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ساخت واژگان به صورت خودکار برای تحلیل نظرات در حوزه بورس Automatically generate sentiment lexicon for the Persian stock market 1 1 با رشد چشمگیر رسانههای اجتماعی، افراد و سازمانها به طور فزایندهای از افکار عمومی در این رسانهها برای تصمیم گیری خود استفاده می‌کنند. هدف تحلیل احساسات، استخراج خودکار احساسات افراد از این شبکه‌های اجتماعی می‌باشد. شبکه‌های اجتماعی مرتبط به بازارهای مالی، از جمله بازارهای سهام، اخیرا مورد توجه بسیاری از افراد و سازمان‌ها قرار گرفته است. افراد در این شبکه‌ها نظرات و عقاید خود را در مورد هر سهم در قالب یک پست یا توییت، به اشتراک می‌گذارند. در واقع تحلیل احساسات در این حوزه، سنجش نگرش افراد به هر سهم است. یکی از رویکردهای پایه‌ای و اصلی در تحلیل خودکار احساسات روش‌های مبتنی بر واژگان است. اغلب واژگان‌های مرسوم به صورت دستی استخراج شده‌اند که فرایندی بسیار دشوار و هزینه بر است. در این مقاله روشی جدید جهت استخراج یک واژگان به صورت خودکار در حوزه شبکه‌های اجتماعی بورسی ارائه شده است. یک ویژگی خاص این شبکه‌ها، وجود اطلاعات قیمتی هر سهم در هر روز است. با در نظر گرفتن وضعیت قیمتی سهم در روز درج نظر برای آن سهم، واژگانی را برای بهبود کیفیت عقیده کاوی در این شبکه‌ها استخراج نمودیم. برای ارزیابی واژگان‌های تولید شده با استفاده از روش پیشنهادی، آن را با نسخه فارسی واژگان SentiStrength که با هدف استفاده عمومی طراحی شده است، مقایسه نمودیم. نتایج آزمایشات 20 درصد بهبود را در معیار صحت نسبت به استفاده از واژگان عمومی نشان می‌دهد. 2 With the significant growth of social media, individuals and organizations are increasingly using public opinion in these media to make their own decisions. The purpose of Sentiment Analysis is to automatically extract peoplechr('39')s emotions from these social networks. Social networks related to financial markets, including stock markets, have recently attracted the attention of many individuals and organizations. People on these social networks share their opinions and ideas about each share in the form of a post or tweet. In fact, sentiment analysis in this area is measuring peoplechr('39')s attitudes toward each share. One of the basic approaches in automatic analysis of emotions is lexicon-based methods. Most conventional lexicon is manually extracted, which is a very difficult and costly process. In this article, a new method for extracting a lexicon automatically in the field of stock social networks is proposed. A special feature of these networks is the availability of price information per share. Taking into account the price information of the share on the day of tweeting for that share, we extracted lexicon to improve the quality of opinion mining in these social networks. To evaluate the lexicon produced using the proposed method, we compared it with the Persian version of the SentiStrength lexicon, which is designed for general purpose. Experimental results show a 20% improvement in accuracy compared to the use of general lexicon. 3 20 2021/06/20 1400/3/30 2022/08/29 1401/6/7 مرتضی آهنگری آهنگرکلائی Morteza Ahangari Ahangarkolaei دانشگاه گلستان ایران m.ahangari98@stu.gu.ac.ir علی سبطی Ali Sebti دانشگاه گلستان ایران a.sebti@gu.ac.ir مهدی یعقوبی Mehdi Yaghoubi دانشگاه گلستان ایران m.yaghoubi@gu.ac.ir Sentiment Analysis Opinion Mining Lexicon Creation Persian Lexicon تحلیل احساسات عقیده کاوی ساخت واژگان واژگان فارسی [1] B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends® in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.##[2] B. Liu and L. Zhang, "A survey of opinion mining and sentiment analysis," in Mining text data: Springer, 2012, pp. 415-463.##[3] I. Chaturvedi, E. Cambria, R. E. Welsch, and F. Herrera, "Distinguishing between facts and opinions for sentiment analysis: Survey and challenges," Information Fusion, vol. 44, pp. 65-77, 2018.##[4] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, "Lexicon-based methods for sentiment analysis," Computational linguistics, vol. 37, no. 2, pp. 267-307, 2011.##[5] G. A. Miller, WordNet: An electronic lexical database. MIT press, 1998.##[6] B. Liu, "Sentiment analysis and opinion mining," Synthesis lectures on human language technologies, vol. 5, no. 1, pp. 1-167, 2012.##[7] S. Li, "Sentiment classification using subjective and objective views," International Journal of Computer Applications, vol. 80, no. 7, 2013.##[8] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up? Sentiment classification using machine learning techniques," arXiv preprint cs/0205070, 2002.##[9] Y. Jo and A. H. Oh, "Aspect and sentiment unification model for online review analysis," in Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 815-824.##[10] D. Maynard and A. Funk, "Automatic detection of political opinions in tweets," in Extended Semantic Web Conference, 2011: Springer, pp. 88-99.##[11] K. Dashtipour, A. Hussain, Q. Zhou, A. Gelbukh, A. Y. Hawalah, and E. Cambria, "PerSent: A freely available Persian sentiment lexicon," in International Conference on Brain Inspired Cognitive Systems, 2016: Springer, pp. 310-320.##[12] P. D. Turney, "Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews," arXiv preprint cs/0212032, 2002.##[13] S. Rani and P. Kumar, "Deep learning based sentiment analysis using convolution neural network," Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3305-3314, 2019.##[14] G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, "Sentiment analysis of comment texts based on BiLSTM," Ieee Access, vol. 7, pp. 51522-51532, 2019.##[15] M. Rhanoui, M. Mikram, S. Yousfi, and S. Barzali, "A CNN-BiLSTM model for document-level sentiment analysis," Machine Learning and Knowledge Extraction, vol. 1, no. 3, pp. 832-847, 2019.##[16] M. Ahmad, S. Aftab, and I. Ali, "Sentiment analysis of tweets using svm," Int. J. Comput. Appl, vol. 177, no. 5, pp. 25-29, 2017.##[17] M. Ahmad, S. Aftab, M. S. Bashir, N. Hameed, I. Ali, and Z. Nawaz, "SVM optimization for sentiment analysis," Int. J. Adv. Comput. Sci. Appl, vol. 9, no. 4, pp. 393-398, 2018.##[18] K. Korovkinas, P. Danėnas, and G. Garšva, "SVM and k-means hybrid method for textual data sentiment analysis," Baltic Journal of Modern Computing, vol. 7, no. 1, pp. 47-60, 2019.##[19] L. Dey, S. Chakraborty, A. Biswas, B. Bose, and S. Tiwari, "Sentiment analysis of review datasets using naive bayes and k-nn classifier," arXiv preprint arXiv:1610.09982, 2016.##[20] V. Narayanan, I. Arora, and A. Bhatia, "Fast and accurate sentiment classification using an enhanced Naive Bayes model," in International Conference on Intelligent Data Engineering and Automated Learning, 2013: Springer, pp. 194-201.##[21] M. S. Hajmohammadi and R. Ibrahim, "A SVM-based method for sentiment analysis in Persian language," International Conference on Graphic and Image Processing (ICGIP 2012), vol. 8768, p. 876838, 2013.##[22] M. Saraee and A. Bagheri, "Feature selection methods in Persian sentiment analysis," International Conference on Application of Natural Language to Information Systems, pp. 303-308, 2013.##[23] A. S. H. Basari, B. Hussin, I. G. P. Ananta, and J. Zeniarja, "Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization," Procedia Engineering, vol. 53, no. 7, pp. 453-462, 2013.##[24] T. S. Ataei, K. Darvishi, S. Javdan, B. Minaei-Bidgoli, and S. Eetemadi, "Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian," arXiv preprint arXiv:1908.01815, 2019.##[25] K. Dashtipour, M. Gogate, A. Adeel, C. Ieracitano, H. Larijani, and A. Hussain, "Exploiting deep learning for persian sentiment analysis," International Conference on Brain Inspired Cognitive Systems, pp. 597-604, 2018.##[26] E. Cambria, D. Olsher, and D. Rajagopal, "SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis," in Proceedings of the AAAI Conference on Artificial Intelligence, 2014, vol. 28, no. 1.##[27] M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004, pp. 168-177.##[28] L. Deng and J. Wiebe, "Mpqa 3.0: An entity/event-level sentiment corpus," in Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, 2015, pp. 1323-1328.##[29] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "SentiFul: A lexicon for sentiment analysis," IEEE Transactions on Affective Computing, vol. 2, no. 1, pp. 22-36, 2011.##[30] A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in LREC, 2006, vol. 6: Citeseer, pp. 417-422.##[31] S. M. Mohammad and P. D. Turney, "Crowdsourcing a word-emotion association lexicon," Computational intelligence, vol. 29, no. 3, pp. 436-465, 2013.##[32] M. E. Basiri, A. R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A framework for sentiment analysis in persian," Open transactions on information processing, vol. 1, no. 3, pp. 1-14, 2014.##[33] M. E. Basiri and A. Kabiri, "Sentence-level sentiment analysis in Persian," in 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), 2017: IEEE, pp. 84-89.##[34] S. M. Mohammad, S. Kiritchenko, and X. Zhu, "NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets," arXiv preprint arXiv:1308.6242, 2013.##[35] M. Thelwall, K. Buckley, and G. Paltoglou, "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, vol. 63, no. 1, pp. 163-173, 2012.##[36] M. E. Basiri and A. Kabiri, "Translation is not enough: comparing lexicon-based methods for sentiment analysis in Persian," in 2017 International Symposium on Computer Science and Software Engineering Conference (CSSE), 2017: IEEE, pp. 36-41.##[37] B. Sabeti, P. Hosseini, G. Ghassem-Sani, and S. A. Mirroshandel, "LexiPers: An ontology based sentiment lexicon for Persian," arXiv preprint arXiv:1911.05263, 2019.##[38] F. Amiri, S. Scerri, and M. Khodashahi, "Lexicon-based sentiment analysis for Persian text," in Proceedings of the International Conference Recent Advances in Natural Language Processing, 2015, pp. 9-16.##[39] R. Dehkharghani, "Sentifars: A persian polarity lexicon for sentiment analysis," ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), vol. 19, no. 2, pp. 1-12, 2019.##[40] E. Haddi, X. Liu, and Y. Shi, "The role of text pre-processing in sentiment analysis," Procedia Computer Science, vol. 17, pp. 26-32, 2013.##[41] 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.##[42] M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, "Sentiment strength detection in short informal text," Journal of the American society for information science and technology, vol. 61, no. 12, pp. 2544-2558, 2010.##[43] G. Shafer, A mathematical theory of evidence. Princeton university press, 1976.##[1] B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends® in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.##[2] B. Liu and L. Zhang, "A survey of opinion mining and sentiment analysis," in Mining text data: Springer, 2012, pp. 415-463.##[3] I. Chaturvedi, E. Cambria, R. E. Welsch, and F. Herrera, "Distinguishing between facts and opinions for sentiment analysis: Survey and challenges," Information Fusion, vol. 44, pp. 65-77, 2018.##[4] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, "Lexicon-based methods for sentiment analysis," Computational linguistics, vol. 37, no. 2, pp. 267-307, 2011.##[5] G. A. Miller, WordNet: An electronic lexical database. MIT press, 1998.##[6] B. Liu, "Sentiment analysis and opinion mining," Synthesis lectures on human language technologies, vol. 5, no. 1, pp. 1-167, 2012.##[7] S. Li, "Sentiment classification using subjective and objective views," International Journal of Computer Applications, vol. 80, no. 7, 2013.##[8] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up? Sentiment classification using machine learning techniques," arXiv preprint cs/0205070, 2002.##[9] Y. Jo and A. H. Oh, "Aspect and sentiment unification model for online review analysis," in Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 815-824.##[10] D. Maynard and A. Funk, "Automatic detection of political opinions in tweets," in Extended Semantic Web Conference, 2011: Springer, pp. 88-99.##[11] K. Dashtipour, A. Hussain, Q. Zhou, A. Gelbukh, A. Y. Hawalah, and E. Cambria, "PerSent: A freely available Persian sentiment lexicon," in International Conference on Brain Inspired Cognitive Systems, 2016: Springer, pp. 310-320.##[12] P. D. Turney, "Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews," arXiv preprint cs/0212032, 2002.##[13] S. Rani and P. Kumar, "Deep learning based sentiment analysis using convolution neural network," Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3305-3314, 2019.##[14] G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, "Sentiment analysis of comment texts based on BiLSTM," Ieee Access, vol. 7, pp. 51522-51532, 2019.##[15] M. Rhanoui, M. Mikram, S. Yousfi, and S. Barzali, "A CNN-BiLSTM model for document-level sentiment analysis," Machine Learning and Knowledge Extraction, vol. 1, no. 3, pp. 832-847, 2019.##[16] M. Ahmad, S. Aftab, and I. Ali, "Sentiment analysis of tweets using svm," Int. J. Comput. Appl, vol. 177, no. 5, pp. 25-29, 2017.##[17] M. Ahmad, S. Aftab, M. S. Bashir, N. Hameed, I. Ali, and Z. Nawaz, "SVM optimization for sentiment analysis," Int. J. Adv. Comput. Sci. Appl, vol. 9, no. 4, pp. 393-398, 2018.##[18] K. Korovkinas, P. Danėnas, and G. Garšva, "SVM and k-means hybrid method for textual data sentiment analysis," Baltic Journal of Modern Computing, vol. 7, no. 1, pp. 47-60, 2019.##[19] L. Dey, S. Chakraborty, A. Biswas, B. Bose, and S. Tiwari, "Sentiment analysis of review datasets using naive bayes and k-nn classifier," arXiv preprint arXiv:1610.09982, 2016.##[20] V. Narayanan, I. Arora, and A. Bhatia, "Fast and accurate sentiment classification using an enhanced Naive Bayes model," in International Conference on Intelligent Data Engineering and Automated Learning, 2013: Springer, pp. 194-201.##[21] M. S. Hajmohammadi and R. Ibrahim, "A SVM-based method for sentiment analysis in Persian language," International Conference on Graphic and Image Processing (ICGIP 2012), vol. 8768, p. 876838, 2013.##[22] M. Saraee and A. Bagheri, "Feature selection methods in Persian sentiment analysis," International Conference on Application of Natural Language to Information Systems, pp. 303-308, 2013.##[23] A. S. H. Basari, B. Hussin, I. G. P. Ananta, and J. Zeniarja, "Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization," Procedia Engineering, vol. 53, no. 7, pp. 453-462, 2013.##[24] T. S. Ataei, K. Darvishi, S. Javdan, B. Minaei-Bidgoli, and S. Eetemadi, "Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian," arXiv preprint arXiv:1908.01815, 2019.##[25] K. Dashtipour, M. Gogate, A. Adeel, C. Ieracitano, H. Larijani, and A. Hussain, "Exploiting deep learning for persian sentiment analysis," International Conference on Brain Inspired Cognitive Systems, pp. 597-604, 2018.##[26] E. Cambria, D. Olsher, and D. Rajagopal, "SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis," in Proceedings of the AAAI Conference on Artificial Intelligence, 2014, vol. 28, no. 1.##[27] M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004, pp. 168-177.##[28] L. Deng and J. Wiebe, "Mpqa 3.0: An entity/event-level sentiment corpus," in Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, 2015, pp. 1323-1328.##[29] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "SentiFul: A lexicon for sentiment analysis," IEEE Transactions on Affective Computing, vol. 2, no. 1, pp. 22-36, 2011.##[30] A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in LREC, 2006, vol. 6: Citeseer, pp. 417-422.##[31] S. M. Mohammad and P. D. Turney, "Crowdsourcing a word-emotion association lexicon," Computational intelligence, vol. 29, no. 3, pp. 436-465, 2013.##[32] M. E. Basiri, A. R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A framework for sentiment analysis in persian," Open transactions on information processing, vol. 1, no. 3, pp. 1-14, 2014.##[33] M. E. Basiri and A. Kabiri, "Sentence-level sentiment analysis in Persian," in 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), 2017: IEEE, pp. 84-89.##[34] S. M. Mohammad, S. Kiritchenko, and X. Zhu, "NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets," arXiv preprint arXiv:1308.6242, 2013.##[35] M. Thelwall, K. Buckley, and G. Paltoglou, "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, vol. 63, no. 1, pp. 163-173, 2012.##[36] M. E. Basiri and A. Kabiri, "Translation is not enough: comparing lexicon-based methods for sentiment analysis in Persian," in 2017 International Symposium on Computer Science and Software Engineering Conference (CSSE), 2017: IEEE, pp. 36-41.##[37] B. Sabeti, P. Hosseini, G. Ghassem-Sani, and S. A. Mirroshandel, "LexiPers: An ontology based sentiment lexicon for Persian," arXiv preprint arXiv:1911.05263, 2019.##[38] F. Amiri, S. Scerri, and M. Khodashahi, "Lexicon-based sentiment analysis for Persian text," in Proceedings of the International Conference Recent Advances in Natural Language Processing, 2015, pp. 9-16.##[39] R. Dehkharghani, "Sentifars: A persian polarity lexicon for sentiment analysis," ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), vol. 19, no. 2, pp. 1-12, 2019.##[40] E. Haddi, X. Liu, and Y. Shi, "The role of text pre-processing in sentiment analysis," Procedia Computer Science, vol. 17, pp. 26-32, 2013.##[41] 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.##[42] M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, "Sentiment strength detection in short informal text," Journal of the American society for information science and technology, vol. 61, no. 12, pp. 2544-2558, 2010.##[43] G. Shafer, A mathematical theory of evidence. Princeton university press, 1976.## ##

بهبود کیفیت سرویس شبکه های خودرویی با استفاده توامان واحدهای کنارجاده ای و خودروهای پارک شده در محیط شهری Vehicular Networks QoS Improvement by Simultaneous Use of RSUs and Parked Vehicles in Urban Scenarios 1 1 با رشد جمعیت شهری، وسایل نقلیه نیز رشد چشمگیری را تجربه کرده ­اند. افزایش خودروها منجر به بروز چالش­هایی در ارائه سرویس­های  ایمنی ، ترافیک و رفاه شده است. جهت رفع این چالش­ها، سامانه ­های حمل­ و­نقل هوشمند ارائه شده ­اند. یکی از تکنولوژی های کلیدی در سامانه ­های حمل ­و­نقل هوشمند در جهت رفع این چالش­ها، شبکه های خودرویی هستند. اما این شبکه ­ها در ارائه مطلوب سرویس ­ها با چالش­ هایی از قبیل گسستگی شبکه در نقاط کم ­چگالی و کمبود ظرفیت شبکه در نقاط دارای ازدحام مواجه هستند. جهت غلبه بر این مشکلات، واحدهای کنارجاده ­ای در محیط شهری نصب می­ گردند اما هزینه ­ی بالای نصب و نگهداری این واحدها، از نصب گسترده ­ی آنها در محیط شهری جلوگیری می ­کند. از این رو نیاز است تا تعداد کمینه ­ای از این واحدها در محیط شهری و در نقاط مناسب و موردنیاز جهت رفع چالش های مذکور، نصب گردد. وجود خودروهای پارک شده در محیط شهری و در مکان ­های از پیش تعیین شده، امکان استفاده از آنها را به عنوان واحدهای کنارجاده­ ای مقدور می­ سازد. از این­رو نیاز است تا در چیدمان واحدهای کنارجاده ای در محیط شهری، موقعیت پارکینگ ­ها نیز مدنظر قرار گیرد. در این مقاله، مدلی مبتنی بر برنامه ­ریزی دودویی جهت ایجاد حداقل پوشش موردنیاز در محیط شهری جهت رفع چالش­ های مطرح شده با لحاظ نمودن خودروهای پارک شده به عنوان واحدکنارجاده­ ای ارائه شده است. در این مدل در نقاط پارکینگ  از گره­ های پارک ­شده به عنوان واحدهای کنارجاده ای جهت تکمیل پوشش شبکه و افزایش ظرفیت آن بهره می­ برند. همچنین قیدهایی جهت تعیین حداقل پوشش موردنیاز و جلوگیری از پوشش­ های چندگانه به منظور کاهش هزینه­ های نصب به مدل افزوده شده است. درنهایت راهکار ارائه شده با استفاده از شبیه ­سازهای OMNeT++ ،SUMO و Veins مورد ارزیابی قرار ­گرفته است. جهت صحت ­سنجی مدل ارائه شده، ارزیابی در دو نقشه متفاوت، با تعداد متنوعی از واحدهای کنارجاده­ ای و حالات ترافیکی متفاوت صورت گرفته است و پارامترهای کارایی نرخ گم­شدن بسته ­ها و تاخیر دریافت سرویس مورد اندازه­ گیری واقع شده ­اند. نتایج حاصل از شبیه ­سازی حاکی از بهبود پارامتر تاخیر دریافت سرویس به طور متوسط در دو نقشه به میزان 39 و 43  درصد و نرخ گم شدن بسته­ ها به میزان 47 و 49 درصد در مقایسه با دیگر راه کارهای مرتبط می ­باشد. 2 By growth in urban population, vehicles have also experienced increased significantly. The increased of vehicles has led to challenges in the services of safety, traffic and comfort. Congestion in urban areas is one of the main examples of increasing the number of vehicles, which has also led to environmental challenges. To address these challenges, Intelligent Transportation Systems (ITS) have been developed. One of the key technologies in ITS to solve these challenges is vehicular networks. These networks increase the efficiency of transportation systems in urban and highway areas by providing a wide range of services. However, these networks face challenges such as network partitioning in low-density areas and lack of network capacity in dense areas to providing proper services. Such challenges will reduce the efficiency of vehicular networks and transportation systems in urban and highway scenarios. To overcome these problems, roadside units are deployed in urban environments, but the high cost of installation and maintenance of RSUs prevents their widespread installation in urban areas. Therefore, it is necessary to install a minimum number of these units in the urban environment and in suitable and necessary places to meet these challenges. The presence of parked vehicles in urban areas and in predetermined places, makes it possible to use them as RSUs. Therefore, it is necessary to consider the location of parking lots in the placement of roadside units in the urban environment so that parked vehicles in these places can be used as roadside units to meet these challenges. Therefore, it is necessary to consider the location of parking lots in urban areas in the placement of RSUs. In this paper, a BIP model for RSUs installation is developed to provide the minimum required coverage by considering parked vehicles as RSUs in the urban area to meet the mentioned challenges. In this model, parked vehicles in parking lots are used as RSUs to increase coverage and capacity of the network. Moreover, constraints have been added to the model to achieve the minimum required coverage and minimize multiple co-coverage to reduce installation costs. Therefore, in the proposed model, in addition to considering the parked vehicles in the parking lots as roadside units and restrictions to prevent multiple coverages in order to reduce installation costs, providing minimum coverage to improve the efficiency of ITS services is also considered. Finally, the proposed solution is evaluated using OMNeT++, SUMO and Veins. To validate the proposed model, the evaluation was repeated in two different maps, with a different number of RSUs and different traffic scenarios, and Packet Loss Rate and Service Delay were measured as performance parameters. The results of the simulation show the improvement of the service delay parameter in the two maps by 39% and 43% and the packet loss rate by 47% and 49% compared to other related work. 21 38 2021/06/202021/07/11 1400/4/20 2022/08/292022/05/11 1401/2/21 نیک‌محمد بلوچ‌زهی Nik-Mohamma Balouchzahi دانشگاه سیستان و بلوچستان ایران balouchzahi@ece.usb.ac.ir رخشاالسادات کشفی Rakhshsadat Kashfi دانشگاه سیستان و بلوچستان ایران rakhshakashfi@pgs.usb.ac.ir مریم بیدار Maryam Bidar دانشگاه سیستان و بلوچستان ایران maryambidar6030@gmail.com Vehicular Networks Road Side Units Parked Vehicles Intelligent Transportation Systems QoS Improvement شبکه های خودرویی واحدهای کنارجاده ای خودروهای پارک شده سامانه های حمل و نقل بهبود کیفیت سرویس [1] "statista," 2020. [Online]. Available: https://www.statista.com.##[2] C. a. Graham, "INRIX Global Traffic Scorecard," 2018.##[3] N.-M. Balouchzahi, M. Fathy and A. Akbari, "Optimal road side units placement model based on binary integer programming for efficient traffic information advertisement and discovery in vehicular environment," IET Intelligent Transport Systems, vol. 9, no. 9, pp. 851-861, 2015.##[4] F. Outay, A.-U.-H. Yasar and E. Shakshuki, "A Review of Intelligent Transport System and Its People's Needs Considerations for Traffic Management's Policy Framework in a Developing Country: People's Needs Considerations for ITS Policy," in Global Advancements in Connected and Intelligent Mobility: Emerging Research and Opportunities, Hershey, PA, 2020, pp. 166-195.##[5] Z. Ahmed, S. Naz and J. Ahmed, "Minimizing transmission delays in vehicular ad hoc networks by optimized placement of road-side unit, " Wireless Networks, pp. 1-10, 2020.##[6] M. Fogue, J. A. Sanguesa, F. J. Martinez and J. M. Marquez-Barja, "Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms, " Computer Science and Electrical Engineering, 2018.##[7] Z. Gao, D. Chen, S. Cai and H.-C. Wu, "Optimal and Greedy Algorithms for the One-Dimensional RSU Deployment Problem with New Model," IEEE Transactions on Vehicular Technology, vol. 11, 2018.##[8] J. Barrachina, P. Garrido and M. Fogue, "Road Side Unit Deployment: A Density-Based Approach, " IEEE Intelligent Transportation Systems Magazine, vol. 5, no. 3, pp. 30-39, 2013.##[9] F. Hagenauer, C. Sommer, T. Higuchi, O. Altintas and F. Dressler, "Parked Cars as Virtual Network Infrastructure:, " in Mobile Computing and Networking, 2017.##[10] A. Reis, S. Sargento and O. Tonguz, "Parked Cars are Excellent Roadside Units, " IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 9, pp. 2490-2502, 2017.##[11] S. B. Chaabene, T. Yeferny and S. B. Yahia, "A Roadside Unit Placement Scheme for Vehicular Ad-hoc Networks, " in International Conference on Advanced Information Networking and Applications, Matsue, 2019.##[12] D. L. Moura, R. S. Cabral, A. T. Sales and L.L., "An evolutionary algorithm for roadside unit deployment with betweenness centrality preprocessing," Future Generation Computer Systems, vol. 88, pp. 776-784, 2018.##[13] J. F. M. Sarubbi, T. R. Silva, F. V. C. Martins, E. F. Wanner and C. M. Silva, "A GRASP based heuristic for Deployment Roadside Units in VANETs, " in IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal, 2017.##[14] H. Yang, Z. Jia and G. Xie, "Delay-Bounded and Cost-Limited RSU Deployment in Urban Vehicular Ad Hoc Networks, " Sensors (Basel), vol. 18, 2018.##[15] S. Mehar, S. M. Senouci, A. Kies and M. M. Zoulikha, "An Optimized Roadside Units (RSU) placement for delay-sensitive applications in vehicular networks, " in 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, 2015.##[16] A. Olia, H. Abdelgawad, B. Abdulhai and S. Razavi, "Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment, " Intelligent Transportation Systems, vol. 21, no. 4, pp. 296-309 , 2017.##[17] Z. Wang, J. Zheng, Y. Wu and N. Mitton, "A Centrality-based RSU Deployment Approach for Vehicular Ad Hoc Networks, " in International Conference on Communications, 2017.##[18] K. Ota, T. Kumrai, M. Dong, J. Kishigami and M. Guo, "Smart infrastructure design for Smart Cities, " IT Professional, vol. 19, no. 5, pp. 42-49, 2017.##[19] Y. Wang, J. Zheng and N. Mitton, "Delivery delay analysis for roadside unit deployment in vehicular ad hoc networks with intermittent connectivity, " IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8591-8602, 2016.##[20] Z. Lamb and D. Agrawal, "Data-Driven Approach for Targeted RSU Deployment in an Urban Environment, " IEEE Intelligent Vehicles Symposium , 2017.##[21] A. Guerna and S. Bitam, "GICA: An evolutionary strategy for roadside units deployment in vehicular networks, " in 2019 International Conference on Networking and Advanced Systems (ICNAS), Annaba, 2019.##[22] H. Gong and L. Yu, "Content Downloading with the Assistance of Roadside Cars for Vehicular Ad Hoc Networks, " Mobile Information Systems, vol. 2017, 2017.##[23] O. K. Tonguz and W. Viriyasitavat, "Cars as Roadside Units: A Self-Organizing Network Solution, " IEEE Communications Magazine, vol. 51, no. 12, pp. 112-120, 2013.##[24] "OpenStreetMap," 2021. [Online]. Available: https://www.openstreetmap.org/. [Accessed 2021].##[25] D. Krajzewicz, J. Erdmann, M. Behrisch and L. Bieker, "Recent Development and Applications of SUMO - Simulation of Urban MObility, " International Journal On Advances in Systems and Measurements, vol. 5, no. 3, pp. 128-138, December 2012.##[26] [Online]. Available: http://www.omnetpp.org/.##[27] C. Sommer, R. German and F. Dressler, "Bidirect2ionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis, " IEEE Transactions on Mobile Computing, vol. 10, no. 1, pp. 3-15, January 2011.##[28] N.-M. Balouchzahi and M. Rajaei, "Efficient Traffic Information Dissemination and Vehicle Navigation for Lower Travel Time in Urban Scenario Using Vehicular Networks," Wireless Personal Communications, vol. 80, no. 3, pp. 1-17, 2019.##[1] "statista," 2020. [Online]. Available: https://www.statista.com.##[2] C. a. Graham, "INRIX Global Traffic Scorecard," 2018.##[3] N.-M. Balouchzahi, M. Fathy and A. Akbari, "Optimal road side units placement model based on binary integer programming for efficient traffic information advertisement and discovery in vehicular environment," IET Intelligent Transport Systems, vol. 9, no. 9, pp. 851-861, 2015.##[4] F. Outay, A.-U.-H. Yasar and E. Shakshuki, "A Review of Intelligent Transport System and Its People's Needs Considerations for Traffic Management's Policy Framework in a Developing Country: People's Needs Considerations for ITS Policy," in Global Advancements in Connected and Intelligent Mobility: Emerging Research and Opportunities, Hershey, PA, 2020, pp. 166-195.##[5] Z. Ahmed, S. Naz and J. Ahmed, "Minimizing transmission delays in vehicular ad hoc networks by optimized placement of road-side unit, " Wireless Networks, pp. 1-10, 2020.##[6] M. Fogue, J. A. Sanguesa, F. J. Martinez and J. M. Marquez-Barja, "Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms, " Computer Science and Electrical Engineering, 2018.##[7] Z. Gao, D. Chen, S. Cai and H.-C. Wu, "Optimal and Greedy Algorithms for the One-Dimensional RSU Deployment Problem with New Model," IEEE Transactions on Vehicular Technology, vol. 11, 2018.##[8] J. Barrachina, P. Garrido and M. Fogue, "Road Side Unit Deployment: A Density-Based Approach, " IEEE Intelligent Transportation Systems Magazine, vol. 5, no. 3, pp. 30-39, 2013.##[9] F. Hagenauer, C. Sommer, T. Higuchi, O. Altintas and F. Dressler, "Parked Cars as Virtual Network Infrastructure:, " in Mobile Computing and Networking, 2017.##[10] A. Reis, S. Sargento and O. Tonguz, "Parked Cars are Excellent Roadside Units, " IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 9, pp. 2490-2502, 2017.##[11] S. B. Chaabene, T. Yeferny and S. B. Yahia, "A Roadside Unit Placement Scheme for Vehicular Ad-hoc Networks, " in International Conference on Advanced Information Networking and Applications, Matsue, 2019.##[12] D. L. Moura, R. S. Cabral, A. T. Sales and L.L., "An evolutionary algorithm for roadside unit deployment with betweenness centrality preprocessing," Future Generation Computer Systems, vol. 88, pp. 776-784, 2018.##[13] J. F. M. Sarubbi, T. R. Silva, F. V. C. Martins, E. F. Wanner and C. M. Silva, "A GRASP based heuristic for Deployment Roadside Units in VANETs, " in IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal, 2017.##[14] H. Yang, Z. Jia and G. Xie, "Delay-Bounded and Cost-Limited RSU Deployment in Urban Vehicular Ad Hoc Networks, " Sensors (Basel), vol. 18, 2018.##[15] S. Mehar, S. M. Senouci, A. Kies and M. M. Zoulikha, "An Optimized Roadside Units (RSU) placement for delay-sensitive applications in vehicular networks, " in 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, 2015.##[16] A. Olia, H. Abdelgawad, B. Abdulhai and S. Razavi, "Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment, " Intelligent Transportation Systems, vol. 21, no. 4, pp. 296-309 , 2017.##[17] Z. Wang, J. Zheng, Y. Wu and N. Mitton, "A Centrality-based RSU Deployment Approach for Vehicular Ad Hoc Networks, " in International Conference on Communications, 2017.##[18] K. Ota, T. Kumrai, M. Dong, J. Kishigami and M. Guo, "Smart infrastructure design for Smart Cities, " IT Professional, vol. 19, no. 5, pp. 42-49, 2017.##[19] Y. Wang, J. Zheng and N. Mitton, "Delivery delay analysis for roadside unit deployment in vehicular ad hoc networks with intermittent connectivity, " IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8591-8602, 2016.##[20] Z. Lamb and D. Agrawal, "Data-Driven Approach for Targeted RSU Deployment in an Urban Environment, " IEEE Intelligent Vehicles Symposium , 2017.##[21] A. Guerna and S. Bitam, "GICA: An evolutionary strategy for roadside units deployment in vehicular networks, " in 2019 International Conference on Networking and Advanced Systems (ICNAS), Annaba, 2019.##[22] H. Gong and L. Yu, "Content Downloading with the Assistance of Roadside Cars for Vehicular Ad Hoc Networks, " Mobile Information Systems, vol. 2017, 2017.##[23] O. K. Tonguz and W. Viriyasitavat, "Cars as Roadside Units: A Self-Organizing Network Solution, " IEEE Communications Magazine, vol. 51, no. 12, pp. 112-120, 2013.##[24] "OpenStreetMap," 2021. [Online]. Available: https://www.openstreetmap.org/. [Accessed 2021].##[25] D. Krajzewicz, J. Erdmann, M. Behrisch and L. Bieker, "Recent Development and Applications of SUMO - Simulation of Urban MObility, " International Journal On Advances in Systems and Measurements, vol. 5, no. 3, pp. 128-138, December 2012.##[26] [Online]. Available: http://www.omnetpp.org/.##[27] C. Sommer, R. German and F. Dressler, "Bidirect2ionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis, " IEEE Transactions on Mobile Computing, vol. 10, no. 1, pp. 3-15, January 2011.##[28] N.-M. Balouchzahi and M. Rajaei, "Efficient Traffic Information Dissemination and Vehicle Navigation for Lower Travel Time in Urban Scenario Using Vehicular Networks," Wireless Personal Communications, vol. 80, no. 3, pp. 1-17, 2019.## ##

الگوسازی موضوع‌ها بر پایه‌ی روش بیز گوناگونی Topic Modeling Based on Variational Bayes Method 1 1 در این مقاله، برپایه‌ی روش بیز گوناگونی، نشان می‌دهیم که روش تخصیص پنهان دیریکله که یک مدل احتمالاتی مولّد است و در پردازش زبان‌های طبیعی، متن‌کاوی، کاهش ابعاد، و زیست‌داده‌ورزی کاربرد دارد،  نسبت به روش تحلیل معنایی پنهان احتمالاتی در مدل‌بندی داده‌ها عملکرد بهتری دارد. در این باره، ابتدا یک مدل بیزی را در مدل‌سازی موضوع‌ها شرح می‌دهیم. آنگاه با روش بیز گوناگونی و الگوریتم امیدریاضی-بیشینه‌سازی (EM) پارامترهای مدل را برآورد می‌کنیم. سپس الگوریتم ارائه شده، موسوم به الگوریتم EM گوناگونی، را برپایه‌ی یک مجموعه‌داده‌ی نوشتاری از داده‌های واقعی در زمینه‌ی تحلیل داده‌های خبری پیاده‌سازی می‌کنیم و مدل‌بندی زبانی را بر اساس ملاک سرگشتگی بررسی می‌کنیم، و دقت خوشه‌بندی موضوع‌ها و کاربرد کاهش ابعاد داده‌های حجیم را با کمک ماشین بردار پشتیبان می‌سنجیم. همچنین در مقایسه‌ای دیگر، کاربرد الگوریتم پیشنهادی را در پالایش همکارانه بررسی می‌کنیم. 2 The Latent Dirichlet Allocation (LDA) model is a generative model with several applications in natural language processing, text mining, dimension reduction, and bioinformatics. It is a powerful technique in topic modeling in text mining, which is a data mining method to categorize documents by their topic. Basic methods for topic modeling, including TF-IDF, unigram, and mixture of unigrams successfully deployed in modern search engines. Although these methods have some useful benefits, they don’t provide much summarization and reduction. To overcome these shortcomings, the latent semantic analysis (LSA) has been proposed, which uses singular value decomposition (SVD) of word-document matrix to compress big collection of text corpora. User’s search key words can be queried by making a pseudo-document vector. The next improvement step in topic modeling was probabilistic latent semantic analysis (PLSA), which has a close relation to LSA and matrix decomposition with SVD. By introducing of exchangeability for the words in documents, the topic modeling has been proceeded beyond PLSA and leads to LDA model. We consider a corpus  contains M  documents, each document  has  words, and each word is an indicator from one of  vocabularies. We defined a generative model for generation of each document as follows. For each document draw its topic  from  and repeatedly for each  draw topic of each word  from  and draw each word from the probability matrix of  with probability of . We can repeat this procedure to generate whole documents of corpus. We want to find corpus related parameters  and  as well as latent variables  and  for each document. Unfortunately, the posterior  is intractable, and we have to choose an approximation scheme. In this paper we utilize LDA for collection of discrete text corpora. We describe procedures for inference and parameter estimation. Since computing posterior distribution of hidden variables given a document is intractable to compute in general, we use approximate inference algorithm called variational Bayes method. The basic idea of variational Bayes is to consider a family of adjustable lower bound on the posterior, then finds the tightest possible one. To estimate optimal hyper-parameters in the model, we used the empirical Bayes method, as well as a specialized expectation-maximization (EM) algorithm called variational-EM algorithm. The results are reported in document modeling, text classification, and collaborative filtering. The topic modeling of LDA and PLSA models are compared on a Persian news data set. It has been observed that LDA has perplexity between  and , while the PLSA has perplexity between  and , which shows domination of LDA over PLSA. The LDA model has also been applied for dimension reduction in a document classification problem, along with the support vector machines (SVM) classification method. Two competitor models are compared, first trained on a low-dimensional representation provided by LDA and the second trained on all documents of corpus, with accuracies  and , respectively, this means we lose accuracy but it remains in reasonable range when we use LDA model for dimensionality reduction. Finally, we used the LDA and PLSA methods along with the collaborative filtering for MovieLens 1m data set, and we observed that the predictive-perplexity of LDA changes from  to  while it changes from  to  for PLSA, again showing the domination of the LDA method. 39 58 2021/06/202021/07/112021/04/19 1400/1/30 2022/08/292022/05/112023/02/22 1401/12/3 وحید حیدری Vahid Heidari دانشگاه تهران ایران vahid.heidari@ut.ac.ir سید محمود طاهری S. Mahmoud Taheri دانشگاه تهران ایران sm_taheri@ut.ac.ir مرتضی امینی Morteza Amini دانشگاه تهران ایران morteza.amini@ut.ac.ir Variational Bayes method Latent Dirichlet allocation Expectation-Maximization algorithm Machine learning Natural language processing روش بیز گوناگونی تخصیص پنهان دیریکله الگوریتم امیدریاضی-بیشینه‌سازی یادگیری ماشین پردازش زبان‌های طبیعی [1] M. S. Rasoli, B. Minaei Bidgoli, H. Faili, and M. Aminian, "Unsupervised Persian Verb Valency Induction," Signal and Data Processing, vol. 9, no. 2, 3-12, 2013.##[2] E. Asgarian, M. Kahani, and S. Sharifi, "HesNegar: Persian Sentiment WordNet," Signal and Data Processing, vol. 15, no. 1, pp. 71-86, 2018.##[3] H. Faili, "Phrasal Verb Translation from English to Persian Using Statistical Parsing," Signal and Data Processing, vol. 7, no. 1, pp. 66-76, 2010.##[4] H. Faili, H. Ghader, and M. Analoui, "A Bayesian Model for Supervised Grammar Induction," Signal and Data Processing, vol. 9, no. 1, pp. 19-34, 2012.##[5] B. Masoudi, and R. G. Saeid, "Farsi Word Sense Disambiguation with LDA Topic Model," Signal and Data Processing, vol. 12, no. 4, pp. 117-125, 2016.##]6[ E. Asgari, and J.-C. Chappelier, "Linguistic ]1[ Analysis of Persian Poems, "Proceedings of the Second Workshop on Computational Linguistics for Literature, Atlanta, Georgia, pp. 23-31, 2013.##]7[ D. Blei, A. Ng, and J. Michael, "Latent Dirichlet Allocation," Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.##]8[ S. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. Harshman, "Indexing by Latent Semantic Analysis," Journal of the American Society for Information Science, vol. 41, pp. 391-407, 1990.##https://doi.org/10.1002/(SICI)1097-4571(199009)41:63.0.CO;2-9##]9[ Y. Du, Y. Yi, X. Li, X. Chen, Y. Fan, and F. Su, "Extracting and Tracking Hot Topics of Micro-blogs Based on Improved Latent Dirichlet Allocation," Engineering Applications of Artificial Intelligence, vol. 87, pp. 103279, 2020.##]10[ C. Geigle, "Inference Methods for Latent Dirichlet Allocation,", Course notes (cs598cxz advanced topics in information retrieval), Department of Computer Science, University of Illinois at Urbana-Champaign, 2016.##]11[ Y. Gong, Q. Zhang, and X. Huang, "Hashtag Recommendation for Multimodal Microblog Posts," Neurocomputing, vol. 272, pp. 170-177, 2018.##]12[ M. Hoffman, D. Blei, and F. Bach, "Online Learning for Latent Dirichlet Allocation," Advances in Neural Information Processing Systems. pp. 856-864, 2010.##]13[ T. Hofmann, "Probabilistic Latent Semantic Indexing," SIGIR '99. pp. 50-57, 1999.##]14[ T. Hofmann, "Probabilistic Latent Semantic Analysis," UAI'99. pp. 289-296, 1999.##]15[ T. Hofmann, "Unsupervised Learning by Probabilistic Latent Semantic Analysis," Machine Learning, vol. 42, pp. 177-196, 2001.##]16[ H. Jelodar, Y. Wang, C. Yuan, X. Feng, X. Jiang, Y. Li, and L. Zhao, "Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey," Multimedia Tools Applications, vol. 78, pp. 15169-15211, 2019.##]17[ D. Jurafsky, and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, USA: Prentice Hall PTR, 2000.##]18[ J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of Massive Datasets, USA: Cambridge University Press, 2014.##]19[ B. Liu, C. Wang, Y. Wang, K. Zhang, and C. Wang, "Microblog Topic Mining Based on FR-DATM," Chinese Journal of Electronics, vol. 27, pp. 334-341, 2018.##]20[ X. Liu, Y. Gao, Z. Cao, and G. Sun, "LDA-based Topic Mining of Microblog Comments," Journal of Physics: Conference Series, vol. 1757, pp. 012118, 2021.##]21[ Y. Lu, Q. Mei, and C. Zhai, "Investigating Task Performance of Probabilistic Topic Models: An Empirical Study of PLSA and LDA," Information Retrieval, vol. 14, pp. 178-203, 2011.##]22[ H. F. Maxwell, and K. Joseph, "The MovieLens Datasets: History and Context," ACM Transactions on Interactive Intelligent Systems, vol. 5, 2015.##]23[ T. Minka, "Estimating a Dirichlet Distribution,", Technical report, M.I.T., 2000.##]24[ K. P. Morphy, Machine Learning: A Probabilistic Perspective, London, England: MIT Press, 2012.##]25[ A. Raj, M. Stephens, and J. K. Pritchard, "fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets," Genetics, vol. 197, pp. 573-589, 2014.##]26[ V. Smidl, and A. Quinn, The Variational Bayes Method in Signal Processing, Berlin Heidelberg, Germany: Springer, 2006.##[1] م. رسولی, ب. مینایی‌بیدگلی, ه. فیلی, م. امینیان, "استخراج بی ناظر ظرفیت فعل در زبان فارسی," پردازش علائم و داده‌ها, دوره ۹,شماره ۲, صفحات ۱۲-۳, ۱۳۹۱.##]1[ M. S. Rasoli, B. Minaei Bidgoli, H. Faili, and M. Aminian, "Unsupervised Persian Verb Valency Induction," Signal and Data Processing, vol. 9, no. 2, 3-12, 2013.##[2] ا. عسکریان, م. کاهانی, ش. شریفی, "حس‌نگار: شبکۀ واژگان فارسی", پردازش علائم و داده‌ها, دوره ۱۵, شمارۀ ۱, صفحات ۸۶-۷۱, ۱۳۹۷.##]2[ E. Asgarian, M. Kahani, and S. Sharifi, "HesNegar: Persian Sentiment WordNet," Signal and Data Processing, vol. 15, no. 1, pp. 71-86, 2018.##[3] ه. فیلی, "استفاده از تجزیه‌گرهای احتمالاتی زبان طبیعی جهت بهبود ترجمۀ افعال گروهی انگلیسی به فارسی," پردازش علائم و داده‌ها, دوره ۷, شماره ۱, صفحات ۷۶-۶۵, ۱۳۸۹.##]3[ H. Faili, "Phrasal Verb Translation from English to Persian Using Statistical Parsing," Signal and Data Processing, vol. 7, no. 1, pp. 66-76, 2010.##[4] ه. فیلی, ح. قادر, م. آنالویی, "یک الگوی بیزی برای استخراج با مربی گرامر زبان طبیعی," پردازش علائم و داده‌ها, دوره ۹, شماره ۱, صفحات ۳۴-۱۹, ۱۳۹۱.##]4[ H. Faili, H. Ghader, and M. Analoui, "A Bayesian Model for Supervised Grammar Induction," Signal and Data Processing, vol. 9, no. 1, pp. 19-34, 2012.##[5] ب. مسعودی, س. قوچانی, "رفع ابهام معنایی واژگان مبهم فارسی با مدل موضوعی LDA," پردازش علائم و داده‌ها, دوره ۱۲, شماره ۴, صفحات ۱۲۵-۱۱۷, ۱۳۹۴.##]5[ B. Masoudi, and R. G. Saeid, "Farsi Word Sense Disambiguation with LDA Topic Model," Signal and Data Processing, vol. 12, no. 4, pp. 117-125, 2016.##]6[ E. Asgari, and J.-C. Chappelier, "Linguistic ]1[ Analysis of Persian Poems, "Proceedings of the Second Workshop on Computational Linguistics for Literature, Atlanta, Georgia, pp. 23-31, 2013.##]7[ D. Blei, A. Ng, and J. Michael, "Latent Dirichlet Allocation," Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.##]8[ S. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. Harshman, "Indexing by Latent Semantic Analysis," Journal of the American Society for Information Science, vol. 41, pp. 391-407, 1990.##https://doi.org/10.1002/(SICI)1097-4571(199009)41:63.0.CO;2-9##]9[ Y. Du, Y. Yi, X. Li, X. Chen, Y. Fan, and F. Su, "Extracting and Tracking Hot Topics of Micro-blogs Based on Improved Latent Dirichlet Allocation," Engineering Applications of Artificial Intelligence, vol. 87, pp. 103279, 2020.##]10[ C. Geigle, "Inference Methods for Latent Dirichlet Allocation,", Course notes (cs598cxz advanced topics in information retrieval), Department of Computer Science, University of Illinois at Urbana-Champaign, 2016.##]11[ Y. Gong, Q. Zhang, and X. Huang, "Hashtag Recommendation for Multimodal Microblog Posts," Neurocomputing, vol. 272, pp. 170-177, 2018.##]12[ M. Hoffman, D. Blei, and F. Bach, "Online Learning for Latent Dirichlet Allocation," Advances in Neural Information Processing Systems. pp. 856-864, 2010.##]13[ T. Hofmann, "Probabilistic Latent Semantic Indexing," SIGIR '99. pp. 50-57, 1999.##]14[ T. Hofmann, "Probabilistic Latent Semantic Analysis," UAI'99. pp. 289-296, 1999.##]15[ T. Hofmann, "Unsupervised Learning by Probabilistic Latent Semantic Analysis," Machine Learning, vol. 42, pp. 177-196, 2001.##]16[ H. Jelodar, Y. Wang, C. Yuan, X. Feng, X. Jiang, Y. Li, and L. Zhao, "Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey," Multimedia Tools Applications, vol. 78, pp. 15169-15211, 2019.##]17[ D. Jurafsky, and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, USA: Prentice Hall PTR, 2000.##]18[ J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of Massive Datasets, USA: Cambridge University Press, 2014.##]19[ B. Liu, C. Wang, Y. Wang, K. Zhang, and C. Wang, "Microblog Topic Mining Based on FR-DATM," Chinese Journal of Electronics, vol. 27, pp. 334-341, 2018.##]20[ X. Liu, Y. Gao, Z. Cao, and G. Sun, "LDA-based Topic Mining of Microblog Comments," Journal of Physics: Conference Series, vol. 1757, pp. 012118, 2021.##]21[ Y. Lu, Q. Mei, and C. Zhai, "Investigating Task Performance of Probabilistic Topic Models: An Empirical Study of PLSA and LDA," Information Retrieval, vol. 14, pp. 178-203, 2011.##]22[ H. F. Maxwell, and K. Joseph, "The MovieLens Datasets: History and Context," ACM Transactions on Interactive Intelligent Systems, vol. 5, 2015.##]23[ T. Minka, "Estimating a Dirichlet Distribution,", Technical report, M.I.T., 2000.##]24[ K. P. Morphy, Machine Learning: A Probabilistic Perspective, London, England: MIT Press, 2012.##]25[ A. Raj, M. Stephens, and J. K. Pritchard, "fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets," Genetics, vol. 197, pp. 573-589, 2014.##]26[ V. Smidl, and A. Quinn, The Variational Bayes Method in Signal Processing, Berlin Heidelberg, Germany: Springer, 2006.## ##

بهبود دقت داده‌های فناوری تیکه‌نگاری صوتی با استفاده از روش آستانه‌گذاری‌ فضای ‌فازی Improving accuracy of Acoustic Tomographic data using Phase-Space Thresholding method 1 1 فناوری تیکه­نگاری صوتی، یکی از شاخه‌های دانش سنجش‌ازدور جهت پایش منابع آب سطحی ‌است. داده‌های پرت در این فناوری تابه‌حال با استفاده از روش انحراف معیار تشخیص داده‌شده و حذف می­شده­اند. در این تحقیق، از روش آستانه­گذاری فضای فازی به‌ منظور تشخیص داده­های پرت و  از روش میانگین چهار نقطه در طرفین هر داده پرت جهت جایگزینی داده‌های پرت تشخیص داده‌شده استفاده‌شده است. داده­های مورد استفاده قرار گرفته در این تحقیق شامل مجموعه داده 12 روزه برداشت شده از رودخانه گونو واقع در شهر میوشی، استان هیروشیما ژاپن بوده است. مجموعاً  8017 داده، معادل با  32 %  از 25031 داده اولیه، به‌عنوان نقاط پرت شناسایی و جایگزین شدند. همچنین مقدار انحراف معیار داده­ها پیش از انجام فرایند پرت­کاوی 206/0 و پس از روش آستانه­گذاری فضای فازی به 119/0 رسید. نتایج نشان داد که روش آستانه‌گذاری فضای فازی نسبت به روش انحراف معیار از دقت و عملکرد بالاتری در شناسایی نقاط پرت برخوردار است. در نهایت مشاهده شد که مقایسه خطای نسبی اندازه‌گیری دبی بین روش آستانه­گذاری فضای فازی و دبی-اشل (به عنوان مرجع) در اکثر نقاط کمتر از ۲۰ درصد است. درحالی‌که این مقدار برای روش انحراف معیار و دبی-اشل به بیش از 50 درصد می‌رسد. 2 The acquisition of reliable flow velocity and streamflow estimates is vital and essential in aquatic studies.   Acoustic Tomography Technology is a branch of remote sensing science which innovatively developed for continuous monitoring of surface water currents in oceans, seas, and in recent years in rivers and is a promising method to measure Flow characteristics such as velocity & discharge with high accuracy and continuously in time. The output of this system impressed by the influence of unknown factors and after the initial processing of raw data, some spikes appear in the data. Although the developers of this system have stated that a source of spurious data can be a complex salinity distribution in estuarine regions, failure to identify these outliers will cause errors in measurements and increase the error of data mining and time series forecasting algorithms. In the previous studies, the spikes removed using the standard deviation method without any replacement.  In this study, Phase-Space Thresholding (PST) is proposed to detect and remove the spikes, which was developed for despiking output of Acoustic Doppler Velocimeter (ADV) data. This method combines three concepts: 1) the differentiation enhances the high-frequency portion of a signal, 2) the expected maximum of a normal, random series is given by the universal threshold, and 3) the good data cluster in a dense cloud in phase space. These concepts are used to construct an ellipsoid in a three-dimensional phase-space, then points which lie outside the threshold ellipsoid are designated as spikes. An important advantage of this method in comparison with various other methods is that it requires no parameters. Furthermore, another advantage of this method against the standard deviation method is the replacement of detected spikes with a reliable value. for replacement of detected spike’s values, we used the mean value of two adjacent data points on either side of the detected spike. After 6 iterations of implementing the PST method on the input dataset a total of 8017 data, which is 32% of 25031 data were identified as spikes and replaced with a correct value. Moreover, the standard deviation value before despiking was 0/206 and after applying the PST method improves to 0/119. This change in standard deviation value shows that the data dispersion around signal mean reduces due to despiking process. The results show that the PST method has higher accuracy in comparison with the standard deviation approach. Finally, it was observed that the comparison of the relative discharge error between the output of the PST method and the rating curve data (as a reference) is almost less than 20%. While this value exceeds 50% in the comparison between the standard deviation and the rating curve data. 59 68 2021/06/202021/07/112021/04/192021/04/15 1400/1/26 2022/08/292022/05/112023/02/222022/05/11 1401/2/21 امیرحسین حسن آبادی AmirHosein Hasanabadi دانشگاه علم و صنعت ایران ایران AmirhoseinHsnabadi@gmail.com ابراهیم جباری Ebrahim Jabbari دانشگاه علم و صنعت ایران ایران jabbari@iust.ac.ir مسعود بحرینی مطلق Masoud Bahreinimotlagh موسسه تحقیقات آب ایران m.bahreini@wri.ac.ir حسین علیزاده *, Hossein Alizadeh دانشگاه علم و صنعت ایران ایران alizadeh@iust.ac.ir یوسف الفت میری Yousef Olfatmiri دانشگاه علم و صنعت ایران ایران yousefolfatmiri@gmail.com Despking Acoustic Tomography Phase-Space Thresholding پرت‌کاوی تیکه‌نگاری صوتی داده‌‌کاوی آستانه‌گذاری فضای فازی [1] K. Kawanisi, A. Kaneko, S. Nigo, and M. Soltaniasl, "New acoustic system for continuous measurement of river discharge and water temperature," Water Sci. Eng., vol. 3,no.1,pp.47-55,2010,doi: 10.3882/j.issn.1674-2370.2010.01.005.##]2 [مسعود بحرینی مطلق و همکاران،(1398)، "امکان سنجی پایش سیلاب با استفاده از دستگاه تیکه¬نگاری صوتی رودخانه¬ای و تعیین دقت اندازه¬گیری، حداق و حداکثر برد اندازه¬گیری"، اکوهیدرولوژی، دوره6، شماره3،پاییز 1398، ص 592-585##]3 [حداد، پریسا، پایان¬نامه کارشناسی ارشد (1396)، عنوان: تشخیص داده پرت در داده¬های سری زمانی با الگوریتم داده¬کاوی، دانشگاه آزاد اسلامی، واحد مرودشت، دانشکده مهندسی، گروه کامپیوتر و فناوری اطلاعات.##]4 [ مسعود بحرینی مطلق و همکاران،(1399)، "امکان سنجی پایش خودکار رودخانه¬های ایران با استفاده از فناوری تیکه‌نگاری¬ صوتی رودخانه¬ای 50 کیلوهرتز"، مجله انجمن مهندسی صوتیات ایران، سال هشتم، شماره1، pp. 14-21.##]5 [مسعود بحرینی مطلق و همکاران،(1398)، "امکان سنجی پایش جریان¬های خلیج فارس با استفاده از فناوری تیکه¬نگاری صوتی دریایی 10 کیلوهرتز"، نشریه مهندسی دریا، سال پانزدهم، شماره30، پاییز و زمستان 1398، (131-138) .##]6 [مسعود بحرینی مطلق و همکاران،(1399)، "پایش پیوسته دمای آب با استفاده از فناوری تیکه¬نگاری صوتی"، نشریه مهندسی عمران امیرکبیر، دوره 51 شماره5، سال 1399.##]7[ مسعود بحرینی¬مطلق و همکاران. 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تشخیص اسلحه دستی بااستفاده از مدل شبکه‎‎های عصبی کانولوشنال سه بعدی Detection of handgun using 3D convolutional neural network model (3DCNNs) 1 1 از آنجاییکه رفتار افراد در ویدئوها بصورت سیگنال‎های سه بعدی است و جستجوی یک عمل خاص بسیار دشوار می‎باشد، لذا نیاز به یک تکنیک مناسب جهت تشخیص خودکار دزدان مسلح در ویدئو‎های امنیتی در حال ضبط می‎باشد. در این مقاله روشی سریع و کارا جهت شناسایی موقعیت افراد و سپس تشخیص اسلحه در دست آنها، با استخراج فریم‌های تصاویر برگرفته از ویدئوها و بدون حذف نقاط اصلی، ارائه شده است. در مرحله نخست و بمنظور استخراج فریم‌های تصاویر برگرفته از ویدئوها، الگوریتم جداسازی با نرخ فریم مشخص اعمال خواهد شد و تمامی تصاویر در یک پوشه قرار می‎گیرند. سپس روی تمامی تصاویر بدست آمده طبقه‎بند(HC)  Haar Cascade اعمال شده تا نقاط کلیدی یا فریم‌های مربوط به تصاویر کل بدن استخراج شوند و باقی پس‎زمینه‎ها از تصاویر حذف گردند. در انتها، نمونه‌های هر ویدئو در قالب ماتریس چهار بعدی شامل تعداد دنباله فریم‌های هر ویدئو، عرض، ارتفاع و تعداد کانال تصویر به شبکه 3DCNNs ارسال می‌شود تا سلاح در تصاویر شناسایی شوند. لذا نوآوری مقاله ترکیب طبقه‎بند HCو  3DCNNs بمنظور افزایش سرعت و کارایی تشخیص اسلحه می‎باشد. همچنین بمنظور بررسی دقت مدل پیشنهادی، از پارامترهای نرخ مثبت صحیح و مثبت کاذب، مقدار پیش بینی مثبت و نرخ تشخیص کاذب استفاده‎ می‎شود.   2 Since the behavior of people in the videos are in 3D signals format and they are long, it is difficult to search for a specific action. Therefore, a suitable technique in live security videos is required to detect ongoing armed thieves to reduce the occurrence of crime and theft. The innovation of this paper is to provide a rapid and efficient method for detecting guns in frames of images taken from videos without deleting the main points. The hierarchy of object recognition is that in order to extract frames from images derived from videos, the separation algorithm will be applied at a specified frame rate and all images will be placed in a folder. Then, video samples are divided into three categories of training, validation and testing, and using Haar Cascade (HC) classification, the frames of whole body images are extracted and the rest of the backgrounds are removed from the images. The reason for choosing this method is that the HC classification is resistant to rotation of images and also this algorithm has shown good performance compared to complex calculations. Therefore, in our proposed model, we will use this algorithm as a whole body diagnosis. This is done by detecting the Region of Interest (ROI) area by cutting the selected areas, followed by subtracting the background to eliminate unwanted backgrounds. All key points of selection and extraction are stored inside a folder. Finally, all images are sent to 3D convolutional Neural Networks (3DCNNs) to detect weapons in the images. Finally, in order to evaluate the performance of the system in terms of accuracy, it is used with correct positive rate parameters, false positive rate, positive prediction value and false detection rate. As can be seen in the results of the tests, the highest gun detection rate is related to the 3DCNNs model with a detection rate of 96.1%, followed by the best detection model rate related to YOLO V3 and with a detection rate of 95.6%.   69 79 2021/06/202021/07/112021/04/192021/04/152021/08/15 1400/5/24 2022/08/292022/05/112023/02/222022/05/112023/07/8 1402/4/17 سارا معتمد Sara Motamed دانشگاه آزاد اسلامی ایران samotamed@gmail.com الهام عسکری Elham Askari دانشگاه آزاد اسلامی ایران askary.elham@gmail.com 3D Neural Networks (3DCNNs) Haar-Cascade (HC) Classification Object Recognition Full Body Recognition. شبکه‎های عصبی سه بعدی (3DCNN) طبقه‎بندی Haar Cascade (HC) بازشناسی اشیاء شناسایی کل بدن. [1] Enrquez, F., Soria, L. M., Alvarez-Garca, J. A., Caparrini, F. S., Velasco, F., Deniz, O. , Vallez, N. "Vision and crowdsensing technology for an optimal response in physical-security", International Conference on Computational Science, Springer, pp. 15 - 26, 2019.##[2] Tessler, R. A., Mooney, S. J., Witt, C. E., O'Connell, K., Jenness, J., Vavilala, M. S., Rivara, F. P. "Use of _rearms in terrorist attacks: di_erences between the United States, Canada, Europe, Australia, and New Zealand", JAMA internal medicine. Vol. 177, pp.1865 - 1868. 2017.##[3] Nercessian, S., Panetta, K., Agaian, S. "Automatic detection of potential threat objects in x-ray luggage scan images", IEEE Conference on Technologies for Homeland Security, IEEE. pp. 504 - 509. 2008.##[4] Xiao, Z., Lu, X., Yan, J., Wu, L., Ren, L. "Automatic detection of concealed pistols using passive millimeter wave imaging", IEEE International Conference on Imaging Systems and Techniques (IST), IEEE. pp. 1 - 4. 2015.##[5] Flitton, G., Breckon, T. P., Megherbi, N. "A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery", Pattern Recognition. 2420 - 2436. 2013.##[6] Tiwari, R. K., Verma, G. K. "A computer vision based framework for visual gun detection using Harris interest point detector", Procedia Computer##Science. Vol. 54, pp. 703 - 712. 2015.##[7] Halima, N. B., Hosam, O. "Bag of words based surveillance system using support vector machines", Int. J. Secur. Appl. Vol. 10, pp. 331- 346. 2016.##[8] Gelana, F., Yadav, A. "Firearm detection from surveillance cameras using image processing and machine learning techniques", Smart Innovations in Communication and Computational Sciences, Springer. pp. 25- 34. 2019.##[9] Girshick, R. "Fast R-CNN", Proceedings of the IEEE international conference on computer vision. pp. 1440 - 1448. 2015.##[10] Ren, S., He, K., Girshick, R., Sun, J. "Faster R-CNN: Towards real-time object detection with region proposal networks", Advances in Neural Information Processing Systems. pp. 91 - 99. 2015.##[11] Verma, G. K., Dhillon, A. "A handheld gun detection using faster R-CNN deep learning", Proceedings of the 7th International Conference on##Computer and Communication Technology. pp. 84 - 88. 2017.##[12] IMFDB: Internet Movie Firearms Database, http://www.imfdb.org/ wiki/Main_Page, 2020.##[13] Olmos, R., Tabik, S., Herrera, F. "Automatic handgun detection alarm in videos using deep learning", Neurocomputing. Vol. 275, pp. 66 - 72. 2018.##[14] Redmon, J., Divvala, S., Girshick, R., Farhadi, A. "You only look once: Uni_ed, real-time object detection", Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779 - 788. 2016.##[15] Redmon, J., Farhadi, A. "Yolo9000: better, faster, stronger", Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7263 - 7271. 2017.##[16] Farhadi, A., Redmon, J. "Yolov3: An incremental improvement", Computer Vision and Pattern Recognition. 2018.##[17] de Azevedo Kanehisa, R. F., de Almeida Neto, A. "Firearm detection using convolutional neural networks", ICAART. Vol. 2, pp. 707 - 714. 2019.##[18] Susarla, P., Agrawal, U., Jayagopi, D. B. "Human weapon-Activity recognition in surveillance videos using structural-RNN," MedPRAI '18, Rabat, Morocco, pp.101-108. 2018.##[19] Qi., D, Tan., W, Liu., Z, Yao., Q, Liu., J, "A dataset and system for real-time gun detection in surveillance video using deep learning ," IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2021.##[20] Narejo, S., Pandey, B., Esenarro vargas, D., Rodriguez, R., Anjum, M.R. "Weapon detection using YOLOV3 for smart surveillance system," Mathematical Problems in Engineering, pp. 1-9. 2021.##[21] Velasco-Mata., A, Ruiz-Santaquiteria., J, Vallez., N, Deniz., O, "Using human pose information for handgun detection," Neural Computing and Applications, 33: pp.17273-17286. 2021.##[22] Pishchulin, L., Jain, A., Andriluka, M., Thormahlen, T., Schiele, B. 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"Activity recognition using biomechanical model based pose estimation", European Conference on Smart Sensing and Context, Springer. pp. 42 - 55. 2010.##[27] Eiert, S. "Activity Recognition from 2D pose using an LSTM RNN", 2020.##[28] Luvizon, D. C., Picard, D., Tabia, H. "2D/3D pose estimation and action recognition using multitask deep learning", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5137 - 5146. 2018.##[29] Velasco-Mata, A. "Human pose information as an improvement factor for handgun detection", Master's thesis, Escuela Superior de Inform_atica (UCLM), 2020.##[30] Puad, A., Tahir2, M. D. "Human Gait Silhouettes Extraction Using Haar Cascade Classifier on OpenCV", International Conference on Modelling & Simulation. Vol. 25, pp. 105 - 111. 2017.##[31] Grega, M., Matiola'nski, A., Guzik, P., Leszczuk, M. "Automated detection of firearms and knives in a CCTV image", Sensors. Vol. 16. 2017.##[32] http://www.imfdb.org/ wiki/Main_Page.##[33] Schmidt, A., Kasiński, A. "The performance of the haar cascade classifiers applied to the face and eyes detection," in Computer Recognition Systems 2, vol. 45, pp. 816-823, 2007.##[34] Lienhart, R., Kuranov, A., Pisarevsky, V., Report, M. R. L. T. "Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection." in Joint Pattern Recognition Symposium, pp. 297-304. 2003.##[35] Paulo Menezes, J., Carlos Barreto, J. "Face Tracking Based On Haar-Like Features And Eigenfaces," in IFAC/EURON Symposium on Intelligent Autonomous Vehicles , pp. 1-6, 2004.##[36] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick, C. L. "Microsoft COCO: Common Objects in Context", European conference on computer vision, Springer. pp. 740 - 755. 2014.##[37] Almaadeed, N., Elharrouss, O., Q'AlMaadeed,S., Bouridane, A., Beghdadi, A. "A Novel Approach for Robust Multi Human Action Recognition and Summarization based on 3D Convolutional Neural Networks", Computer Vision and Pattern Recognition, pp. 1-12. 2021.##[1] Enrquez, F., Soria, L. M., Alvarez-Garca, J. A., Caparrini, F. S., Velasco, F., Deniz, O. , Vallez, N. "Vision and crowdsensing technology for an optimal response in physical-security", International Conference on Computational Science, Springer, pp. 15 - 26, 2019.##[2] Tessler, R. A., Mooney, S. J., Witt, C. E., O'Connell, K., Jenness, J., Vavilala, M. S., Rivara, F. P. "Use of _rearms in terrorist attacks: di_erences between the United States, Canada, Europe, Australia, and New Zealand", JAMA internal medicine. Vol. 177, pp.1865 - 1868. 2017.##[3] Nercessian, S., Panetta, K., Agaian, S. "Automatic detection of potential threat objects in x-ray luggage scan images", IEEE Conference on Technologies for Homeland Security, IEEE. pp. 504 - 509. 2008.##[4] Xiao, Z., Lu, X., Yan, J., Wu, L., Ren, L. "Automatic detection of concealed pistols using passive millimeter wave imaging", IEEE International Conference on Imaging Systems and Techniques (IST), IEEE. pp. 1 - 4. 2015.##[5] Flitton, G., Breckon, T. P., Megherbi, N. "A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery", Pattern Recognition. 2420 - 2436. 2013.##[6] Tiwari, R. K., Verma, G. K. "A computer vision based framework for visual gun detection using Harris interest point detector", Procedia Computer##Science. Vol. 54, pp. 703 - 712. 2015.##[7] Halima, N. B., Hosam, O. "Bag of words based surveillance system using support vector machines", Int. J. Secur. Appl. Vol. 10, pp. 331- 346. 2016.##[8] Gelana, F., Yadav, A. "Firearm detection from surveillance cameras using image processing and machine learning techniques", Smart Innovations in Communication and Computational Sciences, Springer. pp. 25- 34. 2019.##[9] Girshick, R. "Fast R-CNN", Proceedings of the IEEE international conference on computer vision. pp. 1440 - 1448. 2015.##[10] Ren, S., He, K., Girshick, R., Sun, J. "Faster R-CNN: Towards real-time object detection with region proposal networks", Advances in Neural Information Processing Systems. pp. 91 - 99. 2015.##[11] Verma, G. K., Dhillon, A. "A handheld gun detection using faster R-CNN deep learning", Proceedings of the 7th International Conference on##Computer and Communication Technology. pp. 84 - 88. 2017.##[12] IMFDB: Internet Movie Firearms Database, http://www.imfdb.org/ wiki/Main_Page, 2020.##[13] Olmos, R., Tabik, S., Herrera, F. "Automatic handgun detection alarm in videos using deep learning", Neurocomputing. Vol. 275, pp. 66 - 72. 2018.##[14] Redmon, J., Divvala, S., Girshick, R., Farhadi, A. "You only look once: Uni_ed, real-time object detection", Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779 - 788. 2016.##[15] Redmon, J., Farhadi, A. "Yolo9000: better, faster, stronger", Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7263 - 7271. 2017.##[16] Farhadi, A., Redmon, J. "Yolov3: An incremental improvement", Computer Vision and Pattern Recognition. 2018.##[17] de Azevedo Kanehisa, R. F., de Almeida Neto, A. "Firearm detection using convolutional neural networks", ICAART. Vol. 2, pp. 707 - 714. 2019.##[18] Susarla, P., Agrawal, U., Jayagopi, D. B. "Human weapon-Activity recognition in surveillance videos using structural-RNN," MedPRAI '18, Rabat, Morocco, pp.101-108. 2018.##[19] Qi., D, Tan., W, Liu., Z, Yao., Q, Liu., J, "A dataset and system for real-time gun detection in surveillance video using deep learning ," IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2021.##[20] Narejo, S., Pandey, B., Esenarro vargas, D., Rodriguez, R., Anjum, M.R. "Weapon detection using YOLOV3 for smart surveillance system," Mathematical Problems in Engineering, pp. 1-9. 2021.##[21] Velasco-Mata., A, Ruiz-Santaquiteria., J, Vallez., N, Deniz., O, "Using human pose information for handgun detection," Neural Computing and Applications, 33: pp.17273-17286. 2021.##[22] Pishchulin, L., Jain, A., Andriluka, M., Thormahlen, T., Schiele, B. "Articulated people detection and pose estimation: Reshaping the future", IEEE Conference on Computer Vision and Pattern Recognition, IEEE. pp. 3178 - 3185. 2012.##[23] Gkioxari, G., Hariharan, B., Girshick, R., Malik, J. "Using k-pose lets for detecting people and localizing their key points", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3582 - 3589. 2014.##[24] Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y. A. "OpenPose: real time multi-person 2D pose estimation using Part Annity Fields", IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 43, pp.172 - 186. 2019.##[25] Thurau, C., Hlav_ac, V. "Pose primitive based human action recognition in videos or still images", in: 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE. pp. 1 - 8. 2008.##[26] Reiss, A., Hendeby, G., Bleser, G., Stricker, D. "Activity recognition using biomechanical model based pose estimation", European Conference on Smart Sensing and Context, Springer. pp. 42 - 55. 2010.##[27] Eiert, S. "Activity Recognition from 2D pose using an LSTM RNN", 2020.##[28] Luvizon, D. C., Picard, D., Tabia, H. "2D/3D pose estimation and action recognition using multitask deep learning", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5137 - 5146. 2018.##[29] Velasco-Mata, A. "Human pose information as an improvement factor for handgun detection", Master's thesis, Escuela Superior de Inform_atica (UCLM), 2020.##[30] Puad, A., Tahir2, M. D. "Human Gait Silhouettes Extraction Using Haar Cascade Classifier on OpenCV", International Conference on Modelling & Simulation. Vol. 25, pp. 105 - 111. 2017.##[31] Grega, M., Matiola'nski, A., Guzik, P., Leszczuk, M. "Automated detection of firearms and knives in a CCTV image", Sensors. Vol. 16. 2017.##[32] http://www.imfdb.org/ wiki/Main_Page.##[33] Schmidt, A., Kasiński, A. "The performance of the haar cascade classifiers applied to the face and eyes detection," in Computer Recognition Systems 2, vol. 45, pp. 816-823, 2007.##[34] Lienhart, R., Kuranov, A., Pisarevsky, V., Report, M. R. L. T. "Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection." in Joint Pattern Recognition Symposium, pp. 297-304. 2003.##[35] Paulo Menezes, J., Carlos Barreto, J. "Face Tracking Based On Haar-Like Features And Eigenfaces," in IFAC/EURON Symposium on Intelligent Autonomous Vehicles , pp. 1-6, 2004.##[36] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick, C. L. "Microsoft COCO: Common Objects in Context", European conference on computer vision, Springer. pp. 740 - 755. 2014.##[37] Almaadeed, N., Elharrouss, O., Q'AlMaadeed,S., Bouridane, A., Beghdadi, A. "A Novel Approach for Robust Multi Human Action Recognition and Summarization based on 3D Convolutional Neural Networks", Computer Vision and Pattern Recognition, pp. 1-12. 2021.## ##

تشخیص داده‌های پَرت در داده‌های جریانی با استفاده از مدل مبتنی بر QLattice و یادگیری آنلاین Outlier Detection on Data Streams Using a QLattice-based Model and Online Learning 1 1 تشخیص داده‌های پَرت در جریان داده (داده‌های جریانی)، که ویژگی‌های خاصی نظیر نامحدود بودن و گذرا بودن را دارند، چالش‌های زیادی دارد. برای این منظور، در این پژوهش، یک رویکرد مبتنی بر مدل طبقه­بندی QLattice، که بر مبنای محاسبات کوانتوم کار می­کند و در کاربرد مورد هدف عملکرد بهتری نسبت به دیگر روش‌های طبقه‌بندی دارد، معرفی می‌کنیم. با توجه به امکان تغییر توزیع داده­ها در طول زمان در داده‌های جریانی، طرحی برای بهره‌گیری از یادگیری افزایشی آنلاین نیز در روش پیشنهادی ارائه می‌شود. با توجه به نامحدود بودن جریان داده­ها و حافظه­ی پردازشی محدود، فرآیند تشخیص بر روی پنجره‌ای از داده‌ها که همواره با داده‌های نمونه‌برداری شده از پنجره‌های قبلی به‌روزرسانی می‌شود، اعمال می‌گردد. تابعی نیز برای حل مشکل نامتوازن بودن داده­ها طراحی شده که از روش نمونه­برداری برای حل این مشکل بهره می­گیرد. نتایج آزمایشات نشان می­دهد که رویکرد پیشنهادی دقت عملکرد بهتری نسبت به روش­های دیگر دارد. 2 With the advancement of computer science, the dramatic developments in data mining area and their increasing applications, the identification of outlier or anomaly data has also become one of the most important research topics. In most applications, the outlier data contain beneficial information that can be used to gain useful knowledge. Today, there are a large number of applications on data streams, in the vast majority of which the discovery of outlier/anomaly data is very important and in some cases vital. Detection of anomalies is an important way for detecting frauds, network intrusion detection, detection of abnormal behaviors in monitoring systems, and other rare events that are always of great importance; but they are often difficult to identify. Most of the existing efficient outlier detection algorithms have been designed for the static data. While outlier detection is more challenging in data streams, where data are generating continuously and has especial properties such as infinity and transience. In this research, we introduce an approach based on the QLattice classification model, which works based on the quantum computing and performs better in the intended application than other classification methods. Given the possibility of changing the distribution of data over time in streaming data, a scheme to take advantage of online incremental learning is also applied in the proposed method. Considering the unlimited data flow and limited processing memory, the detection process is applied to a window of data that is constantly updated with data sampled from previous windows. A function is also designed to solve the problem of data imbalance, which uses the random sampling technique to solve this issue. The results of experiments obtained on benchmark datasets show that the proposed approach has better performance than other methods. 81 98 2021/06/202021/07/112021/04/192021/04/152021/08/152021/04/18 1400/1/29 2022/08/292022/05/112023/02/222022/05/112023/07/82022/05/11 1401/2/21 سحر فردین Sahar Fardin دانشگاه شهید مدنی آذربایجان ایران s.fardin@azaruniv.ac.ir مهدی هاشم‌زاده Mahdi Hashemzadeh دانشگاه شهید مدنی آذربایجان ایران meh_hashemzadeh@yahoo.com Outlier detection Data streams Online learning Incremental learning Data mining تشخیص داده پرت جریان داده یادگیری آنلاین یادگیری افزایشی داده‌کاوی [1] Y. Djenouri, D. Djenouri, and J. C.-W. Lin, "Trajectory Outlier Detection: New Problems and Solutions for Smart Cities," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 15, no. 2, pp. 1-28, 2021.##[2] A. Belhadi, Y. Djenouri, G. Srivastava, D. Djenouri, A. Cano, and J. C.-W. Lin, "A Two-Phase Anomaly Detection Model for Secure Intelligent Transportation Ride-Hailing Trajectories," IEEE Transactions on Intelligent Transportation Systems, 2020.##[3] M. Hashemzadeh and A. Zademehdi, "Fire detection for video surveillance applications using ICA K-medoids-based color model and efficient spatio-temporal visual features," Expert Systems with Applications, vol. 130, pp. 60-78, 2019.##[4] M. Hashemzadeh, G. Pan, and M. Yao, "Counting moving people in crowds using motion statistics of feature-points," Multimedia tools and applications, vol. 72, no. 1, pp. 453-487, 2014.##[5] M. Hashemzadeh, G. Pan, Y. Wang, M. Yao, and J. Wu, "Combining velocity and location-specific spatial clues in trajectories for counting crowded moving objects," International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 02, p. 1354003, 2013.##[6] M. Hashemzadeh and N. Farajzadeh, "Combining keypoint-based and segment-based features for counting people in crowded scenes," Information Sciences, vol. 345, pp. 199-216, 2016.##[7] N. Farajzadeh, A. Karamiani, and M. Hashemzadeh, "A fast and accurate moving object tracker in active camera model," Multimedia Tools and Applications, vol. 77, no. 6, pp. 6775-6797, 2018.##[8] S. Sadik and L. Gruenwald, "Research issues in outlier detection for data streams," Acm Sigkdd Explorations Newsletter, vol. 15, no. 1, pp. 33-40, 2014.##[9] J. Han, M. Kamber, and J. Pei, "Data mining: concepts and techniques, Waltham, MA," Morgan Kaufman Publishers, vol. 10, pp. 978-1, 2012.##[10] D. M. Hawkins, Identification of outliers. Springer, 1980.##[11] S. Mehta, "Concept drift in streaming data classification: Algorithms, Platforms and issues," Procedia computer science, vol. 122, pp. 804-811, 2017.##[12] V. Hodge and J. Austin, "A survey of outlier detection methodologies," Artificial intelligence review, vol. 22, no. 2, pp. 85-126, 2004.##[13] M. Singh and R. Pamula, "ADINOF: adaptive density summarizing incremental natural outlier detection in data stream," Neural Computing and Applications, pp. 1-17, 2021.##[14] M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, "Outlier detection for temporal data: A survey," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9, pp. 2250-2267, 2013.##[15] Y. Yang, L. Chen, and C. Fan, "ELOF: fast and memory-efficient anomaly detection algorithm in data streams," Soft Computing, pp. 1-12, 2020.##[16] L. Chen, W. Wang, and Y. Yang, "CELOF: Effective and fast memory efficient local outlier detection in high-dimensional data streams," Applied Soft Computing, vol. 102, p. 107079, 2021.##[17] S. Thudumu, P. Branch, J. Jin, and J. J. Singh, "A comprehensive survey of anomaly detection techniques for high dimensional big data," Journal of Big Data, vol. 7, no. 1, pp. 1-30, 2020.##[18] M. V. Joshi, R. C. Agarwal, and V. Kumar, "Mining needle in a haystack: classifying rare classes via two-phase rule induction," in Proceedings of the 2001 ACM SIGMOD international conference on Management of data, 2001, pp. 91-102.##[19] S. Hawkins, H. He, G. Williams, and R. Baxter, "Outlier detection using replicator neural networks," in International Conference on Data Warehousing and Knowledge Discovery, 2002: Springer, pp. 170-180.##[20] M. U. Togbe et al., "Anomaly Detection for Data Streams Based on Isolation Forest Using Scikit-Multiflow," in International Conference on Computational Science and Its Applications, 2020: Springer, pp. 15-30.##[21] G. Han, J. Tu, L. Liu, M. Martínez-García, and Y. Peng, "Anomaly Detection Based on Multidimensional Data Processing for Protecting Vital Devices in 6G-Enabled Massive IIoT," IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5219-5229, 2021.##[22] N. M. R. SURI and G. Athithan, Outlier detection: techniques and applications. Springer, 2019.##[23] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne, "On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms," Data Mining and Knowledge Discovery, vol. 8, no. 3, pp. 275-300, 2004.##[24] C. C. Aggarwal, S. Y. Philip, J. Han, and J. Wang, "A framework for clustering evolving data streams," in Proceedings 2003 VLDB conference, 2003: Elsevier, pp. 81-92.##[25] I. Assent, P. Kranen, C. Baldauf, and T. Seidl, "Anyout: Anytime outlier detection on streaming data," in International Conference on Database Systems for Advanced Applications, 2012: Springer, pp. 228-242.##[26] F. Angiulli and F. Fassetti, "Detecting distance-based outliers in streams of data," in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007, pp. 811-820.##[27] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, "LOF: identifying density-based local outliers," in ACM sigmod record, 2000, vol. 29, no. 2: ACM, pp. 93-104.##[28] G. S. Na, D. Kim, and H. Yu, "DILOF: Effective and memory efficient local outlier detection in data streams," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 1993-2002.##[29] M. Salehi, C. Leckie, J. C. Bezdek, T. Vaithianathan, and X. Zhang, "Fast memory efficient local outlier detection in data streams," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, pp. 3246-3260, 2016.##[30] J. Gao, W. Ji, L. Zhang, A. Li, Y. Wang, and Z. Zhang, "Cube-based incremental outlier detection for streaming computing," Information Sciences, vol. 517, pp. 361-376, 2020.##[31] X. Qin, L. Cao, E. A. Rundensteiner, and S. Madden, "Scalable Kernel Density Estimation-based Local Outlier Detection over Large Data Streams," in EDBT, 2019, pp. 421-432.##[32] F. T. Liu, K. M. Ting, and Z.-H. Zhou, "Isolation forest," in 2008 Eighth IEEE International Conference on Data Mining, 2008: IEEE, pp. 413-422.##[33] Z. Ding and M. Fei, "An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window," IFAC Proceedings Volumes, vol. 46, no. 20, pp. 12-17, 2013.##[34] S. C. Tan, K. M. Ting, and T. F. Liu, "Fast anomaly detection for streaming data," in Twenty-Second International Joint Conference on Artificial Intelligence, 2011.##[35] S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, "Unsupervised real-time anomaly detection for streaming data," Neurocomputing, vol. 262, pp. 134-147, 2017.##[36] A. Tuor, S. Kaplan, B. Hutchinson, N. Nichols, and S. Robinson, "Deep learning for unsupervised insider threat detection in structured cybersecurity data streams," arXiv preprint arXiv:1710.00811, 2017.##[37] B. V. Ashok, "QLattice Environment and Feyn QGraph Models - A new Perspective towards Deep Learning," Zenodo, 2020.##[38] M. Machado. "A new kind of AI." https://medium.com/abzuai/a-new-kind-of-ai-7665f8198877 (accessed.##[39] K. B. T. Jelen. https://docs.abzu.ai/docs/guides/qlattice.html (accessed.##[40] C. Cave. "Opening the black box." https://medium.com/abzuai/opening-the-black-box-247a63ce553e (accessed.##[41] J. Brownlee, "Why one-hot encode data in machine learning," Machine Learning Mastery, 2017.##[42] M. DelSole. "What is One Hot Encoding and How to Do It." https://medium.com/@michaeldelsole/what-is-one-hot-encoding-and-how-to-do-it-f0ae272f1179 (accessed.##[43] A. Fernández, S. Garcia, F. Herrera, and N. V. Chawla, "SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary," Journal of artificial intelligence research, vol. 61, pp. 863-905, 2018.##[44] P. Soltanzadeh and M. Hashemzadeh, "RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem," Information Sciences, vol. 542, pp. 92-111, 2021.##[45] "Overview of Online Machine Learning in Big Data Streams," in Encyclopedia of Big Data Technologies, S. Sakr and A. Y. Zomaya Eds. Cham: Springer International Publishing, 2019, pp. 1239-1239.##[46] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," in 2009 IEEE symposium on computational intelligence for security and defense applications, 2009: IEEE, pp. 1-6.##[47] "Strategies to scale computationally: bigger data." https://scikit-learn.org/0.15/modules/scaling_strategies.html (accessed 2018).##[1] Y. Djenouri, D. Djenouri, and J. C.-W. Lin, "Trajectory Outlier Detection: New Problems and Solutions for Smart Cities," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 15, no. 2, pp. 1-28, 2021.##[2] A. Belhadi, Y. Djenouri, G. Srivastava, D. Djenouri, A. Cano, and J. C.-W. Lin, "A Two-Phase Anomaly Detection Model for Secure Intelligent Transportation Ride-Hailing Trajectories," IEEE Transactions on Intelligent Transportation Systems, 2020.##[3] M. Hashemzadeh and A. Zademehdi, "Fire detection for video surveillance applications using ICA K-medoids-based color model and efficient spatio-temporal visual features," Expert Systems with Applications, vol. 130, pp. 60-78, 2019.##[4] M. Hashemzadeh, G. Pan, and M. Yao, "Counting moving people in crowds using motion statistics of feature-points," Multimedia tools and applications, vol. 72, no. 1, pp. 453-487, 2014.##[5] M. Hashemzadeh, G. Pan, Y. Wang, M. Yao, and J. Wu, "Combining velocity and location-specific spatial clues in trajectories for counting crowded moving objects," International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 02, p. 1354003, 2013.##[6] M. Hashemzadeh and N. Farajzadeh, "Combining keypoint-based and segment-based features for counting people in crowded scenes," Information Sciences, vol. 345, pp. 199-216, 2016.##[7] N. Farajzadeh, A. Karamiani, and M. Hashemzadeh, "A fast and accurate moving object tracker in active camera model," Multimedia Tools and Applications, vol. 77, no. 6, pp. 6775-6797, 2018.##[8] S. Sadik and L. Gruenwald, "Research issues in outlier detection for data streams," Acm Sigkdd Explorations Newsletter, vol. 15, no. 1, pp. 33-40, 2014.##[9] J. Han, M. Kamber, and J. Pei, "Data mining: concepts and techniques, Waltham, MA," Morgan Kaufman Publishers, vol. 10, pp. 978-1, 2012.##[10] D. M. Hawkins, Identification of outliers. Springer, 1980.##[11] S. Mehta, "Concept drift in streaming data classification: Algorithms, Platforms and issues," Procedia computer science, vol. 122, pp. 804-811, 2017.##[12] V. Hodge and J. Austin, "A survey of outlier detection methodologies," Artificial intelligence review, vol. 22, no. 2, pp. 85-126, 2004.##[13] M. Singh and R. Pamula, "ADINOF: adaptive density summarizing incremental natural outlier detection in data stream," Neural Computing and Applications, pp. 1-17, 2021.##[14] M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, "Outlier detection for temporal data: A survey," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9, pp. 2250-2267, 2013.##[15] Y. Yang, L. Chen, and C. Fan, "ELOF: fast and memory-efficient anomaly detection algorithm in data streams," Soft Computing, pp. 1-12, 2020.##[16] L. Chen, W. Wang, and Y. Yang, "CELOF: Effective and fast memory efficient local outlier detection in high-dimensional data streams," Applied Soft Computing, vol. 102, p. 107079, 2021.##[17] S. Thudumu, P. Branch, J. Jin, and J. J. Singh, "A comprehensive survey of anomaly detection techniques for high dimensional big data," Journal of Big Data, vol. 7, no. 1, pp. 1-30, 2020.##[18] M. V. Joshi, R. C. Agarwal, and V. Kumar, "Mining needle in a haystack: classifying rare classes via two-phase rule induction," in Proceedings of the 2001 ACM SIGMOD international conference on Management of data, 2001, pp. 91-102.##[19] S. Hawkins, H. He, G. Williams, and R. Baxter, "Outlier detection using replicator neural networks," in International Conference on Data Warehousing and Knowledge Discovery, 2002: Springer, pp. 170-180.##[20] M. U. Togbe et al., "Anomaly Detection for Data Streams Based on Isolation Forest Using Scikit-Multiflow," in International Conference on Computational Science and Its Applications, 2020: Springer, pp. 15-30.##[21] G. Han, J. Tu, L. Liu, M. Martínez-García, and Y. Peng, "Anomaly Detection Based on Multidimensional Data Processing for Protecting Vital Devices in 6G-Enabled Massive IIoT," IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5219-5229, 2021.##[22] N. M. R. SURI and G. Athithan, Outlier detection: techniques and applications. Springer, 2019.##[23] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne, "On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms," Data Mining and Knowledge Discovery, vol. 8, no. 3, pp. 275-300, 2004.##[24] C. C. Aggarwal, S. Y. Philip, J. Han, and J. Wang, "A framework for clustering evolving data streams," in Proceedings 2003 VLDB conference, 2003: Elsevier, pp. 81-92.##[25] I. Assent, P. Kranen, C. Baldauf, and T. Seidl, "Anyout: Anytime outlier detection on streaming data," in International Conference on Database Systems for Advanced Applications, 2012: Springer, pp. 228-242.##[26] F. Angiulli and F. Fassetti, "Detecting distance-based outliers in streams of data," in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007, pp. 811-820.##[27] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, "LOF: identifying density-based local outliers," in ACM sigmod record, 2000, vol. 29, no. 2: ACM, pp. 93-104.##[28] G. S. Na, D. Kim, and H. Yu, "DILOF: Effective and memory efficient local outlier detection in data streams," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 1993-2002.##[29] M. Salehi, C. Leckie, J. C. Bezdek, T. Vaithianathan, and X. Zhang, "Fast memory efficient local outlier detection in data streams," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, pp. 3246-3260, 2016.##[30] J. Gao, W. Ji, L. Zhang, A. Li, Y. Wang, and Z. Zhang, "Cube-based incremental outlier detection for streaming computing," Information Sciences, vol. 517, pp. 361-376, 2020.##[31] X. Qin, L. Cao, E. A. Rundensteiner, and S. Madden, "Scalable Kernel Density Estimation-based Local Outlier Detection over Large Data Streams," in EDBT, 2019, pp. 421-432.##[32] F. T. Liu, K. M. Ting, and Z.-H. Zhou, "Isolation forest," in 2008 Eighth IEEE International Conference on Data Mining, 2008: IEEE, pp. 413-422.##[33] Z. Ding and M. Fei, "An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window," IFAC Proceedings Volumes, vol. 46, no. 20, pp. 12-17, 2013.##[34] S. C. Tan, K. M. Ting, and T. F. Liu, "Fast anomaly detection for streaming data," in Twenty-Second International Joint Conference on Artificial Intelligence, 2011.##[35] S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, "Unsupervised real-time anomaly detection for streaming data," Neurocomputing, vol. 262, pp. 134-147, 2017.##[36] A. Tuor, S. Kaplan, B. Hutchinson, N. Nichols, and S. Robinson, "Deep learning for unsupervised insider threat detection in structured cybersecurity data streams," arXiv preprint arXiv:1710.00811, 2017.##[37] B. V. Ashok, "QLattice Environment and Feyn QGraph Models - A new Perspective towards Deep Learning," Zenodo, 2020.##[38] M. Machado. "A new kind of AI." https://medium.com/abzuai/a-new-kind-of-ai-7665f8198877 (accessed.##[39] K. B. T. Jelen. https://docs.abzu.ai/docs/guides/qlattice.html (accessed.##[40] C. Cave. "Opening the black box." https://medium.com/abzuai/opening-the-black-box-247a63ce553e (accessed.##[41] J. Brownlee, "Why one-hot encode data in machine learning," Machine Learning Mastery, 2017.##[42] M. DelSole. "What is One Hot Encoding and How to Do It." https://medium.com/@michaeldelsole/what-is-one-hot-encoding-and-how-to-do-it-f0ae272f1179 (accessed.##[43] A. Fernández, S. Garcia, F. Herrera, and N. V. Chawla, "SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary," Journal of artificial intelligence research, vol. 61, pp. 863-905, 2018.##[44] P. Soltanzadeh and M. Hashemzadeh, "RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem," Information Sciences, vol. 542, pp. 92-111, 2021.##[45] "Overview of Online Machine Learning in Big Data Streams," in Encyclopedia of Big Data Technologies, S. Sakr and A. Y. Zomaya Eds. Cham: Springer International Publishing, 2019, pp. 1239-1239.##[46] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," in 2009 IEEE symposium on computational intelligence for security and defense applications, 2009: IEEE, pp. 1-6.##[47] "Strategies to scale computationally: bigger data." https://scikit-learn.org/0.15/modules/scaling_strategies.html (accessed 2018).## ##

بهینه‌سازی انرژی ساختمان با استفاده از الگوریتم گرگ خاکستری و شبکه عصبی مصنوعی Building energy optimization using gray wolf algorithm and an artificial neural network 1 1 یکی از بزرگترین مشکلات پیش روی بشر تأمین انرژی با توجه به کاهش منابع و قیمت تمام شده‌ی زیاد است. سهم بزرگی از مصرف انرژی در جهان را ساختمان‌های اداری دارند. در این مقاله روش نوین مدیریت انرژی ساختمان مطرح می‌شود که به کمک شبکه‌های اینترنت اشیا میزان مصرف انرژی در ساختمان‌های اداری کنترل می‌شود. روش پیشنهادی شامل دو مرحله می‌باشد: مرحله اول با استفاده از شبکه عصبی مصنوعی و شش پارامتر دمای بیرون ساختمان، دمای نقطه تنظیم، تابش خورشید، اشغال، دمای ساعت قبل و ساعت روز به عنوان ورودی به شبکه عصبی پرسپترون داده می‌شود و خروجی این مرحله دمای درون ساختمان و میزان مصرف انرژی است. مرحله دوم با استفاده از الگوریتم گرگ خاکستری بهینه دما برای هر بخش ساختمان در هر ساعت از شبانه‌روز تعیین می‌شود. میزان مصرف انرژی و قیمت تمام شده انرژی برای ساختمان با استفاده از نرم‌افزار متلب محاسبه می‌شود که نتیجه آن کاهش چشمگیر مصرف انرژی و بهینه‌سازی قیمت تمام شده انرژی در ساختمان‌های اداری است. روش پیشنهادی کاهش مصرف انرژی 22 کیلو وات ساعت در ساعات ابتدایی صبح را نشان‌ می‌دهد 2 One of the biggest problems facing the human being is energy supply due to reduced resource and cost. The largest share of energy consumption in the world has been allocated to the construction sector. The main sources of energy supply are coal, natural gas and oil, all of which are non-renewable and will be completed in the near future. Major energy consumers can be referred to household, industrial, agricultural, general, commercial, and street lighting. Among the energy consumers, the share of domestic and office sectors is higher than other consumers, and attention to reducing energy consumption and energy losses in the construction sector is an unavoidable necessity. In this paper, a new method of building energy management is proposed, which, with the help of internet networks of objects, controls the energy consumption of buildings. An administrative building with six areas is considered. The proposed method consists of two phases: the first phase, which is the prediction stage, is performed using artificial neural network and six parameters: outside temperature of the building, set point temperature, sun radiation, occupancy, previous temperature and the hour of the day are given as inputs to the perceptron neural network and the output of this phase is inside temperature of building and the energy consumption, which is given as input to the next phase. The second phase uses the gray wolf algorithm to determine the optimal temperature for each part of the building at any hour of the day. The energy consumption and cost of the building are calculated using the software of MATLAB, which results in a significant reduction in energy consumption and energy cost optimization in the office. The proposed method shows a reduction in energy consumption of 22 Kw/h in the early morning hours. 99 112 2021/06/202021/07/112021/04/192021/04/152021/08/152021/04/182019/09/6 1398/6/15 2022/08/292022/05/112023/02/222022/05/112023/07/82022/05/112023/07/18 1402/4/27 آیدین افکاریان خیابان Aydin afkarian khiaban دانشگاه آزاد شبستر ایران Aydin.afkarian@gmail.com وحید مجیدنژاد vahid Majidnezhad دانشگاه آزاد شبستر ایران vahidmn@iaushab.ac.ir Energy building Energy cost Internet of things Gray wolf algorithm انرژی ساختمان قیمت تمام شده انرژی شبکه اینترنت اشیا الگوریتم گرگ خاکستری. H., Omar, et al. "IoT-based interactive dual mode smart home automation." 2019 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2019.‏##C. Franco, et al.". The Internet of Things for Smart Urban Ecosystems". Springer, 2019.‏##K. Huh Seyoung, "Managing IoT devices using blockchain platform," in 9th international conference on advanced communication technology (ICACT), 2017.##M. Miettinen, "IoT Sentinel: Automated device-type identification for security enforcement in IoT," in IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017.##A. K. Harish "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, vol. 56, pp. 1272-1292, 2016.##M. H. Magalhães, "Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior," Energy and Buildings , vol. 151, pp. 332-343, 2017.##K. Martin,A. Greene, "Prediction Model of California Residential Buildings' Energy Consumption," CSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction, pp. 55-62., 2012##K.. Papantoniou, "Building optimization and control algorithms implemented in existing BEMS using a web based energy management and control system," Energy and Buildings, Vol.98, pp. 45-55, 2015.##G. Mehreen "Understanding the energy consumption and occupancy of a multi-purpose academic building," Energy and Buildings, Vol.87, pp. 155-165, 2015.##A. C., Menezes, et al. "Estimating the energy consumption and power demand of small power equipment in office buildings." Energy and Buildings, Vol.75, pp. 199-209, 2014.‏##J. Reynolds, "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, vol. 151, pp. 729-739, 2018.##J.Wang, y. Junqi, Y. Nan, Y. Zhang, and X. Yang. "Research on Chaotic Time Series Prediction Model for Building Energy Consumption." In IOP Conference Series: Earth and Environmental Science, vol. 242, no. 6, p. 062037. IOP Publishing, 2019.##M. Laurent, "Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network." Building and Environment, vol45, pp.739-746, 2010.##K.Jad, Z. Alameddine, and P. Hollmuller. "Understanding and bridging the energy performance gap in building retrofit." Energy, vol.122, pp. 217-222, 2017.##Reynolds, J., Ahmad, M. W., Rezgui, Y., & Hippolyte, J. L. ,"Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm". Applied energy, Vol. 235, pp.699-713, 2019.##Ö.Lale, A. Baykasoğlu, and S. Kulluk. "A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner." Applied Soft Computing, vol.10, pp. 304-317, 2010.##R. Naveen, and C. Raghavendra. "Rule extraction from differential evolution trained radial basis function network using genetic algorithms." 2009 IEEE International Conference on Automation Science and Engineering. IEEE, 2009.##W. Xin, and S. Lin. "Pruning And Retraining Method For A Convolution Neural Network." U.S. Patent Application, vol. 15, pp. 429-438, 2019.##Mirjalili, S., Aljarah, I., Mafarja, M., Heidari, A.A. and Faris, H., "Grey Wolf optimizer: theory, literature review, and application in computational fluid dynamics problems". Nature-inspired optimizers, pp.87-105, 2019.##I. Aljarah, et al. "Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach." Knowledge and Information Systems, vol. 62, 1-33, 2019.##H., Omar, et al. "IoT-based interactive dual mode smart home automation." 2019 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2019.‏##C. Franco, et al.". The Internet of Things for Smart Urban Ecosystems". Springer, 2019.‏##K. Huh Seyoung, "Managing IoT devices using blockchain platform," in 9th international conference on advanced communication technology (ICACT), 2017.##M. Miettinen, "IoT Sentinel: Automated device-type identification for security enforcement in IoT," in IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017.##A. K. Harish "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, vol. 56, pp. 1272-1292, 2016.##M. H. Magalhães, "Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior," Energy and Buildings , vol. 151, pp. 332-343, 2017.##K. Martin,A. Greene, "Prediction Model of California Residential Buildings' Energy Consumption," CSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction, pp. 55-62., 2012##K.. Papantoniou, "Building optimization and control algorithms implemented in existing BEMS using a web based energy management and control system," Energy and Buildings, Vol.98, pp. 45-55, 2015.##G. Mehreen "Understanding the energy consumption and occupancy of a multi-purpose academic building," Energy and Buildings, Vol.87, pp. 155-165, 2015.##A. C., Menezes, et al. "Estimating the energy consumption and power demand of small power equipment in office buildings." Energy and Buildings, Vol.75, pp. 199-209, 2014.‏##J. Reynolds, "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, vol. 151, pp. 729-739, 2018.##J.Wang, y. Junqi, Y. Nan, Y. Zhang, and X. Yang. "Research on Chaotic Time Series Prediction Model for Building Energy Consumption." In IOP Conference Series: Earth and Environmental Science, vol. 242, no. 6, p. 062037. IOP Publishing, 2019.##M. Laurent, "Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network." Building and Environment, vol45, pp.739-746, 2010.##K.Jad, Z. Alameddine, and P. Hollmuller. "Understanding and bridging the energy performance gap in building retrofit." Energy, vol.122, pp. 217-222, 2017.##Reynolds, J., Ahmad, M. W., Rezgui, Y., & Hippolyte, J. L. ,"Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm". Applied energy, Vol. 235, pp.699-713, 2019.##Ö.Lale, A. Baykasoğlu, and S. Kulluk. "A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner." Applied Soft Computing, vol.10, pp. 304-317, 2010.##R. Naveen, and C. Raghavendra. "Rule extraction from differential evolution trained radial basis function network using genetic algorithms." 2009 IEEE International Conference on Automation Science and Engineering. IEEE, 2009.##W. Xin, and S. Lin. "Pruning And Retraining Method For A Convolution Neural Network." U.S. Patent Application, vol. 15, pp. 429-438, 2019.##Mirjalili, S., Aljarah, I., Mafarja, M., Heidari, A.A. and Faris, H., "Grey Wolf optimizer: theory, literature review, and application in computational fluid dynamics problems". Nature-inspired optimizers, pp.87-105, 2019.##I. Aljarah, et al. "Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach." Knowledge and Information Systems, vol. 62, 1-33, 2019.## ##

مروری بر آسیب‌پذیری شبکه‌های عصبی عمیق نسبت به نمونه‌های خصمانه و رویکرد‌های مقابله با آن‌ها A survey on vulnerability of deep neural networks to adversarial examples and defense approaches to deal with them 1 1 امروزه شبکه‌های عصبی به‌عنوان بارزترین ابزار مطرح در هوش‌مصنوعی و یادگیری ماشین شناخته شده‌ و در حوزه‌های مالی و بانکداری، کسب ‌و کار، تجارت، سلامت، پزشکی، بیمه، رباتیک، هواپیمایی، خودرو، نظامی و سایر حوزه‌ها مورد استفاده قرار می‌گیرند. در سال‌های اخیر موارد متعددی از آسیب‌پذیری شبکه‌های عصبی عمیق نسبت به حملاتی مطرح شده که غالباً با افزودن اختلالات جمع‌شونده و غیر جمع‌شونده بر داده ورودی ایجاد می‌شوند. این اختلالات با وجود نامحسوس بودن در ورودی از دیدگاه عامل انسانی، خروجی شبکه آموزش دیده را تغییر می‌دهند. به اقداماتی که شبکه‌های عصبی عمیق را نسبت به حملات مقاوم‌ می‌نمایند، دفاع اطلاق می‌شود. برخی از روش‌های حمله مبتنی بر ابزارهایی نظیر گرادیان شبکه نسبت به ورودی، به دنبال شناسایی اختلال می‌باشند و برخی دیگر به تخمین آن ابزارها می‌پردازند و در تلاش هستند تا حتی بدون داشتن اطلاعاتی از آن‌ها، به اطلاعات آن‌ها دست پیدا کنند. رویکردهای دفاع نیز برخی روی تعریف تابع هزینه بهینه و همچنین معماری شبکه مناسب و برخی دیگر بر جلوگیری و یا اصلاح داده قبل از ورود به شبکه متمرکز می‌شوند. همچنین برخی رویکردها به تحلیل میزان مقاوم‌بودن شبکه نسبت به این حملات و ارائه محدوده اطمینان متمرکز شده‌اند. در این مقاله سعی شده است تا جدیدترین پژوهش‌ها در زمینه آسیب‌پذیری شبکه‌های عصبی عمیق  بررسی و مورد نقد قرار گیرند و کارایی آن‌ها با انجام آزمایش‌هایی مقایسه شود. در آزمایشات صورت گرفته در بین حملات محصور‌شده به l∞  و l2 ، روش AutoAttack کارایی بسیار بالایی دارد. البته باتوجه به برتری روش‌ AutoAttack نسبت به روش‌هایی نظیر MIFGSM، PGD و DeepFool این روش برای اجرا، مدت زمان بیشتری به خاطر ترکیبی بودن ساختار درونی آن نسبت به سایر روش‌های همردیف خود نیاز دارد. همچنین به مقایسه برخی از رویکردهای پرکاربرد دفاع در مقابل نمونه‌های خصمانه نیز پرداخته شد و از بین روش‌های مبتنی بر نواحی محصورشده به l∞   حول داده، روش آموزش خصمانه مبتنی بر مشتقات PGD با پارامترهای مشخص، از سایر روش‌ها بهتر در مقابل اغلب روش‌های حمله مقاوم بوده است. لازم به ذکر است که روش‌های مختلف حمله خصمانه و دفاع نسبت به آن حملات که در این مقاله مورد بررسی قرار گرفت است در یک قالب مناسب و منعطف کدنویسی شده است. این قالب کدنویسی به عنوان یک پشتوانه پایدار ویژه تحقیق و پژوهش در حوزه یادگیری ماشین استاندارد و یادگیری ماشین خصمانه ویژه پژوهشگران و علاقه‌مندان از طریق آدرسhttps://github.com/khalooei/Robustness-framework  در دسترس  قرار گرفته است. 2 Nowadays the most commonly used method in various tasks of machine learning and artificial intelligence are neural networks. In spite of their different uses, neural networks and Deep neural networks (DNNs) have some vulnerabilities. A little distortion or adversarial perturbation in the input data for both additive and non-additive cases can be led to change the output of the trained model, and this could be a kind of DNN vulnerability. Despite the imperceptibility of the mentioned disturbance for human beings, DNN is vulnerable to these changes. Creating and applying any malicious perturbation named “attack”, penetrates DNNs and makes them incapable of doing the duty assigned to them. In this paper different attack approaches were categorized based on the signal applied in the attack procedure. Some approaches use the gradient signal for detecting the vulnerability of DNN and try to create a powerful attack. The other ones create a perturbation in a blind situation and change a portion of the input to create a potential malicious perturbation. Adversarial attacks include both black-box and White-box situations. White-box situation focuses on training loss function and the architecture of the model but black box situation focuses on the approximation of the main model and dealing with the restriction of the input-output model request. Making a deep neural network resilient against attacks is named “defense”. Defense approaches are divided into three categories. One of them tries to modify the input, the other one makes some changes in the developed model and also changes the loss function of the model. In the third defense approach some networks are first used for purification and refinement of the input before passing it to the main network. Furthermore, an analytical approach was presented for the entanglement and disentanglement representation of inputs of the trained model. The gradient is a very powerful signal usually used in learning and an attacking approaches. Besides, adversarial training is a well-known approach in changing a loss function method to defend against adversarial attacks. In this study the most recent research on the vulnerability of DNN through a critical literature review was presented. Literature and our experiments indicate that the projected gradient descent (PGD) and AutoAttack methods are successful approaches in the l2 and l∞  bounded attacks, respectively. Furthermore, our experiments indicate that AutoAttack is much more time-consuming than the other methods. In the defense concept, different experiments were conducted to compare different attacks in the adversarial training approaches. Our experimental results indicate that the PGD is much more efficient in adversarial training than the fast gradient sign method (FGSM) and its deviations like MIFGSM and covers a wider range of generalizations of the trained model on predefined datasets. Furthermore, AutoAttack integration with adversarial training works well, but it is not efficient in low epoch numbers. Aside from that, it has been proven that adversarial training is time-consuming. Furthermore, we released our code for researchers or individuals interested in extending or evaluating predefined models for standard and adversarial machine learning projects. A more detailed description of the framework can be found at https://github.com/khalooei/Robustness-framework . 113 144 2021/06/202021/07/112021/04/192021/04/152021/08/152021/04/182019/09/62021/01/19 1399/10/30 2022/08/292022/05/112023/02/222022/05/112023/07/82022/05/112023/07/182023/07/5 1402/4/14 محمد خالوئی Mohammad khalooei دانشگاه صنعتی امیرکبیر ایران khalooei@aut.ac.ir محمد مهدی همایون پور Mohammad Mehdi Homayounpour دانشگاه صنعتی امیرکبیردانشگاه صنعتی امیرکبیر ایران homayoun@aut.ac.ir مریم امیرمزلقانی Maryam Amirmazlaghani دانشگاه صنعتی امیرکبیر ایران mazlaghani@aut.ac.ir vulnerability of neural network robustness attack defense neural network آسیب‌پذیری شبکه‌های عصبی مقاوم‌سازی حمله دفاع شبکه‌های عصبی [1] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.##[2] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.##[3] A. H. Marblestone, G. Wayne, and K. P. 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تعیین ضرایب شاخص های ارزیابی عملکرد شعب با استفاده از الگوریتم ژنتیک دوهدفه پیشنهادی Determining the coefficients of branch performance evaluation indicators using proposed two-objective genetic algorithm 1 1 با اختصاص بخش قابل توجهی از بودجه مربوط به حقوق و دستمزد بانک­ها به شیوه پرداخت مبتنی بر عملکرد توجه به پتانسیل­های کسب و کاری شعب اهمیت یافته است. از این رو مسئله تعییـن ضرایب اهمیت شاخص­های ارزیابی عملکرد مبتنی بر فضای کسب و کاری به یک چالش برای مدیران بانکی تبدیل شده است. در ایـن مقاله مسئله بهینه­سازی ضرایب اهمیت شاخص­های ارزیابی عملکرد شعب در یکی از بانک­های دولتی ایران با در نظرگرفتن فضای کسب و کاری شعب مورد بررسی قرار گرفته است. برای این منظور یک رویکرد دو مرحله­ای ارائه شده در گام اول از یک روش خوشه­بندی رایج برای تعیین فضای کسب و کاری هر شعبه استفاده شده و در گام دوم یک الگوریتم ژنتیک دوهدفه نوین به منظور بهینه­سازی ضرایب اهمیت هر خوشه پیشنهاد شده است. روش پیشنهادی با چهار روش شناخته شده مقایسه شده و نتایج در مواردی عملکرد موثر روش پیشنهادی را نشان می­دهد. 2 ABSTRACT Nowadays, we are witnessing financial markets becoming more competitive, and banks are facing many challenges to attract more deposits from depositors and increase their fee income. Meanwhile, many banks use performance-based incentive plans to encourage their employees to achieve their short-term goals. In the meantime, fairness in the payment of bonuses is one of the important challenges of banks, because not paying attention to this issue can become a factor that destroys the motivation among employees and prevents the bank from achieving its short-term and mid-term goals. This article is trying to tackle the problem of optimizing the coefficients of branch performance evaluation indicators based on their business environment in one of the state banks of Iran. In this article, a two-objective genetic algorithm is proposed to solve the problem. This article is comprised of four main sections. The first section is dedicated to the problem definition which is what is our meaning of optimizing the importance coefficients of branches based on the business environment. The second section is about our proposed solution for the defined problem. In the third section, we are comparing the performance of the proposed two-objective genetic algorithm on the defined problem with the performance of four well-known multi-objective algorithms including NSGAII, SPEAII, PESAII, and MOEA/D. And finally, the set of ZDT problems which is a standard set of multi-objective problems is taken into account for evaluating the general performance of the proposed algorithm comparing four well-known multi-objective algorithms. Our proposed solution for solving the problem of optimizing branch performance coefficients includes two main steps. First, identifying the business environment of the branches and second, optimizing the coefficients with the proposed two-objective genetic algorithm. In the first step, the k-means clustering algorithm is applied to cluster branches with similar business environments. In the second step, to optimize the coefficients, it is necessary to specify the fitness functions. The defined problem is a two-objective problem, the first objective is to minimize the deviation of the real performance of the branches from the expected performance of them, and the second objective is to minimize the deviation of the coefficients from the coefficients determined by the experts. To solve this two-objective problem, a two-objective genetic algorithm is proposed. In this article, two approaches are adopted to compare the proposed solution performance. In the first stage, the results of applying the proposed two-objective genetic algorithm have been compared with the results of applying four well-known multi-objective genetic algorithms on the problem of optimizing the coefficients. The results of this comparison show that the proposed algorithm has outperformed the other compared methods based on the S indicator and run time, and it is also ranked second after the NSGAII algorithm in terms of the HV indicator. Finally, for evaluating the performance of the proposed algorithm with other well-known methods, the set of ZDT problems including ZDT1, ZDT2, ZDT3, ZDT4, and ZDT6 has also been taken into consideration. At this stage, the performance of the proposed algorithm has been compared with the four mentioned algorithms based on four key indicators, including GD, S, H, and run time. The results show, the proposed algorithm has outperformed significantly in terms of run time in all five ZDT problems. In terms of GD indicator, the performance of our proposed algorithm is located in the first or second rank among all considered algorithms. In addition, in terms of S and H indicators in many cases, the proposed algorithm outperformed the other well-known algorithms. 145 162 2021/06/202021/07/112021/04/192021/04/152021/08/152021/04/182019/09/62021/01/192021/05/11 1400/2/21 2022/08/292022/05/112023/02/222022/05/112023/07/82022/05/112023/07/182023/07/52023/06/2 1402/3/12 الهام حامدی Elham Hamedi دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران ایران elhamhamedi84@gmail.com میترا میرزارضایی Mitra Mirzarezaee دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران ایران mirzarezaee@srbiau.ac.ir Branch business space coefficients of performance evaluation indicators clustering optimization two-objective genetic algorithm. فضای کسب و کاری شعب ضرایب اهمیت شاخص‌های ارزیابی عملکرد خوشه‌بندی بهینه‌سازی الگوریتم ژنتیک چندهدفه C. R. Atencia, J. D. Ser and D. Camacho, "Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning," Swarm and Evolutionary Computation, vol. 44, pp. 480-495, 2018. ##K. Deb, S. Agrawal, A. Pratap and M. T, "A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II," in International Conference on Parallel Problem Solving from Nature, 2000. ##S. Hiwa, M. Nishioka, T. Hiroyasu and M. Miki, "Novel Search Scheme for Multi-Objective Evolutionary Algorithms to Obtain Well-Approximated and Widely Spread Pareto Solutions," Swarm and Evolutionary Computation, vol. 22, pp. 30-46, 2015. ##P. K. Jamwal, B. Abdikenov and S. Hussain, "Evolutionary Optimization Using Equitable Fuzzy Sorting Genetic Algorithm (EFSGA)," EEE Access, vol. 7, pp. 8111 - 8126, 2019. ##X. Lu, Y. Tan, W. Zheng and M. Lili, "A Decomposition Method Based on Random Objective Division for MOEA/D in Many-Objective Optimization," IEEE Access, vol. 8, pp. 103550 - 103564, 2020. ##N. Patel and N. Padhiyar, "Fast Mesh-Sorting in Multi-objective Optimization," IFAC-PapersOnLine, vol. 48, no. 8, pp. 936-941, 2015. ##Y. Salgueiro, R. Falcon and R. Bello, "Multiobjective Variable Mesh Optimization," Annals of Operations Research, 2017. ##F. Wang, Y. Li, H. Zhang, T. Hu and X. Shen, "An adaptive weight vector guided evolutionary algorithm for preference-based multi-objective optimization," Swarm and Evolutionary Computation, vol. 49, p. 220–233, 2019. ##W. Wang, K. Li, X. Tao and F. Gu, "An improved MOEA/D algorithm with an adaptive evolutionary strategy," Information Sciences, vol. 539, pp. 1-15, 2020. ##Q. Zhang and H. Li, "MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition," IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol. 11, no. 6, pp. 712 - 731, 2007. ##E. Zitzler, K. Deb and L. Thiele, "Comparison of Multiobjective Evolutionary Algorithms: Empirical Results," Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000.## ##

ادغام ویژگی های طیفی و مکانی تصاویر ابر طیفی به کمک طبقه بند شبکه عصبی Feature fusion by neural network classification in remotely sensed hyperspectral images 1 1 در تصاویر ابرطیفی که توسط سنجنده های از راه دور بدست می آیند، می توان تفکیک بین کلاس ها را دقیق تر و با جزئیات بیشتر بدست آورد. از آنجایی که ابعاد بالای داده ابرطیفی و تعداد کم نمونه های آموزشی، طبقه بندی تصاویر ابرطیفی را مشکل می سازد. به دنبال تکنیک هایی هستیم که در هنگام کمبود تعداد نمونه های آموزشی دقت طبقه بندی قابل قبولی داشته باشد. لذا بکارگیری تکنیک هایی که علاو  بر کاهش تعداد نمونه های آموزشی، دقت طبقه بندی را  بالاتر ببرد حائز اهمیت می گردد. این مقاله از روش طبقه بند شبکه عصبی در طبقه بندی تصاویر ابرطیفی به کمک ادغام ویژگی طیفی و مکانی در دو روش پشته و روش مبتنی بر گراف دودویی بهره گرفته است. علاوه بر روش متداول پشته یاstack ،استفاده از روش گراف دودویی ناحیه ای به منظور ادغام مناسب اطلاعات طیفی و مکانی یک روش مطلوب برای استفاده همزمان از اطلاعات طیفی در کنار اطلاعات  مکانی (Feature Fusion)  در طبقه بندی تصویر ابرطیفی می باشد. در هریک ازاین روش ها طبقه  بند شبکه عصبی روی ویژگیهای طیفی و  مکانی به صورت مجزاو ادغام شده بکار گرفته شده است و سپس با عملکرد طبقه بند ماشین بردار پشتیبان در شرایط مشابه مقایسه شده است. نتایج طبقه بندی بیانگر برتری طبقه بند شبکه عصبی است.   2 Hyper-spectral image classification is a popular topic in the field of remote sensing.Hyperspectral images (HSI) have rich spectral information and spatial information. Traditional hyperspectral image (HSI) classification methods typically use the spectral features and do not make full use of the spatial or other features of the HSI. In general, the classification approaches classify input data by considering the spectral information of the data to produce a classification map in order to discriminate different classes of interest. The pixel-wise classification approaches classify each pixel autonomously without considering information about spatial structures, further enhancement of classification results can be obtain by considering spatial dependences between pixels. However, how to fuse and utilize spectral-spatial features more efficiently is a challenging task. So the combination of spectral information and spatial information has become an effective means to obtain good classification results. Specifically, firstly, the principal component analysis (PCA) algorithm is used to extract the first principal component in the original hyperspectral image. Secondly, the   residual network Gabor, GLCM and MP   are introduced for each band to extract the spatial information of the image. Thirdly, the image is classified by using SVM to get the final classification result. In this paper, we have used the neural network classifier in the classification of hyperspectral images by integrating spectral and spatial properties in two methods stack and the method based on binary graphs. In spite of   the traditional stack method, the use of local binary graph method to properly integrate spectral and spatial information is a desirable method for the simultaneous use of spectral information along with spatial information (Feature Fusion) in hyperspectral image classification. In each of these methods, the neural network classifier is applied to the spectral and spatial features separately and then compared with the performance of the support vector machine classifier in similar conditions. The classification results show that the proposed method can outperform other traditional   classification techniques  163 174 2021/06/202021/07/112021/04/192021/04/152021/08/152021/04/182019/09/62021/01/192021/05/112022/06/12 1401/3/22 2022/08/292022/05/112023/02/222022/05/112023/07/82022/05/112023/07/182023/07/52023/06/22023/07/17 1402/4/26 فاطمه سادات میری Fatemeh Miri دانشکده برق و کامپیوتر ایران f_miri_61@yahoo.com سید ابوالفضل حسینی Seyed Hosseini دانشکده برق و کامپیوتر ایران abolfazl.hosseini@modares.ac.ir رامین شقاقی کندوان Ramin Shaghaghi Kandovan دانشکده برق و کامپیوتر ایران ramin.shaghaghi@gmail.com feature fusion spectral spatial neural network SVM classification طبقه بندی ادغام ویژگی طیفی و مکانی شبکه عصبی تصاویر ابرطیفی [1] M. Imani and H. Ghassemian, "Pansharpening optimisation using multiresolution analysis and sparse representation," International Journal of Image and Data Fusion, vol. 12, no. 4, pp. 1-23, 2017.##[2] M. Imani and H. Ghassemian, "Feature reduction of hyperspectral images Discriminant analysis and the first principal component," Journal of AI and Data Mining, vol. 3, no. 5, pp. 1-9, 2015.##[3] M. Imani and H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 102, no. 5, pp. 1-13, 2015.##[4] A. Taghipour, H. Ghassemian, and F. Mirzapour, "Anomaly detection of hyperspectral imagery using differential morphological profile" in Electrical Engineering (ICEE), 2016 24th Iranian Conference on, 2016, vol. 12, no. 4, pp. 1219-1223, 2016.##[5] A. Taghipour and H. Ghassemian, "Hyperspectral Anomaly Detection Using Attribute Profiles" IEEE Geoscience and Remote Sensing Letters,vol. 12, no. 4, pp. 13-15, 2017.##[6] Z. Zhu, "Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications" ISPRS Journal of Photogrammetry and Remote Sensing, vol. 130, no. 13, pp. 370-384, 2017.##[7] Maryam Vafadar; Hassan Ghassemian, " Hyperspectral anomaly detection using Modified Principal component analysis reconstruction error" ,2017 Iranian Conference on Electrical Engineering (ICEE), DOI:10. 1109/Iranian CEE. 2017. 7985332.##[8] Hamid Nourollahi, S. Abolfazl Hosseini*, Ali Shahzadi & Ramin Shaghaghi Kandovan, Signal detection Using Rational Function Curve Fitting, Signal and Data Processing Journal Serial 54, Volume 19, Number 4 (3-2023), jsdp. rcisp. ac. ir##[9] R. Rajabi and H. Ghassemian, "Sparsity constrained graph regularized NMF for spectral unmixing of hyperspectral data," Journal of the Indian Society of Remote Sensing, vol. 43, no. 13, pp. 269-278, 2015.##[10] F. Kowkabi, H. Ghassemian, and A. Keshavarz, "Hyperspectral endmember extraction and unmixing by a novel spatial-spectral preprocessing module," in Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, vol. 12, no. 4, pp. 33823385, 2016.##[11] Hongmin Gao; Zhonghao Chen ; Feng Xu " Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples" ; International Journal of Applied Earth Observation and Geoinformation Volume 107, March 2022##[12] H. Ghassemian and D. A. Landgrebe, "object-oriented feature extraction method for image data compaction," IEEE Control Systems Magazine, vol. 8, no. 3, pp. 42 - 48, 1988.##[13] M. Fauvel, Y. Tarabalka and J. A. Benedi, "Advances in Spectral-Spatial Classification of Hyperspectral Images," Proceedings of the IEEE, vol. 101, no. 3, pp. 652 - 675, 2013.##[14] Mathieu FauvelMathieu FauvelJocelyn ChanussotJocelyn ChanussotJon Atli BenediktssonJon Atli BenediktssonJohannes R. SveinssonJohannes R. Sveinsson " Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles", "IEEE Transactions on Geoscience and Remote Sensing 46, 11 - part 2 (2008) 3804-3814"##[15] Siyuan Hao, Yufeng Xia, Lijian Zhou, etc Yuanxin Ye; Wei Wang "Spectral and Spatial Feature Fusion for Hyperspectral Image Classification" Journals & Magazines, IEEE Geoscience and Remote Sensing Letters, Volume: 19,2022##[16] W. Liao, M. Dalla Mura and J. Chanussot, "Fusion of Spectral and Spatial Information for Classification of Hyperspectral Remote-Sensed Imagery by Local Graph," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 583 - 594, 2016.##[17] Maryam Imani, Hassan Ghassemian, "An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges" Elsevier Information Fusion 59 (2020) 59-83, 2020.##[18] M. Imani and H. Ghassemian, "GLCM, Gabor, and morphology profiles fusion for hyperspectral image classification," 2016 24th Iranian Conference on Electrical Engineering (ICEE), pp. 460 - 465, 2016.##[19] Maryam Imani, Hassan Ghassemian, "An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges", journal Elsevier Information Fusion 59 (2020) 59-83##[20] Hosseini S Abolfazl, Beitollahi Mersedeh, "Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression", IEEE 2017.##[21] Debaleena Datta, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi, and Jaeyoung Choi," Hyperspectral Image Classification: Potentials, Challenges, and Future Directions "Hindawi Computational Intelligence and Neuroscience Volume 2022.##[1] M. Imani and H. Ghassemian, "Pansharpening optimisation using multiresolution analysis and sparse representation," International Journal of Image and Data Fusion, vol. 12, no. 4, pp. 1-23, 2017.##[2] M. Imani and H. Ghassemian, "Feature reduction of hyperspectral images Discriminant analysis and the first principal component," Journal of AI and Data Mining, vol. 3, no. 5, pp. 1-9, 2015.##[3] M. Imani and H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 102, no. 5, pp. 1-13, 2015.##[4] A. Taghipour, H. Ghassemian, and F. Mirzapour, "Anomaly detection of hyperspectral imagery using differential morphological profile" in Electrical Engineering (ICEE), 2016 24th Iranian Conference on, 2016, vol. 12, no. 4, pp. 1219-1223, 2016.##[5] A. Taghipour and H. Ghassemian, "Hyperspectral Anomaly Detection Using Attribute Profiles" IEEE Geoscience and Remote Sensing Letters,vol. 12, no. 4, pp. 13-15, 2017.##[6] Z. Zhu, "Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications" ISPRS Journal of Photogrammetry and Remote Sensing, vol. 130, no. 13, pp. 370-384, 2017.##[7] Maryam Vafadar; Hassan Ghassemian, " Hyperspectral anomaly detection using Modified Principal component analysis reconstruction error" ,2017 Iranian Conference on Electrical Engineering (ICEE), DOI:10. 1109/Iranian CEE. 2017. 7985332.##[8] Hamid Nourollahi, S. Abolfazl Hosseini*, Ali Shahzadi & Ramin Shaghaghi Kandovan, Signal detection Using Rational Function Curve Fitting, Signal and Data Processing Journal Serial 54, Volume 19, Number 4 (3-2023), jsdp. rcisp. ac. ir##[9] R. Rajabi and H. Ghassemian, "Sparsity constrained graph regularized NMF for spectral unmixing of hyperspectral data," Journal of the Indian Society of Remote Sensing, vol. 43, no. 13, pp. 269-278, 2015.##[10] F. Kowkabi, H. Ghassemian, and A. Keshavarz, "Hyperspectral endmember extraction and unmixing by a novel spatial-spectral preprocessing module," in Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, vol. 12, no. 4, pp. 33823385, 2016.##[11] Hongmin Gao; Zhonghao Chen ; Feng Xu " Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples" ; International Journal of Applied Earth Observation and Geoinformation Volume 107, March 2022##[12] H. Ghassemian and D. A. Landgrebe, "object-oriented feature extraction method for image data compaction," IEEE Control Systems Magazine, vol. 8, no. 3, pp. 42 - 48, 1988.##[13] M. Fauvel, Y. Tarabalka and J. A. Benedi, "Advances in Spectral-Spatial Classification of Hyperspectral Images," Proceedings of the IEEE, vol. 101, no. 3, pp. 652 - 675, 2013.##[14] Mathieu FauvelMathieu FauvelJocelyn ChanussotJocelyn ChanussotJon Atli BenediktssonJon Atli BenediktssonJohannes R. SveinssonJohannes R. Sveinsson " Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles", "IEEE Transactions on Geoscience and Remote Sensing 46, 11 - part 2 (2008) 3804-3814"##[15] Siyuan Hao, Yufeng Xia, Lijian Zhou, etc Yuanxin Ye; Wei Wang "Spectral and Spatial Feature Fusion for Hyperspectral Image Classification" Journals & Magazines, IEEE Geoscience and Remote Sensing Letters, Volume: 19,2022##[16] W. Liao, M. Dalla Mura and J. Chanussot, "Fusion of Spectral and Spatial Information for Classification of Hyperspectral Remote-Sensed Imagery by Local Graph," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 583 - 594, 2016.##[17] Maryam Imani, Hassan Ghassemian, "An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges" Elsevier Information Fusion 59 (2020) 59-83, 2020.##[18] M. Imani and H. Ghassemian, "GLCM, Gabor, and morphology profiles fusion for hyperspectral image classification," 2016 24th Iranian Conference on Electrical Engineering (ICEE), pp. 460 - 465, 2016.##[19] Maryam Imani, Hassan Ghassemian, "An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges", journal Elsevier Information Fusion 59 (2020) 59-83##[20] Hosseini S Abolfazl, Beitollahi Mersedeh, "Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression", IEEE 2017.##[21] Debaleena Datta, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi, and Jaeyoung Choi," Hyperspectral Image Classification: Potentials, Challenges, and Future Directions "Hindawi Computational Intelligence and Neuroscience Volume 2022.## ##

وارسی ویژگی دسترس‌پذیری در سیستم‌های تبدیل گراف با رویکرد کشف وابستگی‌ شرطی بین قوانین Checking reachability property of systems specified through graph transformation with the approach of discovery conditional dependency between the rules 1 1 وارسی مدل[1] یکی از مؤثرترین تکنیک­های صحت سنجی خودکار ویژگی‌های سیستم‌های سخت‌افزاری و نرم‌افزاری است. در حالت کلی، در این روش، مدلی از سیستم موردنظر تولید می­شود و تمام حالات ممکن در گراف فضای حالت مورد کاوش قرار می‌گیرد تا بتواند خطاها و الگوهای نامطلوب را پیدا کند. در سیستم‌های بزرگ و پیچیده تولید تمام فضای حالت منجر به مشکل انفجار فضای حالت[2] می­شود. تحقیقات اخیر نشان می‌دهند که کاوش در فضای حالت با استفاده از روش‌های هوشمندانه، می‌تواند ایده امیدوارکننده‌ای باشد. ازاین‌رو در این تحقیق ابتدا مدلی از سیستم موردنظر ایجاد می‌شود، سپس بخشی از فضای حالت مدل، تولیدشده و با استفاده از احتمالات شرطی، وابستگی بین قوانین موجود در فضای حالت کشف می‌شوند. پس از آن، با کمک وابستگی‌های کشف‌شده، بقیه فضای حالت مدل را به‌طور هوشمندانه مورد کاوش قرار می‌گیرد. در این مقاله روشی برای وارسی ویژگی دسترس‌پذیری[3] در سیستم‌های نرم‌افزاری پیچیده و بزرگ که به زبان رسمی تبدیل گراف[4] (GTS) مدل شده‌اند، ارائه می‌شود. روش پیشنهادی در GROOVE که یک مجموعه ابزار منبع باز برای طراحی و بررسی وارسی مدل سیستم‌های تبدیل گراف می‌باشد، پیاده‌سازی شده است. نتایج آزمایش‌های تجربی نشان می‌دهند که رویکرد پیشنهادی نسبت به روش‌های قبلی سریع‌تر بوده و مثال‌های نقض[5]/شاهد[6]کوتاه‌تری تولید می‌کند.   [1] Model checking [2] State space explosion [3] Reachability property [4] Graph transformation system [5] Counterexample [6] Witness   2 Model checking is among the most effective techniques for automatic verification of hardware and software systems’ properties. Generally, in this method, a model of the desired system is generated and all possible states are explored in the space state graph to find errors and undesirable patterns. In models of large and complex systems, if the size of the generated state space is too extensive so that not all available states can be explored due to computational restrictions, the problem of state space explosion occurs. In fact, this problem confines the validation process in model verification systems. To use the model checking technique, the system must be described in a formal language. Graphs are very beneficial and intuitive tools for describing and modeling software systems. Correspondingly, graph transformation system provides a proper tool for formal description of software system features as well as their automatic verification. Various techniques have been investigated in the researches to reduce the effect of state space explosion problem in the model checking process. Some of these methods try to reduce the required memory by reducing the number of cases explored. Among others are symbolic model checking, partial-order reduction, symmetry reduction, and scenario-driven model checking. In a complex system, these algorithms, along with conventional methods such as DFS or BFS search algorithms may not afford any complete answer due to the explosion of state space. Hence, the use of intelligent methods such as knowledge-based techniques, datamining, machine learning, and meta-heuristic algorithms which do not entail full state space exploration could be advantageous. Recent researches attest that exploring the state space using intelligent methods could be a promising idea. Therefore, an intelligent method is used in this research to explore the state space of large and complex systems. Accordingly in this paper, first a model of the desired system is created using graph conversion system. Then a portion of the state space of the model is generated. Afterwards, using the conditional probability table, the dependencies between the rules in the paths toward the goal state are discovered. Finally, by means of the discovered dependencies, the rest of the model state space is intelligently explored. In other words, only promising paths, i.e. those who match the detected dependencies are explored to reach the goal state. It is worth noting that the first goal of the proposed approach is to find a goal state, i.e., one in which either the safety property is violated or the reachability property is satisfied in the shortest possible time. The second less important goal is to reduce the number of explored states in the graph of the state space until reaching the goal state. This paper provides a way to check the availability feature in complex and large software systems modeled in the official graph transformation language. The suggested method is implemented in GROOVE which is an open source toolset for designing and model checking graph transformation systems. The results of experimental tests indicate that the proposed approach is faster than the previous methods and produces a shorter counterexample/witness. 175 194 2021/06/202021/07/112021/04/192021/04/152021/08/152021/04/182019/09/62021/01/192021/05/112022/06/122021/06/10 1400/3/20 2022/08/292022/05/112023/02/222022/05/112023/07/82022/05/112023/07/182023/07/52023/06/22023/07/172023/06/2 1402/3/12 جعفر پرتابیان jaafar partabian دانشگاه ازاد اسلامی لامرد ایران jaafar_partabian@yahoo.com Model checking State space explosion Graph transformation system Conditional probability table Reachability property وارسی مدل انفجار فضای حالت سیستم تبدیل گراف جدول وابستگی شرطی ویژگی دسترس‌پذیری [1] Baier C, Katoen JP, Larsen KG (2008) Principles of model checking, vol 2620264. MIT press, Cambridge##[2] Chicano F, Francisco M, Ferreira M, Alba E (2011) Comparing metaheuristic algorithms for error detection in java programs. In: International symposium on Search based software engineering, vol 6956. Springer, Berlin, Heidelberg, pp 82-96##[3] Taentzer G, Ehrig K, Guerra E, Lara JD, Lengyel L, Levendovszky T, Prange U, Varro D, Varro-Gyapay S (2005) Model transformation by graph transformation: a comparative study. In: Proceedings of model transformations in practice workshop, Models conference, Montego Bay, Jamaica##[4] Rafe V, Hajvali M (2013) Designing an architectural style for pervasive healthcare systems. J Med Syst 37(2):1-13##[5] Heckel R, Thöne S (2005) Behavioral refinement of graph transformation-based models. Electron Notes the or Comput Sci 127(3):101-111##[6] Engels G, Hausmann JH, Heckel R, Sauer S (2000) Dynamic meta modeling: a graphical approach to the operational semantics of behavioral diagrams in UML. In: International conference on the unified modeling language. Springer, Berlin Heidelberg, pp 323-337##[7] Mens T (2006) On the use of graph transformations for model refactoring. In: Generative and transformational techniques in software engineering. Springer, Berlin Heidelberg, pp 219-257##[8] Rafe V, Rahmani AT (2008) Formal analysis of workflows using UML 2.0 activities and graph transformation systems. In: International colloquium on theoretical aspects of computing. Springer Berlin, Heidelberg, pp 305-318##[9] Naddaf M, Rafe V (2014) Performance modeling and analysis of software architectures specified through graph transformations. Comput Inform 32(4):797-826##[10] Clarke E, McMillan K, Campos S, Hartonas-Garmhausen V (1996) Symbolic model checking. In: International conference on computer aided verification, vol 1102. Springer, Berlin, Heidelberg, pp 419-422##[11] Godefroid P, Van Leeuwen J, Hartmanis J, Goos G, Wolper P (1996) Partial-order methods for the verification of concurrent systems: an approach to the state-explosion problem, vol 1032. Springer, Heidelberg##[12] Lafuente AL (2003) Symmetry reduction and heuristic search for error detection in model checking. In: Workshop on model checking and artificial intelligence##[13] Rafe V (2013) Scenario-driven analysis of systems specified through graph transformations. J Vis Lang Comput 24(2):136-145##[14] Alba E, Chicano F (2008) Searching for liveness property violations in concurrent systems with ACO. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, pp 1727-1734##[15] Rafe V, Moradi M, Yousefian R, Nikanjam A (2015) A meta-heuristic solution for automated refutation of complex software systems specified through graph transformations. Appl Soft Comput 33:136-149##[16] Pira E, Rafe V, Nikanjam A (2019) Using evolutionary algorithms for reachability analysis of complex software systems specified through graph transformation, Reliability Engineering and System Safety, vol 191##[17] Partabian J, Rafe V, Parvin H, Nejatian S (2019), An Approach Based on Knowledge Exploration for State Space Management in Checking Reachability of Complex Software Systems, soft computing, vol. 24, pp.1-16.##[18] Yasrebi M, Rafe V, Parvin H, Nejatian S (2020), An efficient approach to state space management in model checking of complex software system using machine learning technique, journal of intelligent & Fuzzy system, vol.38, no. 2, pp. 1761-1773.##[19] Rensink A, Boneva I, Kastenberg H, Staijen T (2010) User manual for the GROOVE tool set. Department of Computer Science, University of Twente, Enschede##[20] Peng H, Tahar S (1998) A survey on compositional verification. Technical Report, Department of Electrical and Computer Engineering, Concordia University##[21] Roever WP (1998) The need for compositional proof systems: a survey. In: Compositionality: the significant difference. Springer, Berlin, Heidelberg, pp 1-22##[22] Godefroid P (1990) Using partial orders to improve automatic verification methods. In: International conference on computer aided verification. Springer, Berlin, Heidelberg, pp 176-185##[23] Valmari A (1988) Error detection by reduced reachability graph generation. In: Proceedings of the 9th European workshop on application and theory of petri nets, pp 95-112##[24] Godefroid P, Wolper P (1993) Using partial orders for the efficient verification of deadlock freedom and safety properties. Formal Methods Syst Des 2(2):149-164##[25] Godefroid P, Van Leeuwen J, Hartmanis J, Goos G, Wolper P (1996) Partial-order methods for the verification of concurrent systems: an approach to the state-explosion problem, vol 1032. Springer, Heidelberg##[26] Lafuente AL (2003) Symmetry reduction and heuristic search for error detection in model checking. In: Workshop on model checking and artificial intelligence##[27] Wolper P, Leroy D (1993) Reliable hashing without collision detection. In: International conference on computer aided verification. Springer, Berlin Heidelberg, pp 59-70##[28] Stern U, Dill D (1995) Improved probabilistic verification by hash compaction. In: Advanced research working conference on correct hardware design and verification methods, vol 987. Springer, Berlin, Heidelberg, pp 206-224##[29] Stern U, Dill DL (1996) A new scheme for memory-efficient probabilistic verification. In: Formal description techniques IX. Springer, US, pp 333-348##[30] Courcoubetis C, Vardi M, Wolper P, Yannakakis M (1992) Memory efficient algorithms for the verification of temporal properties. In: Computer-aided verification, vol 1, issue 2-3. Springer, US, pp 129-142##https://doi.org/10.1007/BF00121128##[31] Isenberg T, Steenken D, Wehrheim H (2013) Bounded model checking of graph transformation systems via SMT solving. In: Formal techniques for distributed systems, vol 7892. Springer, Berlin, Heidelberg, pp 178-192##[32] Sivaraja H, Gopalakrishnan G (2003) Random walk based heuristic algorithms for distributed memory model checking. Electron Notes Theory Comput Sci 89(1):51-67##[33] Yang CH (1999) Prioritized model checking. Ph.D. thesis, Stanford Universit##[34] Edelkamp S, Lafuente AL, Leue S (2001) Directed explicit model checking with HSF-SPIN. In: Proceedings of the 8th international SPIN workshop on Model checking of software. Springer, New York, pp 57-79##[35] Yang CH, Dill DL (1998) Validation with guided search of the state space. In: Proceedings of the 35th annual design automation conference, pp 599-604##[36] Groce A, Visser W (2004) Heuristics for model checking Java programs. Int J Software Tools Technology Transf (STTT) 6(4):260-276##[37] Edelkamp S, Reffel F (1998) OBDDs in heuristic search. In: Annual conference on artificial intelligence. Springer, Berlin Heidelberg, pp 81-92##[38] Maeoka J, Tanabe Y, Ishikawa F (2016) Depth-first heuristic search for software model checking. In: Computer and information science, vol 614. Springer International Publishing, pp 75-96##[39] Edelkamp S, Leue S, Lafuente AL (2004) Directed explicit-state model checking in the validation of communication protocols. Int J Softw Tools Technol (STTT) 5(2-3):247-267##[40] Estler HC, Wehrheim H (2011) Heuristic search-based planning for graph transformation systems. KEPS 2011:54##[41] Snippe E (2011) Using heuristic search to solve planning problems in GROOVE. In: 14th Twente student conference on IT. University of Twente##[42] Ziegert S (2014) Graph transformation planning via abstraction. arXiv preprint arXiv: 1407.7933##[43] Elsinga JW (2016) On a framework for domain independent heuristics in graph transformation planning. Master's thesis, University of Twente##[44] Duarte LM, Foss L, Wagner FR, Heimfarth T (2010) Model checking the ant colony optimization. In: Hinchey M, Kleinjohann B, Kleinjohann L, Lindsay P, Rammig FJ, Timmis J, Wolf M (eds) Distributed, parallel and biologically inspired systems, vol 329. Springer, Berlin, Heidelberg, pp 221-232##[45] Alba E, Chicano F (2007a) Ant colony optimization for model checking. In: International conference on computer aided systems theory. Springer, Berlin, Heidelberg, pp 523-530##[46] Francesca G, Santone A, Vaglini G, Villani ML (2011) Ant colony optimization for deadlock detection in concurrent systems. In: 2011 IEEE 35th annual computer software and applications conference. IEEE, pp 108-117##[47] Alba E, Chicano F (2008) Searching for liveness property violations in concurrent systems with ACO. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, pp 1727-1734##[48] Kumazawa T, Yokoyama C, Takimoto M, Kambayashi Y (2016) Ant colony Optimization based model checking extended by smell like pheromone. In: Proceedings of the 9th EAI international conference on bio-inspired information and communications technologies (formerly BIONETICS), pp 214-220##https://doi.org/10.4108/eai.21-4-2016.151156##[49] Takada K, Takimoto M, Kumazawa T, Kambayashi Y (2017) ACO based model checking extended by smell-like pheromone with hop counts. In: Harmony search algorithm: proceedings of the 3rd international conference on harmony search algorithm, vol 514 (ICHSA 2017). Springer, p 52##[50] Foster H, Uchitel S, Magee J, Kramer J (2006) LTSA-WS: a tool for model-based verification of web service compositions and choreography. In: Proceedings of the 28th international conference on software engineering, Shanghai, China, pp 771-774##[51] Godefroid P, Khurshid S (2002) Exploring very large state spaces using genetic algorithms. In: International conference on tools and algorithms for the construction and analysis of systems. Springer, Berlin Heidelberg, pp 266-280##[52] Alba E, Chicano F, Ferreira M, Gomez-Pulido J (2008) Finding deadlocks in large concurrent java programs using genetic algorithms. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, pp 1735-1742##[53] Godefroid P (1997) Model checking for programming languages using Verisoft. In: Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on principles of programming languages. ACM, pp 174-186##[54] Chicano F, Francisco M, Ferreira M, Alba E (2011) Comparing metaheuristic algorithms for error detection in java programs. In: International symposium on Search based software engineering, vol 6956. Springer, Berlin, Heidelberg, pp 82-96##[55] Edelkamp S, Jabbar S, Lafuente AL (2006) Heuristic search for the analysis of graph transition systems. In: International conference on graph transformation. Springer, Berlin, Heidelberg, pp 414-429##[56] Holzmann GJ (1997) The model checker SPIN. IEEE Trans Softw Eng 23(5):279-295##[57] Yousefian R, Aboutorabi S, Rafe V (2016) A greedy algorithm versus metaheuristic solutions to deadlock detection in graph transformation systems. J Intell Fuzzy Syst 31(1):137-149##[58] Yang XS (2010) A new meta heuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), vol 284, pp 65-74##[59] Pira E, Rafe V, Nikanjam A (2016) EMCDM: efficient model checking by data mining for verification of complex software systems specified through architectural styles. Appl Soft Comput 49:1185-1201##[60] Abowd G, Allen R, Garlan D (1993) Using style to give meaning to software architecture. In: ACM SIGSOFT software engineering notes, vol 18, no. 5. ACM, pp 9-20##[61] Garlan D, Allen R, Ockerbloom J (1994) Exploiting style in architectural design environments. In: ACMSIGSOFT software engineering notes, vol. 19, no. 5. ACM, pp 175-188##[62] Pira E, Rafe V, Nikanjam A (2018) Searching for violation of safety and liveness properties using knowledge discovery in complex systems specified through graph transformations. Inf Softw Technol 97:110-134##[63] Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conferences on very large data bases, VLDB, vol 1215, pp 487-499##[64] Pira E, Rafe V, Nikanjam A (2017) Deadlock detection in complex software systems specified through graph transformation using bayesian optimization algorithm. J Syst Softw 131:181-200##[65] Pelikan M, Goldberg DE, Tsutsui S (2003) Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms, vol 3. Springer, Berlin, Heidelberg, pp 2738-2743##[66] Thomas JP (2000) Design and verification of a coordination protocol for cooperating systems. Soft Comput 4(2):130-140##[67] Zhu W, Han Y, Zhou Q (2018) Performing CTL model checking via DNA computing. Soft Comput.##[68] Godefroid P (1997) Model checking for programming languages using Verisoft. In: Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on principles of programming languages. ACM, pp 174-186##[69] Taentzer G, Ehrig K, Guerra E, Lara JD, Lengyel L, Levendovszky T, Prange U, Varro D, Varro-Gyapay S (2005) Model transformation by graph transformation: a comparative study. In: Proceedings of model transformations in practice workshop, Models conference, Montego Bay, Jamaica##[70] Rafe V, Hajvali M (2013) Designing an architectural style for pervasive healthcare systems. J Med Syst 37(2):1-13##[71] Heckel R, Thöne S (2005) Behavioral refinement of graph transformation-based models. Electron Notes Theory Comput Sci 127(3):101-111##[72] Engels G, Hausmann JH, Heckel R, Sauer S (2000) Dynamic meta modeling: a graphical approach to the operational semantics of behavioral diagrams in UML. In: International conference on the unified modeling language. Springer, Berlin Heidelberg, pp 323-337##[73] Mens T (2006) On the use of graph transformations for model refactoring. In: Generative and transformational techniques in software engineering. Springer, Berlin Heidelberg, pp 219-257##[74] Naddaf M, Rafe V (2014) Performance modeling and analysis of software architectures specified through graph transformations. Comput Inform 32(4):797-826##[75] Rafe V, Rahmani AT (2009) Towards automated software model checking using graph transformation systems and Bogor. J Zhejiang Univ Sci A 10(8):1093-1105##[76] Moradi M. Rafe V. Yousefian R. and Nikanjam A. (2015). A Meta-Heuristic Solution for Automated Refutation of Complex Software Systems Specified through Graph Transformations. Applied Soft Computing. vol. 33, 136-149.##[77] Yousefian R, Rafe V, Rahmani M (2014) A heuristic solution for model checking graph transformation systems. Appl Soft Comput 24:169-180##[78] Edelkamp S, Lafuente AL, Leue S (2001) Protocol verification with heuristic search. In: AAAI symposium on model-based validation of intelligence, pp 75-83##[79] Schmidt A. Model checking of visual modeling languages Master's Thesis Hungary: Budapest University of Technology; 2004##[80] Gaschnig J (1979) Performance measurement and analysis of certain search algorithms. Technical Report. CMU-CS-79-124, Carnegie-Mellon University##[81] Thöne S (2005) Dynamic software architectures: a Style based modeling and refinement technique with graph transformations. Ph.D. Thesis, Faculty of Computer Science, Electrical Engineering, and Mathematics, University of Paderborn##[82] Hausmann JH (2005) Dynamic meta modeling: a semantics description technique for visual modeling techniques. Ph.D. Thesis, Universität Paderborn##[83] S. Ziegert. "Graph transformation planning via abstraction", arXiv preprint arXiv: 1407.7933. 2014.##[1] Baier C, Katoen JP, Larsen KG (2008) Principles of model checking, vol 2620264. MIT press, Cambridge##[2] Chicano F, Francisco M, Ferreira M, Alba E (2011) Comparing metaheuristic algorithms for error detection in java programs. In: International symposium on Search based software engineering, vol 6956. Springer, Berlin, Heidelberg, pp 82-96##[3] Taentzer G, Ehrig K, Guerra E, Lara JD, Lengyel L, Levendovszky T, Prange U, Varro D, Varro-Gyapay S (2005) Model transformation by graph transformation: a comparative study. In: Proceedings of model transformations in practice workshop, Models conference, Montego Bay, Jamaica##[4] Rafe V, Hajvali M (2013) Designing an architectural style for pervasive healthcare systems. J Med Syst 37(2):1-13##[5] Heckel R, Thöne S (2005) Behavioral refinement of graph transformation-based models. Electron Notes the or Comput Sci 127(3):101-111##[6] Engels G, Hausmann JH, Heckel R, Sauer S (2000) Dynamic meta modeling: a graphical approach to the operational semantics of behavioral diagrams in UML. In: International conference on the unified modeling language. Springer, Berlin Heidelberg, pp 323-337##[7] Mens T (2006) On the use of graph transformations for model refactoring. In: Generative and transformational techniques in software engineering. Springer, Berlin Heidelberg, pp 219-257##[8] Rafe V, Rahmani AT (2008) Formal analysis of workflows using UML 2.0 activities and graph transformation systems. In: International colloquium on theoretical aspects of computing. Springer Berlin, Heidelberg, pp 305-318##[9] Naddaf M, Rafe V (2014) Performance modeling and analysis of software architectures specified through graph transformations. Comput Inform 32(4):797-826##[10] Clarke E, McMillan K, Campos S, Hartonas-Garmhausen V (1996) Symbolic model checking. In: International conference on computer aided verification, vol 1102. Springer, Berlin, Heidelberg, pp 419-422##[11] Godefroid P, Van Leeuwen J, Hartmanis J, Goos G, Wolper P (1996) Partial-order methods for the verification of concurrent systems: an approach to the state-explosion problem, vol 1032. Springer, Heidelberg##[12] Lafuente AL (2003) Symmetry reduction and heuristic search for error detection in model checking. In: Workshop on model checking and artificial intelligence##[13] Rafe V (2013) Scenario-driven analysis of systems specified through graph transformations. J Vis Lang Comput 24(2):136-145##[14] Alba E, Chicano F (2008) Searching for liveness property violations in concurrent systems with ACO. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, pp 1727-1734##[15] Rafe V, Moradi M, Yousefian R, Nikanjam A (2015) A meta-heuristic solution for automated refutation of complex software systems specified through graph transformations. Appl Soft Comput 33:136-149##[16] Pira E, Rafe V, Nikanjam A (2019) Using evolutionary algorithms for reachability analysis of complex software systems specified through graph transformation, Reliability Engineering and System Safety, vol 191##[17] Partabian J, Rafe V, Parvin H, Nejatian S (2019), An Approach Based on Knowledge Exploration for State Space Management in Checking Reachability of Complex Software Systems, soft computing, vol. 24, pp.1-16.##[18] Yasrebi M, Rafe V, Parvin H, Nejatian S (2020), An efficient approach to state space management in model checking of complex software system using machine learning technique, journal of intelligent & Fuzzy system, vol.38, no. 2, pp. 1761-1773.##[19] Rensink A, Boneva I, Kastenberg H, Staijen T (2010) User manual for the GROOVE tool set. Department of Computer Science, University of Twente, Enschede##[20] Peng H, Tahar S (1998) A survey on compositional verification. Technical Report, Department of Electrical and Computer Engineering, Concordia University##[21] Roever WP (1998) The need for compositional proof systems: a survey. In: Compositionality: the significant difference. Springer, Berlin, Heidelberg, pp 1-22##[22] Godefroid P (1990) Using partial orders to improve automatic verification methods. In: International conference on computer aided verification. Springer, Berlin, Heidelberg, pp 176-185##[23] Valmari A (1988) Error detection by reduced reachability graph generation. In: Proceedings of the 9th European workshop on application and theory of petri nets, pp 95-112##[24] Godefroid P, Wolper P (1993) Using partial orders for the efficient verification of deadlock freedom and safety properties. Formal Methods Syst Des 2(2):149-164##[25] Godefroid P, Van Leeuwen J, Hartmanis J, Goos G, Wolper P (1996) Partial-order methods for the verification of concurrent systems: an approach to the state-explosion problem, vol 1032. Springer, Heidelberg##[26] Lafuente AL (2003) Symmetry reduction and heuristic search for error detection in model checking. In: Workshop on model checking and artificial intelligence##[27] Wolper P, Leroy D (1993) Reliable hashing without collision detection. In: International conference on computer aided verification. Springer, Berlin Heidelberg, pp 59-70##[28] Stern U, Dill D (1995) Improved probabilistic verification by hash compaction. In: Advanced research working conference on correct hardware design and verification methods, vol 987. Springer, Berlin, Heidelberg, pp 206-224##[29] Stern U, Dill DL (1996) A new scheme for memory-efficient probabilistic verification. In: Formal description techniques IX. Springer, US, pp 333-348##[30] Courcoubetis C, Vardi M, Wolper P, Yannakakis M (1992) Memory efficient algorithms for the verification of temporal properties. In: Computer-aided verification, vol 1, issue 2-3. Springer, US, pp 129-142##https://doi.org/10.1007/BF00121128##[31] Isenberg T, Steenken D, Wehrheim H (2013) Bounded model checking of graph transformation systems via SMT solving. In: Formal techniques for distributed systems, vol 7892. Springer, Berlin, Heidelberg, pp 178-192##[32] Sivaraja H, Gopalakrishnan G (2003) Random walk based heuristic algorithms for distributed memory model checking. Electron Notes Theory Comput Sci 89(1):51-67##[33] Yang CH (1999) Prioritized model checking. Ph.D. thesis, Stanford Universit##[34] Edelkamp S, Lafuente AL, Leue S (2001) Directed explicit model checking with HSF-SPIN. In: Proceedings of the 8th international SPIN workshop on Model checking of software. Springer, New York, pp 57-79##[35] Yang CH, Dill DL (1998) Validation with guided search of the state space. In: Proceedings of the 35th annual design automation conference, pp 599-604##[36] Groce A, Visser W (2004) Heuristics for model checking Java programs. Int J Software Tools Technology Transf (STTT) 6(4):260-276##[37] Edelkamp S, Reffel F (1998) OBDDs in heuristic search. In: Annual conference on artificial intelligence. Springer, Berlin Heidelberg, pp 81-92##[38] Maeoka J, Tanabe Y, Ishikawa F (2016) Depth-first heuristic search for software model checking. In: Computer and information science, vol 614. Springer International Publishing, pp 75-96##[39] Edelkamp S, Leue S, Lafuente AL (2004) Directed explicit-state model checking in the validation of communication protocols. Int J Softw Tools Technol (STTT) 5(2-3):247-267##[40] Estler HC, Wehrheim H (2011) Heuristic search-based planning for graph transformation systems. KEPS 2011:54##[41] Snippe E (2011) Using heuristic search to solve planning problems in GROOVE. In: 14th Twente student conference on IT. University of Twente##[42] Ziegert S (2014) Graph transformation planning via abstraction. arXiv preprint arXiv: 1407.7933##[43] Elsinga JW (2016) On a framework for domain independent heuristics in graph transformation planning. Master's thesis, University of Twente##[44] Duarte LM, Foss L, Wagner FR, Heimfarth T (2010) Model checking the ant colony optimization. In: Hinchey M, Kleinjohann B, Kleinjohann L, Lindsay P, Rammig FJ, Timmis J, Wolf M (eds) Distributed, parallel and biologically inspired systems, vol 329. Springer, Berlin, Heidelberg, pp 221-232##[45] Alba E, Chicano F (2007a) Ant colony optimization for model checking. In: International conference on computer aided systems theory. Springer, Berlin, Heidelberg, pp 523-530##[46] Francesca G, Santone A, Vaglini G, Villani ML (2011) Ant colony optimization for deadlock detection in concurrent systems. In: 2011 IEEE 35th annual computer software and applications conference. IEEE, pp 108-117##[47] Alba E, Chicano F (2008) Searching for liveness property violations in concurrent systems with ACO. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, pp 1727-1734##[48] Kumazawa T, Yokoyama C, Takimoto M, Kambayashi Y (2016) Ant colony Optimization based model checking extended by smell like pheromone. In: Proceedings of the 9th EAI international conference on bio-inspired information and communications technologies (formerly BIONETICS), pp 214-220##https://doi.org/10.4108/eai.21-4-2016.151156##[49] Takada K, Takimoto M, Kumazawa T, Kambayashi Y (2017) ACO based model checking extended by smell-like pheromone with hop counts. In: Harmony search algorithm: proceedings of the 3rd international conference on harmony search algorithm, vol 514 (ICHSA 2017). Springer, p 52##[50] Foster H, Uchitel S, Magee J, Kramer J (2006) LTSA-WS: a tool for model-based verification of web service compositions and choreography. In: Proceedings of the 28th international conference on software engineering, Shanghai, China, pp 771-774##[51] Godefroid P, Khurshid S (2002) Exploring very large state spaces using genetic algorithms. In: International conference on tools and algorithms for the construction and analysis of systems. Springer, Berlin Heidelberg, pp 266-280##[52] Alba E, Chicano F, Ferreira M, Gomez-Pulido J (2008) Finding deadlocks in large concurrent java programs using genetic algorithms. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, pp 1735-1742##[53] Godefroid P (1997) Model checking for programming languages using Verisoft. In: Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on principles of programming languages. ACM, pp 174-186##[54] Chicano F, Francisco M, Ferreira M, Alba E (2011) Comparing metaheuristic algorithms for error detection in java programs. In: International symposium on Search based software engineering, vol 6956. Springer, Berlin, Heidelberg, pp 82-96##[55] Edelkamp S, Jabbar S, Lafuente AL (2006) Heuristic search for the analysis of graph transition systems. In: International conference on graph transformation. Springer, Berlin, Heidelberg, pp 414-429##[56] Holzmann GJ (1997) The model checker SPIN. IEEE Trans Softw Eng 23(5):279-295##[57] Yousefian R, Aboutorabi S, Rafe V (2016) A greedy algorithm versus metaheuristic solutions to deadlock detection in graph transformation systems. J Intell Fuzzy Syst 31(1):137-149##[58] Yang XS (2010) A new meta heuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), vol 284, pp 65-74##[59] Pira E, Rafe V, Nikanjam A (2016) EMCDM: efficient model checking by data mining for verification of complex software systems specified through architectural styles. Appl Soft Comput 49:1185-1201##[60] Abowd G, Allen R, Garlan D (1993) Using style to give meaning to software architecture. In: ACM SIGSOFT software engineering notes, vol 18, no. 5. ACM, pp 9-20##[61] Garlan D, Allen R, Ockerbloom J (1994) Exploiting style in architectural design environments. In: ACMSIGSOFT software engineering notes, vol. 19, no. 5. ACM, pp 175-188##[62] Pira E, Rafe V, Nikanjam A (2018) Searching for violation of safety and liveness properties using knowledge discovery in complex systems specified through graph transformations. Inf Softw Technol 97:110-134##[63] Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conferences on very large data bases, VLDB, vol 1215, pp 487-499##[64] Pira E, Rafe V, Nikanjam A (2017) Deadlock detection in complex software systems specified through graph transformation using bayesian optimization algorithm. J Syst Softw 131:181-200##[65] Pelikan M, Goldberg DE, Tsutsui S (2003) Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms, vol 3. Springer, Berlin, Heidelberg, pp 2738-2743##[66] Thomas JP (2000) Design and verification of a coordination protocol for cooperating systems. Soft Comput 4(2):130-140##[67] Zhu W, Han Y, Zhou Q (2018) Performing CTL model checking via DNA computing. Soft Comput.##[68] Godefroid P (1997) Model checking for programming languages using Verisoft. In: Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on principles of programming languages. ACM, pp 174-186##[69] Taentzer G, Ehrig K, Guerra E, Lara JD, Lengyel L, Levendovszky T, Prange U, Varro D, Varro-Gyapay S (2005) Model transformation by graph transformation: a comparative study. In: Proceedings of model transformations in practice workshop, Models conference, Montego Bay, Jamaica##[70] Rafe V, Hajvali M (2013) Designing an architectural style for pervasive healthcare systems. J Med Syst 37(2):1-13##[71] Heckel R, Thöne S (2005) Behavioral refinement of graph transformation-based models. Electron Notes Theory Comput Sci 127(3):101-111##[72] Engels G, Hausmann JH, Heckel R, Sauer S (2000) Dynamic meta modeling: a graphical approach to the operational semantics of behavioral diagrams in UML. In: International conference on the unified modeling language. Springer, Berlin Heidelberg, pp 323-337##[73] Mens T (2006) On the use of graph transformations for model refactoring. In: Generative and transformational techniques in software engineering. Springer, Berlin Heidelberg, pp 219-257##[74] Naddaf M, Rafe V (2014) Performance modeling and analysis of software architectures specified through graph transformations. Comput Inform 32(4):797-826##[75] Rafe V, Rahmani AT (2009) Towards automated software model checking using graph transformation systems and Bogor. J Zhejiang Univ Sci A 10(8):1093-1105##[76] Moradi M. Rafe V. Yousefian R. and Nikanjam A. (2015). A Meta-Heuristic Solution for Automated Refutation of Complex Software Systems Specified through Graph Transformations. Applied Soft Computing. vol. 33, 136-149.##[77] Yousefian R, Rafe V, Rahmani M (2014) A heuristic solution for model checking graph transformation systems. Appl Soft Comput 24:169-180##[78] Edelkamp S, Lafuente AL, Leue S (2001) Protocol verification with heuristic search. In: AAAI symposium on model-based validation of intelligence, pp 75-83##[79] Schmidt A. Model checking of visual modeling languages Master's Thesis Hungary: Budapest University of Technology; 2004##[80] Gaschnig J (1979) Performance measurement and analysis of certain search algorithms. Technical Report. CMU-CS-79-124, Carnegie-Mellon University##[81] Thöne S (2005) Dynamic software architectures: a Style based modeling and refinement technique with graph transformations. Ph.D. Thesis, Faculty of Computer Science, Electrical Engineering, and Mathematics, University of Paderborn##[82] Hausmann JH (2005) Dynamic meta modeling: a semantics description technique for visual modeling techniques. Ph.D. Thesis, Universität Paderborn##[83] S. Ziegert. "Graph transformation planning via abstraction", arXiv preprint arXiv: 1407.7933. 2014.## ##

تحلیل مصرف انرژی در شبکه حسگر بی‌سیم با مدل مبتنی برحسگری فشرده Energy Consumption Analysis based on Compressive Sensing Model in Wireless Sensor Networks 1 1 مهمترین چالش در شبکه‌های حسگر بی‌سیم، به حداقل رساندن مصرف انرژی در باتری گره‌های حسگر و افزایش طول عمر شبکه است. یکی از تکنیک‌های مؤثر در کاهش مصرف انرژی در این شبکه‌ها، تکنیک حسگری فشرده است که با کاهش داده‌های ارسالی، موجب کاهش مصرف انرژی در شبکه می‌گردد. از طرفی، برای مقابله با چالش مصرف انرژی، شناخت کامل منابع مصرف انرژی در شبکه ضروری است. مدل‌های مختلفی برای تحلیل مصرف انرژی در شبکه حسگر بی‌سیم ارائه شده‌اند، اما مدل کاملی برای تحلیل انرژی مصرفی مبتنی بر حسگری فشرده ارائه نشده است. از این رو وجود مدلی برای تحلیل مصرف انرژی مبتنی برحسگری فشرده ضرورت می‌یابد. بر همین اساس، در این مقاله به ارائه مدلی برای تحلیل مصرف انرژی مبتنی بر حسگری فشرده می‌پردازیم. این مدل می‌تواند به طراحی بهینه شبکه‌های حسگر بی‌سیم مبتنی بر حسگری فشرده با رویکرد بهبود مصرف انرژی کمک مؤثری نماید.   2 Nowadays, wireless sensor networks (WSNs) have found many applications in a variety of topics. The main purpose of these networks is to measure environmental phenomena and to send read data in multi-hop paths to the sink to be exploited by users. The most important challenge in WSNs is to minimize energy consumption in sensor batteries and increase network lifetime. One of the most important techniques for reducing energy consumption in WSNs is the compressive sensing (CS) technique. CS reduces network energy consumption by reducing data transmission in the network and increasing the network lifetime. The use of CS technique in a WSN results in the production of different models of CS signals. These models are based on spatial, temporal and spatio-temporal sensors readings. On the other hand, in order to overcome the challenge of energy consumption, the exact recognition of energy resources in the network is essential.  Energy consumption in a sensor node can be divided into two parts: (a) the energy used for computing; and (b) the energy consumed by the communication. The energy used for the computing consists of three components: 1. sensor energy consumption (data reading), 2. background energy consumption, and 3. energy consumption for processing. The power consumption of the communication includes the following: 1. energy consumption for data transmission; 2. energy consumption for data receiving; 3. energy consumption for sending messages; and 4. energy consumption for receiving messages. Hence, the existence of a model for analyzing energy consumption in a CS-based WSN is necessary. Several models have been developed to analyze energy consumption in a WSN, but there is not a complete model for analyzing energy consumption in a CS-based WSN.  In this paper, we study all energy consumption components mentioned above in a CS-based WSN and present a complete model for energy consumption analysis. This model can optimize the design of CS-based WSNs energy efficiency improvement approach. To evaluate the proposed model, we use this model to analyze energy consumption in the compressive data gathering technique which is a CS-based data aggregation method. Using this model can optimize the design of CS-based WSNs. 195 210 2021/06/202021/07/112021/04/192021/04/152021/08/152021/04/182019/09/62021/01/192021/05/112022/06/122021/06/102019/05/19 1398/2/29 2022/08/292022/05/112023/02/222022/05/112023/07/82022/05/112023/07/182023/07/52023/06/22023/07/172023/06/22020/10/20 1399/7/29 محمدرضا قادری Mohammad Reza Ghaderi دانشگاه آزاد اسلامی واحد تهران جنوب ایران st_mr_ghaderi@azad.ac.ir وحید طباطبا وکیلی Vahid Tabataba Vakili دانشکده برق دانشگاه علم و صنعت ایران ایران vakily@iust.ac.ir منصور شیخان Mansour Sheikhan دانشکده برق دانشگاه آزاد اسلامی واحد تهران جنوب-بلوار آهنگ- گروه مخابرات ایران msheikhn@azad.ac.ir Wireless sensor network Compressive sensing Energy model Compressive data gathering. شبکه حسگر بی سیم حسگری فشرده مدل انرژی تحلیل انرژی تجمیع داده‌های فشرده [1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: a survey," Computer Networks, vol. 38, pp. 393-422, 2002.##[2] Z. Xiong, A. Liveris, and S. Cheng, "Distributed source coding for sensor networks," IEEE Signal Processing Magazine, vol. 21, pp. 80-94, 2004.##[3] J. Lee, and W. Cheng, "Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication," IEEE Sensors Journal, vol. 12, pp. 2891- 2897, 2012.##[4] M. A. Zahhad, O. Amin, M. Farrag, and A. Ali, "An energy consumption model for wireless sensor networks," IEEE 5th International Conference on Energy Aware Computing Systems & Applications, Cairo, Egypt, Dec. 2015.##[5] J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak, "Compressed sensing for networked data," Signal Processing Magazine, vol. 25, pp. 92-101, 2008.##[6] E. Candes and M. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, vol. 25, pp. 21-30, 2008.##[7] M. B. Wakin, M. F. Duarte, S. Sarvotham, D. Baron, and R. G. Baraniuk, "Recovery of jointly sparse signals from few random," in Proc. 15th ACM MobiCom, Beijing, China, pp. 145-156, Sep. 2009.##[8] J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, vol. 53, pp. 4655-4666, Dec. 2007.##[9] A. Kulkarni and T. Mohsenin, "Low overhead architectures for OMP compressive sensing reconstruction algorithm," IEEE Transactions on Circuits and Systems, vol. 64, pp. 1468-1480, 2017.##[10] D. L. Donoho, M. Elad, and V. N. Temlyakov, "Stable recovery of sparse over complete representations in the presence of noise," IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006.##[11] E. Candes and J. Romberg, "Sparsity and incoherence in compressive sampling," Inverse Problems, vol. 23, pp. 969-985, Apr. 2007.##[12] C. Luo, et al. , "Compressive data gathering for large-scale wireless sensor networks," in Proc. of the 15th annual international conference on Mobile computing and networking (Mobicom), pp. 145-156, 2009.##[13] K. C Lan and M. Z. Wei, "A compressibility-based clustering algorithm for hierarchical compressive data gathering," IEEE Sensors Journal, vol. 17, pp. 2550-2562, Apr. 2017.##[14] B. Ali, N. Pissinou, and K. Makki, "Identification and validation of spatio-temporal associations in wireless sensor networks," in Proc. SENSORCOMM, Athens, Greece, pp. 496-501, Jun. 2009.##[15] M. Leinonen and S. Member, "Sequential compressed sensing with progressive signal reconstruction in wireless sensor networks," IEEE Transactions on Wireless Communication, vol. 14, pp. 1622-1635, Mar. 2015.##[16] M. Duarte and R. Baraniuk, "Kronecker product matrices for compressive sensing," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Dallas, TX, USA, pp. 3650-3653, Mar. 2010.##[17] M. Mahmudimanesh, A. Khelil, and N. Suri, "Balanced spatio-temporal compressive sensing for multi-hop wireless sensor networks," IEEE 9th Int. Conf. on Mobile Ad hoc and Sensor Systems, Las Vegas, USA, Oct. 2012.##[18] X. Li, X. Tao, and Z. Chen, "Spatio-temporal compressive sensing based data gathering in wireless sensor networks," IEEE Wireless Communications Letters, vol. 7, pp. 198-201, Apr. 2018.##[19] M. A. Zahhad, O. Amin, M. Farrag, and A. Ali, "Survey on energy consumption models in wireless sensor networks," Open Transactions on Wireless Communications, vol. 1, pp. 63-79, 2014.##[20] C. Karakus, A. C. Gurbuz, and B. Tavli, "Analysis of energy efficiency of compressive sensing in wireless sensor networks," IEEE Sensors Journal, vol. 13, pp. 1999-2008, May 2013.##[21] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Transactions on Wireless Communications, vol. 1, pp. 660-670, 2002.##[22] C. Zhou, M. Wang, W. Qu, and Z. Lu, "A wireless sensor network model considering energy consumption balance," Mathematical Problems in Engineering, vol. 2018, pp. 1-8, 2018.##[23] A. Ali, M. Abo-Zahhad, and M. Farrag, "Modeling of wireless sensor networks with minimum energy consumption," Arabian Journal for Science and Engineering, vol. 42, pp. 2631-2639, Jul. 2017.##[24] M. Ahmad Jan, P. Nanda, and X. He, "Energy evaluation model for an improved centralized clustering hierarchical algorithm in WSN," in Proc. International Conference on Wired/Wireless Internet Communication, WWIC, pp. 154-167, 2013.##[25] V. Shnayder, M. Hempstead, B. Chen, G. W. Allen, and M. Welsh, "Simulating the power consumption of large-scale sensor network applications," in Proc. ACM Conf. Embedded Netw. Sensor Syst., pp. 188-200, 2004.##[26] F. Z. Djiroun and D. Djenouri, "MAC protocols with wake-up radio for wireless sensor networks: a review," IEEE Communications Surveys & Tutorials, vol. 19, pp. 587-618, 2017.##[27] A. Rasul and T. Erlebach, "Reducing idle listening during data collection in wireless sensor networks," 10th International Conference on Mobile Ad-hoc and Sensor Networks, Maui, HI, USA, 2014.##[28] N. N. Minh and M. K. Kim, "Reducing idle listening time in pipeline-forwarding MAC protocols of wireless sensor networks," IEEE International Conference on Advanced Technologies for Communications (ATC), Hanoi, Vietnam, 2016.##[29] S.H. Lee and L. Choi, "ZeroMAC: Toward a zero sleep delay and zero idle listening media access control protocol with ultralow power radio frequency wakeup sensor," International Journal of Distributed Sensor Networks, vol. 13, pp. 1-21, 2017.##[30] M. R. Ghaderi, V. T. Vakili, and M. Sheikhan, "FGAF CDG: fuzzy geographic routing protocol based on compressive data gathering in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, pp. 1-23, Published online 17 May 2019 (##https://doi.org/10.1007/s12652-019-01314-1##[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: a survey," Computer Networks, vol. 38, pp. 393-422, 2002.##[2] Z. Xiong, A. Liveris, and S. Cheng, "Distributed source coding for sensor networks," IEEE Signal Processing Magazine, vol. 21, pp. 80-94, 2004.##[3] J. Lee, and W. Cheng, "Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication," IEEE Sensors Journal, vol. 12, pp. 2891- 2897, 2012.##[4] M. A. Zahhad, O. Amin, M. Farrag, and A. Ali, "An energy consumption model for wireless sensor networks," IEEE 5th International Conference on Energy Aware Computing Systems & Applications, Cairo, Egypt, Dec. 2015.##[5] J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak, "Compressed sensing for networked data," Signal Processing Magazine, vol. 25, pp. 92-101, 2008.##[6] E. Candes and M. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, vol. 25, pp. 21-30, 2008.##[7] M. B. Wakin, M. F. Duarte, S. Sarvotham, D. Baron, and R. G. Baraniuk, "Recovery of jointly sparse signals from few random," in Proc. 15th ACM MobiCom, Beijing, China, pp. 145-156, Sep. 2009.##[8] J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, vol. 53, pp. 4655-4666, Dec. 2007.##[9] A. Kulkarni and T. Mohsenin, "Low overhead architectures for OMP compressive sensing reconstruction algorithm," IEEE Transactions on Circuits and Systems, vol. 64, pp. 1468-1480, 2017.##[10] D. L. Donoho, M. Elad, and V. N. Temlyakov, "Stable recovery of sparse over complete representations in the presence of noise," IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006.##[11] E. Candes and J. Romberg, "Sparsity and incoherence in compressive sampling," Inverse Problems, vol. 23, pp. 969-985, Apr. 2007.##[12] C. Luo, et al. , "Compressive data gathering for large-scale wireless sensor networks," in Proc. of the 15th annual international conference on Mobile computing and networking (Mobicom), pp. 145-156, 2009.##[13] K. C Lan and M. Z. Wei, "A compressibility-based clustering algorithm for hierarchical compressive data gathering," IEEE Sensors Journal, vol. 17, pp. 2550-2562, Apr. 2017.##[14] B. Ali, N. Pissinou, and K. Makki, "Identification and validation of spatio-temporal associations in wireless sensor networks," in Proc. SENSORCOMM, Athens, Greece, pp. 496-501, Jun. 2009.##[15] M. Leinonen and S. Member, "Sequential compressed sensing with progressive signal reconstruction in wireless sensor networks," IEEE Transactions on Wireless Communication, vol. 14, pp. 1622-1635, Mar. 2015.##[16] M. Duarte and R. Baraniuk, "Kronecker product matrices for compressive sensing," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Dallas, TX, USA, pp. 3650-3653, Mar. 2010.##[17] M. Mahmudimanesh, A. Khelil, and N. Suri, "Balanced spatio-temporal compressive sensing for multi-hop wireless sensor networks," IEEE 9th Int. Conf. on Mobile Ad hoc and Sensor Systems, Las Vegas, USA, Oct. 2012.##[18] X. Li, X. Tao, and Z. Chen, "Spatio-temporal compressive sensing based data gathering in wireless sensor networks," IEEE Wireless Communications Letters, vol. 7, pp. 198-201, Apr. 2018.##[19] M. A. Zahhad, O. Amin, M. Farrag, and A. Ali, "Survey on energy consumption models in wireless sensor networks," Open Transactions on Wireless Communications, vol. 1, pp. 63-79, 2014.##[20] C. Karakus, A. C. Gurbuz, and B. Tavli, "Analysis of energy efficiency of compressive sensing in wireless sensor networks," IEEE Sensors Journal, vol. 13, pp. 1999-2008, May 2013.##[21] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Transactions on Wireless Communications, vol. 1, pp. 660-670, 2002.##[22] C. Zhou, M. Wang, W. Qu, and Z. Lu, "A wireless sensor network model considering energy consumption balance," Mathematical Problems in Engineering, vol. 2018, pp. 1-8, 2018.##[23] A. Ali, M. Abo-Zahhad, and M. Farrag, "Modeling of wireless sensor networks with minimum energy consumption," Arabian Journal for Science and Engineering, vol. 42, pp. 2631-2639, Jul. 2017.##[24] M. Ahmad Jan, P. Nanda, and X. He, "Energy evaluation model for an improved centralized clustering hierarchical algorithm in WSN," in Proc. International Conference on Wired/Wireless Internet Communication, WWIC, pp. 154-167, 2013.##[25] V. Shnayder, M. Hempstead, B. Chen, G. W. Allen, and M. Welsh, "Simulating the power consumption of large-scale sensor network applications," in Proc. ACM Conf. Embedded Netw. Sensor Syst., pp. 188-200, 2004.##[26] F. Z. Djiroun and D. Djenouri, "MAC protocols with wake-up radio for wireless sensor networks: a review," IEEE Communications Surveys & Tutorials, vol. 19, pp. 587-618, 2017.##[27] A. Rasul and T. Erlebach, "Reducing idle listening during data collection in wireless sensor networks," 10th International Conference on Mobile Ad-hoc and Sensor Networks, Maui, HI, USA, 2014.##[28] N. N. Minh and M. K. Kim, "Reducing idle listening time in pipeline-forwarding MAC protocols of wireless sensor networks," IEEE International Conference on Advanced Technologies for Communications (ATC), Hanoi, Vietnam, 2016.##[29] S.H. Lee and L. Choi, "ZeroMAC: Toward a zero sleep delay and zero idle listening media access control protocol with ultralow power radio frequency wakeup sensor," International Journal of Distributed Sensor Networks, vol. 13, pp. 1-21, 2017.##[30] M. R. Ghaderi, V. T. Vakili, and M. Sheikhan, "FGAF CDG: fuzzy geographic routing protocol based on compressive data gathering in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, pp. 1-23, Published online 17 May 2019 (##https://doi.org/10.1007/s12652-019-01314-1## ##