proceedings Of The 31st International
Conference On Machine Learning, Beijing, China.
[7] Diederik P. Kingma, Danilo J Rezende, Shakir Mohamad, Max Welling, (2014) “Semi-Supervised
Learning With Deep Generative Models”, Proceedings Of Neural Information Processing Systems
(NIPS), Cornell University, USA.
[8] Pitelis, N., Russell, C., And Agapito, L. (2014) “Semi-Supervised Learning Using An Unsupervised
Atlas”. In Proceedings Of The European Conference On Machine Learning (ECML), Volume LNCS
8725, Pages 565 –580.
[9] Kingma Diederik, Rezende Danilo, Mohamed Shakir, Welling M, (2014) “Semi-Supervised Learning
With Deep Generative Models”, Proceedings Of Neural Information Processing Systems (NIPS).
[10] L. Nie, D. Jiang, S. Yu, H. Song, (2017) “Network Traffic Prediction Based On Deep Belief Network
In Wireless Mesh Backbone Networks”, IEEE Wireless Communication And Networking
Conference, USA.
[11] Chao Yu, Dongxu Wang, Tianpei Yang, Et., (2018) “Adaptive Shaping Reinforcement Learning
Agents Vis Human Reward”, PRICAI Proceedings Part-1, Springer.
[12] Xishun Wang, Minjie Zhang, Fenghui Ren, (2018) “Deep RSD: A Deep Regression Method For
Sequential Data”, PRICAI Proceedings Part-1, Springer.
[13] Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, Et. (2018) “Realistic Evaluation Of
Semi-Supervised Learning Algorithms”, 6th International Conference On Learning
Representations, ICLR, Vancouver, BC, Canada.
[14] Kenndy John, Satran Michael, (2018) “Preventing Memory Leaks In Windows Applications”,
Microsoft Windows Documents.

[15] M.F. Iqbal, M.Z. Zahid, D. Habib, K. John, (2019) “Efficient Prediction Of Network Traffic For Real
Time Applications”, Journal Of Computer Networks And Communications.
[16] Verma. V, Lamb. A, Kannala. J, Bengio. Y, Paz DL, (2019) “Interpolation Consistency Training
For Semi Supervised Learning”, Proceedings Of 28th International Joint Conference On Artificial
Intelligence IJCAI Macao, China.
AUTHORS
Nitin Khosla Mr Khosla has worked about 15 years as Asst. Professor at MNIT in the Department of
Electronics and Communication Engineering before moving to Australia. He acquired
Master of Philosophy (Artificial Intelligence) from Australia, Master of Engineering
(Computer Technology) from AIT Bangkok and Bachelor of Engineering (Electronics)
from MNIT. His expertise is in Artificial Intelligence (neural nets), Software Quality
Assurance and IT Performance Engineering. Also, he is a Certified Quality Test
Engineer, Certified Project Manager and a Quality Lead Assessor. During last 14 years,
he worked in private and public services in New Zealand and Australia as a Senior
Consultant in Software Quality. Currently he is Asst. Director in Australian Federal
Government in Performance and Capacity Management and leading multiple IT
projects.

Intelligent Decision Support Systems For
Admission Management In Higher Education
Institutes
Rajan Vohra1
& Nripendra Narayan Das2
1.
Prosessor, Department of Computer Science & Engineering, Bahra University,
Solan, Himachal Pradesh, India.
2.
Assistant Professor, Department of Computer Science & Information Technology,
ITM University, Gurgaon, Haryana, . India
ABSTRACT
On the basis of their use, the DSS has received positive feedback from the University's decision
makers. Making use of Intelligent Decision Support Systems (IDSS) technologies suited to
provide decision support in the higher education environments, by generating and presenting
relevant information and knowledge which are helpful in taking the decision regarding admission
management in higher education colleges or universities. The university decision makers' needs
and the DSS components are identified with the help of survey done. In this paper the
components of a decision support system (DSS) for developing student admission policies in
higher education institute or in the university and the architecture about DSS based on ERP are
proposed followed by how intelligent DSS in conjunction with ERP helps to overcome the
drawbacks , if ERP is used alone in higher education institutes.
KEYWORDS
Intelligent systems, Decision support, Decision Support Systems (DSS), ERP, Higher
education institutions, knowledge base.

REFERENCES
[1] D. J. Power, “Supporting Decision-Makers: An Expanded Framework”, In Harriger, A.(Editor),
eProceedings Informing Science Conference, Krakow, Poland, June 19-22, 2001, 431-436.
[2] Vasile Paul Bresfelean et. al ,”Towards the development of decision support in academic
environments,” proceedings of the ITI 2009 , 31st international conference on information technology
interface , june 22-25, 2009, Cavtat, Croatia
[3] G. DeSanctis and R. B.Gallupe, “A Foundation for the Study of Group Decision Support
Systems”, Management Science, 33(5), 1987, 589-609.
[4] Marco Semini, Håkon Fauske and Erik Gran “Use of model-driven decision support methods for
supply chain design” SINTEF Technology and Society.
[5] Muneer Alsurori, Juhana Salim,” Information and Communication Technology for Decision-Making
in the Higher Education in Yemen: A Review” 2009 International Conference on Electrical
Engineering and Informatics ,5-7 August 2009, Selangor, Malaysia.
[6] Wang Aihua, Guo Wenge, Xu Guoxiong, Jia Jiyou, Wen Dongmao,” GIS-Based Educational
DecisionMaking System” Proceedings of 2009 IEEE International Conference on Grey Systemss and
Intelligent Services, November 10-12, 2009, Nanjing, China., 2009 IEEE, pp 1198-1202.
[7] Qiusheng Liu, Guofang Liu,” Research on the Framework of Decision Support System Based on
ERP Systems”, 2010 Second International Workshop on Education Technology and Computer
Science, 2010 IEEE.
[8] S. F. Mohd Dahlan and N. A. Yahaya,”A System Dynamics Model for Determining Educational
Capacity of Higher Education Institutions” Second International Conference on Computational
Intelligence, Modelling and Simulation, 2010 IEEE.
[9] P. G. W. Keen and M. S. Scott Morton, “Decision Support Systems: An Organizational Perspective”,
Reading, MA, Addison-Wesley, 1978.

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