ORIGINAL_ARTICLE
A Distant Supervised Approach for Relation Extraction in Farsi Texts
The volume of Farsi information on the Internet has been increasing in recent years. However, most of this information is in the form of unstructured or semi-structured free text. For quick and accurate access to the vast knowledge contained in these texts, the information extraction methods are essential to generate knowledge bases. In recent years, relation extraction as a sub-task of information extraction has received much attention. While many of these systems were developed in English and other well-known languages, the systems for information extraction in Farsi have received less attention from researchers. In this systematic research for semi-automatic relation extraction, Persian Wikipedia articles were presented as reliable and semi-structured sources. In this system, the relation extraction is performed with the assistance of patterns that are automatically obtained with an approach based on distant supervised. In order to apply the distant supervised, the vast knowledge base of Wikidata has been used as a source in perfect synchronization with Wikipedia. The results show that the average precision value for all relations is 76.81%, which indicates an enhancement of precision compared to other methods in Farsi.
https://ijwr.usc.ac.ir/article_128960_7feb1e39fb030f3143006c50b5c0ca98.pdf
2020-12-01
1
8
10.22133/ijwr.2021.253045.1071
Relation Extraction
Information Extraction
Distant Supervision
Persian Wikipedia
Shireen
Atarod
at.shi.1989@gmail.com
1
Department of Computer Engineering, Science and Research Branch, Azad University, Tehran, Iran.
AUTHOR
Alireza
Yari
a_yari@itrc.ac.ir
2
Assistant Professor, ICT Research Institute (ITRC), Tehran, Iran
LEAD_AUTHOR
Emami, H. Shirazi, A. Abdollahzadeh, and M. Hourali, “A Pattern-Matching Method for Extracting Personal Information in Farsi Content”, University Politehnica of Bucharest-Scientific Bulletin, Series C, Electrical Engineering and Computer Science, vol. 78, pp. 125-139, 2016.
1
A. Hearst, “Automatic acquisition of hyponyms from large text corpora”, In Proceedings of the 14th conference on Computational Linguistics,Vol. 2, 1992, 539-545.
2
Rahimipour, M. Shamsfard, and Z. Ansari, “Information Extraction System, Mersad”, In Proceedings of the fifteenth Iran conference on Electric Engineering, 2007.
3
Sharifzadeh and M. Shamsfard, “Automatic Information Extraction on Special Domain”, In Proceedings of nineteenth Annual National Conference on Iran Computer Society, 2014.
4
Brin, “Extracting patterns and relations from the World Wide Web”, International Workshop on The World Wide Web and DatabasesSpringer, Berlin, Heidelberg ,1998, pp. 172-183.
5
Chen, D. Ji, C. L. Tan, and Z. Y. Niu, “Relation extraction using label propagation based semi-supervised learning”, In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2006, pp. 129-136.
6
Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates, “Web-scale information extraction in knowitall, (preliminary results)”, In Proceedings of the 13th international conference on World Wide Web, ACM, 2004, pp. 100-110.
7
Feldman and B. Rosenfeld, “Boosting unsupervised relation extraction by using NER”, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, 2006, pp. 473-481.
8
Hasegawa, S. Sekine, and R. Grishman, “Discovering relations among named entities from large corpora”, In Proceedings of the 42nd Annual Meeting on Association for Computational LinguisticsAssociation for Computational Linguistics, 2004, pp. 415-422.
9
Bach and S. Badaskar, “A review of relation extraction”, Literature review for Language and Statistics II, vol. 2, pp. 1-15, 2007.
10
Grishman, Information Extraction: Capabilities and Challenges, Lecture Notes of Computer Science, 2012.
11
Sudachi Khalese and M. A. Zare Bidaki, “An information framework for automatic answering to Farsi questions based on extracted knowledge from Wikipedia using self-supervised learning”, In Proceedings of 3th International Conference on Applied research in Computer and Information, 2016.
12
Agichtein and L. Gravano, “Snowball, Extracting relations from large plain-text collections”, In Proceedings of the fifth ACM conference on Digital libraries, ACM, 2000, pp. 85-94.
13
Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni, “Open information extraction from the web”, IJCAI. Vol. 7, pp. 2670-2676, 2007.
14
Fader, S. Soderland, and O. Etzioni, “Identifying relations for open information extraction”, Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, 2011, pp. 1535-1545.
15
Wu and D. S.Weld, “Open information extraction using Wikipedia”, In Proceedings of the 48th annual meeting of the association for computational linguistics, Association for Computational Linguistics, 2010, pp. 118-127.
16
Rozenfeld and R. Feldman, “Self-supervised relation extraction from the Web”, Knowledge and Information Systems, vol. 17, no. 1, pp. 17-33, 2008.
17
S. Weld, F. Wu, E. Adar, S. Amershi, J. Fogarty, R. Hoffmann, and M. Skinner, “Intelligence in Wikipedia”, In AAAI, vol. 8, pp.1609-1614, 2008.
18
Konstantinova, “Review of relation extraction methods, what is new out there?”, International Conference on Analysis of Images, Social Networks and Texts, Springer, Cham, 2014, pp. 15-28.
19
Min, R. Grishman, L. Wan, C. Wang, and D. Gondek, “Distant supervision for relation extraction with an incomplete knowledge base”, In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics, Human Language Technologies, 2013, pp. 777-782.
20
Mintz, S .Bills, R. Snow, and D. Jurafsky, “Distant supervision for relation extraction without labeled data”, In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, Association for Computational Linguistics, 2009, pp. 1003-1011.
21
Mosalla Nejad, D. Davoodi Moghadam, and A. Ahmadi, “An effective algorithm for semantic relation extraction in documents based on Wikipedia knowledge base”, Proceedings of 23th Iran Electrical Engineering Conference, 2016, pp. 918-923.
22
Nasser, M. Asgari, and B. Minaei-Bidgoli, “Distant Supervision for Relation Extraction in The Persian Language using Piecewise Convolutional Neural Networks”, 5th International Conference on Web Research (ICWR), IEEE, 2019, pp. 96-99.
23
Ji, K. Liu, S. He, J. Zhao, “Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions”, In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-17), 2017.
24
Xu, S. Reddy, Y. Feng, S. Huang, and D. Zhao, “Question answering on freebase via relation extraction and textual evidence”, arXiv preprint arXiv:1603.00957, 2016.
25
Heydari, Z. Banaian, and V. Reshadat, “Study of information extraction methods based on machine learning and knowledge engineering”, The Second International Conference on Knowledge-Based Research. Tehran, Majlisi University, 2017.
26
Saheb-Nassagh, M. Asgari, and B. Minaei-Bidgoli, “RePersian A Fast Relation Extraction Tool in Persian”, International Journal of Web Research, vol. 2, no. 2, Autumn-Winter, 2019.
27
Asgari-Bidhendi, A. Hadian, and B. Minaei-Bidgoli, “FarsBase: The Persian Knowledge Graph”, Semantic Web, voll. 10, no 6, IOS Press, 2019.
28
Gu, W. Liu, and J. Song, “Relation extraction from Wikipedia leveraging intrinsic patterns”, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE, 2015, pp. 181-186.
29
Heist, S. Hertling, and H. Paulheim, “Language-agnostic relation extraction from abstracts in Wikis”, Information, vol. 9, no. 4, p. 75, 2018.
30
Huang, Y. Jia, J. Huang, and Z. He, “Multi-language person social relation extraction model based on distant supervision”, IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2018, pp. 386-374.
31
Shamsfard and A. Abdollahzadeh Barforosh, “Extracting conceptual knowledge from the text using linguistic and semantic patterns”, Cognitive Science News, vol. 4, no 1, pp. 48-60, 2002
32
Mosallanejad, J. Davoodi Moghadam, and A. Ahmadi, “Presenting an efficient algorithm for extracting semantic relationships in documents, based on the tacit knowledge base of Wikipedia”. 23rd Iranian Electrical Engineering Conference. Tehran, Sharif University of Technology, 2015.
33
Dami, H. Shirazi, and A. Abdullah Zadeh, “Fapedia, a large-scale Persian cognitive database extracted from DBPedia”, 4th Joint Congress of Fuzzy and Intelligent Systems of Iran, Zahedan, University of Sistan and Baluchestan, 2015.
34
Asgari-Bidhendi, M. Nasser, B. Janfada, and B. Minaei-Bidgoli, “Perlex: A Bilingual Persian-English Gold Dataset for Relation Extraction”, Scientific Programming, 2020.
35
Khaleseh Sudachi, “Automatic production of Persian information boxes for individuals using the extraction of information made from Wikipedia articles”, The first national conference on new ideas in electrical and computer engineering, Iran, 2016.
36
Hasili, M. Hosseini Beheshti, and S. Pak Nohad, “Information Extraction, Methods and Applications”, The first international conference on interactive information retrieval, Tehran, University of Tehran, 2016.
37
Fadaei and M. Shamsfard, “Extracting conceptual relations from Persian resources”, In Proceeding of Seventh International Conference on Information Technology, New Generations, 2010, pp. 244-248.
38
Kambhatla, “Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations”, In Proceedings of
39
the ACL 2004 on Interactive poster and demonstration sessions. Association for Computational Linguistics, 2004, pp. 178-181.
40
Surdeanu, J. Tibshirani, R. Nallapati, and C. D. Manning, “Multi-instance multi-label learning for relation extraction”, In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Association for Computational Linguistics, 2012, pp. 455-465.
41
Zelenko, C. Aone, and A. Richardella, “Kernel methods for relation extraction”, Journal of machine learning research, vol. 3(Feb), pp. 1083-1106, 2003.
42
Zhao and R. Grishman, “Extracting relations with integrated information using kernel methods”, In Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, 2005, pp. 419-426.
43
Shireen Atarod received her bachelor’s degree in information technology (IT) engineering from Hamedan University of Technology (HUT), From Hamedan, Iran, in 2012. She received her master’s degree in e-commerce from Science and Research Branch of Islamic Azad University (SRBIAU) in 2018. Her research interests include relation extraction, supervised and semi supervised machine learning, and text mining.
44
Alireza Yari received his B.Sc. degree in control system engineering in 1993 from the University of Tehran, Iran, and M.Sc. and a Ph.D. degree in System engineering in 2000 from Kitami institute of technology, Japan. He is currently doing research in the Information Technology research faculty of Iran Telecom Research Center (ITRC). His research interests include web processing and cyber linguistics application, such as web search engines.
45
ORIGINAL_ARTICLE
The Co-authorship Network of Published Articles in Conferences on Web Research Based on Social Network Analysis
Collaboration in writing scientific articles with the growth of academic exchanges and social interactions of researchers is increasingly expanding. Scientific collaboration gives researchers the opportunity to combine the capabilities and abilities of different scientific and research disciplines, which cannot be done individually. Co-authorship is the most formal manifestation of intellectual collaboration between authors in the production of scientific research. On the other hand, the study of the trend of scientific activities and its dynamics in any specialized field is one of the most important concerns of researchers in that field. In recent years, the use of the social network analysis approach has been proposed as a suitable solution to map the scientific structure of specialized fields and the co-authorship network of researchers. In this research, the papers published in six web research conferences have been analyzed to discover the scientific network and the co-authorship based on the social network analysis approach. The results of the analysis show that in the period, concepts such as social network analysis, Internet of Things, cloud computing, and deep learning have the largest share in articles. Also, based on the number of communities formed, the authors of the conference papers were more inclined to form small scientific groups in the form of universities or research institutes of their respective organizations.
https://ijwr.usc.ac.ir/article_128961_e788c8c644c29f560adf0cea4b50fa38.pdf
2020-12-01
9
15
10.22133/ijwr.2021.261519.1080
Co-authorship Network
Scientific map
conference on web research
social network analysis
Meysam
Alavi
meysamalavi@modares.ac.ir
1
Information Technology dept.,Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Sayed ali
lajevardy
sayedali.lajevardy@modares.aci.ir
2
Information Technology dept., Tarbiat Modares University,Tehran, Iran
AUTHOR
Soheili, M. Cheshme Sohrabi and S. Atashpaykar, “Co-authorship network analysis of Iranian medical science researchers: A social network analysis,” Caspian Journal Of Scientometrics, vol. 2, pp. 24-32, 2015. [In Persian]
1
Li, D. Zhang, P. Luo, and J. Jiang, “Interpreting the formation of co-author networks via utility analysis”, Information Processing & Management, vol. 53, no. 3, pp. 624-639, 2017.
2
Dino, S. Yu, L. Wan, M. Wang, K. Zhang, H. Guo and I. Hussain, “Detecting leaders and key members of scientific teams in co-authorship networks”, Computers & Electrical Engineering, vol. 85, p. 106703, 2020.
3
Assareh and K. Baba'I, “The co-authorship networks of published Articles in the journal of Psychology of Exceptional Individuals Allameh Tabataba'i University and Journal of Exceptional Children”, Knowledge Studies, vol. 1, no. 1, pp.1-17, 2014.[In Persian]
4
Higaki, T. Uetani, S. Ikeda and O. Yamaguchi, “Co-authorship network analysis in cardiovascular research utilizing machine learning (2009–2019)”, International Journal of Medical Informatics, vol. 143, p. 104274, 2020.
5
De Stefano, V. Fuccella, M. P. Vitale and S. Zaccarin, “The use of different data sources in the analysis of co-authorship networks and scientific performance”, Social Networks, vol. 35, no. 3, pp. 370-381, 2013.
6
keywords of high frequency in six conferences
7
trend of keywords
8
information about the five main communities of the scientific network under study.
9
Percentage of total network
10
No. of nodes
11
No. of edges
12
Ratio of edge to node
13
Community density
14
Keyword (based on betweenness centrality)
15
Internet of Things
16
Social Network
17
Cloud Computing
18
Semantic Web
19
Heidari, A. Valipour and B. Bakhtiyari, “Marketing research trend in Iran: An analytical review”, Management Research in Iran, vol. 21, no. 3, pp. 97-119, 2017. [In Persian]
20
Alcaide–Muñoz, M. P. Rodríguez–Bolívar, M. J. Cobo and E. Herrera–Viedma, “Analysing the scientific evolution of e-Government using a science mapping approach”, Government information quarterly, vol. 34, no. 3, pp. 545-555, 2017.
21
Rip, and J. Courtial, “Co-word maps of biotechnology: An example of cognitive scientometrics”, Scientometrics, vol. 6, no. 6, pp. 381-400, 1984.
22
Ahmadi and F. Osareh, “Co-word analysis concept, definition and application”, vol. 28, no. 1, pp. 125-145, 2017. [In Persian]
23
Guns, Y. X. Liu, and D. Mahbuba, “Q-measures and betweenness centrality in a collaboration network: a case study of the field of informetrics”, Scientometrics, vol. 87, no. 1, 133-147, 2011.
24
Furht, Handbook of Social Network Technologies and Applications, Springer, 2010, p. 3.
25
Frank, “Using centrality modeling in network surveys”, Social Networks, vol. 24, no. 4, pp. 385-394, 2002.
26
Fahimifar and F. Sahli, “Co-authorship Network in Scientific Knowledge and Information Science Persian Journals”, Research On Information Science And Public Libraries, vol. 21, pp. 127-151, 2015. [In Persian]
27
Tahmasbi, “Study and map co-authorship network of researchers in tuberculosis and lung disease research center of tehran”, Caspian Journal of Scientometrics, vol. 4, pp. 36-44, 2017. [In Persian]
28
Shams Mourkani and M. A. Ghanei Rad, “social net-work anal-ysis of co-authorship of faculty members in science education based on their foreign articles”, Scientometrics Research Journal, vol. 4, pp. 33-56, 2018. [In Persian]
29
Sattarzadeh, G. Galyani Moghaddam and E. Momeni, “The Analysis of the Structure of Scientific Collaboration Networks in Basic Medical Sciences in the Science Citation Indicator from 1996 to 2013”, Knowledge Studies, vol. 2, no. 6, pp. 1-20, 2016. [In Persian]
30
Jafari, R. Farshid and E. Mostafavi, “Co-authoring Patterns and Subject Trends in Iranian and World Scientific Research in the Field of Information and Knowledge Organization (2001-2020)”, Knowledge Studies, vol. 6, no. 22, pp. 25-54, 2020. [In Persian]
31
Fan, G. Li and R. Law, “Analyzing co-authoring communities of tourism research collaboration”, Tourism Management Perspectives, vol. 33, p. 100607, 2020.
32
A. Xu and V. Chang, “Co-authorship network and the correlation with academic performance”, Internet of Things, vol. 12, p. 100307, 2020.
33
Zhai, Y. Zhai, C. Cong, T. Song, R. Xiang, T. Feng, Z. Liang et al., “Research Progress of Coronavirus Based on Bibliometric Analysis”, International Journal of Environmental Research and Public Health, vol. 17, no. 11, p. 3766, 2020.
34
N. Yan, T. S. Lee and T. P. Lee, “Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): A co-word analysis”, Scientometrics, vol. 105, no. 2, pp. 1285-1300, 2015.
35
Piñeiro-Chousa, M. Á. López-Cabarcos, N. M. Romero-Castro and A. M. Pérez-Pico, “Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front”, Journal of Business Research, vol. 115, pp. 475-485, 2020.
36
Baziyad, S. Shirazi, S. Hosseini and R. Norouzi, “Mapping the intellectual structure of epidemiology with use of co-word analysis”, Journal of Biostatistics and Epidemiology, vol. 5, no. 3, pp. 210-215, 2019.
37
Shahrabi Farahani, M. Alavi, M. Ghasemi and B. Teimourpour, “Scientific Map of Papers Related to Data Mining in Civilica Database Based on Co-Word Analysis”, International Journal of Web Research, vol. 3, no. 1, pp. 11-18, 2020.
38
Meysam Alavi was born in Hamadan, Iran. He received his B.Sc. and an M.Sc. degree in Computer Engineering - Software in 2005 and 2010 respectively. Currently, he is an M.Sc. student in Information Technology at Tarbiat Modares University. His research interests include medical image processing, machine learning, data mining, social network analysis
39
Sayed Ali Lajevardy received his B.Sc in Mechanic Engineering, Amirkabir University, Tehran, Iran in 2008 and received his M.Sc degree in Information Technology, Tarbiat Modares University, Tehran, Iran in 2014. He is PHD senior in Information Technology, Tarbiat Modares University from 2017. His research interests include machine learning, bioinformatics and data gathering.
40
ORIGINAL_ARTICLE
Modeling Opponent Strategy in Multi-Issue Bilateral Automated Negotiation Using Machine Learning
With the emergence of the World Wide Web, Electronic Commerce (E-commerce) has been growing rapidly in the past two decades. Intelligent agents play the main role in making the negotiation between different entities automatically. Automated negotiation allows resolving opponent agents' mutual concerns to reach an agreement without the risk of losing individual profits. However, due to the unknown information about the opponent's strategies, automated negotiation is difficult. The main challenge is how to reveal the optimal information about the opponent's strategy during the negotiation process to propose the best counter-offer. In this paper, we design a buyer agent which can automatically negotiate with the opponent using artificial intelligence techniques and machine learning methods. The proposed buyer agent is designed to learn the opponent's strategies during the negotiation process using four methods: "Bayesian Learning", "Kernel Density Estimation", "Multilayer Perceptron Neural Network", and "Nonlinear Regression". Experimental results show that the use of machine learning methods increases the negotiation efficiency, which is measured and evaluated by parameters such as the rate agreement (RA), average buyer utility (ABU), average seller utility (ASU), average rounds (AR). Rate agreement and average buyer utility have increased from 58% to 74% and 90% to 94%, respectively, and average rounds have decreased from 10% to 0.04%.
https://ijwr.usc.ac.ir/article_128962_352e4d593406fc133c2a145fd61dc9f1.pdf
2020-12-01
16
25
10.22133/ijwr.2021.254096.1075
Multiagent System
Automatic Negotiation, Machine Learning
Opponent Strategy Learning
Opponent's Modeling
e-commerce
Bayesian Learning
Kernel density estimation
Artificial Neural Network
Fatemeh
Mohammadi Ashnani
mohammadi.fateme@ut.ac.ir
1
MSc Graduate in Information Technology Engineering, Department of Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran
AUTHOR
Zahra
Movahedi
zmovahedi@ut.ac.ir
2
Department of Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran
AUTHOR
Kazim
Fouladi
kfouladi@ut.ac.ir
3
Department of Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran
LEAD_AUTHOR
Kenneth Laudon and Carol Traver. E-Commerce: Business, Technology, Society (3rd Edition). Prentice-Hall, Inc., USA, 2006.
1
Dave Chaffey. E-business and E-commerce Management: Strategy, Implementation and Practice. Pearson Education, 2007
2
Sarit Kraus. Automated negotiation and decision making in multiagent environments. In ECCAI Advanced Course on Artificial Intelligence, pages 150–172. Springer, 2001.
3
Peter Braun, Jakub Brzostowski, Gregory Kersten, Jin Baek Kim, Ryszard Kowalczyk, Stefan Strecker, and Rustam Vahidov. E-negotiation Systems and Software Agents: Methods, Models, and Applications, pages 271–300. Springer London, London, 2006.
4
Dean G Pruitt and Peter J Carnevale. Negotiation in social conflict. Buckingham, Open University Press, 1993.
5
Nicholas R Jennings, Peyman Faratin, Alessio R Lomuscio, Simon Parsons, Carles Sierra, and Michael Wooldridge. Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation, 10(2):199–215, 2001.
6
Shaheen Fatima, Sarit Kraus, and Michael Principles of automated negotiation. Cambridge University Press, 2014.
7
Tim Baarslag, Mark J.C. Hendrikx, Koen V. Hindriks, and Catholijn M. Jonker. Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques. Autonomous Agents and Multi-Agent Systems, 30(5):849–898, 9 2016.
8
Usha Kiruthika, Thamarai Selvi Somasundaram, and S. Kanaga Suba Raja. Lifecycle model of a negotiation agent: A survey of automated negotiation techniques. Group Decision and Negotiation, 9, 2020.
9
Park and S. Yang. An automated system based on incremental learning with applicability toward multilateral negotiations. In 2006 SICE-ICASE International Joint Conference, pages 6001–6006, 2006.
10
Dajun Zeng and Katia Sycara. Benefits of learning in negotiation. In Proceedings of the 14th National Conference on Artificial Intelligence, AAAI 97, pages 36–41, 1997.
11
Dajun Zeng and Katia Sycara. Bayesian learning in negotiation. International Journal of Human-Computer Studies, 48(1):125–141, 1998.
12
Jihang Zhang, Fenghui Ren, and Minjie Zhang. Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents. Knowledge-Based Systems, 84:108–120, 2015.
13
Jihang Zhang, Fenghui Ren, and M. Zhang. Prediction of the opponent's preference in bilateral multi-issue negotiation through bayesian learning. In ANAC@AAMAS, 2014.
14
Faezeh Eshragh, Mozhdeh Shahbazi, and Behrouz Far. Real-time opponent learning in automated negotiation using recursive bayesian filtering. Expert Systems with Applications, 128:28–53, 2019.
15
Kashif Imran, Jiangfeng Zhang, Anamitra Pal, Abraiz Khattak, Kafait Ullah, and Sherjeel Mahmood Baig. Bilateral negotiations for electricity market by adaptive agent-tracking strategy. Electric Power Systems Research, 186:106390, 2020.
16
Chao Yu, Fenghui Ren, and Minjie Zhang. Bilateral Negotiation Model Based on Bayesian Learning, pages 75–93. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013.
17
Chongming Hou. Predicting agents tactics in automated negotiation. In Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'04, pages 127–133, 2004.
18
Haithem Mezni and Mokhtar Sellami. A negotiation-based service selection approach using swarm intelligence and kernel density estimation. Software: Practice and Experience, 48(6):1285–1311, 2018.
19
Robert M. Coehoorn and Nicholas R. Jennings. Learning on opponent's preferences to make effective multi-issue negotiation trade-offs. In Proceedings of the 6th International Conference on Electronic Commerce, ICEC '04, page 59–68, New York, NY, USA, 2004.
20
Dirk C. Moosmayer, Alain Yee-Loong Chong, Martin J. Liu, and Bjoern Schuppar. A neural network approach to predicting price negotiation outcomes in business-to-business contexts. Expert Systems with Applications, 40(8):3028 – 3035, 2013.
21
Chun Ching Lee and C. Ou-Yang. A neural networks approach for forecasting the supplier's bid prices in supplier selection negotiation process. Expert Systems with Applications, 36(2, Part 2):2961–2970, 2009.
22
Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, and Kostas Stathis. A deep reinforcement learning approach to concurrent bilateral negotiation, In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Pages 297-303, 2020.
23
M. Farag, S. E. AbdelRahman, R. Bahgat, and A. M. A-Moneim. Towards KDE mining approach for multiagent negotiation. In 2010 The 7th International Conference on Informatics and Systems (INFOS), pages 1–7, 2010.
24
Peyman Faratin, Carles Sierra, and Nick R. Jennings. Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems, 24(3):159 – 182, 1998.
25
Mohammad Irfan Bala, Sheetal Vij, and Debajyoti Mukhopadhyay. Negotiation life cycle: An approach in e-negotiation with prediction. In Suresh Chandra Satapathy, P. S. Avadhani, Siba K. Udgata, and Sada- sivuni Lakshminarayana, editors, ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India, Vol I, pages 505–512, Cham, 2014.
26
Kostas Kolomvatsos and Stathes Hadjieftymiades. On the use of particle swarm optimization and kernel density estimator in concurrent negotiations. Information Sciences, 262:99 –116, 2014.
27
Binmore, K., Vulkan, N. Applying game theory to automated negotiation. NETNOMICS: Economic Research and Electronic Networking 1: 1–9, 1999.
28
Kolomvatsos, Kostas & Panagidi, Kakia & Neokosmidis, Ioannis & Varoutas, Dimitris & Hadjiefthymiades, Stathes. Automated Concurrent Negotiations: An Artificial Bee Colony Approach. Electronic Commerce Research and Applications.19:56-69, 2016.
29
Fatemeh Mohammadi-Ashnani, Zahra Movahedi, Kazim Fouladi-Ghaleh. Using Bayesian Learning for Opponent Modeling in Multiagent Automated Negotiation, Proceedings of the 5th International Conference on Web Research, ICWR 2019, University of Science and Culture, Tehran, Iran, 2019.
30
ORIGINAL_ARTICLE
Resolving Ambiguity Using Word Embeddings for Personalized Information Retrieval in Folksonomy Systems
The diversity and high volume of available information on the web make data retrieval a serious challenge in this environment. On the other hand, obtaining user satisfaction is difficult, which is one of the main challenges of data retrieval systems. Depending on their information about interests and needs for the same keyword, different people expect different responses from Information Retrieval (IR) systems. Achieving this goal requires an effective method to retrieve information. Personalized Information Retrieval (PIR) is an effective method to achieve this goal which is considered by researchers today. Folksonomy is the process that allows users to tag in a specific domain of information in a social environment (tags are accessible to other users). Folksonomy systems are made collaborative tagging systems. Due to the large volume and variety of tags produced, resolving ambiguity is a severe challenge in these systems. In recent years, word embedding methods have been considered by researchers as a successful method to fix the ambiguity of texts.This study proposes a model which, in addition to using word embedding methods to remove tag ambiguity, provides search results in a personalized approach by fixing ambiguity and sentiment analysis combination tailored to users' interests. In this research, different models of word embeddings were applied. The experiments' results show that after applying the fixing ambiguity, the mean accuracy criterion improved by 1.93% and the mean MRR (Mean Reciprocal Rank) by 0.38%.
https://ijwr.usc.ac.ir/article_128964_cfd180f6eff410577eeb06c38b32ec23.pdf
2020-12-01
26
30
10.22133/ijwr.2021.254896.1079
Personalized Information Retrieval
Folksonomy
Fixing Ambiguity
word embedding
Sentiment analysis
Ghazale
Etemadikhou
computer.software1390@gmail.com
1
Department of Computer Engineering, University of Science and Culture, Tehran, Iran
AUTHOR
Fatemeh
Azimzadeh
f.azimzadeh@gmail.com
2
Scientific Information Database(SID), Academic Center for Education, Culture and Research ACECR
LEAD_AUTHOR
Abdolsamad
Karamatfar
keramatfar.a.s@gmail.com
3
Scientific Information Database(SID), Academic Center for Education, Culture and Research ACECR
AUTHOR
Xie et al., "Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy," Information Processing & Management, vol. 52, no. 1, pp. 61-72, 2016.
1
Zhou, X. Wu, W. Zhao, S. Lawless and J. Liu, "Query expansion with enriched user profiles for personalized search utilizing folksonomy data," IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 7, pp. 1536-1548, 2017.
2
Mikolov, K. Chen, G. Corrado and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
3
Mikolov, I. Sutskever, K. Chen, G. S. Corrado and J. Dean, "Distributed representations of words and phrases and their compositionality," in Advances in neuralinformation processing systems, 2013, pp. 3111-3119.
4
Saleh and N. El-Tazi, "Finding semantic relationships in folksonomies," In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), IEEE, 2018, pp. 174-181.
5
Pittaras, G. Giannakopoulos, G. Papadakis, and V. Karkaletsis, "Text classification with semantically enriched word embeddings," Natural Language Engineering, pp. 1-35, 2020.
6
C. Fernández-Reyes, J. Hermosillo-Valadez, and M. Montes-y-Gómez, “A prospect-guided global queryexpansion strategy using word embeddings,” Information Processing & Management, vol. 54, no. 1, pp. 1-13, 2018.
7
Wang, M. Wang, and H. Fujita, "Word sense disambiguation: A comprehensive knowledge exploitation framework," Knowledge-Based Systems, vol. 190, p. 105030, 2020.
8
D. Vo, Q. P. Nguyen, and C.-Y. Ock, "Semantic and syntactic analysis in learning representation based on a Sentiment analysis model," Applied Intelligence, vol. 50, no. 3, pp. 663-680, 2020.
9
Habimana, Y. Li, R. Li, X. Gu, and G. Yu, "Sentiment analysis using deep learning approaches: an overview," Science China Information Sciences, vol. 63, no. 1, pp. 1-36, 2020.
10
C. Dang, M. N. Moreno-García, and F. De la Prieta, "Sentiment analysis based on deep learning: A comparative study," Electronics, vol. 9, no. 3, p. 483, 2020.
11
Luo, S. Chen, G. Xu, and J. Zhou, Trust-based collective view prediction, Springer, 2013.
12
Cai and Q. Li, "Personalized search by tag-based user profile and resource profile incollaborative tagging systems," In Proceedings of the 19th ACM international conference on information and knowledge management, 2010, pp. 969-978.
13
G. Noll and C. Meinel, "Web search personalization via social bookmarking and tagging," In The semantic web, Springer, 2007, pp. 367-380.
14
Xu, S. Bao, B. Fei, Z. Su, and Y. Yu, "Exploring folksonomy for personalized search," In Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, 2008, pp. 155-162.
15
Rong, "word2vec parameter learning explained," arXiv preprint arXiv:1411.2738, 2014.
16
Cambria, D. Olsher, and D. Rajagopal, "SenticNet 3: a common and common-sense knowledge base for cognition-driven Sentiment analysis," In Twenty-eighth AAAI conference on artificial intelligence, 2014.
17
Ghazale Etemadikhu is a graduate student in master of software engineering at the University of Science and Culture, Tehran, Iran. Her research interest is information retrieval.
18
Fatemeh Azimzadeh received a Ph.D. degree in Information Technology from University Putra Malaysia in 2012. Currently, she is an assistant professor in ACECR, Tehran, Iran. She is also the director of SID (Scientific Information Database) in Iran. Her research interests include information retrieval and information quality.
19
Abdalsamad Keramatfar is a Ph.D. student in information technology at the University of Qom and a data scientist at SID. He currently works in natural language processing and machine learning and specifically on multi-thread modeling of context for social media sentiment analysis. His research interests are artificial intelligence and natural language
20
ORIGINAL_ARTICLE
Intelligent Web Advertisement Based on Eye-Tracking and Machine Learning
Building and maintaining brand loyalty is a vital issue for market research departments. Various means, including online advertising, helps with promoting loyalty to the brand amongst users. The present paper studies intelligent web advertisements with an eye-tracking technique that calculates users’ eye movements, gaze points, and heat maps. This paper examines different features of an online ad and their combinations, such as underlining words and personalization by eye-tracking. These characteristics include underlining, changing color, number of words, personalizing, inserting a related photograph, and changing the size and location of the advertisement on a website. They help advertisers to improve their ability to manage the ads by increasing users' attention. Moreover, the current research argues the impact of gender on users' visual behavior for advertising features in different Cognitive Demand (CD) levels of tasks while avoiding interruption of users’ cognitive processes with eye-tracking techniques. Also, it provides users the most relevant advertisement compatible with CD level of a task by Support Vector Machine (SVM) algorithm with high accuracy. This paper consists of two experiments that one of them has two phases. In the first and second experiments, a news website alongside an advertisement and an advertising website is shown to the users. The results of the first experiment revealed that personalizing and underlining the words of the ad grabs more attention from users in a low CD task. Furthermore, darkening the background promotes users' frequency of attention in a high CD task. By analyzing the impact of gender on users' visual behavior, males are attracted to the advertisement with red-colored words sooner than females during the high CD task. Females pay more prolonged and more frequent attention to the ads with red-colored words and larger sizes in the low CD task. The second experiment shows that the gazing start point of users with a right to left mother tongue language direction is mainly in the middle of the advertising website.
https://ijwr.usc.ac.ir/article_131342_15e5d9e1272cf2899c042cd19a3ac6cd.pdf
2020-12-01
31
44
10.22133/ijwr.2021.270721.1086
Web advertisement
Eye-tracking
Intelligent advertisement
Online advertisement
Human-Computer Interaction
Machine Learning
Website Design
User Experience
Yasaman
Mashhadi Hashem Marandi
marandi.yasaman@ut.ac.ir
1
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
AUTHOR
Hedieh
Sajedi
hhsajedi@ut.ac.ir
2
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
LEAD_AUTHOR
AdNews (2013, June13). No one’s paying attention to digital ads…well, 8% are Retrieved from http://www.adnews.com.au/adnews/no-one-s-paying-attention-to-digital-ads-well-8-are.
1
Bang, Hyejin, and Bartosz W. Wojdynski. "Tracking users' visual attention and responses to personalized advertising based on task cognitive demand." Computers in Human Behavior 55 (2016): 867-876.
2
Dehghani, Milad, et al. "Evaluating the influence of YouTube advertising for attraction of young customers." Computers in human behavior 59 (2016): 165-172.
3
Kumar, Naveen, Varun Maheshwari, and Jyoti Kumar. "A comparative study of user experience in online social media branding web pages using eye tracker." 2016 international conference on advances in human machine interaction (HMI). IEEE, 2016.
4
Buscher, Georg, Edward Cutrell, and Meredith Ringel Morris. "What do you see when you're surfing?: using eye tracking to predict salient regions of web pages." Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 2009.
5
Sørum, Hanne. "Design of Public Sector Websites: Findings from an Eye Tracking Study Emphasizing Visual Attention and Usability Metrics." International Conference on Electronic Government and the Information Systems Perspective. Springer, Cham, 2016.
6
Zhang, Jin-Ting, et al. "A new test for functional one-way ANOVA with applications to ischemic heart screening." Computational Statistics & Data Analysis 132 (2019): 3-17
7
Ponzio, Francesco, et al. "A human-computer interface based on the “voluntary” pupil accommodative response." International Journal of Human-Computer Studies 126 (2019): 53-63.
8
Kaur, Akriti, Ashutosh Agrawal, and Pradeep Yammiyavar. "Exploring 3D Interactions for Number Entry and Menu Selection in Virtual Reality Environment." Research into Design for a Connected World. Springer, Singapore, 2019. 781-791.
9
Jerome, Theresa, Leong Wai Shan, and Kok Wei Khong. "Online advertising: a study on Malaysian consumers." Available at SSRN 1644802 (2010).
10
Brajnik, Giorgio, and Silvia Gabrielli. "A review of online advertising effects on the user experience." International Journal of Human-Computer Interaction 26.10 (2010): 971-997.
11
Rodgers, Shelly, and Esther Thorson. "The interactive advertising model: How users perceive and process online ads." Journal of interactive advertising 1.1 (2000): 41-60.
12
Faber, Ronald J., Mira Lee, and Xiaoli Nan. "Advertising and the consumer information environment online." American Behavioral Scientist 48.4 (2004): 447-466.
13
Rodgers, Shelly, and Esther Thorson. "The interactive advertising model: How users perceive and process online ads." Journal of interactive advertising 1.1 (2000): 41-60.
14
ACM SIGCHI: The Association for Computing Machinery Special Interest Group on Computer-Human Interaction (1992). www.sigchi.org. Accessed 14 Mar 2016
15
Frøkjær, Erik, and Kasper Hornbæk. "Metaphors of human thinking for usability inspection and design." ACM Transactions on Computer-Human Interaction (TOCHI) 14.4 (2008): 20.
16
International Organization for Standardization (ISO) 9241-11: Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs) – Part 11: Guidance on Usability (1998)
17
Berger, Jonah, and Katherine L. Milkman. "What makes online content viral?" Journal of marketing research 49.2 (2012): 192-205.
18
Yu, Jay, and Brenda Cude. "‘Hello, Mrs. Sarah Jones! We recommend this product!’Consumers' perceptions about personalized advertising: comparisons across advertisements delivered via three different types of media." International journal of consumer studies 33.4 (2009): 503-514.
19
Lang, A. (2006). Using the limited capacity model of motivated mediated message processing to design effective cancer communication messages. Journal of Communication,56(1),57–S80.
20
Petty, R.E., Barden, J., & Wheeler, S.C.(2002). The elaboration likelihood model of persuasion: health promotions that yield sustained behavioral change. InR.J. DiClemente, A. Crosby, &Kegler (Eds.), Emerging theories in health promotion practice and research(pp.71–99). San Francisco: Jossey-Bass.
21
Malheiros, Miguel, et al. "Too close for comfort: A study of the effectiveness and acceptability of rich-media personalized advertising." Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 2012.
22
Tam, Kar Yan, and Shuk Ying Ho. "Web personalization as a persuasion strategy: An elaboration likelihood model perspective." Information systems research 16.3 (2005): 271-291.
23
Kreuter, Matthew W., and Ricardo J. Wray. "Tailored and targeted health communication: strategies for enhancing information relevance." American journal of health behavior 27.1 (2003): S227-S232.
24
Pavlou, Paul A., and David W. Stewart. "Measuring the effects and effectiveness of interactive advertising: A research agenda." Journal of Interactive Advertising 1.1 (2000): 61-77.
25
Baek, Tae Hyun, and Mariko Morimoto. "Stay away from me." Journal of advertising 41.1 (2012): 59-76.
26
Howard, Daniel J., and Roger A. Kerin. "The effects of personalized product recommendations on advertisement response rates: The “try this. It works!” technique." Journal of consumer psychology 14.3 (2004): 271-279.
27
Li, Cong. "When does web-based personalization really work? The distinction between actual personalization and perceived personalization." Computers in Human Behavior 54 (2016): 25-33.
28
Phelps, Joseph E., Giles D'Souza, and Glen J. Nowak. "Antecedents and consequences of consumer privacy concerns: An empirical investigation." Journal of Interactive Marketing 15.4 (2001): 2-17.
29
Sacirbey, O. "Privacy Concerns, Advertising Hamper E-commerce." IPO Reporter 24.35 (2000): 11-11.
30
Van Doorn, Jenny, and Janny C. Hoekstra. "Customization of online advertising: The role of intrusiveness." Marketing Letters 24.4 (2013): 339-351.
31
Kempf, DeAnna S., Kay M. Palan, and Russell N. Laczniak. "Gender differences in information processing confidence in an advertising context: A preliminary study." ACR North American Advances (1997).
32
Shavitt, Sharon, Pamela Lowrey, and James Haefner. "Public attitudes toward advertising: More favorable than you might think." Journal of advertising research 38.4 (1998): 7-22.
33
Sweller, John. "Cognitive load during problem solving: Effects on learning." Cognitive science 12.2 (1988): 257-285.
34
Sweller, John, Jeroen JG Van Merrienboer, and Fred GWC Paas. "Cognitive architecture and instructional design." Educational psychology review 10.3 (1998): 251-296.
35
Rodgers, Shelly, and Esther Thorson. "The interactive advertising model: How users perceive and process online ads." Journal of interactive advertising 1.1 (2000): 41-60.
36
Hoffman, Donna L., and Thomas P. Novak. "Marketing in hypermedia computer-mediated environments: Conceptual foundations." Journal of marketing 60.3 (1996): 50-68.
37
Wise, Kevin, Hyo Jung Kim, and Jeesum Kim. "The effect of searching versus surfing on cognitive and emotional responses to online news." Journal of Media Psychology 21.2 (2009): 49-59.
38
Wolford, George, and Fred Morrison. "Processing of unattended visual information." Memory & Cognition 8.6 (1980): 521-527.
39
Nielsen, Jakob. " F-Shaped Pattern For Reading Web Content, Jakob Nielsen's Alertbox." http://www. useit. com/alertbox/reading_pattern. html (2006).
40
Nielsen, J.: Designing Web Usability. New Riders Publishing, Indianapolis (2000). ISBN 1-56205-810-X
41
Johansen, Sune Alstrup, and John Paulin Hansen. "Do we need eye trackers to tell where people look?." CHI'06 Extended Abstracts on Human Factors in Computing Systems. ACM, 2006.
42
Ehmke, Claudia, and Stephanie Wilson. "Identifying web usability problems from eye-tracking data." Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI... but not as we know it-Volume 1. British Computer Society, 2007.
43
Bojko, Aga. Eye tracking the user experience: A practical guide to research. Rosenfeld Media, 2013, P.27.
44
Byrne, Michael D., et al. "Eye tracking the visual search of click-down menus." Proceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 1999.
45
Jacob, Robert JK, and Keith S. Karn. "Eye tracking in human-computer interaction and usability research: Ready to deliver the promises." The mind's eye. North-Holland, 2003. 573-605.
46
Carpenter, P. A., and M. A. Just. "Linguistic influences on picture scanning." Eye movements and psychological processes (1976): 459-472.
47
Graf, W., and H. Krueger. "Ergonomic evaluation of user-interfaces by means of eye-movement data." Proceedings of the third international conference on human-computer interaction, Vol. 1 on Work with computers: organizational, management, stress and health aspects. Elsevier Science Inc., 1989.
48
Wedel, Michel, and Rik Pieters. "A review of eye-tracking research in marketing." Review of marketing research. Routledge, 2017. 123-147.
49
Kong, Shaojun, et al. "Web advertisement effectiveness evaluation: Attention and memory." Journal of Vacation Marketing 25.1 (2019): 130-146.
50
Muñoz-Leiva, Francisco, Janet Hernández-Méndez, and Diego Gómez-Carmona. "Measuring advertising effectiveness in Travel 2.0 websites through eye-tracking technology." Physiology & behavior 200 (2019): 83-95.
51
Okano, Masao, and Masami Asakawa. "Eye tracking analysis of consumer's attention to the product message of web advertisements and TV commercials." 2017 5th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2017.
52
Weizhe, Li, and Fei Hu. "Eye-Tracking assisted Human-Computer Interaction on E-Shopping Website: A Preliminary Study." (2020).
53
Jagadale, Pooja Ganesh. "Role of Eye Tracking System To Enhance Life Of Disable People." International Research Journal of Modernization in Engineering Technology and Science 2(11) (2020):715-719.
54
Agarkhed, Jayashree, et al. "Human Computer Interaction System Using Eye-Tracking Features." 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC). IEEE, 2020.
55
Brosens, Jacques, Funmi Adebesin, and Rendani Kruger. "In the eye of the beholder: Teaching user-centered design to information and communication technology students with the help of eye tracking." Handbook of research on diverse teaching strategies for the technology-rich classroom. IGI Global, 2020. 296-318.
56
Gwizdka, Jacek. "Distribution of cognitive load in web search." Journal of the American Society for Information Science and Technology 61.11 (2010): 2167-2187.
57
ORIGINAL_ARTICLE
Intrusion Detection with Low False Alarms using Decision Tree-based SVM Classifier
Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based intrusion detection system using J48 Decision Tree, Support Vector Classifier (SVC) and k-means clustering algorithm in order to reduce false alarm rates and enhance the system performance. J48 decision tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified k-means clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS2017 and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.
https://ijwr.usc.ac.ir/article_134086_001f739968b0bc635f4972673bb29bff.pdf
2020-12-01
45
50
10.22133/ijwr.2021.284583.1091
Intrusion Detection
K-means Clustering
decision tree
Support vector classifier
NSL-KDD dataset
Aliakbar
Tajari Siahmarzkooh
a.tajari@gu.ac.ir
1
Department of Computer Sciences, Faculty of Sciences, Golestan University, Gorgan, Iran
LEAD_AUTHOR
Gupta, A. Garg, A. Singh, S. Batra, N. Kumar, and M. Obaidat, "ProIDS: Probabilistic Data Structures Based Intrusion Detection System for Network Traffic Monitoring", in IEEE Global Communications Conference (GLOBECOM 17), 2017, pp. 1-6.
1
Internet Security Threat Report (ISTR), 2017. URL: https://docs.broadcom.com/doc/istr-22-2017-en.
2
Garg, A. Singh, S. Batra, N. Kumar, and L. Yang, "UAV-Empowered Edge Computing Environment for Cyber-Threat Detection in Smart Vehicles", IEEE Network, Vol. 32, No. 3, pp.42–51, 2018.
3
Garg, K. Kaur, N. Kumar, S. Batra, and M. Obaidat, "HyClass: Hybrid Classification Model for Anomaly Detection in Cloud Environment", in 2018 IEEE International Conference on Communications (ICC), 2018, pp. 1-7.
4
Raman, N. Somu, K. Kirthivasan, R. Lisano, and V. Sriram, "An efficient intrusion detection system based on hyper graph Genetic algorithm for parameter optimization and feature selection in support vector machine, Knowledge-Based Systems, Vol. 134, No. 4, pp. 1-12, 2017.
5
Singh, H. Kumar, and R. Singla, "An intrusion detection system using network traffic profiling and online sequential extreme learning machine", Expert Systems with Applications, Vol. 42, No. 22, pp. 8609-8624, 2015.
6
Guo, Y. Ping, N. Liu, and S. Luo, "A two level hybrid approach for intrusion detection", Neurocomputing, Vol. 214, No. 4, pp. 391-400, 2016.
7
Mazraeh, M. Ghanavati, and S. Neysi, "Intrusion detection system with decision tree and combine method algorithm", International Academic Journal of Science and Engineering, Vol. 3, No. 2, pp. 21-31, 2016.
8
Al-Yaseen, Z. Othman, and M. Nazri, "Multi-level hybrid support vector machine and extreme learning machine based on modifed K-means for intrusion detection system", Expert Systems with Applications, Vol. 67, No. 1, pp. 296-303, 2016.
9
Prasad, A. Reddy, and K. Rao, "BARTD: Bioinspired anomaly based real time detection of under rated App-DDoS attack on web", Journal of King Saud University– Computer and Information Sciences, Vol. 32, No. 1, pp. 73-87, 2017.
10
Singaravelan, R. Arun, D. Arunshunmugam, S. Joy, and D. Murugan, "Inner interruption discovery and defense system by using data mining", Journal of King Saud University-Computer and Information Sciences, 2017, in press.
11
Venkatesan, M. Basha, C. Chellappan, A. Vaish, and P. Dhavachelvan, "Analysis of accounting models for the detection of duplicate requests in web services", Journal of King Saud University- Computer and Information Sciences, Vol. 25, No. 1, pp. 7-24, 2013.
12
Chandola, A. Banerjee, and V. Kumar, "Anomaly detection for discrete sequences: A survey", IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 5, pp. 823–839, 2012.
13
Akoglu, H. Tong, and D. Koutra, "Graph based anomaly detection and description: a survey", Data Mining and Knowledge Discovery, Vol. 29, No. 3, pp. 626–688, 2015.
14
Elik, F. Dadas¸ E. Elik, and A. Dokuz, "Anomaly detection in temperature data using dbscan algorithm", in International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 2011, pp. 91–95.
15
Lv, T. Ma, M. Tang, J. Cao J, Y. Tian, A. Al-Dhelaan, and M. Al-Rodhaan, "An efficient and scalable density-based clustering algorithm for datasets with complex structures", Neurocomputing, Vol. 171, No. 1, pp. 9–22, 2016.
16
Wang, B. Zhang, D. Wang, Y. Jiang, S. Qin, and L. Xue, "Anomaly detection based on probability density function with kullback–leibler divergence", Signal Processing, Vol. 126, No. 1, pp. 12–17, 2016.
17
Song, Y. Sun, G. Han, and J. Rodrigues, "Intrusion detection based on hybrid classifiers for smart grid", Computers & Electrical Engineering, Vol. 93, No. 4, pp. 285-298, 2021.
18
Gu, and S. Lu, "An effective intrusion detection approach using SVM with naïve Bayes feature embedding", Computers & Security, Vol. 103, No. 3, pp. 315–329, 2021.
19
Garg, and S. Batra, "Flexible Subspace Clustering: A Joint Feature Selection and K-Means Clustering Framework", Big Data Research, Vol. 23, No. 1, pp. 211-231, 2021.
20
Tao, Y. Zhang, and Q. Wang, "Fuzzy c-mean clustering-based decomposition with GA optimizer for FSM synthesis targeting to low power ", Engineering Applications of Artificial Intelligence, Vol. 68, No. 2, pp. 40-52, 2018.
21
Bezdek, R. Ehrlich, and W. Full, "Fcm: The fuzzy c-means clustering algorithm", Computers & Geosciences, Vol. 10, No. 2, pp. 191–203, 1984.
22
Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set", In Proceedings of the 2009 IEEE Symposium on Computational Intelligence, 2009.
23
Thakkar, and R. Lohiya, " A Review of the Advancement in Intrusion Detection Datasets ", Procedia Coputer Science, Vol. 167, No. 2, pp. 636-645, 2020.
24
Aliakbar Tajari Siahmarzkooh received the B.Sc. degree in Computer Engineering from Ferdowsi University of Iran in 2009, and the M.Sc. and Ph.D. degree in Computer Science from University of Tabriz, Iran in 2012 and 2017, respectively. He has been working with the Department of Computer Sciences, Golestan University, since 2017, where he is now an assistant professor. His current research interests include network security, data mining and artificial intelligence.
25