jist-202605211820260521182303CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-143710272021936Chukwunonso HenryNwokoyeNjideka N.MbeledoguChikweUmeugoji1027202123625110.52547/jist.9.36.236http://jist.ir/en/Article/15452http://jist.ir/en/Article/Download/15452http://jist.ir/en/Article/Download/15452http://jist.ir/en/Article/Download/15452http://jist.ir/en/Article/Download/15452http://jist.ir/en/Article/Download/15452http://jist.ir/en/Article/Download/15452http://jist.ir/en/Article/Download/15452[1] E. Wurtz, “Intercultural communication on web sites: A cross-cultural analysis of web sites from high-context cultures and low-context cultures”, J. of Computer-Mediated Communication, vol. 11, 2006, pp. 274–299. [2] E. T Hall, The Silent Language, New York: Doubleday. 1976. [3] G. Hofstede, Culture’s consequences: International Differences in Work-Related Values. UK: Sage, 1984. [4] D. Morley and K. Robins, Spaces of Identity: Global Media, Electronic Landscapes and Cultural Boundaries. US: Routledge, 1995. [5] M. D. Myers, and F. B. Tan, “Beyond models of national culture in information systems research”, Journal of Global Information Management, vol. 10, no. 2, pp. 23-56, 2002. [6] L. Thorlacius, “Visuel Kommunikation pa Web sites,” Ph.D. dissertation, Roskilde Universite, 2002. [7] K. F. Gygi, and J. H. Spyridakis, “Developing a cultural model to support web site localization: a case study of Uzbek school websites,” in Annual Conference Society for Technical Communication, vol. 54, 2007, pp. 1-20. [8] A. Yeratziotis, and D. Greunen, “E-government – putting service at your fingertips,” in IST-Africa 2009 Conference & Exhibition, 2009, pp. 1–15. [9] M. Herselman, and D. Greunen, “Global survey on culture differences and context in using e-government systems: a pilot study,” in Second International ICST Conference, AFRICOM, 2010, pp. 46–68. [10] R. Vatrapu, and M. A. Pérez-Quiñones, “Culture and international usability testing: The effects of culture in structured interviews”, Journal of Usability Studies, vol. 1, no. 4, pp.156-170, 2006. [11] F. T. Moura, N. Singh and W. Chun, “The influence of culture in website design and users’ perceptions: Three systematic reviews,” Journal of Electronic Commerce Research, vol. 17, no. 4, pp. 312-339, 2016. [12] R. Alexander, D. Murray, and N. Thompson, “Cross-cultural web design guidelines,” W4A 2017, 2017, pp. 1-16. [13] I. Umeh and C. H. Nwokoye, “Culture Context Profiles: A Case of Institutional Websites in Nigeria”, Global Journal of Computer Science and Technology: G Interdisciplinary, vol. 17, pp. 1-17, 2017. [14] I. O. Oyefolahan, A. A. Sule, S. A. Adepoju, and F. Babakano, “Keeping with the global trends: An evaluation of accessibility and usability of Nigerian banks website,” I. J. Information Engineering and Electronic Business, vol. 2, pp. 44-53, 2019. [15] SEO, Assessed on: July 24, 2020. [Online]. Available from https://seositecheckup.com/seo-audit/flash-test/. [16] Smallseotools, Assessed on: Aug 19, 2020. [Online]. Available from https://smallseotools.com/website-link-analyzer-tool/. [17] J. Ouellet, J. Kang and A. Girouard, “Cultural and linguistic variables in usability testing: A Canadian evaluation of international students,” Journal of Usability Studies, vol. 16, pp. 121–147, 2021. [18] C. Sik-Lanyi and E. Orbán-Mihálykó, “Analyses of websites was done for healthcare in European countries,” Arabian Journal for Science and Engineering, vol. 44, pp. 9171–9190, 2019. [19] Z. Nong and S. M. Gainsbury, “Website design features: exploring how social cues present in the online environment may impact risk taking,” Human behavior and emerging technologies, vol. 2, no. 1, pp. 1 – 35, 2019. [20] N. Al-Shammari, “E-Commerce in Saudi Arabia: characteristics of a trustworthy usable e-commerce websites,” International Journal of Information Science, vol. 9, pp. 6-15, 2019. [21] R. Alexander, N. Thompson and D. Murray, “Towards cultural translation of websites: a large-scale study of Australian, Chinese, and Saudi Arabian design preferences,” Behaviour & Information Technology, vol. 36, pp. 351 – 363, 2017. [22] L. Al-Omairi, H. Al-Samarraie, A. I. Alzahrani and N. Alalwan, "Students' intention to adopt e-government learning services: a developing country perspective," Library Hi Tech, vol. 39, pp. 308-334, 2020. [23] Nigeria Airforce, January 2020. Assessed on: June 20, 2021. [Online]. Available from https://airforce.mil.ng./ [24] Nigeria Navy, March 2020. Assessed on: June 20, 2021. [Online]. Available from http://www.navy.mil.ng/. [25] Nigeria Army, January 2019. Assessed on: June 20, 2021. [Online]. Available from https://www.army.mil.ng/. [26] Nigeria Police Force, January 2019. Assessed on: June 20, 2021. [Online]. Available from https://www.npf.gov.ng/. [27] Nigeria Prisons Service, January 2019. Assessed on: May 5, 2019. [Online]. Available from http://www.prisons.gov.ng/. [28] Nigeria Immigration Service, January 2019. Assessed on: June 20, 2021. [Online]. Available from https://immigration.gov.ng/. [29] Nigeria Security and Civil Defense Corps, January 2019. Assessed on: June 20, 2021. [Online]. Available from http://nscdc.gov.ng/. [30] Nigeria Customs Service, January 2019. Assessed on: June 20, 2021. [Online]. Available from https://www.customs.gov.ng/. [31] Federal Road Safety Corps, January 2019. Assessed on: June 20, 2021. [Online]. Available from https://frsc.gov.ng/. [32] Federal Fire Service, January 2019. Assessed on: June 20, 2021. [Online]. Available from http://fedfire.gov.ng/. [33] Defense Intelligence Agency, January 2019. Assessed on: June 20, 2021. [Online]. Available from http://www.dia.gov.ng/. [34] National Drug Law Enforcement Agency, January 2019. Assessed on: June 20, 2021. [Online]. Available from https://ndlea.gov.ng/. [35] Independent Corrupt Practices and Other Related Offences Commission, January 2019. Assessed on: June 20, 2021. [Online]. Available from http://icpc.gov.ng/. [36] Economic and Financial Crimes Commission, January 2020. Assessed on: June 20, 2021. [Online]. Available from https://efccnigeria.org/efcc/. [37] M. Cutts, How many links per page. Gadgets, Google, and SEO, 2009, January 2020. Assessed on: January 30, 2020. [Online]. Available from https://www.mattcutts.com/blog/how-many-links-per-page/. [38] S. Köszegi, R. Vetschera, and G. E. Kersten, “National cultural differences in the use and perception of Internet-based NSS: Does high or low context matter?” Assessed on: January 30, 2020. [Online]. Available from interneg.concordia.ca/views/bodyfiles/paper/2003/09.pdf.AbbasKhamsehMohammad AliMirfallah LialestaniRezaRadfar1027202127528410.52547/jist.9.36.275http://jist.ir/en/Article/15537http://jist.ir/en/Article/Download/15537http://jist.ir/en/Article/Download/15537http://jist.ir/en/Article/Download/15537http://jist.ir/en/Article/Download/15537http://jist.ir/en/Article/Download/15537http://jist.ir/en/Article/Download/15537http://jist.ir/en/Article/Download/15537[1] S. Nambisan, M. Wright and M. Feldman, "The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes," RESEARCH POLICY 48 (8): Art. No. 103773, 2019 OCT. [2] Young and P. Rogers, "A Review of Digital Transformation in Mining," MINING METALLURGY & EXPLORATION 36, pp. 683-699, AUG 2019. [3] Savastano, C. Amendola, F. Bellini and F. D’Ascenzo, "Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review," Sustainability 2019, 11, 891, 9 February 2019. [4] Cozmiuc and . I. Petrisor, "Industrie 4.0 by Siemens: Steps Made Today," JOURNAL OF CASES ON INFORMATION TECHNOLOGY 20 (2): Art. No. UNSP 3, 2018 APR-JUN. [5] Wikipedia, "Industry_4.0," 2016. [Online]. Available: https://en.wikipedia.org/wiki/Industry_4.0. [6] H. S. Birkel, J. Veile, J. M. Müller and E. Hartmann, "Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers," Sustainability 2019, 11, 384, 2019. [7] J. Reis, M. Amorim, N. Melão and P. Matos, "Digital Transformation: A Literature Review and Guidelines for Future Research," Springer International Publishing AG, part of Springer Nature , p. 411–421, 2018. [8] V. Bosilj Vuksic, L. Invancic and D. Susa Vugec, "A preliminary Litereture Review of Digital Transformation Case Studies," in Conference Proceedings, Rome Italy Sep 17-18, Rome Italy, 2018. [9] P. C. Verhoefa, T. Broekhuizena and Y. Bartb, "Digital transformation: A multidisciplinary reflection and research agenda," Journal of Business Research, 2019. [10] Scholz, Roland W; Bartelsman, Eric J; Diefenbach, Sarah, "Unintended Side Effects of the Digital Transition:European Scientists’ Messages from a Proposition-Based Expert Round Table," Sustainability 2018, 10, 2001, 2018. [11] K. S. R. Warner and M. Waeger, "Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal," LONG RANGE PLANNING 52 (3), pp. 326-349, 2019 JUN. [12] L. Downes and P. Nunes , "Big-bang disruption," Harv Bus Rev91(3), p. 44–56, 2013. [13] E. Henriette, M. Feki and I. Boughzala, "The Shape of Digital Transformation: A Systematic Literature Review," in Mediterranean Conference on Information Systems MCIS 2015 Proceedings, 2015. [14] C. Matt, T. Hess and A. Benlian, "Digital Transformation Strategies," Bus Inf Syst Eng 57(5), p. 339–343, 2015. [15] M. Eling and . M. Lehmann, "The Impact of Digitalization on the Insurance Value Chain and the Insurability of Risks," The International Association for the Study of Insurance Economics, 2017 . [16] T. Borangiu,. D. Trentesaux, A. Thomas, P. Leitao and . J. Barata, "Digital transformation of manufacturing through cloud services and resource virtualization," COMPUTERS IN INDUSTRY 108, pp. 150-162, 2019 JUN. [17] B. Hinings,. T. Gegenhuber and R. Greenwood, "Digital innovation and transformation: An institutional perspective," INFORMATION AND ORGANIZATION 28 (1), pp. 52-61, 2018 MAR. [18] A. Cozzolino, . G. Verona and Frank T. R, "Unpacking the Disruption Process: New Technology,Business Models, and Incumbent Adaptation," Journal of Management Studies 55:7, November 2018. [19] . J. Butschan, . S. Heidenreich, B. Weber and . T. Kraemer, "TACKLING HURDLES TO DIGITAL TRANSFORMATION - THE ROLE OF COMPETENCIES FOR SUCCESSFUL INDUSTRIAL INTERNET OF THINGS (IIoT) IMPLEMENTATION," INTERNATIONAL JOURNAL OF INNOVATION MANAGEMENT 23 (4): Art. No. UNSP 1950036, 2019 MAY. [20] G. Vial, "Understanding digital transformation: A review and a research agenda," Journal of Strategic Information Systems, 2019. [21] Accenture, "Digital Transformation Initiative Mining and Metals Industry," In collaboration with Accenture, Cologny/Geneva, January 2017. [22] D. S. a. W. R. Vinit Parida, "Reviewing Literature on Digitalization, Business Model Innovation, and Sustainable Industry: Reviewing Literature on Digitalization, Business," Licensee MDPI, Basel, Switzerland., 2019. [23] Y. Sabria, G. J. Michelia and . C. Nuurb, "Exploring the impact of innovation implementation on supply chain configuration," Journal of Engineering and Technology Management, 2018. [24] M.-S. Denner , L. C. Pu¨schel and M. Ro¨glinger, "How to Exploit the Digitalization Potential of Business Processes," Springer Fachmedien Wiesbaden GmbH, part of Springer Nature , 2017. [25] V. Nissen, "Digital Transformation of the Consulting Industry-Introduction and Overview," DIGITAL TRANSFORMATION OF THE CONSULTING INDUSTRY: EXTENDING THE TRADITIONAL DELIVERY MODEL , pp. 1-58, 2018. [26] Bentley, Duncan, "Timeless principles of taxpayer protection: how they adapt to digital disruption," EJOURNAL OF TAX RESEARCH 16 (3), pp. 679-713, 2019. [27] I. Troshania, M. Janssenb, A. Lymerc and L. D. Parkerd, "Digital transformation of business-to-government reporting: An institutional work perspective," International Journal of Accounting Information Systems, 2018. [28] B. Alexey V, "MULTINATIONAL BUSINESS IN THE ERA OF GLOBAL DIGITAL REVOLUTION," MIROVAYA EKONOMIKA I MEZHDUNARODNYE OTNOSHENIYA 62 (9), pp. 5-17, 2018 SEP. [29] N. Pourebrahimi , A. Kordnaeij, K. H. Hamid and A. Azar, "Developing A Digital Banking Framework in the Iranian Banks: Prerequisites and Facilitators," International Journal of E-Business Research (IJEBR) 14(4), pp. 65-77, 2018. [30] E. Z. Milian, M. d. M. Spinola and M. M. de Carvalho, "Fintechs: A literature review and research agenda," ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 34: Art. No. 100833, pp. Milian EZ (Milian, Eduardo Z.); Spinola MD (Spinola, Mauro de M.); de Carvalho MM (de Carvalho, Marly M.), 2019 MAR-APR. [31] B. J. Drasch, A. Schweizer and N. Urbach, "Integrating the 'Troublemakers': A taxonomy for cooperation between banks and fintechs," JOURNAL OF ECONOMICS AND BUSINESS 100, pp. 26-42, 2018 NOV-DEC. [32] T. Schwarzmüller, P. Brosi, . D. Duman and I. M. .Welpe, "How Does the Digital Transformation Affect Organizations? Key Themes of Change in Work Design and Leadership," mrev, 29 (2), pp. 114-138, 2017. [33] A. Benlian and . I. Haffke, "Does mutuality matter? Examining the bilateral nature and effects of CEO–CIO mutual understanding," Journal of Strategic Information Systems, p. 104–126, 2017. [34] J. Dugstad, T. Eide, E. R. Nilsen and H. Eide, "Towards successful digital transformation through co-creation: a longitudinal study of a four-year implementation of digital monitoring technology in residential care for persons with dementia," BMC Health Services Research, 2019. [35] M. P. Schoenermark, "Medical Data Management - Approach, Concepts, Strategic and Operative Implications," LARYNGO-RHINO-OTOLOGIE 98, pp. S220-S236, 2019 MAR. [36] A. Zimmermann, . R. Schmidt, . K. Sandkuhl and Others, "Leveraging Analytics for Digital Transformation of Enterprise Services and Architectures," Springer International Publishing Switzerland , 2016. [37] J. Barata and P. Rupino da Cunha, "The Viable Smart Product Model: Designing Products that Undergo Disruptive Transformations," Cybernetics and Systems, 2019. [38] J. Kaivo-oja, S. Roth and L. Westerlund, "Futures of Robotics. Human Work in Digital Transformation," International Journal of Technology Management, March 2017. [39] P. Nagy and B. Koles, "The digital transformation of human identity: Towards a conceptual model of virtual identity in virtual worlds," The International Journal of Research into New Media Technologies Vol. 20(3), p. 276–292, 2014. [40] S. Mendhurwar and R. Mishra, "Integration of social and IoT technologies:architectural framework for digital transformation and cyber security challenges," Enterprise Information Systems, 2019. [41] I. Ilvone, . S. Thalmann, . M. Manhart and C. Sillaber, "Reconciling digital transformation and knowledg protection: a research agenda," Knowledge Management Research & Practice, 2018. [42] F. Kowsari and A. Darush, "Investigating the Impact of Organizational Culture on Innovation with Regard to the Mediating Role of Knowledge Sharing," Journal of Management Systems, vol. 3, no. 4, pp. 22-44, 2017. [43] S. M. Seyedi, N. Shahidi and S. Hadadnia, "Studying the Relationship Between Knowledge Management Effectiveness and Administrative Innovation Among Shiraz Manufacturing Firms," Journal of Management System, vol. 1, no. 1, pp. 45-63, 2015. [44] M. Petticrew and H. Roberts, Systematic Reviews in the Social Sciences: A Practical Guide, wiley, 2006.KalaivaniKUlagapriyaKSarithaAAshutosh Kumar1027202122623510.52547/jist.9.36.226http://jist.ir/en/Article/15560http://jist.ir/en/Article/Download/15560http://jist.ir/en/Article/Download/15560http://jist.ir/en/Article/Download/15560http://jist.ir/en/Article/Download/15560http://jist.ir/en/Article/Download/15560http://jist.ir/en/Article/Download/15560http://jist.ir/en/Article/Download/15560[1] F.Y. Osisanwo, J.E.T.Akinsola, O. Awodele, J.O. Hinmikaiye, O. Olakanmi, and J. Akinjobi, “Supervised Machine Learning Algorithms: Classification and Comparison”, International Journal of Computer Trends and Technology, Vol. 48, No. 3, 2017, pp. 128-138. [2] H. Al-Shehri et al., "Student performance prediction using Support Vector Machine and K-Nearest Neighbor," in IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 2017, pp. 1-4. [3] S. Hossain, D. Sarma, F. Tuj-Johora, J. Bushra, S. Sen and M. Taher, "A Belief Rule Based Expert System to Predict Student Performance under Uncertainty", in 22nd International Conference on Computer and Information Technology (ICCIT), 2019, pp. 1-6. [4] E. S. Bhutto, I. F. Siddiqui, Q. A. Arain and M. Anwar, "Predicting Students’ Academic Performance Through Supervised Machine Learning",in International Conference on Information Science and Communication Technology (ICISCT), 2020, pp. 1-6. [5] Khalil Ahammad, Partha Chakraborty, Evana Akter, Umme Fomey, and Saifur Rahman, “A Comparative Study of Different Machine Learning Techniques to Predict the Result of an Individual Student Using Previous Performances”, International Journal of Computer Science and Information Security, Vol. 18, No. 1, 2021, pp. 5-10. [6] M. B. Shah, M. Kaistha and Y. Gupta, "Student Performance Assessment and Prediction System using Machine Learning", in 4th International Conference on Information Systems and Computer Networks (ISCON), 2019, pp. 386-390. [7] Nurafifah Mohammad Suhaimi, ShahAlam, Selangor, Shuzlina Abdul-Rahman, Sofianita Mutalib, Nurzeatul Hamimah Abdul Hamid, Ariff Md Ab Malik, “Review on Predicting Students’, Graduation Time Using Machine Learning Algorithms”, International Journal of Modern Education and Computer Science, Vol. 7, 2019, pp. 1-13. [8] Fan Yang, Frederick W.B. Li, “Study on student performance estimation, student progress analysis, and student potential prediction based on data mining”, Computers & Education, Vol. 123, 2018, pp. 97-108. [9] Reynold A. Rustia, Ma. Melanie A. Cruz, Michael Angelo P. Burac, Thelma D. Palaoag, “Predicting Student's Board Examination Performance using Classification Algorithms”, in 7th International Conference on Software and Computer Applications, 2018, pp. 233–237. [10] Jiawei Han, Micheline Kamber, Jian Pei, “Data Mining Concepts and Techniques”, USA: Morgan Kaufmann Publishers Elsevier, 2012. [11] Saishruthi Swaminathan, “Logistic Regression – Detailed Overview”, Canada: Towards Data Science – Medium Publication, 2018. [12] Mandy Sidana, “Intro to types of Classification Algorithms in Machine Learning”, USA: Medium Publication, 2017. [13] Gongde Guo, Hui Wang, David Bell, Yaxin Bi, Kieran Greer, “KNN Model-Based Approach in Classification”, Lecture Notes in Computer Science, Vol. 2888, 2003, pp. 986-996. [14] Simon Tong, Daphne Koller, “Support Vector Machine Active Learning with Applications to Text Classification”, Journal of Machine Learning Research, Vol. 2, No.1, 2001, pp. 45-66. [15] H Zhang, J Zimmerman, D Nettleton, DJ Nordman, “Random forest prediction intervals”, The American Statistician, Vol.74, Issue 4, 2019, pp. 392-406. [16] Farhad Malik, “Must Know Mathematical Measures for Every Data Scientist”, USA: Medium Publication, 2018. [17] R.J. Howarth, “r2 (R Squared)”, Dictionary of Mathematical Geosciences: With Historical Notes, Switzerland: Springer, 2017, pp. 503-527. [18] Aditya Mishra, “Metrics to Evaluate your Machine Learning Algorithm”, Canada: Towards Data Science, Medium Publication, 2018. [19] Haiyi Zhang, Di Li, “Naïve Bayes Text Classifier”, in IEEE International Conference on Granular Computing, 2007, pp. 708. [20] Nawzat Sadiq Ahmed, Mohammed Hikmat Sadiq, “Clarify of the Random Forest Algorithm in an Educational Field”, in IEEE International Conference on Advanced Science and Engineering, 2018, pp.179-184. [21] Dataset,https://archive.ics.uci.edu/ml/datasets/Student+Performance. [22] Sotiris Kotsiantis, “Supervised Machine Learning: a review of classification techniques”, Informatica, Vol. 31, No. 3, 2007, pp. 249-268. [23] S.B.Kotsiantis, I.D. Zaharakis, P.E.Pintelas, “Machine Learning: A Review of Classification Techniques and Combining Techniques”, Artificial Intelligence Review, Vol. 26, No. 3, 2006, pp.159-190. [24] R. Choudhary and H. K. Gianey, "Comprehensive Review on Supervised Machine Learning Algorithms," in IEEE International Conference on Machine Learning and Data Science (MLDS), 2017, pp. 37-43. [25] C. A. Ul Hassan, M. S. Khan and M. A. Shah, "Comparison of Machine Learning Algorithms in Data classification," in 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1-6. [26] O. Obulesu, M. Mahendra and M. Thrilok Reddy, "Machine Learning Techniques and Tools: A Survey," in International Conference on Inventive Research in Computing Applications (ICIRCA), 2018, pp. 605-611.PhatNguyen HuuLocHoang Bao1027202125226210.52547/jist.9.36.252http://jist.ir/en/Article/15594http://jist.ir/en/Article/Download/15594http://jist.ir/en/Article/Download/15594http://jist.ir/en/Article/Download/15594http://jist.ir/en/Article/Download/15594http://jist.ir/en/Article/Download/15594http://jist.ir/en/Article/Download/15594http://jist.ir/en/Article/Download/15594[1] Z. Pala and N. Inanc, “Smart Parking Applications Using RFID Technology,” 2007 1st Annual RFID Eurasia, 2007, pp. 1-3. [2] J. P. Benson et al., “Car-Park Management using Wireless Sensor Networks,” Proceedings. 2006 31st IEEE Conference on Local Computer Networks, 2006, pp. 588-595. [3] A. Kianpisheh, N. Mustaffa, P. Limtrairut, and P. Keikhosrokiani, “Smart Parking System (SPS) architecture using ultrasonic detector,” Int. J. Softw. Eng. its Appl., vol. 6, no. 3, pp. 51–58, 2012. [4] G. Ostojic, S. Stankovski, M. Lazarevic and V. Jovanovic, “Implementation of RFID Technology in Parking Lot Access Control System,” 2007 1st Annual RFID Eurasia, 2007, pp. 1-5. [5] J. Wolff, T. Heuer, Haibin Gao, M. Weinmann, S. Voit and U. Hartmann, “Parking monitor system based on magnetic field senso,” 2006 IEEE Intelligent Transportation Systems Conference, 2006, pp. 1275-1279. [6] N. H. H. Mohamad Hanif, Mohd Hafiz Badiozaman and H. Daud, “Smart parking reservation system using short message services (SMS),” 2010 International Conference on Intelligent and Advanced Systems, 2010, pp. 1-5. [7] A. Sayeeraman and P. S. Ramesh, “Zigbee and gsm based secure vehicle parking management and reservation system,” J. Theor. Appl. Inf. Technol., vol. 37, no. 2, pp. 199–203, 2012. [8] H. Ichihashi, T. Katada, M. Fujiyoshi, A. Notsu and K. Honda, “Improvement in the performance of camera based vehicle detector for parking lot,” International Conference on Fuzzy Systems, 2010, pp. 1-7. [9] R. Yusnita, F. Norbaya, and N. Basharuddin. “Intelligent parking space detection system based on image processing,” International Journal of Innovation, Management and Technology, vol. 3, no. 3, pp. 232-235, 2012. [10] S. Banerjee, P. Choudekar and M. K. Muju, "Real time car parking system using image processing," 2011 3rd International Conference on Electronics Computer Technology, 2011, pp. 99-103. [11] S. Khaled, and H. Tounsi, “Parking space detection system using video images,” Transportation Research Record: J. of the Transportation Research Board 2537, no. 1, pp. 137-147, 2015. [12] S. H. Pratap, O. P. Uniyal, and K. Joshi, “An approach to implement cost efficient space detection technology with lower complexity for smart parking system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 3, pp. 415-419, 2015. [13] S. Saleh Al-Amri, N. V. Kalyankar, and Khamitkar S, “Image segmentation by using thershold techniques,” Journal of Computing. vol. 2, no. 5, 2010. [14] S. Li, H. Dawood and P. Guo, “Comparison of linear dimensionality reduction methods in image annotation,” 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI), 2015, pp. 355-360. [15] R. Mehmood, R. Bie, H. Dawood and H. Ahmad, "Fuzzy clustering by fast search and find of density peaks," 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI), 2015, pp. 258-261. [16] A. R. Pathak, M. Pandey,S. Rautaray, “Application of deep learning for object detection,” Proceding Comput. Sci, vol. 132, pp. 1706–1717, 2018. [17] X. Wang, “Deep learning in object recognition, detection, and segmentation,” Trends Signal Process, vol. 8, pp. 217–382, 2016. [18] M. Shafiee, B. Chywl, F. Li, A. Wong, “Fast YOLO: A fast you only look once system for real-time embedded object detection in video,” arXiv:1709.05943, 2017. [19] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: unified, real-time object detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788. [20] R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587. [21] S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017. [22] A. Mauri, R. Khemmar, B. Decoux, N. Ragot, R. Rossi, R. Trabelsi, R. Boutteau, R. Ertaud, J. Savatier, “Deep learning for real-time 3D multi-object detection, localisation, and tracking: application to smart mobility,” Sensors, vol. 20, no. 2, pp. 1-15, 2020. [23] J. Sang, Z. Wu, P. Guo P, et al, “An improved YOLOv2 for vehicle detection,” Sensors (Basel), vol 18, no. 12, pp. 1-15, 2018. [24] J. C. Nascimento, A. J. Abrantes and J. S. Marques, “An algorithm for centroid-based tracking of moving objects,” 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999, pp. 3305-3308, vol.6. [25] M. Everingham, L. V. Gool, C.K.I. Williams, J. Winn, A. Zisserman, “The pascal visual object classes (voc) challenge,” International J. of Computer Vision, vol. 88, no. 2, pp. 303–338, 2010. [26] G. R. Gonalves, S.P.G. Silva,D. Menotti, W.R. Schwartz, “Benchmark for license plate character segmentation,” J. of Electronic Imaging, vol. 25, no. 5, pp. 1–5, 2016. [27] S. M. Silva and C.R. Jung, “License plate detection and recognition in unconstrained scenarios,” Lecture Notes in Computer Science, vol 11216, Springer, Cham, pp. 593–609, 2018. [28] P.N. Huu, L.H. Bao, and H.L. The, “Proposing an image enhancement algorithm using CNN for applications of face recognition system,” J. Adv. Math. Comput. Sci., vol. 34, no. 6, pp. 1–14, 2020. [29] Computer Vision, “Aggregate data”, Online: https://thigiacmaytinh.com/tai-nguyen-xu-ly-anh/tong-hop-data-xu-ly-anh/ (accessed Jun. 30, 2020). [30] T.Y. Lin, et al, “Microsoft COCO: Common objects in context,” in ECCV 2014: Computer Vision – ECCV 2014, vol. 8693, pp. 740-755, 2014. [31] K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778. [32] W. Liu, et al., “SSD: Single shot multiBox detector,” In ECCV 2016: Computer Vision – ECCV 2016, vol. 9905, pp. 21-37, 2016. [33] J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517-6525. [34] A. G. Howard, et. al, “Mobilenets: efficient convolutional neural networks for mobile vision applications,” arXiv:1704.04861, pp. 1-9, 2017. [35] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510-4520. [36] D. Erhan, C. Szegedy, A. Toshev and D. Anguelov, “Scalable object detection using deep neural networks,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2155-2162. [37] C. Szegedy, S. Reed, D. Erhan, D. Anguelov, “Scalable, high-quality object detection,” arXiv:1412.1441 v3, pp. 1-10, 2015. [38] R. Girshick, “Fast R-CNN,” 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448. [39] M. Abadi, et. al, “TensorFlow: a system for large-scale machine learning,” In Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation (OSDI'16). USENIX Association, USA, pp. 265–283, 2016.HamedAgahiKimiaRezaei1027202128529610.52547/jist.9.36.285http://jist.ir/en/Article/15630http://jist.ir/en/Article/Download/15630http://jist.ir/en/Article/Download/15630http://jist.ir/en/Article/Download/15630http://jist.ir/en/Article/Download/15630http://jist.ir/en/Article/Download/15630http://jist.ir/en/Article/Download/15630http://jist.ir/en/Article/Download/15630[1] R. C. Gonzalez and R. E. Woods, "Digital image processing," ed: Prentice hall Upper Saddle River, 2002. [2] D. Marmanis, K. Schindler, J. D. Wegner, S. Galliani, M. Datcu, and U. Stilla, "Classification with an edge: Improving semantic image segmentation with boundary detection," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 135, pp. 158-172, 2018. [3] I. Sobel, "Camera models and machine perception [PhD Dissertation]," ed: Stanford, CA: Stanford University, 1970. [4] J. M. Prewitt, "Object enhancement and extraction," Picture processing and Psychopictorics, vol. 10, no. 1, pp. 15-19, 1970. [5] J. Canny, "A computational approach to edge detection," IEEE Transactions on pattern analysis and machine intelligence, no. 6, pp. 679-698, 1986. [6] L. S. Davis, "A survey of edge detection techniques," Computer graphics and image processing, vol. 4, no. 3, pp. 248-270, 1975. [7] L. Bin and M. S. Yeganeh, "Comparison for image edge detection algorithms," IOSR Journal of Computer Engineering, vol. 2, no. 6, pp. 1-4, 2012. [8] L. Romani, M. Rossini, and D. Schenone, "Edge detection methods based on RBF interpolation," Journal of Computational and Applied Mathematics, vol. 349, pp. 532-547, 2019. [9] S. Eser and A. Derya, "A new edge detection approach via neutrosophy based on maximum norm entropy," Expert Systems with Applications, vol. 115, pp. 499-511, 2019. [10] Y. He and L. M. Ni, "A novel scheme based on the diffusion to edge detection," IEEE Transactions on Image Processing, vol. 28, no. 4, pp. 1613-1624, 2018. [11] R. K. Bhogal and A. Agrawal, "Image Edge Detection Techniques Using Sobel, T1FLS, and IT2FLS," in Information and Communication Technology for Intelligent Systems: Springer, 2019, pp. 303-317. [12] A. Banharnsakun, "Artificial bee colony algorithm for enhancing image edge detection," Evolving Systems, vol. 10, no. 4, pp. 679-687, 2019. [13] O. P. Verma and A. S. Parihar, "An optimal fuzzy system for edge detection in color images using bacterial foraging algorithm," IEEE Transactions on Fuzzy Systems, vol. 25, no. 1, pp. 114-127, 2016. [14] M. D. Ansari, A. R. Mishra, and F. T. Ansari, "New divergence and entropy measures for intuitionistic fuzzy sets on edge detection," International Journal of Fuzzy Systems, vol. 20, no. 2, pp. 474-487, 2018. [15] W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang, "Deepcontour: A deep convolutional feature learned by positive-sharing loss for contour detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3982-3991. [16] S. Xie and Z. Tu, "Holistically-nested edge detection," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1395-1403. [17] D. Xu, W. Ouyang, X. Alameda-Pineda, E. Ricci, X. Wang, and N. Sebe, "Learning deep structured multi-scale features using attention-gated crfs for contour prediction," in Advances in Neural Information Processing Systems, 2017, pp. 3961-3970. [18] R. P. Olenick, T. M. Apostol, D. L. Goodstein, and A. Arons, "The Mechanical Universe: Introduction to Mechanics and Heat," ed: AAPT, 1986. [19] E. Rashedi and H. Nezamabadi-Pour, "A stochastic gravitational approach to feature based color image segmentation," Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp. 1322-1332, 2013. [20] G. Sun, A. Zhang, Y. Yao, and Z. Wang, "A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding," Applied Soft Computing, vol. 46, pp. 703-730, 2016. [21] J. J. D. M. S. Junior, A. R. Backes, and P. C. Cortez, "Color texture classification based on gravitational collapse," Pattern Recognition, vol. 46, no. 6, pp. 1628-1637, 2013. [22] G. Sun, Q. Liu, Q. Liu, C. Ji, and X. Li, "A novel approach for edge detection based on the theory of universal gravity," Pattern Recognition, vol. 40, no. 10, pp. 2766-2775, 2007. [23] C. Lopez-Molina, H. Bustince, J. Fernández, P. Couto, and B. De Baets, "A gravitational approach to edge detection based on triangular norms," Pattern Recognition, vol. 43, no. 11, pp. 3730-3741, 2010. [24] O. P. Verma and R. Sharma, "An optimal edge detection using universal law of gravity and ant colony algorithm," in 2011 World Congress on Information and Communication Technologies, 2011: IEEE, pp. 507-511. [25] O. P. Verma, R. Sharma, M. Kumar, and N. Agrawal, "An optimal edge detection using gravitational search algorithm," Lecture Notes on Software Engineering, vol. 1, no. 2, p. 148, 2013. [26] F. Deregeh and H. Nezamabadi-Pour, "A new gravitational image edge detection method using edge explorer agents," Natural Computing, vol. 13, no. 1, pp. 65-78, 2014. [27] G. Sun et al., "Gravitation-based edge detection in hyperspectral images," Remote Sensing, vol. 9, no. 6, p. 592, 2017. [28] D. Wang, J. Yin, C. Tang, X. Cheng, and B. Ge, "Color edge detection using the normalization anisotropic Gaussian kernel and multichannel fusion," IEEE Access, vol. 8, pp. 228277-228288, 2020. [29] J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, "Bi-directional cascade network for perceptual edge detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3828-3837. [30] K. Li, Y. Tian, B. Wang, Z. Qi, and Q. Wang, "Bi-Directional Pyramid Network for Edge Detection," Electronics, vol. 10, no. 3, p. 329, 2021. [31] Y. Lu, C. He, Y. F. Yu, G. Xu, H. Zhu, and L. Deng, "Vector co‐occurrence morphological edge detection for colour image," IET Image Processing, 2021. [32] S. Anand and G. Sangeethapriya, "Gaussian modulated hyperbolic tangent high pass filter for edge detection in noisy images," arXiv preprint arXiv:2005.11432, 2020. [33] S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: theory and application," Advances in Engineering Software, vol. 105, pp. 30-47, 2017. [34] C. Lopez-Molina, B. De Baets, and H. Bustince, "Generating fuzzy edge images from gradient magnitudes," Computer Vision and Image Understanding, vol. 115, no. 11, pp. 1571-1580, 2011. [35] N. Otsu, "A threshold selection method from gray-level histograms," IEEE transactions on systems, man, and cybernetics, vol. 9, no. 1, pp. 62-66, 1979. [36] M. Mafarja, I. Aljarah, H. Faris, A. I. Hammouri, A.-Z. Ala’M, and S. Mirjalili, "Binary grasshopper optimisation algorithm approaches for feature selection problems," Expert Systems with Applications, vol. 117, pp. 267-286, 2019. [37] X. Zhang, Q. Miao, H. Zhang, and L. Wang, "A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery," Mechanical Systems and Signal Processing, vol. 108, pp. 58-72, 2018. [38] U. SIPI, "The usc-sipi image database," ed, 2016. [39] P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, "Contour detection and hierarchical image segmentation," IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 5, pp. 898-916, 2010. [40] S. E. Umbaugh, Computer imaging: digital image analysis and processing. CRC press, 2005. [41] C. E. Shannon, "A mathematical theory of communication," Bell system technical journal, vol. 27, no. 3, pp. 379-423, 1948. [42] R. Xiaofeng and L. Bo, "Discriminatively trained sparse code gradients for contour detection," in Advances in neural information processing systems, 2012, pp. 584-592. [43] K.-K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool, "Convolutional oriented boundaries," in European Conference on Computer Vision, 2016: Springer, pp. 580-596. [44] I. Kokkinos, "Pushing the boundaries of boundary detection using deep learning," arXiv preprint arXiv:1511.07386, 2015. [45] P. Isola, D. Zoran, D. Krishnan, and E. H. Adelson, "Crisp boundary detection using pointwise mutual information," in European Conference on Computer Vision, 2014: Springer, pp. 799-814. [46] G. Bertasius, J. Shi, and L. Torresani, "High-for-low and low-for-high: Efficient boundary detection from deep object features and its applications to high-level vision," in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 504-512. [47] Y. Liao, S. Fu, X. Lu, C. Zhang, and Z. Tang, "Deep-learning-based object-level contour detection with CCG and CRF optimization," in 2017 IEEE International Conference on Multimedia and Expo (ICME), 2017: IEEE, pp. 859-864. [48] W. Yupei, Z. Xin, and K. Huang, "Deep crisp boundaries," 2017: CVPR. [49] S. Hallman and C. C. Fowlkes, "Oriented edge forests for boundary detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1732-1740.MasoodGhayoomi1027202126327410.52547/jist.9.36.263http://jist.ir/en/Article/15662http://jist.ir/en/Article/Download/15662http://jist.ir/en/Article/Download/15662http://jist.ir/en/Article/Download/15662http://jist.ir/en/Article/Download/15662http://jist.ir/en/Article/Download/15662http://jist.ir/en/Article/Download/15662http://jist.ir/en/Article/Download/15662[1] F. de Saussure, Cours de linguistique générale, C. Bally, A. Sechehaye, and A. Riedlinger, Eds. Lausanne, Paris: Payot, 1916. [2] J. Lyons, Language and Linguistics: An Introduction. Cambridge, UK: Cambridge University Press, 1981. [3] L. Wittgenstein, Philosophical Investigations. Oxford, UK: Blackwell Publishing Ltd, 1953. [4] Z. S. Harris, “Distributional structure,” Word, vol. 23, no. 10, pp. 146–162, 1954. [5] J. R. Firth, “A synopsis of linguistic theory 1930-1955,” Studies in Linguistic Analysis (special volume of the Philological Society), pp. 1–32, 1957. [6] G. A. Miller and W. G. Charles, “Contextual correlates of semantic similarity,” Language and Cognitive Processes, vol. 6, no. 1, pp. 1–28, 1991. [7] Y. Peirsman and D. Geeraerts, “Predicting strong associations on the basis of corpus data,” in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2009, pp. 648–656. [8] T. K. Landauer and S. T. Dumais, “A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge,” Psychological Review, vol. 104, no. 2, pp. 211–240, 1997. [9] M. Sahlgren, The Word-space Model: Using Distributional Analysis to Represent Syntagmatic and Paradigmatic Relations Between Words in High-dimensional Vector Spaces. Ph.D. dissertation, Stockholm University, Stockholm, Sweden, 2006. [10] Z. S. Harris, A Theory of Language and Information: A Mathematical Approach. Oxford, England: Oxford University Press, 1991. [11] D. Lin, “Automatic retrieval and clustering of similar words,” in Proceedings of the 17th international conference on Computational linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 1998, pp. 768–774. [12] S. Padó and M. Lapata, “Dependency-based construction of semantic space models,” Computational Linguistics, vol. 33, no. 2, pp. 161–199, June 2007. [13] O. Levy and Y. Goldberg, “Dependency-based word embeddings,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 2. Baltimore, Maryland: Association for Computational Linguistics, June 2014, pp. 302–308. [14] K. M. Hermann and P. Blunsom, “The role of syntax in vector space models of compositional semantics,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 1, Sofia, Bulgaria, 2013, pp. 894–904. [15] L. Song, Z. Wang, H. Mi, and D. Gildea, “Sense embedding learning for word sense induction,” in Proceedings of the 5th Joint Conference on Lexical and Computational Semantics. The *SEM 2016 Organizing Committee, 2016, pp. 85–90. [16] D. M. Blei, A. Ng, and M. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003. [17] D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2020, https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf. [18] S. K. Jauhar, C. Dyer, and E. Hovy, “Ontologically grounded multi-sense representation learning for semantic vector space models,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Denver, Colorado: Association for Computational Linguistics, May 2015, pp. 683–693. [19] S. Rothe and H. Schütze, “AutoExtend: Extending word embeddings to embeddings for synsets and lexemes,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1. Beijing, China: Association for Computational Linguistics, July 2015, pp. 1793–1803. [20] S. Ramprasad and J. Maddox, “CoKE: Word sense induction using contextualized knowledge embeddings,” in Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering, 2019. [21] J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for word representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, vol. 14, 2014, pp. 1532–1543. [22] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Proceedings of the 26th International Conference on Neural Information Processing Systems, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2013, pp. 3111–3119. [23] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. Berkeley, California: University of California Press, 1967, pp. 281–297. [24] E. Huang, R. Socher, C. D. Manning, and A. Ng, “Improving word representations via global context and multiple word prototypes,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, vol. 1. Jeju Island, Korea: Association for Computational Linguistics, July 2012, pp. 873–882. [25] M. Ghayoomi, “Density measure in context clustering for distributional semantics of word sense induction,” Journal of Information Systems and Telecommunication, vol. 8, no. 1, pp. 15–24, 2020. [26] P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, no. 1, pp. 53–65, November 1987. [27] D. M. Blei, M. I. Jordan, T. L. Griffiths, and J. B. Tenenbaum, “Hierarchical topic models and the nested Chinese restaurant process,” in Proceedings of the 16th International Conference on Neural Information Processing Systems. MIT Press, 2003, pp. 17–24. [28] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, E. Simoudis, J. Han, and U. M. Fayyad, Eds. AAAI Press, 1996, pp. 226–231. [29] A. Neelakantan, J. Shankar, A. Passos, and A. McCallum, “Efficient nonparametric estimation of multiple embeddings per word in vector space,” in Processing of the Conference on Empirical Methods in Natural Language. Doha, Qatar: Association for Computational Linguistics, 2014. [30] J. Li and D. Jurafsky, “Do multi-sense embeddings improve natural language understanding?” in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2015, pp. 1722–1732. [31] J. Wang, M. Bansal, K. Gimpel, B. D. Ziebart, and C. T. Yu, “A sensetopic model for word sense induction with unsupervised data enrichment,” Transactions of the Association for Computational Linguistics, vol. 3, pp. 59–71, 2015. [32] A. Amrami and Y. Goldberg, “Word sense induction with neural biLM and symmetric patterns,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, 2018, pp. 4860–4867. [33] M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1. New Orleans, Louisiana: Association for Computational Linguistics, 2018, pp. 2227–2237. [34] D. Alagić, J. Šnajder, and S. Padó, “Leveraging lexical substitutes for unsupervised word sense induction,” in Proceedings of the 32nd Conference of the Association for the Advancement of Artificial Intelligence. New Orleans, LA, 2018. [35] E. A. Corrêa and D. R. Amancio, “Word sense induction using word embeddings and community detection in complex networks,” Physica A: Statistical Mechanics and its Applications, vol. 523, pp. 180–190, 2019. [36] B. Perozzi, R. Al-Rfou’, V. Kulkarni, and S. Skiena, “Inducing language networks from continuous space word representations,” in Complex Networks, P. Contucci, R. Menezes, A. Omicini, and J. Poncela-Casasnovas, Eds. Cham: Springer International Publishing, 2014, pp. 261–273. [37] P. T. Tallo, Using Sentence Embeddings for Word Sense Induction. Master’s thesis, Electrical Engineering and Computer Science, University of Cincinnati, Ohio, USA, 2020. [38] Q. Dong and Y. Wang, “Enhancing medical word sense inventories using word sense induction: A preliminary study,” in Proceedings of the 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, in conjunction with the 46th International Conference on Very Large Data Bases, 2020, pp. 151–167. [39] S. Arora, Y. Li, Y. Liang, T. Ma, and A. Risteski, “Linear algebraic structure of word senses, with applications to polysemy,” Transactions of the Association for Computational Linguistics, vol. 6, pp. 483–495, 2018. [40] S. Manandhar, I. P. Klapaftis, D. Dligach, and S. S. Pradhan, “Semeval-2010 task 14: Word sense induction & disambiguation,” in Proceedings of the 5th International Workshop on Semantic Evaluation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010, pp. 63–68. [41] G. M. Salton, A. Wong, and C.-S. Yang, “A vector space model for automatic indexing,” Communications of the ACM, vol. 18, no. 11, pp. 613–620, November 1975. [42] M. Ghayoomi, “Finding the meaning of Persian words automatically using word embedding,” Iranian Journal of Information Processing & Management, vol. 35, no. 1, pp. 25–50, 2019. [43] S. Assi, “Farsi linguistic database (FLDB),” International Journal of Lexicography, vol. 10, no. 3, p. 5, 1997. [44] A. AleAhmad, H. Amiri, E. Darrudi, M. Rahgozar, and F. Oroumchian, “Hamshahri: A standard Persian text collection,” Knowledge-Based Systems, vol. 22, no. 5, pp. 382–387, 2009. [45] M. Bijankhan, “naqše peykarehāye zabāni dar neveštane dasture zabān: mo‘arrefiye yek narmafzāre rāyāneyi [“The role of corpora in writing a grammar: Introducing a software”],” Journal of Linguistics, vol. 19, no. 2, pp. 48–67, 2004. [46] M. Bijankhan, J. Sheykhzadegan, M. Bahrani, and M. Ghayoomi, “Lessons from building a Persian written corpus: Peykare,” Language Resources and Evaluation, vol. 45, no. 2, pp. 143–164, 2011. [47] C. Shaoul and C. Westbury, “The Westbury Lab Wikipedia Corpus,” 2010. http://www.psych.ualberta.ca/~westburylab/downloads/westburylab.wikicorp.download.html. [48] M. Shamsfard, H. S. Jafari, and M. Ilbeygi, “STeP-1: A set of fundamental tools for Persian text processing,” in Proceedings of the 7th International Conference on Language Resources and Evaluation, N.Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias, Eds. Valletta, Malta: European Language Resources Association (ELRA), May 19–21 2010, pp. 859–865. [49] C. J. V. Rijsbergen, Information Retrieval, 2nd ed. Newton, MA, USA: Butterworth-Heinemann, 1979. [50] A. Rosenberg and J. Hirschberg, “V-measure: A conditional entropy-based external cluster evaluation measure,” in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, Czech Republic: Association for Computational Linguistics, June 2007, pp. 410–420. [51] B. E. Dom, An Information-theoretic External Cluster-validity Measure. IBM, Tech. Rep., 2001. [52] M. Meilă, “Comparing clusterings – an information based distance,” Journal of Multivariate Analysis, vol. 98, no. 5, pp. 873–895, May 2007. [53] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, Minnesota: Association for Computational Linguistics, 2019, pp. 4171–4186.Katkoori Arun KumarRavi Boda1027202121822510.52547/jist.9.36.218http://jist.ir/en/Article/15688http://jist.ir/en/Article/Download/15688http://jist.ir/en/Article/Download/15688http://jist.ir/en/Article/Download/15688http://jist.ir/en/Article/Download/15688http://jist.ir/en/Article/Download/15688http://jist.ir/en/Article/Download/15688http://jist.ir/en/Article/Download/15688[1] Linda G. Shapiro and George C. Stockman: “Computer Vision”., New Jersey, Prentice-Hall, 2001, pp 279-325. [2] R.O Duda and P.E. Hart, " Pattern Classification and Scene Analysis ", John Wiley & Sons, New-York, 1973, vol. 3, pp 731-749. [3] N. Papamarkos, B. Gatos, “A new approach for multilevel threshold Selection”. Graphics Models Image Processing, 1994, vol 56, issue 5, pp 357-370. [4] S. Boukharouba, J.M. Rebordao, P.L. Wendel, “An amplitude segmentation method based on the distribution function of an image”. Computer Vision Graphics Image Processing, 1985, vol. 29 issue 1, pp 47-59. [5] Y.-T. Kao, E. Zahara and I-W. Kao, “A Hybridized Approach to Data Clustering,” Expert Systems with Applications, 2008, Vol. 34, No. 3, pp.1754-1762. [6] “The essential guide to brain tumors”, National brain tumor society (NBTS), 2007. [7] Molka DHIEB, Sabeur Masmoudi, Mohamed Ben Messa OUD, and Faten Ben Arfia “2-D Entropy Image Segmentation on Thresholding Based on Particle Swarm Optimization (PSO)”, in 1st International Conference on Advanced Technologies for Signal and Image processing (ATSIP), 2014, pp. 143-147, doi: 10.1109/ATSIP.2014.6834594. [8] Bo Chen , Qing-hua Zou, Yan Li “A new image segmentation model with local statistical characters based on variance minimization”, Applied Mathematical Modelling, 2014, vol. 39, issue 12, pp 3227-3235. [9] Mala, C., Sridevi, M. “Multilevel threshold selection for image segmentation using soft computing techniques”, Soft Computing, 2016, vol. 20, issue 5, pp 1793–1810. [10] H. Zhang, J. Fritts, and S. Goldman, “An Entropy-based objective evaluation method for image segmentation”, in Proc. SPIE-Storage and Retrieval Methods and Application for Watershed Transform Multimedia, 2004, pp 38-49. [11] Horst K. Hahn et al,“ The Skull stripping Problem in MRI solved by a Single 3D”, Proc. MICCAI,LNCS, Springer, Berlin, 2000, pp 134-143. [12] Habba Maryam, Ameur Mustapha and Jabrane Younes , “A multilevel thresholding method for image segmentation based on multiobjective particle swarm optimization”, International Conference on wireless technologies, Embedded and Intelligent systems (WITS), 2017, pp. 1-6, doi: 10.1109/WITS.2017.7934620. [13] Pham TX, Siarry P, Oulhadj H. “A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.” Magnetic Resonance Imaging, 2019, vol 61, pp 41-65. [14] Coello Coello CA, González Brambila S, Figueroa Gamboa J, Castillo Tapia MG, Hernández Gómez R, “Evolutionary multiobjective optimization: open research areas and some challenges lying ahead”, Complex & Intelligent Systems, 2020, vol.6, pp 221-236. [15] Hu W, Yen GG, “Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system”, IEEE Transactions on Evolutionary Computation, 2015, vol 19, issue 1, pp 1–18. [16] S. B. Emami, N. Nourafza, S. Fekri–Ershad, "A method for diagnosing of Alzheimer's disease using the brain emotional learning algorithm and wavelet feature", Journal of Intelligent Procedures in Electrical Technology, vol. 13, no. 52, pp. 1-15, 2021. [17] G. Mahesh Kumar, K. Arun Kumar, P. Rajashekar Reddy, J. Tarun Kumar, “A Novel Approach of Tumor Detection in Brain using MRI Scan Images”, Research J. Pharm. and Tech., 2020, vol 13, issue 12, pp 5914-5918.