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سامانۀ مدیریت اعتماد مبتنی‌بر آنتروپی جهت کاهش رفتارهای بدخواهانه در سامانه‌های مدیریت اعتماد بر اساس نظریه اخلاق اطلاعاتی Entropy-Based Trust Management System for Mitigating Malicious Behaviors in Trust Management Systems, Considering Information Ethics Theory 1 1 سامانه­‌های مدیریت اعتماد در محیط‌­های تعاملی، جایی که یک عامل باید در مورد استفاده از یک خدمات تصمیم بگیرد، استفاده می‌شوند. به‌دلیل فراگیرشدن این سامانه‌­ها، موجودیت­‌های بدخواه انگیزۀ­ مضاعفی برای تأثیرگذاری بر سامانه­‌های مدیریت اعتماد و ایجاد خدشه در فرایند تصمیم­‌گیری دارند. با وجود روش‌­های ارائه‌شده برای کاهش فعالیت­‌های مخرب در الگو­های اعتماد قبلی، بسیاری از آنها نتوانستند به‌طور مؤثر این مشکل را حل کنند. به‌عنوان مثال، مقابله با تغییر رفتار عامل­ها یک مشکل رایج برای بسیاری از الگو­های اعتماد است. علاوه‌بر این، هیچ رویکرد قوی، منعطف و تطبیقی ارائه نشده‌است و این مشکل همچنان وجود دارد. در این مقاله، رویکرد جدیدی برای جلوگیری از اعمال مخرب و شناسایی ناهنجاری‌ها با استفاده از یک سامانۀ مدیریت اعتماد مبتنی‌بر آنتروپی با قابلیت تشخیص ویژگی‌های ذاتی اقدامات عامل­‌ها، اعم از اینکه اقدام مخرب باشد یا خیر، ارائه شده‌است. محاسبۀ اعتماد بر اساس ساختار آنتروپی، از نظریۀ اخلاق اطلاعات الهام گرفته‌شده‌است. با استفاده از این روش، اقدامات مخربی که سامانۀ مدیریت اعتماد را مختل می­کنند، پالایش می­شوند و در نتیجه، دقت محاسبۀ اعتماد افزایش می‌­یابد. نتایج تجربی حاصل از شبیه­‌سازی نشان می‌­دهد که عملکرد سامانۀ پیشنهادی از نظر دقت محاسبه، قابلیت اعتماد خدمات و همچنین، تشخیص رفتار مخرب عامل­ها قابل‌قبول است. به‌طور ویژه، الگوی پیشنهادی از نظر سرعت انطباق با تغییرات محیطی و رفتارهای متغیر عامل­‌ها، حدود ده درصد نسبت به الگوهای اعتماد شناخته‌شده بهبود یافته‌است.   2 Trust management systems are used in interactive environments, where an agent needs to make a decision about using a service. Due to the preponderance of these systems, malicious entities have strong incentives to influence trust management systems and divert their decisions. In spite of approaches presented in previous trust models to mitigate the malicious activities, many of them could not cope with the problem efficiently. For example, tackling the variable behavior of agents is a common failure point for many trust models. Moreover, no rigid, flexible and adaptive general approach has been presented and the problem somehow remains. This paper presents a novel approach to prevent malicious actions and identify anomalies using an entropy-based trust management system. The system is capable of recognizing the intrinsic characteristics of actions, determining whether they are malicious or not. To achieve this, the information environment is divided into four main parts based on entropy changes. Trust calculation in this system relies on an entropy structure derived from information ethics theory. To enhance the system’s resistance and resilience against malicious behavior, it is important to understand the nature of the actions performed by the agents. To accomplish this, we define the patterns of entropy changes for the four parts and use these patterns to identify and refine the nature of actions as good, bad, or insignificant. The simulation-based experimental results indicate that the proposed system shows promising performance in terms of accurately calculating trust and detecting malicious behavior. Specifically, the proposed system exhibits a 10 percent advantage over well-known trust systems with regards to swiftly adapting to environmental changes and diverse agent behaviors. Moreover, the observed experiments have displayed a notable trend in the fluctuation of good, bad, and insignificant actions. The results indicate a consistent increase in the number of good actions and a corresponding decrease in bad actions. Put simply, the method demonstrates improvement over time through repeated system implementations. This improvement can be attributed to the agents’ heightened honesty as they gain a better understanding of the nature of their actions. Additionally, the provision of feedback on their behavior plays a pivotal role in reinforcing more accurate decision-making within the system. 3 22 2023/04/1 1402/1/12 2023/07/18 1402/4/27 امیر خشکبارچی Amir Khoshkbarchi دانشگاه صنعتی امیر کبیر ایران a.khoshkbarchi@aut.ac.ir حمید رضا شهریاری Hamid Reza Shahriari دانشگاه صنعتی امیر کبیر ایران shahriari@aut.ac.ir Trust Information Ethics Theory Entropy Service Discovery Service-Oriented environment اعتماد نظریه اخلاق اطلاعاتی آنتروپی کشف خدمات محیط مبتنی‌بر سامانه مبتنی بر سرویس مبتنی‌بر خدمات [1] Z. Su et al., “Reliable and resilient trust management in distributed service provision networks,” dl.acm.org, vol. 9, no. 3, Jun. 2015, doi: 10.1145/2754934.##[2] D. Chen, F. Lai, and Z. Lin, “A trust model for online peer-to-peer lending: a lender’s perspective,” Information Technology and Management, vol. 15, no. 4, pp. 239–254, Dec. 2014, doi: 10.1007/S10799-014-0187-Z.##[3] M. Tang, X. Dai, J. Liu, and J. Chen, “Towards a trust evaluation middleware for cloud service selection,” Future Generation Computer Systems, vol. 74, pp. 302–312, Sep. 2017, doi: 10.1016/J.FUTURE.2016.01.009.##[4] B. Lahno, “Trust. The Tacit Demand,” Ethical Theory and Moral Practice, vol. 2, no. 4. Springer, pp. 433–435, 1999.##[5] M. Ryan, “In AI We Trust: Ethics, Artificial Intelligence, and Reliability,” Sci Eng Ethics, vol. 26, no. 5, pp. 2749–2767, Oct. 2020, doi: 10.1007/S11948-020-00228-Y/TABLES/1.##[6] J.-H. Cho, K. Chan, and S. Adali, “A survey on trust modeling,” ACM Computing Surveys (CSUR), vol. 48, no. 2, pp. 1–40, 2015.##[7] D. M. Messick and R. M. Kramer, “Trust as a form of shallow morality.,” 2001.##[8] Y. Yamamoto, “A morality based on trust: Some reflections on Japanese morality,” Philos East West, vol. 40, no. 4, pp. 451–469, 1990.##[9] L. Floridi, “Information ethics, its nature and scope,” ACM SIGCAS Computers and Society, vol. 36, no. 3, pp. 21–36, 2006.##[10] L. Floridi, The ethics of information. 2013. Accessed: Jun. 21, 2022. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=_XHcAAAAQBAJ&oi=fnd&pg=PP1&dq=The+ethics+of+information&ots=fZkH8VOxSZ&sig=QhCILoi1OTYVGbnRvifDNM5lwHY##[11] L. Floridi and J. W. Sanders, “Entropy as evil in information ethics,” Etica & Politica, Special Issue on Computer Ethics, vol. 1, no. 2, 1999.##[12] D. De Siqueira Braga, M. Niemann, B. Hellingrath, and F. B. De Lima Neto, “Survey on computational trust and reputation models,” ACM Comput Surv, vol. 51, no. 5, Aug. 2018, doi: 10.1145/3236008.##[13] J. Wang et al., “A survey on trust evaluation based on machine learning,” dl.acm.org, vol. 53, no. 5, Sep. 2020, doi: 10.1145/3408292.##[14] A. J. Bidgoly and B. T. Ladani, “Modeling and quantitative verification of trust systems against malicious attackers,” Comput J, vol. 59, no. 7, pp. 1005–1027, 2016.##[15] F. Dini and G. Spagnolo, “Buying reputation on eBay: Do recent changes help?,” International Journal of Electronic Business, vol. 7, no. 6, pp. 581–598, 2009.##[16] A. Jøsang and J. Golbeck, “Challenges for robust trust and reputation systems,” in Proceedings of the 5th International Workshop on Security and Trust Management (SMT 2009), Saint Malo, France, 2009, vol. 5, no. 9.##[17] R. Kerr and R. Cohen, “Smart cheaters do prosper: defeating trust and reputation systems,” in Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 2, 2009, pp. 993–1000.##[18] K. Arshad and K. Moessner, “Robust collaborative spectrum sensing based on beta reputation system,” in 2011 Future Network & Mobile Summit, 2011, pp. 1–8.##[19] M. Momani, K. Aboura, and S. Challa, “RBATMWSN: recursive Bayesian approach to trust management in wireless sensor networks,” in 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, 2007, pp. 347–352.##[20] P. Shi and H. Chen, “RASN: Resist on-off attack for wireless sensor networks,” in Proceedings of the 2012 international conference on computer application and system modeling, 2012, pp. 690–693.##[21] D. Wang, T. Muller, Y. Liu, and J. Zhang, “Towards robust and effective trust management for security: A survey,” in 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, 2014, pp. 511–518.##[22] O. A. Wahab, J. Bentahar, H. Otrok, and A. Mourad, “A survey on trust and reputation models for Web services: Single, composite, and communities,” Decis Support Syst, vol. 74, pp. 121–134, 2015.##[23] N. Kandhoul, S. K. Dhurandher, and I. Woungang, “T_CAFE: a trust based security approach for opportunistic IoT,” IET Communications, vol. 13, no. 20, pp. 3463–3471, 2019.##[24] S. Sicari, A. Rizzardi, L. A. Grieco, and A. Coen-Porisini, “Security, privacy and trust in Internet of Things: The road ahead,” Computer networks, vol. 76, pp. 146–164, 2015.##[25] X. Wang, L. Liu, and J. Su, “RLM: A general model for trust representation and aggregation,” IEEE Trans Serv Comput, vol. 5, no. 1, pp. 131–143, 2010.##[26] F. Ahmad, F. Kurugollu, A. Adnane, R. Hussain, and F. Hussain, “MARINE: Man-in-the-middle attack resistant trust model in connected vehicles,” IEEE Internet Things J, vol. 7, no. 4, pp. 3310–3322, 2020.##[27] A. Vasudeva and M. Sood, “Survey on sybil attack defense mechanisms in wireless ad hoc networks,” Journal of Network and Computer Applications, vol. 120, pp. 78–118, 2018.##[28] Y. Ruan and A. Durresi, “A survey of trust management systems for online social communities–trust modeling, trust inference and attacks,” Knowl Based Syst, vol. 106, pp. 150–163, 2016.##[29] B. Khosravifar, J. Bentahar, M. Gomrokchi, and R. Alam, “CRM: An efficient trust and reputation model for agent computing,” Knowl Based Syst, vol. 30, pp. 1–16, 2012.##[30] B. Sun and D. Li, “A comprehensive trust-aware routing protocol with multi-attributes for WSNs,” IEEE Access, vol. 6, pp. 4725–4741, 2017.##[31] K. Hoffman, D. Zage, and C. Nita-Rotaru, “A survey of attack and defense techniques for reputation systems,” ACM Computing Surveys (CSUR), vol. 42, no. 1, pp. 1–31, 2009.##[32] S. Vavilis, M. Petković, and N. Zannone, “A reference model for reputation systems,” Decis Support Syst, vol. 61, pp. 147–154, 2014.##[33] F. G. Mármol and G. M. Pérez, “Towards pre-standardization of trust and reputation models for distributed and heterogeneous systems,” Comput Stand Interfaces, vol. 32, no. 4, pp. 185–196, 2010.##[34] Z. Noorian and M. Ulieru, “The state of the art in trust and reputation systems: a framework for comparison,” Journal of theoretical and applied electronic commerce research, vol. 5, no. 2, pp. 97–117, 2010.##[35] D. D. S. Braga, M. Niemann, B. Hellingrath, and F. B. D. L. Neto, “Survey on computational trust and reputation models,” ACM Computing Surveys (CSUR), vol. 51, no. 5, pp. 1–40, 2018.##[36] S. Chen, Y. Zhang, P. Liu, and J. Feng, “Coping with traitor attacks in reputation models for wireless sensor networks,” in 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010, pp. 1–6.##[37] L. F. Perrone and S. C. Nelson, “A study of on-off attack models for wireless ad hoc networks,” in 2006 1st Workshop on Operator-Assisted (Wireless Mesh) Community Networks, 2006, pp. 1–10.##[38] D. Wang, T. Muller, J. Zhang, and Y. Liu, “Quantifying robustness of trust systems against collusive unfair rating attacks using information theory,” in Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.##[39] D. Wang, T. Muller, A. A. Irissappane, J. Zhang, and Y. Liu, “Using Information Theory to Improve the Robustness of Trust Systems.,” in AAMAS, 2015, pp. 791–799.##[40] J.-H. Cho, A. Swami, and R. Chen, “Modeling and analysis of trust management with trust chain optimization in mobile ad hoc networks,” Journal of Network and Computer Applications, vol. 35, no. 3, pp. 1001–1012, 2012.##[41] A. Aldini, “Formal approach to design and automatic verification of cooperation-based networks,” IARIA Int J Adv Internet Technol, vol. 6, no. 1, p. 2, 2013.##[42] T. Muller, “Semantics of trust,” in International Workshop on Formal Aspects in Security and Trust, 2010, pp. 141–156.##[43] A. Herbon, and D. Tsadikovich. "An efficient entropy-based stopping rule for mitigating risk factors in supply nets." International Journal of Production Economics 260 2023.##[44] A. Khoshkbarchi and H. R. Shahriari, “Improving Agents Trust in Service-Oriented Environment Based on Entropy Structure and Information Ethics Principles,” Int J Hum Comput Interact, 2022, doi: 10.1080/10447318.2022.2115639.##[45] L. Floridi, The ethics of information. 2013. Accessed: Jun. 15, 2022. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=_XHcAAAAQBAJ&oi=fnd&pg=PP1&dq=Ethics+of+Information+2013+floridi&ots=fZkH2-PvUS&sig=Wxpr4W8OpzGGiVWMDesAf4Czwrg##[46] “Floridi, L., & Sanders, J. W. (1999). Entropy as... - Google Scholar.” https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Floridi%2C+L.%2C+%26+Sanders%2C+J.+W.+%281999%29.+Entropy+as+evil+in+information+ethics.+Etica+%26+Politica%2C+Special+Issue+on+Computer+Ethics%2C+1%282%29.&btnG= (accessed Jun. 15, 2022).##[47] P. Resnick and R. Zeckhauser, “Trust among strangers in Internet transactions: Empirical analysis of eBay’s reputation system,” in The Economics of the Internet and E-commerce, Emerald Group Publishing Limited, 2002.##[48] K. RahimiZadeh, and P. Kabiri. "Trust‐based routing method using a mobility‐based clustering approach in mobile ad hoc networks." Security and Communication Networks 7, no. 11, 2014.##[49] A. Khoshkbarchi, and H.R. Shahriari. "Coping with unfair ratings in reputation systems based on learning approach." Enterprise Information Systems 11, no. 10 (2017): 1481-1499.##[1] Z. Su et al., "Reliable and resilient trust management in distributed service provision networks," dl.acm.org, vol. 9, no. 3, Jun. 2015, doi: 10.1145/2754934.##[2] D. Chen, F. Lai, and Z. Lin, "A trust model for online peer-to-peer lending: a lender's perspective," Information Technology and Management, vol. 15, no. 4, pp. 239-254, Dec. 2014, doi: 10.1007/S10799-014-0187-Z.##[3] M. Tang, X. Dai, J. Liu, and J. Chen, "Towards a trust evaluation middleware for cloud service selection," Future Generation Computer Systems, vol. 74, pp. 302-312, Sep. 2017, doi: 10.1016/J.FUTURE.2016.01.009.##[4] B. Lahno, "Trust. The Tacit Demand," Ethical Theory and Moral Practice, vol. 2, no. 4. Springer, pp. 433-435, 1999.##[5] M. Ryan, "In AI We Trust: Ethics, Artificial Intelligence, and Reliability," Sci Eng Ethics, vol. 26, no. 5, pp. 2749-2767, Oct. 2020, doi: 10.1007/S11948-020-00228-Y/TABLES/1.##[6] J.-H. Cho, K. Chan, and S. Adali, "A survey on trust modeling," ACM Computing Surveys (CSUR), vol. 48, 2015.##[7] D. M. Messick and R. M. Kramer, "Trust as a form of shallow morality.," 2001.##[8] Y. Yamamoto, "A morality based on trust: Some reflections on Japanese morality," Philos East West, vol. 40, no. 4, pp. 451-469, 1990.##[9] L. Floridi, "Information ethics, its nature and scope," ACM SIGCAS Computers and Society, vol. 36, no. 3, 2006.##[10] L. Floridi, The ethics of information. 2013. Accessed: Jun. 21, 2022. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=_XHcAAAAQBAJ&oi=fnd&pg=PP1&dq=The+ethics+of+information&ots=fZkH8VOxSZ&sig=QhCILoi1OTYVGbnRvifDNM5lwHY##[11] L. Floridi and J. W. Sanders, "Entropy as evil in information ethics," Etica & Politica, Special Issue on Computer Ethics, vol. 1, no. 2, 1999.##[12] D. De Siqueira Braga, M. Niemann, B. Hellingrath, and F. B. De Lima Neto, "Survey on computational trust and reputation models," ACM Comput Surv, vol. 51, no. 5, Aug. 2018, doi: 10.1145/3236008.##[13] J. Wang et al., "A survey on trust evaluation based on machine learning," dl.acm.org, vol. 53, no. 5, Sep. 2020, doi: 10.1145/3408292.##[14] A. J. Bidgoly and B. T. Ladani, "Modeling and quantitative verification of trust systems against malicious attackers," Comput J, vol. 59, no. 7, pp. 1005-1027, 2016.##[15] F. Dini and G. Spagnolo, "Buying reputation on eBay: Do recent changes help?," International Journal of Electronic Business, vol. 7, no. 6, pp. 581-598, 2009.##[16] A. Jøsang and J. Golbeck, "Challenges for robust trust and reputation systems," in Proceedings of the 5th International Workshop on Security and Trust Management (SMT 2009), Saint Malo, France, 2009, vol. 5, no. 9.##[17] R. Kerr and R. Cohen, "Smart cheaters do prosper: defeating trust and reputation systems," in Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 2, 2009, pp. 993-1000.##[18] K. Arshad and K. Moessner, "Robust collaborative spectrum sensing based on beta reputation system," in 2011 Future Network & Mobile Summit, 2011, pp. 1-8.##[19] M. Momani, K. Aboura, and S. Challa, "RBATMWSN: recursive Bayesian approach to trust management in wireless sensor networks," in 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, 2007, pp. 347-352.##[20] P. Shi and H. Chen, "RASN: Resist on-off attack for wireless sensor networks," in Proceedings of the 2012 international conference on computer application and system modeling, 2012, pp. 690-693.##[21] D. Wang, T. Muller, Y. Liu, and J. Zhang, "Towards robust and effective trust management for security: A survey," in 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, 2014.##[22] O. A. Wahab, J. Bentahar, H. Otrok, and A. Mourad, "A survey on trust and reputation models for Web services: Single, composite, and communities," Decis Support Syst, vol. 74, pp. 121-134, 2015.##[23] N. Kandhoul, S. K. Dhurandher, and I. Woungang, "T_CAFE: a trust based security approach for opportunistic IoT," IET Communications, vol. 13, no. 20, 2019.##[24] S. Sicari, A. Rizzardi, L. A. Grieco, and A. Coen-Porisini, "Security, privacy and trust in Internet of Things: The road ahead," Computer networks, vol. 76, pp. 146-164, 2015.##[25] X. Wang, L. Liu, and J. Su, "RLM: A general model for trust representation and aggregation," IEEE Trans Serv Comput, vol. 5, no. 1, pp. 131-143, 2010.##[26] F. Ahmad, F. Kurugollu, A. Adnane, R. Hussain, and F. Hussain, "MARINE: Man-in-the-middle attack resistant trust model in connected vehicles," IEEE Internet Things J, vol. 7, no. 4, pp. 3310-3322, 2020.##[27] A. Vasudeva and M. Sood, "Survey on sybil attack defense mechanisms in wireless ad hoc networks," Journal of Network and Computer Applications, vol. 120, pp. 78-118, 2018.##[28] Y. Ruan and A. Durresi, "A survey of trust management systems for online social communities-trust modeling, trust inference and attacks," Knowl Based Syst, vol. 106, 2016.##[29] B. Khosravifar, J. Bentahar, M. Gomrokchi, and R. Alam, "CRM: An efficient trust and reputation model for agent computing," Knowl Based Syst, vol. 30, pp. 1-16, 2012.##[30] B. Sun and D. Li, "A comprehensive trust-aware routing protocol with multi-attributes for WSNs," IEEE Access, vol. 6, pp. 4725-4741, 2017.##[31] K. Hoffman, D. Zage, and C. Nita-Rotaru, "A survey of attack and defense techniques for reputation systems," ACM Computing Surveys (CSUR), vol. 42, no. 1, pp. 1-31, 2009.##[32] S. Vavilis, M. Petković, and N. Zannone, "A reference model for reputation systems," Decis Support Syst, vol. 61, pp. 147-154, 2014.##[33] F. G. Mármol and G. M. Pérez, "Towards pre-standardization of trust and reputation models for distributed and heterogeneous systems," Comput Stand Interfaces, vol. 32, no. 4, pp. 185-196, 2010.##[34] Z. Noorian and M. Ulieru, "The state of the art in trust and reputation systems: a framework for comparison," Journal of theoretical and applied electronic commerce research, vol. 5, no. 2, pp. 97-117, 2010.##[35] D. D. S. Braga, M. Niemann, B. Hellingrath, and F. B. D. L. Neto, "Survey on computational trust and reputation models," ACM Computing Surveys (CSUR), vol. 51, no. 2018.##[36] S. Chen, Y. Zhang, P. Liu, and J. Feng, "Coping with traitor attacks in reputation models for wireless sensor networks," in 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010, pp. 1-6.##[37] L. F. Perrone and S. C. Nelson, "A study of on-off attack models for wireless ad hoc networks," in 2006 1st Workshop on Operator-Assisted (Wireless Mesh) Community Networks, 2006, pp. 1-10.##[38] D. Wang, T. Muller, J. Zhang, and Y. Liu, "Quantifying robustness of trust systems against collusive unfair rating attacks using information theory," in 24th International Joint Conference on Artificial Intelligence, 2015.##[39] D. Wang, T. Muller, A. A. Irissappane, J. Zhang, and Y. Liu, "Using Information Theory to Improve the Robustness of Trust Systems.," in AAMAS, 2015, pp. 791-799.##[40] J.-H. Cho, A. Swami, and R. Chen, "Modeling and analysis of trust management with trust chain optimization in mobile ad hoc networks," Journal of Network and Computer Applications, vol. 35, no. 3, pp. 1001-1012, 2012.##[41] A. Aldini, "Formal approach to design and automatic verification of cooperation-based networks," IARIA Int J Adv Internet Technol, vol. 6, no. 1, p. 2, 2013.##[42] T. Muller, "Semantics of trust," in International Workshop on Formal Aspects in Security and Trust, 2010.##[43] A. Herbon, and D. Tsadikovich. "An efficient entropy-based stopping rule for mitigating risk factors in supply nets." International Journal of Production Economics ,2023.##[44] A. Khoshkbarchi and H. R. Shahriari, "Improving Agents Trust in Service-Oriented Environment Based on Entropy Structure and Information Ethics Principles," Int J Hum Comput Interact, 2022.##[45] L. Floridi, The ethics of information. 2013. Accessed: Jun. 15, 2022.##[46] Floridi, L., & Sanders, J. W. (1999). "Entropy as evil" - Google Scholar.##https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Floridi%2C+L.%2C+%26+Sanders%2C+J.+W.+%281999%29.+Entropy+as+evil+in+information+ethics.+Etica+%26+Politica%2C+Special+Issue+on+Computer+Ethics%2C+1%282%29.&btnG= (accessed Jun. 15, 2022).##[47] P. Resnick and R. Zeckhauser, "Trust among strangers in Internet transactions: Empirical analysis of eBay's reputation system," in The Economics of the Internet and E-commerce, Emerald Group Publishing Limited, 2002.##[48] K. RahimiZadeh, and P. Kabiri. "Trust‐based routing method using a mobility‐based clustering approach in mobile ad hoc networks." Security and Communication Networks 7, no. 11, 2014.##[49] A. Khoshkbarchi, and H.R. Shahriari. "Coping with unfair ratings in reputation systems based on learning approach." Enterprise Information Systems 11, no. 10 (2017): 1481-1499. ##

یادگیری برخط داده‌های جریانی نامتوازن دارای رانش مفهوم به‌وسیله نظریه باور و تابع آشوب Online Learning for Imbalanced Data Streams with Concept Drift by Belief Theory and Chaotic Function 1 1 خصوصیات داده‌های جریانی در گذر زمان ناپایدار بوده و توزیع طبقات متحمل تغییرات می‌شوند؛ بنابراین مدل‌های یادگیری اغلب نیاز به تطبیق با رانش مفاهیم دارند. در این مقاله، با هدف حل دو چالش نبود توازن میان طبقات مشاهده‌شده و وقوع رانش مفهوم، طبقه‌بند داده‌های جریانی نامتوازن دارای رانش مفهوم ارائه شده است. روش پیشنهادی سعی در حذف داده‌های جریانی مرزی و نوفه‌ای با کمک خوشه‌بندی دارد. داده‌ها با کمک تابع باور وزن­دهی شده است و با درنظرگرفتن برچسب داده‌ها، نمونه­افزایی در نواحی کم­تراکم طبقه کمینه و با رویکرد آشوبی انجام می‌گیرد. سپس، با تعریف حدود آستانه، رانش مفهوم پدید آمده از نوع تدریجی و افزایشی شناسایی می‌شود. پیش‌بینی برچسب به‌وسیله طبقه‌بند ترکیبی و رأی­گیری وزن‌دار بیشینه انجام می‌پذیرد. عملکرد روش پیشنهادی بر روی مجموعه­داده‌های پایگاه داده UCI به‌وسیله روش LOO ارزیابی و با طبقه‌بندهای مرز دانش مقایسه شده­است. نتایج آزمایش‌ها نشان‌دهنده برتری روش پیشنهادی از نظر معیارهای ارزیابی است.   2 Continual learning from data streams is a pivotal aspect of machine learning, requiring the development of algorithms capable of adapting to incoming data. However, the ongoing evolution of data streams presents a formidable challenge as previously acquired knowledge may become outdated. This challenge, known as concept drift, demands timely detection for the effective adaptation of learning models. While various drift detectors have been proposed, they often assume a relatively balanced class distribution. In scenarios with imbalanced data streams, these detectors may exhibit bias toward majority classes, overlooking shifts in minority classes. Moreover, the imbalance among classes can change over time, with roles shifting between majority and minority classes, especially when relationships among classes become complex due to overlapping regions. In this paper, a novel classification method is introduced for imbalanced streaming data affected by concept drift. The proposed method continuously monitors arriving streams to detect and adapt to both imbalances and concept drift. Upon receiving a new block of data, the proposed method employs the k-means clustering approach to identify non-dense regions and performs oversampling for minority classes. Cluster centers are selected using the belief function to address overlapping issues between majority and minority classes. Utilizing a chaotic approach, the new sample is added based on its neighborhood and the size of thresholds that cover time intervals and classification errors. Finally, the label prediction process is done by ensemble learning and weighted majority voting. Experiments conducted on benchmark datasets from the UCI database evaluate the performance of the proposed method using Leave-One-Out (LOO) validation and comparisons with state-of-the-art methods. The results demonstrate the superiority of the proposed method across various evaluation criteria, highlighting its effectiveness in addressing imbalanced streaming data with concept drift. 23 34 2023/04/12021/07/1 1400/4/10 2023/07/182023/07/5 1402/4/14 جواد حمیدزاده Javad Hamidzadeh دانشگاه سجاد ایران J_Hamidzadeh@sadjad.ac.ir محمدعلی رشیدی محمودی Mohammad Ali Rashidi Mahmoodi دانشگاه سجاد ایران ma.rashidi191@sadjad.ac.ir منا مرادی Mona Moradi دانشگاه سجاد ایران monamoradi0@gmail.com Belief Theory Concept Drift Data Stream Imbalanced Data Online Classification تغییر مفهوم داده جریانی داده نامتوازن طبقه‌بندی برخط نظریه باور [1] G. Douzas, R. Rauch, and F. Bacao, "G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE," Expert Systems with Applications, vol. 183, p. 115230, 2021, doi: ##https://doi.org/10.1016/j.eswa.2021.115230##[2] J. Engelmann and S. Lessmann, "Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning," Expert Systems with Applications, vol. 174, p. 114582, 2021, doi: ##https://doi.org/10.1016/j.eswa.2021.114582##[3] X. Xie, H. Liu, S. Zeng, L. Lin, and W. Li, "A novel progressively undersampling method based on the density peaks sequence for imbalanced data," Knowledge-Based Systems, vol. 213, p. 106689, 2021, doi: ##https://doi.org/10.1016/j.knosys.2020.106689##[4] G. Douzas, F. Bacao, and F. Last, "Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE," Information Sciences, vol. 465, pp. 1-20, 2018.##[5] Z. Xu, D. Shen, T. Nie, Y. Kou, N. Yin, and X. Han, "A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data," Information Sciences, vol. 572, pp. 574-589, 2021, doi: ##https://doi.org/10.1016/j.ins.2021.02.056##[6] Z. Li, W. Huang, Y. Xiong, S. Ren, and T. Zhu, "Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm," Knowledge-Based Systems, vol. 195, p. 105694, 2020, doi: 10.1016/j.knosys.2020.105694.##[7] E. S. Page, "Continuous inspection schemes," Biometrika, vol. 41, no. 1/2, pp. 100-115, 1954.##[8] D. Siegmund, Sequential analysis: tests and confidence intervals. Springer Science & Business Media, 2013.##[9] O. A. Mahdi, E. Pardede, and N. Ali, "KAPPA as Drift Detector in Data Stream Mining," Procedia Computer Science, vol. 184, pp. 314-321, 2021.##[10] J. Gama, P. Medas, G. Castillo, and P. Rodrigues, "Learning with Drift Detection," Berlin, Heidelberg, 2004: Springer Berlin Heidelberg, in Advances in Artificial Intelligence - SBIA 2004, pp. 286-295.##[11] M. Baena-Garcıa, J. del Campo-Ávila, R. Fidalgo, A. Bifet, R. Gavalda, and R. Morales-Bueno, "Early drift detection method," in Fourth international workshop on knowledge discovery from data streams, 2006, vol. 6, pp. 77-86.##[12] A. Bifet and R. Gavalda, "Learning from time-changing data with adaptive windowing," in Proceedings of the 2007 SIAM international conference on data mining, 2007: SIAM, pp. 443-448.##[13] P. Domingos and G. Hulten, "Mining high-speed data streams," in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000: ACM, pp. 71-80.##[14] G. Liu, H. Cheng, Z. Qin, Q. Liu, and C. Liu, "E-CVFDT: An improving CVFDT method for concept drift data stream," in 2013 International Conference on Communications, Circuits and Systems (ICCCAS), 2013, vol. 1, pp. 315-318, doi: 10.1109/ICCCAS.2013.6765241.##[15] S. A. Jadhav and S. Kosbatwar, "Concept-adapting Very Fast Decision Tree with Misclassification Error," 2016.##[16] I. Frias-Blanco, J. del Campo-Ávila, G. Ramos-Jimenez, R. Morales-Bueno, A. Ortiz-Diaz, and Y. Caballero-Mota, "Online and non-parametric drift detection methods based on Hoeffding's bounds," IEEE Transactions on Knowledge Data Engineering, vol. 27, no. 3, pp. 810-823, 2014.##[17] A. Pesaranghader and H. L. Viktor, "Fast hoeffding drift detection method for evolving data streams," in Joint European conference on machine learning and knowledge discovery in databases, 2016: Springer, pp. 96-111.##[18] A. Pesaranghader, H. Viktor, and E. Paquet, "Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams," Machine Learning, vol. 107, no. 11, pp. 1711-1743, 2018.##[19] Y. Yuan, Z. Wang, and W. Wang, "Unsupervised concept drift detection based on multi-scale slide windows," Ad Hoc Networks, vol. 111, p. 102325, 2021.##[20] A. Feitosa Neto and A. M. P. Canuto, "EOCD: An ensemble optimization approach for concept drift applications," Information Sciences, vol. 561, pp. 81-100, 2021, doi: ##https://doi.org/10.1016/j.ins.2021.01.051##[21] D. H. Jeong and J. M. Lee, "Ensemble learning based latent variable model predictive control for batch trajectory tracking under concept drift," Computers & Chemical Engineering, vol. 139, p. 106875, 2020, doi: ##https://doi.org/10.1016/j.compchemeng.2020.106875##[22] W. Liu, H. Zhang, Z. Ding, Q. Liu, and C. Zhu, "A comprehensive active learning method for multiclass imbalanced data streams with concept drift," Knowledge-Based Systems, vol. 215, p. 106778, 2021, doi: ##https://doi.org/10.1016/j.knosys.2021.106778##[23] P. Zyblewski, R. Sabourin, and M. Woźniak, "Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams," Information Fusion, vol. 66, pp. 138-154, 2021, doi: 10.1016/j.inffus.2020.09.004.##[24] J. Hamidzadeh and M. Moradi, "Improving Chernoff criterion for classification by using the filled function," jsdp, vol. 19, no. 3, pp. 105-118, 2022, doi: 10.52547/jsdp.19.3.105.##[25] J. Pouramini, B. Minaei-Bidgoli, and M. Esmaeili, "A Novel One Sided Feature Selection Method for Imbalanced Text Classification," jsdp, vol. 16, no. 1, pp. 21-40, 2019, doi: 10.29252/jsdp.16.1.21.##[26] E. Yasrebi Naeini and m. hatami, "Improving Imbalanced Data Classification Accuracy by using Fuzzy Similarity Measure and Subtractive Clustering," jsdp, vol. 19, no. 2, pp. 27-38, 2022, doi: 10.52547/jsdp.19.2.27.##[27] Y. Wang, Y. Zhang, and Y. Wang, "Mining Data Streams with Skewed Distribution by Static Classifier Ensemble," in Opportunities and Challenges for Next-Generation Applied Intelligence, B.-C. Chien and T.-P. Hong Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 65-71.##[28] S. Chen and H. He, "Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach," Evolving Systems, vol. 2, no. 1, pp. 35-50, 2011, doi: 10.1007/s12530-010-9021-y.##[29] R. N. Lichtenwalter and N. V. Chawla, "Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams," in New Frontiers in Applied Data Mining, Berlin, Heidelberg, T. Theeramunkong et al., Eds., 2010// 2010: Springer Berlin Heidelberg, pp. 53-75.##[30] G. Ditzler and R. Polikar, "Incremental Learning of Concept Drift from Streaming Imbalanced Data," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2283-2301, 2013, doi: 10.1109/TKDE.2012.136.##[31] R. R. Yager and L. Liu, Classic works of the Dempster-Shafer theory of belief functions. Springer, 2008.##[32] M. A. A. Abdualrhman and M. Padma, "CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream," in Emerging Research in Electronics, Computer Science and Technology: Springer, 2019, pp. 597-612.##[33] M. M. W. Yan, "Accurate detecting concept drift in evolving data streams," ICT Express, 2020.##[1] G. Douzas, R. Rauch, and F. Bacao, "G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE," Expert Systems with Applications, vol. 183, p. 115230, 2021, doi: ##https://doi.org/10.1016/j.eswa.2021.115230##[2] J. Engelmann and S. Lessmann, "Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning," Expert Systems with Applications, vol. 174, p. 114582, 2021, doi: ##https://doi.org/10.1016/j.eswa.2021.114582##[3] X. Xie, H. Liu, S. Zeng, L. Lin, and W. Li, "A novel progressively undersampling method based on the density peaks sequence for imbalanced data," Knowledge-Based Systems, vol. 213, p. 106689, 2021, doi: ##https://doi.org/10.1016/j.knosys.2020.106689##[4] G. Douzas, F. Bacao, and F. Last, "Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE," Information Sciences, vol. 465, pp. 1-20, 2018.##[5] Z. Xu, D. Shen, T. Nie, Y. Kou, N. Yin, and X. Han, "A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data," Information Sciences, vol. 572, pp. 574-589, 2021, doi: ##https://doi.org/10.1016/j.ins.2021.02.056##[6] Z. Li, W. Huang, Y. Xiong, S. Ren, and T. Zhu, "Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm," Knowledge-Based Systems, vol. 195, p. 105694, 2020, doi: 10.1016/j.knosys.2020.105694.##[7] E. S. Page, "Continuous inspection schemes," Biometrika, vol. 41, no. 1/2, pp. 100-115, 1954.##[8] D. Siegmund, Sequential analysis: tests and confidence intervals. Springer Science & Business Media, 2013.##[9] O. A. Mahdi, E. Pardede, and N. Ali, "KAPPA as Drift Detector in Data Stream Mining," Procedia Computer Science, vol. 184, pp. 314-321, 2021.##[10] J. Gama, P. Medas, G. Castillo, and P. Rodrigues, "Learning with Drift Detection," Berlin, Heidelberg, 2004: Springer Berlin Heidelberg, in Advances in Artificial Intelligence - SBIA 2004, pp. 286-295.##[11] M. Baena-Garcıa, J. del Campo-Ávila, R. Fidalgo, A. Bifet, R. Gavalda, and R. Morales-Bueno, "Early drift detection method," in Fourth international workshop on knowledge discovery from data streams, 2006, vol. 6, pp. 77-86.##[12] A. Bifet and R. Gavalda, "Learning from time-changing data with adaptive windowing," in Proceedings of the 2007 SIAM international conference on data mining, 2007: SIAM, pp. 443-448.##[13] P. Domingos and G. Hulten, "Mining high-speed data streams," in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000: ACM, pp. 71-80.##[14] G. Liu, H. Cheng, Z. Qin, Q. Liu, and C. Liu, "E-CVFDT: An improving CVFDT method for concept drift data stream," in 2013 International Conference on Communications, Circuits and Systems (ICCCAS), 2013, vol. 1, pp. 315-318, doi: 10.1109/ICCCAS.2013.6765241.##[15] S. A. Jadhav and S. Kosbatwar, "Concept-adapting Very Fast Decision Tree with Misclassification Error," 2016.##[16] I. Frias-Blanco, J. del Campo-Ávila, G. Ramos-Jimenez, R. Morales-Bueno, A. Ortiz-Diaz, and Y. Caballero-Mota, "Online and non-parametric drift detection methods based on Hoeffding's bounds," IEEE Transactions on Knowledge Data Engineering, vol. 27, no. 3, pp. 810-823, 2014.##[17] A. Pesaranghader and H. L. Viktor, "Fast hoeffding drift detection method for evolving data streams," in Joint European conference on machine learning and knowledge discovery in databases, 2016: Springer, pp. 96-111.##[18] A. Pesaranghader, H. Viktor, and E. Paquet, "Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams," Machine Learning, vol. 107, no. 11, pp. 1711-1743, 2018.##[19] Y. Yuan, Z. Wang, and W. Wang, "Unsupervised concept drift detection based on multi-scale slide windows," Ad Hoc Networks, vol. 111, p. 102325, 2021.##[20] A. Feitosa Neto and A. M. P. Canuto, "EOCD: An ensemble optimization approach for concept drift applications," Information Sciences, vol. 561, pp. 81-100, 2021, doi: ##https://doi.org/10.1016/j.ins.2021.01.051##[21] D. H. Jeong and J. M. Lee, "Ensemble learning based latent variable model predictive control for batch trajectory tracking under concept drift," Computers & Chemical Engineering, vol. 139, p. 106875, 2020, doi: ##https://doi.org/10.1016/j.compchemeng.2020.106875##[22] W. Liu, H. Zhang, Z. Ding, Q. Liu, and C. Zhu, "A comprehensive active learning method for multiclass imbalanced data streams with concept drift," Knowledge-Based Systems, vol. 215, p. 106778, 2021, doi: ##https://doi.org/10.1016/j.knosys.2021.106778##[23] P. Zyblewski, R. Sabourin, and M. Woźniak, "Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams," Information Fusion, vol. 66, pp. 138-154, 2021, doi: 10.1016/j.inffus.2020.09.004.##[24] J. Hamidzadeh and M. Moradi, "Improving Chernoff criterion for classification by using the filled function," jsdp, vol. 19, no. 3, pp. 105-118, 2022, doi: 10.52547/jsdp.19.3.105.##[25] J. Pouramini, B. Minaei-Bidgoli, and M. Esmaeili, "A Novel One Sided Feature Selection Method for Imbalanced Text Classification," jsdp, vol. 16, no. 1, pp. 21-40, 2019, doi: 10.29252/jsdp.16.1.21.##[26] E. Yasrebi Naeini and m. hatami, "Improving Imbalanced Data Classification Accuracy by using Fuzzy Similarity Measure and Subtractive Clustering," jsdp, vol. 19, no. 2, pp. 27-38, 2022, doi: 10.52547/jsdp.19.2.27.##[27] Y. Wang, Y. Zhang, and Y. Wang, "Mining Data Streams with Skewed Distribution by Static Classifier Ensemble," in Opportunities and Challenges for Next-Generation Applied Intelligence, B.-C. Chien and T.-P. Hong Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 65-71.##[28] S. Chen and H. He, "Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach," Evolving Systems, vol. 2, no. 1, pp. 35-50, 2011, doi: 10.1007/s12530-010-9021-y.##[29] R. N. Lichtenwalter and N. V. Chawla, "Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams," in New Frontiers in Applied Data Mining, Berlin, Heidelberg, T. Theeramunkong et al., Eds., 2010// 2010: Springer Berlin Heidelberg, pp. 53-75.##[30] G. Ditzler and R. Polikar, "Incremental Learning of Concept Drift from Streaming Imbalanced Data," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2283-2301, 2013, doi: 10.1109/TKDE.2012.136.##[31] R. R. Yager and L. Liu, Classic works of the Dempster-Shafer theory of belief functions. Springer, 2008.##[32] M. A. A. Abdualrhman and M. Padma, "CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream," in Emerging Research in Electronics, Computer Science and Technology: Springer, 2019, pp. 597-612.##[33] M. M. W. Yan, "Accurate detecting concept drift in evolving data streams," ICT Express, 2020.## ##

ارائه رویکردی نوین در بخشبندی تصاویر دیجیتال به‌وسیله الگوریتم ژنتیک و جنگل تصادفی A New Approach for Digital Image Segmentation with Genetic Algorithm and Random Forest 1 1 در این پژوهش رویکردی نوین برای بخش‌بندی تصویر بر اساس الگوریتم ژنتیک و جنگل تصادفی معرفی می‌شود. در بخش‌بندی تصویر سعی می‌شود، اجزای مختلف تصویر از یکدیگر جدا شوند. در این فرایند به‌تمامی پیکسل‌های داخل تصویر برچسبی داده‌می‌شود؛ به‌نحوی که پیکسل‌های با برچسب یکسان ویژگی‌های مشترکی را داشته‌باشند. در روش پیشنهادی این ویژگی‌ها با به‌کارگیری فیلترهای تصویری به‌دست آورده می‌شود. با ترکیب این ویژگی‌ها و با الگوریتم جنگل تصادفی به‌عنوان طبقه‌بند بخش‌بندی تصاویر انجام‌می‌شود. فیلترهای تصویری استفاده‌شده دارای تعدادی ابر پارامتر هستند که تنظیم درست این ابر پارامترها بر کارایی الگوریتم مؤثر است. در این مقاله انتخاب این ابرپارامترها به‌وسیله الگوریتم ژنتیک انجام‌می‌شود. ابرپارامترهای فیلترهای گابور به‌عنوان ژن‌های کروموزوم الگوریتم ژنتیک در نظر گرفته‌می‌شود. تابع برازندگی f1-score حاصل از اجرای الگوریتم جنگل تصادفی برای بخش‌بندی تصویر تعریف می‌شود. یافتن مقادیر مناسب ابرپارامترهای فیلترهای گابور و افزایش f1-score در بخش‌بندی تصویر نسبت به سایر روش‌های مورد بررسی از دستاوردهای این پژوهش است.   2 In this study, a new method for image segmentation by genetic algorithms and random forest is resented. The main objective of image segmentation is to distinguish different components within an image, achieved by labeling pixels based on shared characteristics. In this novel approach, these distinguishing features are derived through the application of image filters (Gabor filters). The random forest algorithm is then employed as a classifier to perform image segmentation according to extracted features from these filters. The image filters utilized come with various hyperparameters, and tuning of these parameters significantly enhances the algorithm's performance. The proposed methodology distinguishes itself by employing a genetic algorithm to fine-tune the hyperparameters of Gabor filters. In this context, the hyperparameters are treated as genes within the chromosome of the genetic algorithm. The success of this optimization is evaluated using f1-score, a metric derived from the random forest algorithm's execution in image segmentation. This step ensures that the selected hyperparameters contribute to optimal segmentation results. The achievement of this research lies not only in the implementation of this novel approach but also in surpassing the performance of other investigated methods through the enhancement of the f1-score in image segmentation. Key to the success of the proposed method is the careful consideration of hyperparameters and their role in defining the characteristics crucial for accurate image segmentation. The use of genetic algorithms not only automates this parameter tuning process but also ensures that the algorithm adapts and evolves to find the most suitable values for the hyperparameters of Gabor filters. As a result, the research contributes to the broader field of image segmentation by providing a robust and effective methodology, demonstrating superior performance compared to alternative methods. In conclusion, this study introduces an approach to image segmentation, leveraging the synergies between genetic algorithms, random forest, and image filters. The research not only emphasizes the importance of hyperparameter tuning but also showcases the effectiveness of the proposed methodology through the optimization of Gabor filter parameters. The overall impact of this work is evident in the improved f1-score achieved in image segmentation, establishing it as a noteworthy advancement in the field. 35 44 2023/04/12021/07/12022/09/4 1401/6/13 2023/07/182023/07/52023/12/11 1402/9/20 فریبا نمیرانیان Fariba Namiranian دانشگاه یزد ایران fariba.namiranian75@gmail.com علی محمد لطیف AliMohammad Latif دانشگاه یزد ایران alatif@yazd.ac.ir Image Segmentation Genetic Algorithm RImage Segmentation Genetic Algorithm Random Forest Algorithm Gabor Filter Feature Extractionandom Forest Algorithm بخش‌بندی تصویر الگوریتم ژنتیک الگوریتم جنگل تصادفی فیلتر گابور استخراج ویژگی [1] C. Tan, S. Ying, L. Gongfa, T. Bo, X. Shuang and Z. Fei, "Image Segmentation Technology based on Genetic Algorithm, " in International Conference on Digital Signal Processing, 2019.##[2] T. Wang, Y. Yao, Y. Chen, M. Zhang, F. Tao and Hichem, "Auto-Sorting System Toward Smart Factory based on Deep Learning for Image Segmentation, " IEEE Sensors Journal, vol. 18, no. 20, pp. 8493 - 8501, 2018.##[3] A. Chaudhry, M. Hassan and A. Khan, "Robust Segmentation and Intelligent Decision System for Cerebrovascular Disease, " Medical & Biological Engineering & Computing, vol. 54, p. 1903-1920, 2016.##[4] C. Yuan, Z. Liu and Zhang, "Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles, " Journal of Intelligent & Robotic Systems, vol. 88, pp. 635-654, 2017.##[5] T. Maruyama, H. Norioa, S. 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Murugesan, "A Two-Level Approach to Color Space-Based Image Segmentation using Genetic Algorithm and Feed-Forward Neural Network, " Advances in Artificial Intelligence and Data Engineering, pp.67-78, 2020.##[10] L. Xiao, H. Ouyang, Ch. Fan, T. Umer, R.C. Poonia,Sh. Wan," Gesture Image Segmentation with Otsu's Method based on Noise Adaptive Angle Threshold", Multimedia Tools and Applications,pp35619-35640,2020##[11] G. Xu, X. Li, B. Lei, K. Lv, " Unsupervised Color Image Segmentation with Color-alone Feature using Region Growing Pulse Coupled Neural Network", Neurocomputing, vol.306, pp.1-16,2018##[12] PB. Chanda and SK. Sarkar, "Study on Efficient DRLSE-Oriented Edge-Based Medical Image Segmentation of Cardiac Images, " Emerging Technologies in Data Mining and Information Security, vol. 164, pp. 823-831, 2021.##[13] F. Jiang, Q. Gu, H. Hao, N.Li," Feature Extraction and Grain Segmentation of Sandstone Images Based on Convolutional Neural Networks", International Conference on Pattern Recognition (ICPR), pp. 2636-2641, 2018.##[14] Y. Songa, B. Heb, P. Liuc and T. Yand, "Side Scan Sonar Image Segmentation and Synthesis based on Extreme Learning Machine, " Applied Acoustics, vol. 146, pp. 56-65, 2019.##[15] K. Roopa, G. Neena and K. Narender, "Image Segmentation using Improved Genetic Algorithm, " International Journal of Engineering and Advanced Technology, vol. 9, no. 1, pp. 1784-1792, 2019.##[16] S. K. Saha, S. Pradhan and S.V. Barai, "Use of Machine Learning based Technique to X-ray Microtomographic Images of Concrete for Phase Segmentation at Meso-scale," Construction and Building Materials, vol. 249, 2020.##[17] V.R. Patil, T.H. Jaware," Random Forest and Gabor Filter Bank Based Segmentation Approach for Infant Brain MRI", Applied Information Processing Systems, vol.1354, pp. 265-272, 2022.##[18] P. Asadi, L.E. Beckingham," Intelligent framework for mineral segmentation and fluid-accessible surface area analysis in scanning electron microscopy", Applied Geochemistry, vol.143, 2022.##[19] A. Fauzi, L.E. Lubis, "Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization", Indonesian Journal of Electrical Engineering and Computer Science, vol.29, no. 3, pp. 1590-1596, 2023.##[20] Z.Shahidizandi, A.Latif, "Developing a modern method in circle detection in digital images by using genetic algorithm", Journal of Machine Vision and Image Processing,vol. 8, no. 1, pp.35-44,2021##[21] El-Diraby, S. M. Piryonesi and E. Tamer, "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index, " Journal of Infrastructure Systems, vol. 26, no. 1, 2020.##[22] V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo and M. Chica-Riv, "Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural networks, Random forest, Regression Trees and Support Vector Machines, " Ore Geology Reviews, vol. 71, pp. 804-818, 2015.##[23] T. Vijayan, M.Sangeetha, A. Kumaravel, B. Karthik "Gabor Filter and Machine learning Based Diabetic Retinopathy Analysis and Detection," Microprocessors and Microsystems, 2020.##[24] Y. Xua, W. Yuxin, Y. Jie, C. Qian and W. Xueding, "Medical Breast Ultrasound Image Segmentation by Machine Learning, " Ultrasonics, vol. 91, pp. 1-9, 2019.##[25] "https://drive.google.com/file/d/1HWtBaSa-LTyAMgf2uaz1T9o1sTWDBajU/view", visited on 2 july 2022##[26] A.Karimi,L S. Hoseini. "An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization." JSDP,pp.45-54,2018##[1] C. Tan, S. Ying, L. Gongfa, T. Bo, X. Shuang and Z. 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الگوی یادگیری جمعی بهبود‌یافته با هوش ازدحامی جهت پیش‌بینی ریزش مشترکان تلفن همراه Improved Ensemble Learning Model by Swarm Intelligence for Mobile Subscribers’ Churn Prediction 1 1 ازآنجاکه در شرکت‌های مخابرات همراه، هزینۀ حفظ مشتریان فعلی بسیار کمتر از هزینۀ جذب مشتریان جدید است، پیش‌بینی دقیق امکان ریزش هریک از مشتریان و جلوگیری از آن، امری ضروری ‌است. بنابراین، پژوهشگران روش‌های کارآمدی را با استفاده از ابزارهای داده‌کاوی و هوش ‌مصنوعی برای شناسایی مشتریانی که قصد روی‌گردانی دارند، ارائه کرده‌اند. در این مقاله، ما به‌منظور بهبود فرایند پیش‌بینی ریزش مشتری، یک راهکار مؤثر مبتنی‌بر یادگیری جمعی پیشنهاد می‌کنیم که در آن از الگوریتم بهینه‌سازی گرگ خاکستری، به‌منظور انتخاب ویژگی‌های مؤثر و همچنین تنظیم شاخص‌های آزاد الگوی پیشنهادی، استفاده شده‌است. سپس، به‌منظور ارزیابی عملکرد روش پیشنهادی، آن را با استفاده از دو مجموعه‌دادۀ ریزش مشتری شبیه‌سازی کرده و نتایج حاصل را به کمک معیارهای ارزیابی شامل صحت، دقت، یادآوری، امتیاز F1 و AUC با سایر روش‌های مشابه مقایسه کرده‌ایم. نتایج به‌دست‌آمده برتری روش پیشنهادی بر سایر راهکارهای ارزیابی‌شده را نشان می‌دهد.   2 In today’s competitive world, companies need to analyze, identify and predict the behavior of their customers and respond to their demands earlier than their competitors. Moreover, in many industries such as mobile telecommunications, the cost of maintaining existing customers (customer retention) is much lower than the cost of attracting a new customer. Therefore, the problem of identifying customers who are going to leave the company, so-called Customer Churn Prediction (CCP), and preventing them by offering Incentives is essential in these industries. In this direction, researchers have presented competent methods using data mining and artificial intelligence tools to identify potential churners. Machine learning (ML) methods are one of the most powerful and widely used techniques to deal with the CCP problem, since they can properly extract and learn complex relationships between the customers’ attributes and their churn intention. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), Logistic Regression (LR), and Naïve Bayes (NB) are among the well-known ML models utilized in numerous studies to tackle the CCP problem. Also, ensemble learning techniques such as Adaboost, Gradient Boost, and Extreme Gradient Boost (XG_boost) have been widely used to solve the CCP problem since they can aggregate the capabilities of multiple ML models. Hence, in order to improve the process of predicting customer churn, in this paper we propose a novel ensemble learning based approach, which is designed based on the two-level stacking technique. We employ six prominent ML models in each level of our proposed ensemble model including MLP, SVM-RBF, DT, NB, KNN, and LR. We also benefit from the Gray Wolf Optimization (GWO) algorithm as an efficient swarm intelligence based search algorithm to select the most effective features and also adjust the hyper-parameters in the proposed model. We have implemented our proposed model using Python and simulated it on two well-known customer churn datasets in the telecom market (IBM_Telco and Duke_Cell2Cell) to evaluate its performance. In this direction, we first demonstrated the optimal features’ subset and the parameter values obtained from applying the GWO algorithm on each dataset. Next, we compared the performance of the proposed ensemble model with each of the base learners using common evaluation criteria including accuracy, precision, recall, F1 score and AUC. The results show that the proposed ensemble model can collect the capabilities of all the base learners and it works better than each of the basic ML models. Afterward, we compared the obtained results from the suggested model with the common ensemble models (Adaboost, Gradient_boost, XG_boost, and Cat_boost) The experimental results show the superiority of the proposed method over other evaluated ensemble models in all the evaluation criteria. Eventually, our method is compared with two recent CCP approaches introduced in the literature. This analysis reveals that, except for the recall criterion in the Duke_Cell2Cell dataset, our introduced method achieves superior results compared to the considered approaches in both datasets.   45 66 2023/04/12021/07/12022/09/42022/10/25 1401/8/3 2023/07/182023/07/52023/12/112022/12/26 1401/10/5 بیژن مرادی bijan moradi دانشگاه ازاد اسلامی واحد پرند و رباط کریم ایران bijanmoradi52@yahoo.com مهران خلج Mehran Khalaj دانشگاه ازاد اسلامی واحد پرند و رباط کریم ایران mkhalaj@rkiau.ac.ir علی تقی زاده هرات Ali Taghizadeh Harat دانشگاه ازاد اسلامی واحد پرند و رباط کریم ایران Customer Churn Prediction Mobile Telecommunication Ensemble Learning Swarm Intelligence Gray Wolf Optimization Algorithm پیش‌بینی ریزش مشتری مخابرات همراه یادگیری جمعی هوش ازدحامی الگوریتم بهینه‌سازی گرگ خاکستری [1] W. 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بهبود نظرکاوی فارسی مبتنی بر قطبیت و متوازن‌سازی کلمات مهم مثبت و منفی (مطالعه موردی: نظرات دیجی‌کالا برای موبایل) Improving Persian Opinion Mining based on Polarity and Balancing Positive and Negative Keywords (case study: Digikala reviews for mobile) 1 1 بسیاری از شبکه‌های اجتماعی و سایت‌ها به مردم اجازه می­دهند تا احساسات و نظرات خود را در مورد محصولات و خدمات مختلف به اشتراک بگذارند. در این مقاله روشی جدید مبتنی بر قطبیت نظرات مثبت و منفی فارسی درباره محصولات تلفن همراه از سایت دیجی‌کالا و داده­های سنتی­پرس ارائه شده است. نتیجه اجرا با الگوریتم­های بیز ساده، ماشین بردار پشتیبان، کاهش گرادیان تصادفی، رگرسیون لجستیک، جنگل تصادفی و یادگیری عمیق مانند شبکه عصبی کانولوشن و حافظه کوتاه‌مدت متوالی بر اساس پارامترهایی مانند صحت، بازیابی، معیار فیشر و دقت، موردتوجه قرار گرفته شده است. روش پیشنهادی روی داده­های دیجی‌کالا، با الگوریتم­های بیز ساده بین 10 تا 34 درصد و ماشین بردار پشتیبان بین 5 تا 24 درصد و کاهش گرادیان تصادفی بین 7 تا 38 درصد و رگرسیون لجستیک بین 5 تا 38 درصد و جنگل تصادفی بین 4 تا 22 درصد و روش شبکه عصبی کانولوشن به میزان 4 درصد افزایش دقت را به همراه داشته است. هم­چنین در داده­های سنتی­پرس با الگوریتم­های بیز ساده بین 12 تا 46 درصد و ماشین بردار پشتیبان بین 5 تا 46 درصد و کاهش گرادیان تصادفی بین 5 تا 35 درصد و رگرسیون لجستیک بین 6 تا 46 درصد و جنگل تصادفی بین 4 تا 46 درصد دقت نسبت به قبل از اعمال روش پیشنهادی به‌دست آمده است. 2 In recent years, the massive growth of generated content by the users in social networks and online marketing sites, allows people to share their feelings and opinions in a variety of opinions about different products and services. Sentiment analysis is an important factor for better decision-making that is done using natural language processing (NLP), computational methods, and text analysis to extract the polarity of unstructured documents. The complexity of human languages and sentiment analysis have created a challenging research context in computer science and computational linguistics. Many researchers used supervised machine learning algorithms such as Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Logistic Regression (LR) Random Forest (RF), and deep learning algorithms such as Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM). Some researchers have used Dictionary-based methods. Despite the existence of effective techniques in text mining, there are still unresolved challenges. Note that user comments are unstructured texts; Therefore, in order to structure the textual inputs, parsing is usually done along with adding some features, linguistic interpretations and removing additional items, and inserting the next terms in the database, then extracting the patterns in the structured data and finally the outputs will evaluate and interpret. The imbalance of data with the difference in the number of samples in each class of a dataset is an important challenge in the learning phase. This phenomenon breaks the performance of the classifications because the machine does not learn the features of the unpopulated classes well. In this paper, words are weighted based on the prescribed dictionary to influence the most important words on the result of the opinion mining by giving higher weight. On the other hand, the combination of the adjacent words using n-gram methods will improve the outcome. The dictionaries are highly related to the domain of the application. Some words in an application are important but in mobile comments are not impressive. Another challenge is the unbalanced train data, in which the number of positive sentences is not equal to the number of negative sentences. In this paper, two ideas are applied to build an efficient opinion mining algorithm. First, we build a precise dictionary for mobile Persian comments, and the second idea is to balance the positive and negative comments in train data. In summary, the main achievements of the current research can be mentioned: creating a weighted comprehensive dictionary in the field of mobile phone opinions to increase the accuracy of opinion analysis, balancing positive and negative opinions to improve the accuracy of opinion analysis, and eliminating the negative effect of overfitting and providing a precise approach to Determining the polarity of users' opinions about mobile phones using machine learning and recurrent deep learning algorithms. This new method is presented on mobile phone products from the Digikala site and Senti-Pers data. The result is performed with Naive Bayesian, Support Vector Machine, Stochastic Gradient Descent, Logistic Regression, Random Forest, and deep learning methods such as Convolutional Neural Network and Long Short-Term Memory based on parameters such as Accuracy, Precision, Retrieval, and F-Measure. The proposed method increases accuracy on Digikala, with NB between 10% and 34% and SVM between 5% and 24%, SGD between 7% and 38%, LR between 5% to 38%, and RF between 4% Up to 22% and CNN by 4%. The results show an accuracy increment on Senti-Pers, with NB between 12% and 46% and SVM between 5% and 46%, SGD between 5% and 35%, LR between 6% to 46%, and RF between 4% Up to 46%.   67 88 2023/04/12021/07/12022/09/42022/10/252020/12/31 1399/10/11 2023/07/182023/07/52023/12/112022/12/262023/12/11 1402/9/20 مهدیه واحدی پور Mahdieh Vahedipoor دانشگاه صنعتی قم ایران vahedipoor.m@qut.ac.ir محبوبه شمسی Mahboubeh Shamsi دانشگاه صنعتی قم ایران shamsi@qut.ac.ir عبدالرضا رسولی کناری Abdolreza Rasouli Kenari دانشگاه صنعتی قم ایران rasouli@qut.ac.ir Sentiment Analysis Opinion Mining Machine Learning Deep Learning Polarity تحلیل احساسات نظرکاوی یادگیری ماشین یادگیری عمیق قطبیت [1] Feldman, Ronen. "Techniques and applications for sentiment analysis." Communications of the ACM 56.4 (2013): 82-89.‏##[2] Gautam, Geetika, and Divakar Yadav. 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بهره ‌برداری از ژانرهای فیلم و داده‌های کاربران به منظور بهبود سامانه های توصیه فیلم Using movie genres and Demographic Information to improve movie recommendation systems 1 1 سامانه‌های توصیه فیلم ابزارهای کارآمدی هستند که به کاربران کمک می‌کنند فیلم‌های مورد علاقه خود را با بررسی علایق قبلی کاربران پیدا کنند. این سامانه‌ها بر اساس امتیاز کاربران به فیلم‌های گذشته و استفاده از آنها برای پیش‌بینی علایق آنها در آینده ایجاد شده‌اند؛ با این حال، امتیازدهی نامناسبی که کاربران ارائه می‌دهند، منجر به ایجاد مشکلی به نام پراکندگی داده ‌می‌شود. این مشکل موجب کاهش کارایی سامانه‌های توصیه فیلم می‌شود. از سوی دیگر، سایر داده‌های موجود مانند ژانر فیلم‌ها و اطلاعات جمعیت‌شناختی کاربران، نقش حیاتی در کمک به روش‌های توصیه‌کننده برای تولید بهتر توصیه‌ها دارند. این مقاله یک روش توصیه فیلم را با استفاده از ژانرهای فیلم و اطلاعات جمعیت‌شناختی کاربران پیشنهاد می‌کند. همچنین ما مدلی کارآمد جهت ارزیابی پروفایل امتیازدهی کاربر و تعیین کمینه امتیاز مورد نیاز برای تولید یک پیش‌بینی دقیق را پیشنهاد می‌کنیم؛ سپس، امتیازات مجازی مناسب با پروفایل‌هایی که امتیازات نامناسبی دارند ترکیب می‌شوند. این امتیازدهی مجازی با استفاده از شباهت مقادیر بین کاربران به‌دست‌آمده از ژانرهای فیلم و اطلاعات جمعیت‌شناختی کاربران محاسبه می‌شوند؛ علاوه بر این، یک معیار مفید برای تعیین میزان قابل‌اعتماد بودن یک بخش معرفی شده‌است که قابلیت اطمینان امتیازدهی مجازی را تضمین می‌کند؛ درنهایت، امتیازهای ناشناخته برای کاربر هدف براساس پروفایل‌های امتیازدهی توسعه‌یافته پیش‌بینی می‌شوند. آزمایش‌های انجام‌شده بر روی دو مجموعه‌داده توصیه فیلم معروف نشان‌می‌دهد که رویکرد پیشنهادی کارآمدتر از سایر توصیه‌کننده‌های مقایسه شده‌است.   2 Movie recommendation systems are efficient tools to help users find their relevant movies by investigating the previous interests of users. These systems are established on considering the ratings of users provided for movies in the past and using them to predict their interests in the future. However, users mainly provide insufficient ratings leading to make a problem called data sparsity. This problem makes reducing the effectiveness of movie recommendation systems. On the other hand, other available data such as genres of movies and demographic information of users play a vital role in assisting recommenders in order to better produce recommendations. This paper proposes a movie recommendation method utilizing the movies’ genres and users’ demographic information. In particular, we propose an effective model to evaluate the user’s rating profile and determine the minimum number of ratings required to produce an accurate prediction. Then, appropriate virtual ratings are incorporated into the profiles with insufficient ratings to expand them. These virtual ratings are calculated using similarity values between users obtained by genres of movies and demographic information of users. Furthermore, an effective measure is introduced to determine how much an item is reliable. This measure guarantees the virtual ratings’ reliability. Finally, unknown ratings for target user are predicted based on the expanded rating profiles. Experiments performed on two well-known movie recommendation datasets demonstrate that the proposed approach is more efficient than other compared recommenders. We propose a movie recommender system in this paper by employing the genres of movies and demographic information of users to address the above-mentioned challenges. To this end, first of all, a model is developed in order to determine whether the target user’s rating profile is appropriate to produce accurate recommendations or not. In other words, the developed model determines how many ratings are required for each user to generate an accurate prediction with a high probability. This criterion is used to demonstrate that a rating profile contains sufficient ratings for producing reliable recommendations or not. Then, the quality of rating profiles containing insufficient ratings is boosted using an effective profile expansion technique which incorporates some virtual ratings to these profiles. These virtual ratings are calculated using the similarity values between users which are computed according to the genres of movies and demographic information of users. Moreover, the reliability values of users and items are calculated using appropriate reliability measurements to guarantee that the incorporated virtual ratings are reliable. Experimental results on two movie recommendation datasets indicate the superiority of the proposed approach in respect to other models. In the following, we provide a list of the main contributions of this paper: We develop a model in order to evaluate the users’ rating profiles and determine how many ratings are required for generating an accurate prediction. We propose a powerful profile expansion technique which incorporates some virtual ratings to user-item ratings matrix for improving its quality. Movies’ genres and users’ demographic information are used as additional data in the proposed movie recommender system. The reliability measures of users and items are used in the proposed method to guarantee the reliability of calculated virtual ratings. The proposed method generates a denser user-item ratings matrix than the original matrix which results in alleviating data sparsity problem significantly.    The remaining parts of this paper are structured as follows: in section 2, related works are investigated, section 3 includes the details of the proposed method, section 4 refers to the discussion of experimental results, and section 5 provides some conclusions about the paper Movie recommendation systems are efficient tools to help users find their relevant movies by investigating the previous interests of users. These systems are established on considering the ratings of users provided for movies in the past and using them to predict their interests in the future. However, users mainly provide insufficient ratings leading to make a problem called data sparsity. This problem makes reducing the effectiveness of movie recommendation systems. On the other hand, other available data such as genres of movies and demographic information of users play a vital role in assisting recommenders in order to better produce recommendations. This paper proposes a movie recommendation method utilizing the movies’ genres and users’ demographic information. In particular, we propose an effective model to evaluate the user’s rating profile and determine the minimum number of ratings required to produce an accurate prediction. Then, appropriate virtual ratings are incorporated into the profiles with insufficient ratings to expand them. These virtual ratings are calculated using similarity values between users obtained by genres of movies and demographic information of users. Furthermore, an effective measure is introduced to determine how much an item is reliable. This measure guarantees the virtual ratings’ reliability. Finally, unknown ratings for target user are predicted based on the expanded rating profiles. Experiments performed on two well-known movie recommendation datasets demonstrate that the proposed approach is more efficient than other compared recommenders. 89 106 2023/04/12021/07/12022/09/42022/10/252020/12/312022/08/10 1401/5/19 2023/07/182023/07/52023/12/112022/12/262023/12/112023/06/2 1402/3/12 صمد محمدی samad mohammadi گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران ایران mohamadi601@gmail.com واهه آغازاریان vahe aghazarian گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران ایران aghazarian@iauctb.ac.ir علیرضا هدایتی alireza hedayati گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران ایران samad601@gmail.com Recommender systems movies cold start data sparsity demographic information genre سامانه های توصیه فیلم شروع سرد پراکندگی داده ها اطلاعات جمعیت شناختی ژانر 1] H. A. Rahmani, M. Aliannejadi, S. Ahmadian, M. Baratchi, M. Afsharchi, and F. 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دادگان پرسش و پاسخ زبان فارسی Farsi Question and Answer Dataset (FarsiQuAD) 1 1 پاسخ سریع و دقیق به سؤالات مطرح­شده به زبان طبیعی یکی از اهداف مهم در توسعه سامانه‌های پرسش‌وپاسخ است که در آن رایانه یک متن و سؤال را درک و پاسخ دقیق را برای کاربر ارائه می‌کند. با اینکه پیشرفت‌های زیادی در این حوزه صورت‌گرفته­است، اما همچنان جزء مسائلی است که نیاز به ارتقا، به­خصوص برای زبان‌های غیر انگلیسی مثل زبان فارسی‌ است. در این مقاله دادگان پرسش‌وپاسخ زبان فارسی (FarsiQuAD) [1]  که توسط انسان از مقالات ویکی‌پدیای فارسی تهیه شده، در دو نسخه منتشر شده‌است. نسخه یک شامل  10000+ پرسش‌وپاسخ و نسخه دوم این مجموعه شامل بیش از 145000+ جفت پرسش­وپاسخ ‌است. این دادگان قابلیت تجمیع با نسخه انگلیسی SQuAD و سایر دادگان زبان‌های دیگر را دارد که از این استاندارد استفاده کرده باشند و برای عموم منتشر شده‌است[2]. این دادگان جهت ساخت مدل‌های هوش مصنوعی مبتنی بر یادگیری عمیق و برای استفاده در سامانه‌های پرسش و پاسخ زبان فارسی‌است. نتایج این پژوهش نشان می‌دهد دادگان پرسش‌وپاسخ زبان فارسی ایجادشده می‌تواند پاسخ به سؤالات مطرح­شده به زبان طبیعی فارسی را با معیار تطابق دقیق[3] 78 درصد و معیار F1 87 درصد برساند که هنوز نیازمند ارتقا ‌است.    [1] Exact match [2] https://github.com/Forutanrad/FarsiQuAD [3] Exact match 2 A fast and accurate response to questions posed in natural language is a fundamental objective in the advancement of question and answer systems. These systems involve computers comprehending textual content and questions, and subsequently, delivering precise answers to users. Despite significant advancements in this field, there remains room for improvement, particularly when dealing with languages other than English, such as Persian. In this article, we present the Persian language question and answer dataset, known as FarsiQuAD. This dataset was meticulously crafted by human annotators, drawing from Persian Wikipedia articles. FarsiQuAD is made available in two versions: Version 1 comprises over 10,000 questions and answers, while Version 2 offers an extensive collection of over 145,000 rows. This dataset is designed to seamlessly integrate with the English version of SQuAD and other databases in various languages adhering to this standard, and it is open to the public. These data serve as valuable resources for the development of artificial intelligence models based on deep learning and for the enhancement of Persian language question and answer systems. The research findings reveal that the FarsiQuAD dataset is capable of providing answers to questions posed in the natural Persian language with an exact matching accuracy of 78% and an F1 score of 87%. However, there is still room for improvement in achieving even higher accuracy levels. This project arises from the critical need for non-English languages to have access to more data for training deep learning models, especially in the domain of factoid questions. Hence, the primary objective of this article is to introduce the newly created dataset. Prior to this effort, well-known datasets like SQuAD predominantly focused on English, and similar datasets has been developed in other languages, including French, German, Korean, and Japanese. Nevertheless, the dearth of question datasets in the Persian language was evident. The quality and diversity of questions are pivotal aspects, and as this dataset continues to grow, it will contribute to the broader landscape of research in this domain, allowing for valuable cross-linguistic comparisons and integration with research conducted in other languages.   107 120 2023/04/12021/07/12022/09/42022/10/252020/12/312022/08/102022/09/1 1401/6/10 2023/07/182023/07/52023/12/112022/12/262023/12/112023/06/22023/12/11 1402/9/20 جواد فروتن راد Javad ForutanRad دانشگاه صنعتی مالک اشتر ایران Forutanrad@gmail.com مریم حورعلی Maryam HourAli دانشگاه صنعتی مالک اشتر ایران mhourali@mut.ac.it محمدعلی کیوان راد MohammadAli KeyvanRad دانشگاه صنعتی مالک اشتر ایران keyvanrad@aut.ac.ir Question And Answer Dataset Question And Answer systems Reading comprehension Deep Learning Natural Language Processing دادگان پرسش‌وپاسخ زبان فارسی سیستم‌های پرسش‌وپاسخ درک مطلب یادگیری عمیق پردازش زبان طبیعی [1]. Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250, 2016.‏##[2]. Yuanjun Li, Yuzhu Zhang, Question Answering on SQuAD 2.0 Dataset, s. University, Editor, 2018.##[3]. d'Hoffschmidt, M., Belblidia, W., Brendlé, T., Heinrich, Q., & Vidal, M. FQuAD: French question answering dataset. arXiv preprint arXiv:2002.06071, 2020.##[4]. Möller, T., Risch, J., & Pietsch, M. Germanquad and germandpr: Improving non-english question answering and passage retrieval. arXiv preprint arXiv:2104.12741, 2021.##[5].임승영, 김명지, & 이주열. KorQuAD: 기계독해를 위한 한국어 질의응답 데이터셋. 한국정보과학회 학술발표논문집, 539-541, 2018.##[6].김영민, 임승영, 이현정, 박소윤, & 김명지. KorQuAD 2.0: 웹문서 기계독해를 위한 한국어 질의응답 데이터셋. 정보과학회논문지, 47(6), 577-586, 2020.##[7]. So, B., Byun, K., Kang, K., & Cho, S. Jaquad: Japanese question answering dataset for machine reading comprehension. arXiv preprint arXiv:2202.01764, 2022.##[8]. Ayoubi MY Sajjad & Davoodeh Persianqa: a dataset for persian question answering. https://github.com/SajjjadAyobi/PersianQA, 2021.##[9]. Mozafari, J., Fatemi, A., & Nematbakhsh, M. A. BAS: an answer selection method using BERT language model. arXiv preprint arXiv:1911.01528, 2019.##[10]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. Attention is all you need. Advances in neural information processing systems, 30, 2017.##[11]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.##[12]. Farahani, M., Gharachorloo, M., Farahani, M., & Manthouri, M. Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters, 53(6), 3831-3847, 2021.##[13]. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.##[14]. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.‏##[15]. Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291.‏##[16]. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116.‏##[17]. Persian Wikipedia. Available from: https://github.com/miladfa7/Persian-Wikipedia-Dataset##[1]. Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250, 2016.‏##[2]. Yuanjun Li, Yuzhu Zhang, Question Answering on SQuAD 2.0 Dataset, s. University, Editor, 2018.##[3]. d'Hoffschmidt, M., Belblidia, W., Brendlé, T., Heinrich, Q., & Vidal, M. FQuAD: French question answering dataset. arXiv preprint arXiv:2002.06071, 2020.##[4]. Möller, T., Risch, J., & Pietsch, M. Germanquad and germandpr: Improving non-english question answering and passage retrieval. arXiv preprint arXiv:2104.12741, 2021.##[5].임승영, 김명지, & 이주열. KorQuAD: 기계독해를 위한 한국어 질의응답 데이터셋. 한국정보과학회 학술발표논문집, 539-541, 2018.##[6].김영민, 임승영, 이현정, 박소윤, & 김명지. KorQuAD 2.0: 웹문서 기계독해를 위한 한국어 질의응답 데이터셋. 정보과학회논문지, 47(6), 577-586, 2020.##[7]. So, B., Byun, K., Kang, K., & Cho, S. Jaquad: Japanese question answering dataset for machine reading comprehension. arXiv preprint arXiv:2202.01764, 2022.##[8]. Ayoubi MY Sajjad & Davoodeh Persianqa: a dataset for persian question answering. https://github.com/SajjjadAyobi/PersianQA, 2021.##[9]. Mozafari, J., Fatemi, A., & Nematbakhsh, M. A. BAS: an answer selection method using BERT language model. arXiv preprint arXiv:1911.01528, 2019.##[10]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. Attention is all you need. Advances in neural information processing systems, 30, 2017.##[11]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.##[12]. Farahani, M., Gharachorloo, M., Farahani, M., & Manthouri, M. Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters, 53(6), 3831-3847, 2021.##[13]. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.##[14]. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.‏##[15]. Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291.‏##[16]. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116.‏##[17]. Persian Wikipedia. Available from: https://github.com/miladfa7/Persian-Wikipedia-Dataset## ##

تحلیل احساسات مبتنی بر جنبه با استفاده از شبکه رمزگذار توجه Aspect-Based Sentiment Analysis using the Attentional Encoder Network 1 1 پردازش زبان طبیعی به­ طور قابل توجهی در حال رشد و با ظهور تارنمای جهانی و موتورهای جستجو بسیار مورد توجه قرار گرفته است و پژوهش­گران شاهد انفجاری در اطلاعات به زبان­های مختلف شدند. تحلیل احساسات یکی از فعال­ترین زمینه ­های مطالعاتی در پردازش زبان طبیعی است که بر طبقه بندی متن تمرکز دارد و به­منظور شناسایی، استخراج و تجزیه و تحلیل اطلاعات ذهنی از منابع متنی استفاده می‌شود. تحلیل احساسات مبتنی بر جنبه  یک روش تحلیل متن است که نظرات را بر اساس جنبه طبقه ­بندی و احساسات مربوط به هر جنبه را مشخص می­ کند. این تحلیل می­تواند برای تحلیل خودکار بازخورد نظرات مشتریان به بخش ­های مختلف کالا یا خدمات مورد استفاده قرار گیرد و به کارفرمایان برای تمرکز بر نقاط نیازمند ارتقای کیفیت کمک کند.  در این مقاله به معرفی یک معماری جدید مبتنی بر یادگیری عمیق برای تحلیل احساسات مبتنی بر جنبه خواهیم پرداخت. این معماری از یک مدل از دولایه رمزگذار توجه (که یک جایگزین قابل موازی­ سازی و تعاملی  LSTM است و برای محاسبه حالت­ های پنهان جاسازی­ های ورودی اعمال می‌شود) استفاده خواهد کرد. آزمایش این معماری روی سه مجموعه ­داده مختلف شامل رستوران ها و لپتاپ‌هاSemEval 2014 Task 4  و مجموعه داده ACL 14 Twitter  است که در هر سه مجموعه ­داده، قطبیت احساسات مثبت، خنثی و منفی است، انجام شده‌است که مقایسه آن با روش ­های مدرن تحلیل احساس مبتنی بر جنبه، دقت بالای این روش را نشان خواهد داد. برای نمونه، تحلیل احساس مبتنی بر جنبه روی مجموعه داده لپتاپ، 79.15 درصد دقت را نشان داده که نسبت به روش ­های مدرن 4.24 درصد دقت را بالا برده است.   2 Natural language processing is growing significantly and has gained much attention with the advent of the World Wide Web and search engines, and researchers have witnessed an explosion of information in different languages. Sentiment analysis is one of the most active fields of study in natural language processing that focuses on text classification and is used to identify, extract and analyze subjective information from text sources. Aspect-based sentiment analysis is a text analysis technique that classifies comments by aspect and identifies the sentiment associated with each aspect. This analysis can be used to automatically analyze the feedback of customers' comments to different parts of goods or services and help employers to focus on points that need quality improvement. In this paper, we will introduce a new architecture based on deep learning for aspect-based sentiment analysis. This architecture will use an attention-encoder network-based model with multiple multi-head attention and a pointwise convolutional transform (which is a parallelizable and interactive alternative to LSTM and is applied to compute hidden states of input embeddings). Testing this architecture on three different datasets, including restaurants and laptops, SemEval 2014 Task 4 and ACL 14 Twitter dataset, in all three datasets, the polarity of emotions is positive, neutral and negative, which is compared with modern methods of sentiment analysis. Based on the aspect, it will show the high accuracy of this method. For example, the aspect-based sentiment analysis on the Laptop dataset has shown 79.15% accuracy, which has increased the accuracy by 4.24% compared to modern methods.   121 128 2023/04/12021/07/12022/09/42022/10/252020/12/312022/08/102022/09/12022/06/28 1401/4/7 2023/07/182023/07/52023/12/112022/12/262023/12/112023/06/22023/12/112023/12/9 1402/9/18 سمیه کریمی somayeh karimi دانشگاه صنعتی شاهرود ایران somayehkarimi6@yahoo.com فاطمه جعفری نژاد Fatemeh Jafarinejad دانشگاه صنعتی شاهرود ایران jafarinejad@shahroodut.ac.ir Aspect-Based Sentiment Analysis (ABSA) Deep Learning Attentional Encoder Network Multi-Head Attention (MHA) تجزیه و تحلیل احساس مبتنی بر جنبه یادگیری عمیق شبکه رمزگذار توجه توجه چند سر v[1] Z. Rajabi, M. valavi, and M. Hourali, "Sentiment analysis methods in Persian text: A survey," Signal Data Process., vol. 19, no. 2, pp. 107-132, 2022, doi: 10.52547/jsdp.19.2.107.##[2]"https://daneshyari.com/isi/articles/sentiment_anal."##[3] S. Behdenna, F. Barigou, and G. Belalem, "EAI Endorsed Transactions Document Level Sentiment Analysis : A survey," vol. 4, no. 1, pp. 1-8, 2017.##[4] V. S. Jagtap and K. Pawar, "Analysis of different approaches to Sentence-Level Sentiment Classification," Int. J. Sci. Eng. Technol., vol. 2, no. 3, pp. 164-170, 2013, [Online]. Available: http://ijset.com/ijset/ publication/v2s3/paper11.pdf##[5] H. Wan, Y. Yang, J. Du, Y. Liu, K. Qi, and J. Z. Pan, "Target-aspect-sentiment joint detection for aspect-based sentiment analysis," AAAI 2020 - 34th AAAI Conf. Artif. Intell., pp. 9122-9129, 2020, doi: 10.1609/aaai.v34i05.6447.##[6] Y. Kim, "Convolutional neural networks for sentence classification," EMNLP 2014 - 2014 Conf. Empir. Methods Nat. Lang. Process. Proc. Conf., pp. 1746-1751, 2014, doi: 10.3115/v1/d14-1181.##[7] A. K. Sharma, S. Chaurasia, and D. K. Srivastava, "Sentimental Short Sentences Classification by Using CNN Deep Learning Model with Fine Tuned Word2Vec," Procedia Comput. Sci., vol. 167, no. 2019, pp. 1139-1147, 2020, doi: 10.1016/j.procs.2020.03.416.##[8] S. Ramaswamy and N. DeClerck, "Customer perception analysis using deep learning and NLP," Procedia Comput. Sci., vol. 140, pp. 170-178, 2018, doi: 10.1016/j.procs.2018-.10.326.##[9] H. Sadr, M. mohsen Pedram, and M. Teshnehlab, "Efficient Method Based on Combination of Deep Learning Models for Sentiment Analysis of Text," Signal Data Process., vol. 19, no. 1, pp. 19-38, 2022, doi: 10.52547/jsdp.19.1.19.##[10] Y. Wang, M. Huang, L. Zhao, and X. Zhu, "Attention-based LSTM for aspect-level sentiment classification," EMNLP 2016 - Conf. Empir. Methods Nat. Lang. Process. Proc., pp. 606-615, 2016, doi: 10.18653/v1/d16-1058.##[11] T. Chen, R. Xu, Y. He, and X. Wang, "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN," Expert Syst. Appl., vol. 72, pp. 221-230, 2017, doi: 10.1016/j.eswa.2016.10.065.##[12] D. Tang, B. Qin, X. Feng, and T. Liu, "Effective LSTMs for target-dependent sentiment classification," COLING 2016 - 26th Int. Conf. Comput. Linguist. Proc. COLING 2016 Tech. Pap., pp. 3298-3307, 2016.##[13] Y. Song, J. Wang, T. Jiang, Z. Liu, and Y. Rao, "Targeted Sentiment Classification with Attentional Encoder Network," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11730 LNCS, pp. 93-103, 2019, doi: 10.1007/978-3-030-30490-4_9.##[14] M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos, "SemEval-2015 Task 12: Aspect Based Sentiment Analysis," SemEval 2015 - 9th Int. Work. Semant. Eval. co-located with 2015 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. NAACL-HLT 2015 - Proc., pp. 486-495, 2015, doi: 10.18653/v1/s15-2082.##[15] L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu, "Adaptive Recursive Neural Network for target-dependent Twitter sentiment classification," 52nd Annu. Meet. Assoc. Comput. Linguist. ACL 2014 - Proc. Conf., vol. 2, pp. 49-54, 2014, doi: 10.3115/v1/p14-2009.##[16] T. S. Ataei, K. Darvishi, S. Javdan, B. Minaei-Bidgoli, and S. Eetemadi, "Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian," pp. 1-6, 2019.##[17] G. Pang, K. Lu, X. Zhu, J. He, Z. Mo, and Z. Peng, "Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model," vol. 2021, 2021.##[18] Z. Sun, L. Bing, W. Yang, and P. Chen, "Recurrent Attention Network on Memory for Aspect Sentiment Analysis," pp. 452-461, 2017.##[19] R. Wang, "Interactive Attention Encoder Network with Local Context Features for Aspect-Level Sentiment Analysis," no. Iccc, pp. 571-576, 2020, doi: 10.1109/ICCC49849. 2020.9238924.##[20] F. Fan, Y. Feng, and D. Zhao, "Multi-grained Attention Network for Aspect-Level Sentiment Classification," pp. 3433-3442, 2018.##[1] Z. Rajabi, M. valavi, and M. Hourali, "Sentiment analysis methods in Persian text: A survey," Signal Data Process., vol. 19, no. 2, pp. 107-132, 2022, doi: 10.52547/jsdp.19.2.107.##[2]"https://daneshyari.com/isi/articles/sentiment_anal."##[3] S. Behdenna, F. Barigou, and G. Belalem, "EAI Endorsed Transactions Document Level Sentiment Analysis : A survey," vol. 4, no. 1, pp. 1-8, 2017.##[4] V. S. Jagtap and K. Pawar, "Analysis of different approaches to Sentence-Level Sentiment Classification," Int. J. Sci. Eng. Technol., vol. 2, no. 3, pp. 164-170, 2013, [Online]. Available: http://ijset.com/ijset/ publication/v2s3/paper11.pdf##[5] H. Wan, Y. Yang, J. Du, Y. Liu, K. Qi, and J. Z. Pan, "Target-aspect-sentiment joint detection for aspect-based sentiment analysis," AAAI 2020 - 34th AAAI Conf. Artif. Intell., pp. 9122-9129, 2020, doi: 10.1609/aaai.v34i05.6447.##[6] Y. Kim, "Convolutional neural networks for sentence classification," EMNLP 2014 - 2014 Conf. Empir. Methods Nat. Lang. Process. Proc. Conf., pp. 1746-1751, 2014, doi: 10.3115/v1/d14-1181.##[7] A. K. Sharma, S. Chaurasia, and D. K. Srivastava, "Sentimental Short Sentences Classification by Using CNN Deep Learning Model with Fine Tuned Word2Vec," Procedia Comput. Sci., vol. 167, no. 2019, pp. 1139-1147, 2020, doi: 10.1016/j.procs.2020.03.416.##[8] S. Ramaswamy and N. DeClerck, "Customer perception analysis using deep learning and NLP," Procedia Comput. Sci., vol. 140, pp. 170-178, 2018, doi: 10.1016/j.procs.2018-.10.326.##[9] H. Sadr, M. mohsen Pedram, and M. Teshnehlab, "Efficient Method Based on Combination of Deep Learning Models for Sentiment Analysis of Text," Signal Data Process., vol. 19, no. 1, pp. 19-38, 2022, doi: 10.52547/jsdp.19.1.19.##[10] Y. Wang, M. Huang, L. Zhao, and X. Zhu, "Attention-based LSTM for aspect-level sentiment classification," EMNLP 2016 - Conf. Empir. Methods Nat. Lang. Process. Proc., pp. 606-615, 2016, doi: 10.18653/v1/d16-1058.##[11] T. Chen, R. Xu, Y. He, and X. Wang, "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN," Expert Syst. Appl., vol. 72, pp. 221-230, 2017, doi: 10.1016/j.eswa.2016.10.065.##[12] D. Tang, B. Qin, X. Feng, and T. Liu, "Effective LSTMs for target-dependent sentiment classification," COLING 2016 - 26th Int. Conf. Comput. Linguist. Proc. COLING 2016 Tech. Pap., pp. 3298-3307, 2016.##[13] Y. Song, J. Wang, T. Jiang, Z. Liu, and Y. Rao, "Targeted Sentiment Classification with Attentional Encoder Network," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11730 LNCS, pp. 93-103, 2019, doi: 10.1007/978-3-030-30490-4_9.##[14] M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos, "SemEval-2015 Task 12: Aspect Based Sentiment Analysis," SemEval 2015 - 9th Int. Work. Semant. Eval. co-located with 2015 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. NAACL-HLT 2015 - Proc., pp. 486-495, 2015, doi: 10.18653/v1/s15-2082.##[15] L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu, "Adaptive Recursive Neural Network for target-dependent Twitter sentiment classification," 52nd Annu. Meet. Assoc. Comput. Linguist. ACL 2014 - Proc. Conf., vol. 2, pp. 49-54, 2014, doi: 10.3115/v1/p14-2009.##[16] T. S. Ataei, K. Darvishi, S. Javdan, B. Minaei-Bidgoli, and S. Eetemadi, "Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian," pp. 1-6, 2019.##[17] G. Pang, K. Lu, X. Zhu, J. He, Z. Mo, and Z. Peng, "Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model," vol. 2021, 2021.##[18] Z. Sun, L. Bing, W. Yang, and P. Chen, "Recurrent Attention Network on Memory for Aspect Sentiment Analysis," pp. 452-461, 2017.##[19] R. Wang, "Interactive Attention Encoder Network with Local Context Features for Aspect-Level Sentiment Analysis," no. Iccc, pp. 571-576, 2020, doi: 10.1109/ICCC49849. 2020.9238924.##[20] F. Fan, Y. Feng, and D. Zhao, "Multi-grained Attention Network for Aspect-Level Sentiment Classification," pp. 3433-3442, 2018.## ##

بیشینه سازی امتیاز در بازی تصادفی match-3 با استفاده از یادگیری تقویتی عمیق Maximize Score in stochastic match-3 games using reinforcement learning 1 1 بازی‌های رایانه‌ای در سال‌های اخیر نقش مهمی در توسعۀ هوش مصنوعی داشته‌اند. روش‌های گوناگون از جمله روش‌های مبتنی‌بر قوانین، جستجوی درختی و  یادگیری ماشین (یادگیری نظارت‌شده و یادگیری تقویتی) برای ایجاد عامل‌های هوشمند در بازی‌های گوناگون توسعه یافته‌اند. از میان این پژوهش‌ها، می‌توان به پژوهش‌های Deep Blue در بازی شطرنج و AlphaGo در بازی Go اشاره کرد. AlphaGo اولین برنامۀ رایانه‌ای است که یک بازی‌کن حرفه‌ای انسانی Go را شکست داد. همچنین، Deep Blue یک سامانۀ رایانه‌ای حرفه‌ای شطرنج و نخستین برنامه است که در مقابل یک قهرمان جهان، برنده می‌شود. در این مقاله، ما بر روی بازی match-3 تمرکز داریم، که یک بازی محبوب در تلفن‌های همراه و شامل یک فضای حالت تصادفی بسیار بزرگ و تابع پاداش تصادفی است که یادگیری را دشوار می‌کند. در گذشته، پژوهش‌های زیادی در مورد بازی‌های گوناگون، از جمله match-3، انجام شده‌است. هدف اصلی این پژوهش‌ها به‌طور کلی بازی بهینه یا پیش‌بینی دشواری مراحل طراحی‌شده برای بازی‌کنان انسانی بوده‌است. پیش‌بینی دشواری مراحل به توسعه‌دهندگان بازی کمک می‌کند تا کیفیت بازی‌های خود را بهبود بخشند و تجربۀ کاربری بهتری فراهم کنند. در این مقاله، یک عامل هوشمند بر اساس یادگیری تقویتی عمیق ارائه شده‌که هدف آن به بیشینه رساندن امتیاز در بازی match-3 است. یادگیری تقویتی یکی از شاخه‌های یادگیری ماشین است که عامل از طریق تجربیات خود از تعامل با محیط، سیاست بهینه را برای انتخاب اعمال در فضاهای گوناگون یاد می‌گیرد. در یادگیری تقویتی عمیق، الگوریتم‌های یادگیری تقویتی به‌همراه شبکه‌های عصبی عمیق استفاده می‌شوند. در روش پیشنهادی، سازوکار‌های نگاشت گوناگونی برای فضای اعمال و فضای حالت استفاده شده‌است. همچنین، یک ساختار نوآورانه از شبکه‌های عصبی سفارشی‌سازی‌شده برای محیط بازی match-3 پیشنهاد شده‌است تا قابلیت یادگیری فضای حالت بزرگ را به‌دست‌آورد. نوآوری‌های این مقاله را می‌توان بدین شرح خلاصه کرد: روی‌کردی برای نگاشت از فضای اعمال به یک ماتریس دوبعدی ارائه شده که امکان جداکردن اعمال مجاز و غیرمجاز را تسهیل می‌کند. یک روش برای نگاشت از فضای حالت به ورودی شبۀ عصبی عمیق طراحی شده که با کاهش عمق صافی‌های پیچشی، فضای ورودی را کاهش داده و این‌گونه فرایند یادگیری را بهبود می‌بخشد. همچنین، تابع پاداش از طریق جداکردن پاداش‌های تصادفی از پاداش‌های قطعی، فرایند یادگیری را پایدار کرده‌است. مقایسۀ روش پیشنهادی با سایر روش‌های موجود، از جمله PPO، DQN، A3C، روش حریصانه و عوامل انسانی، نشان‌دهندۀ عملکرد برتر روش پیشنهادی در بازی match-3 است.   2 Computer games have played an important role in the development of artificial intelligence in recent years. Throughout the history of artificial intelligence, computer games have been a suitable test environment for evaluating new approaches and algorithms to artificial intelligence. Different methods, including rule-based methods, tree search methods, and machine learning methods (supervised learning and reinforcement learning) have been developed to create intelligent agents in different games. Games have been used as a suitable environment for trial and error, testing different artificial intelligence ideas and algorithms. Among these researches, we can mention the research of Deep Blue in the chess game and AlphaGo in the game Go. AlphaGo is the first computer program to defeat an expert human Go player. Also, Deep Blue is a chess-playing expert system is the first computer program to win a match, against a world champion. In this paper, we focus on the match-3 game. The match-3 game is a popular game in cell phones, which consists of a very large random state space which makes learning difficult. It also has random reward function which makes learning unstable. Many researches have been done in the past on different games, including match-3. The aim of these researches has generally been to play optimally or to predict the difficulty of stages designed for human players. Predicting the difficulty of stages helps game developers to improve the quality of their games and provide a better experience for users. Based on the approach used, past works can be divided into three main categories including search-based methods, machine learning methods and heuristic methods. In this paper, an intelligent agent based on deep reinforcement learning is presented, whose goal is to maximize the score in the match-3 game. Reinforcement learning is one of the approaches that has received a lot of attention recently. Reinforcement learning is one of the branches of machine learning in which the agent learns the optimal policy for choosing actions in different spaces through its experiences of interacting with the environment. In deep reinforcement learning, reinforcement learning algorithms are used along with deep neural networks. In the proposed method, different mapping mechanisms for action space and state space are used. Also, a novel structure of neural network customized for the match-3 game environment has been proposed to achieve the ability to learn large state space. The contributions of this article can be summarized as follow. An approach for mapping the action space to a two-dimensional matrix is presented in which it is possible to easily separate valid and invalid actions. An approach has been designed to map the state space to the input of the deep neural network, which reduces the input space by reducing the depth of the convolutional filter and thus improves the learning process. The reward function has made the learning process stable by separating random rewards from deterministic rewards. The comparison of the proposed method with other existing methods, including PPO, DQN, A3C, greedy method and human agents shows the superior performance of the proposed method in the match-3 game.   129 140 2023/04/12021/07/12022/09/42022/10/252020/12/312022/08/102022/09/12022/06/282022/10/27 1401/8/5 2023/07/182023/07/52023/12/112022/12/262023/12/112023/06/22023/12/112023/12/92023/12/11 1402/9/20 مهدی رعایائی اردکانی Mehdy Roayaei Ardakany دانشگاه تربیت مدرس ایران mroayaei@modares.ac.ir علی افروغه Ali Afroughrh دانشگاه تربیت مدرس ایران ali74afrougheh@gmail.com deep reinforcement learning random game match-3 large state space یادگیری تقویتی عمیق بازی تصادفی match-3 فضای حالت بزرگ [1] M. Campbell, A. J. Hoane, and F. H. Hsu, "Deep Blue," Artificial intelligence, vol. 134, no. 1-2, pp. 57-83, 2002.##[2] D. Silver et al., Mastering the game of Go with deep neural networks and tree search, Nature, vol. 529, no. 7587, pp. 484-489, 2016.##[3] V. Mnih et al., Human-level control through deep reinforcement learning, Nature, vol. 518, no.7540, pp. 529-533, 2015.##[4] H. Van Hasselt, A. Guez, and D. Silver, Deep Reinforcement Learning with Double Q-Learning, Proceedings of the AAAI conference on artificial intelligence, vol. 30, no. 1, Mar. 2016.##[5] Z. Wang, T. Schaul, M. Hessel, H. Van Hasselt, M. Lanctot, and N. De Frcitas, Dueling Network Architectures for Deep Reinforcement Learning, in 33rd International Conference on Machine Learning, ICML 2016, 2016, vol. 4, no. 9, pp. 2939-2947.##[6] V. Mnih et al., Asynchronous Methods for Deep Reinforcement Learning, in Proceedings of the 33rd International Conference on Machine Learning, 2016, vol. 48, pp. 1928-1937.##[7] J. v. Neumann, Zur Theorie der Gesellschaftsspiele, Math. Ann., vol. 100, no. 1, pp. 295-320, Dec. 1928.##[8] D. Knuth, R. M. A., An analysis of alpha-beta pruning, An analysis of alpha-beta pruning, vol. 6, no. 4, pp. 293-326, 1975.##[9] J. Schaeffer, R. Lake, P. Lu, M. B.A., Chinook the world man-machine checkers champion, AI magazine, vol. 17(1), 1996.##[10] M. Enzenberger, M. Müller, B. Arneson, and R. Segal, FUEGO-An open-source framework for board games and go engine based on Monte Carlo tree search, IEEE Transactions on Computational Intelligence and AI in Games, vol. 2, no. 4, pp. 259-270, 2010.##[12] D. Hadar and O. Samuel, Crushing Candy Crush - An AI Project, Hebrew University of Jerusalem, 2015.##[13] E. R. Poromaa, Crushing Candy Crush, KTH Royal Inst. Technol., Stockholm, Sweden, 2017.##[14] S. Purmonen, Predicting game level difficulty using deep neural networks, KTH Royal Institute of Technology, Stockholm, Sweden, 2017.##[15] C. Tesau and G. Tesau, Temporal Difference Learning and TD-Gammon, Communications of the ACM, vol. 38, no. 3, pp. 58-68, 1995.##[16] V. Mnih et al., Playing Atari with Deep Reinforcement Learning, arXiv Prepr. arXiv1312.5602, 2013.##[17] V. 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Gudmundsson et al., Human-Like Playtesting with Deep Learning, in IEEE Conference on Computational Intelligence and Games, CIG, 2018, vol. 2018.##[23] L. Kaiser, M. Babaeizadeh, P. Milos, et al., Model Based Reinforcement Learning for Atari. In International Conference on Learning Representations, 2019.##[24] O. Vinyals, I. Babuschkin, W. M. Wojciech Czarnecki, M. Mathieu, A. Dudzik, J. Chung, et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning, Nature 575, no. 7782 (2019): 350-354.##[25] R. Z., Liu, Pang, Z. Y. Meng, W. Wang, Y. Yu, T., On efficient reinforcement learning for full-length game of StarCraft ii, Journal of Artificial Intelligence Research, 75, 213-260, 2022.##[26] J. Perolat, B. De Vylder, D. Hennes, E. Tarassov, E., F. Strub, V. de Boer, Mastering the game of Stratego with model-free multiagent reinforcement learning. Science, 378 (6623), 990-996, 2022.##[1] M. Campbell, A. J. Hoane, and F. H. 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انتقال دانش تنظیم شده برای یادگیری تقویتی چندعاملی Regularized Knowledge Transfer for Multi-Agent Reinforcement Learning 1 1 یادگیری تقویتی به آموزش مدل‌های یادگیری ماشین برای اتخاذ تصمیمات متوالی اشاره می­کند که در آن یک عامل از طریق تعامل با محیط، آموزش دیده، نتایج این تعامل را مشاهده کرده و بر این اساس، پاداش مثبت یا منفی دریافت می­کند. یادگیری تقویتی کاربردهای زیادی برای سیستم­های چندعاملی  به خصوص در محیط­های پویا و ناشناخته دارد. با این حال، بیش­تر الگوریتم­های یادگیری تقویتی چندعاملی  با مشکلاتی همچون پیچیدگی محاسباتی نمایی برای محاسبه فضای حالت مشترک مواجه هستند که منجر به عدم مقیاس­پذیری الگوریتم­ها درمسائل چندعاملی  واقعی می­شود. کاربردهای یادگیری تقویتی چندعاملی  را می­توان از فوتبال ربات‌ها، شبکه­ها، محاسبات ابری، زمانبندی شغل تا اعزام نیروی واکنشی دسته­بندی کرد. در این مقاله یک الگوریتم جدید به نام انتقال دانش تنظیم‌شده برای یادگیری تقویتی چندعاملی  (RKT-MARL) معرفی می­شود که براساس مدل تصمیم­گیری مارکوف کار می­کند. این الگوریتم برخلاف روش­های یادگیری تقویتی سنتی، مفاهیم تعاملات پراکنده و انتقال دانش را برای رسیدن به تعادل بین عامل­ها استفاده می­کند. علاوه‌بر این، RKT-MARL از سازوکار مذاکره برای یافتن مجموعه تعادل و از روش کمینه واریانس برای انتخاب بهترین عمل در مجموعه تعادل به­دست­آمده استفاده می­کند. همچنین الگوریتم پیشنهادی، دانش مقادیر حالت-عمل را در میان عامل­های مختلف انتقال می­دهد. از طرفی، الگوریتم RKT-MARL مقادیر Q را در حالت­های هماهنگی به عنوان ضریبی از اطلاعات محیطی جاری و دانش قبلی مقداردهی می­کند. به­منظور ارزیابی عملکرد روش پیشنهادی، یک گروه از آزمایش­ها بر روی پنج بازی جهانی انجام­شده و نتایج حاصل بیانگر همگرایی سریع و مقیاس­پذیری بالا در RKT-MARL‌ است.   2 Reinforcement learning (RL) refers to the training of machine learning models to make a sequence of decisions on which an agent learns by interacting with its environment, observing the results of interactions and receiving a positive or negative reward, accordingly. RL has many applications for multi-agent systems, especially in dynamic and unknown environments. However, most multi-agent reinforcement learning (MARL) algorithms suffer from some problems specifically the exponential computational complexity to calculate the joint state-action space, which leads to the lack of scalability of algorithms in realistic multi-agent problems. Applications of MARL can be categorized from robot soccer, networks, cloud computing, job scheduling, and to optimal reactive power dispatch. In the area of reinforcement learning algorithms, there are serious challenges such as the lack of application of equilibrium-based algorithms in practice and high computational complexity to find equilibrium.  On the other hand, since agents have no concept of equilibrium policies, they tend to act aggressively toward their goals, which it results the high probability of collisions. Consequently, in this paper, a novel algorithm called Regularized Knowledge Transfer for Multi-Agent Reinforcement Learning (RKT-MARL) is presented that relies on Markov decision process (MDP) model. RKT-MARL unlike the traditional reinforcement learning methods exploits the sparse interactions and knowledge transfer to achieve an equilibrium across agents. Moreover, RKT-MARL benefits from negotiation to find the equilibrium set. RKT-MARL uses the minimum variance method to select the best action in the equilibrium set, and transfers the knowledge of state-action values across various agents. Also, RKT-MARL initializes the Q-values in coordinate states as coefficients of current environmental information and previous knowledge. In order to evaluate the performance of our proposed method, groups of experiments are conducted on five grid world games and the results show the fast convergence and high scalability of RKT-MARL. Therefore, the fast convergence of our proposed method indicates that the agents quickly solve the problem of reinforcement learning and approach to their goal.   141 160 2023/04/12021/07/12022/09/42022/10/252020/12/312022/08/102022/09/12022/06/282022/10/272019/08/2 1398/5/11 2023/07/182023/07/52023/12/112022/12/262023/12/112023/06/22023/12/112023/12/92023/12/112023/12/11 1402/9/20 نیلوفر علوی Niloofar Alavi دانشگاه صنعتی ارومیه ایران nilooalavi504@it.uut.ac.ir جعفر طهمورث نژاد Jafar Tahmoresnezhad دانشگاه صنعتی ارومیه ایران j.tahmores@it.uut.ac.ir Multi-agent reinforcement learning Knowledge transfer Meta and Nash equilibriums Regularization Sparse interactions Agents negotiations. یادگیری تقویتی چند عاملی انتقال دانش تعادل‌های متا و نش تنظیم‌پذیری تعاملات پراکنده مذاکره بین عامل‌ها. [1] C. Yu, M. Zhang, F. Ren and G. Tan, "Multiagent learning of coordination in loosely coupled multiagent systems," IEEE Transactions on Cybernetics, vol. 45, no.12, pp. 2853-2867, 2015.##[2] J. Kober, J. A. Bagnell and J. Peters, "Reinforcement learning in robotics: A survey," The International Journal of Robotics Research,vol. 32, no. 11, pp. 1238-1274,2013.##[3] R. Babuška, L. Busoniu and B. D. Schutter, "Reinforcement learning for multi-agent systems," IEEE International##Conference on Emerging Technologies and Factory Automation, 2006.##[4] K. Arulkumaran, M. P. Deisenroth, M. Brundage and A. A. Bharath, "A brief survey of deep reinforcement learning," arXiv preprint arXiv:1708.05866, 2017.##[5] Q. Zhang, P. Jiao, Q. Yin and L. Sun, "Coordinated Learning by Model Difference Identification in Multiagent Systems with Sparse Interactions," Discrete Dynamics in Nature and Society, 2016.##[6] A. OroojlooyJadid and D. Hajinezhad, "A review of cooperative multi-agent deep reinforcement learning , arXiv preprint arXiv: 1908.03963, 2019.##[7] A. Nowé, P. Vrancx and Y. M. D. Hauwere, "Game theory and multi-agent reinforcement learning," In Reinforcement Learning, Springer, Berlin, Heidelberg, pp. 441-470, 2012.##[8] F. S. Melo and M. Velso, "Decentralized MDPs with sparse interactions," Artificial Intelligence, vol. 175, no.11, pp. 1757-1789, 2011.##[9] D. S. Bernstein, R. Givan, N. Immerman and S. Zilberstein, "The complexity of decentralized control of Markov decision processes," Mathematics of operations research, vol. 27, no. 4, pp. 819-840, 2002.##[10] A. M. Metelli, M. Mutti and M. Restelli, "Configurable Markov decision processes," In International Conference on Machine Learning, pp. 3491-3500, PMLR, 2018.##[11] L. Zhou, P. Yang, C. Chen and Y. Gao, "Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer," IEEE transactions on cybernetics, vol. 47, no. 5, pp. 1238-1250, 2017.##[12] L. Canese, G.C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Re and S. Spano, "Multi-Agent Reinforcement Learning: A Review of Challenges and Applications," Applied Sciences, p. 4948, 2021.##[13] J. Tahmoresnezhad and S. Hashemi, "Exploiting kernel-based feature weighting and instance clustering to transfer knowledge across domains," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 25, no. 1, pp. 292-307, 2017.##[14] J. Tahmoresnezhad and S. Hashemi, "Visual domain adaptation via transfer feature learning," Knowledge and Information Systems, vol. 50, no. 2, pp. 585-605, 2017.##[15] Y. Hu, Y. Gao and B. An, "Accelerating multiagent reinforcement learning by equilibrium transfer," IEEE transactions on cybernetics, vol. 45, no. 7, pp. 1289-1302, 2015.##[16] C. J. C. H. Watkins, "Learning from delayed rewards," (Doctoral dissertation, King's College, Cambridge), 1989.##[17] Y. M. D. Hauwere, P. Vrancx and A. Nowé, "Learning multi-agent state space representations," In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 715-722, 2010.##[18] Y. Hu, Y. Gao and B. An, "Multiagent reinforcement learning with unshared value functions," IEEE transactions on cybernetics, vol. 45, no. 4, pp. 647-662, 2015.##[19] D. Abel, Y. Jinnai, S. Y. Guo, G. Konidaris and M. Littman, "Policy and Value Transfer in Lifelong Reinforcement Learning," In International Conference on Machine Learning, pp. 20-29, 2018.##[20] Y. Hu, Y. Gao and B. An, "Learning in multi-agent systems with sparse interactions by knowledge transfer and game abstraction," In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 753-761, 2015.##[21] P. Mannion, "Knowledge-based multi-objective multi-agent reinforcement learning (Doctoral dissertation), 2017.##[22] T. Kujirai and T. Yokota, "Breaking Deadlocks in Multi-agent Reinforcement Learning with Sparse Interaction," In Pacific Rim Internatioal Conference on Artifical Intelligence, pp. 746-759, 2019.##[23] Y. Liu, Y. Hu, Y.Gao, Y.Chen and C.Fan, "Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns," In IJCAI, pp. 457-463, 2019.##[24] F. S. Melo and M. Veloso, "Learning of coordination: Exploiting sparse interactions in multiagent systems," In Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 773-780, 2009.##[25] P. Vrancx, Y. M. D. Hauwere and A. Nowé, "Transfer Learning for Multi-agent Coordination," In ICAART (2) , pp. 263-272, 2011.##[26] Melo, Francisco S., and Manuela Veloso. "Decentralized MDPs with sparse interactions," Artificial Intelligence 175.11 (2011).##[27] Sherafati F, Tahmoresnezhad J. Image Classification via Sparse Representation and Subspace Alignment. JSDP 2020; 17 (2) :58-47.##[28] Zandifar M, Tahmoresnezhad J. Sample-oriented Domain Adaptation for Image Classification. JSDP 2019; 16 (3) :148-129.##[1] C. Yu, M. Zhang, F. Ren and G. Tan, "Multiagent learning of coordination in loosely coupled multiagent systems," IEEE Transactions on Cybernetics, vol. 45, no.12, pp. 2853-2867, 2015.##[2] J. Kober, J. A. Bagnell and J. Peters, "Reinforcement learning in robotics: A survey," The International Journal of Robotics Research,vol. 32, no. 11, pp. 1238-1274,2013.##[3] R. Babuška, L. Busoniu and B. D. Schutter, "Reinforcement learning for multi-agent systems," IEEE International##Conference on Emerging Technologies and Factory Automation, 2006.##[4] K. Arulkumaran, M. P. Deisenroth, M. Brundage and A. A. Bharath, "A brief survey of deep reinforcement learning," arXiv preprint arXiv:1708.05866, 2017.##[5] Q. Zhang, P. Jiao, Q. Yin and L. Sun, "Coordinated Learning by Model Difference Identification in Multiagent Systems with Sparse Interactions," Discrete Dynamics in Nature and Society, 2016.##[6] A. OroojlooyJadid and D. Hajinezhad, "A review of cooperative multi-agent deep reinforcement learning , arXiv preprint arXiv: 1908.03963, 2019.##[7] A. Nowé, P. Vrancx and Y. M. D. Hauwere, "Game theory and multi-agent reinforcement learning," In Reinforcement Learning, Springer, Berlin, Heidelberg, pp. 441-470, 2012.##[8] F. S. Melo and M. Velso, "Decentralized MDPs with sparse interactions," Artificial Intelligence, vol. 175, no.11, pp. 1757-1789, 2011.##[9] D. S. Bernstein, R. Givan, N. Immerman and S. Zilberstein, "The complexity of decentralized control of Markov decision processes," Mathematics of operations research, vol. 27, no. 4, pp. 819-840, 2002.##[10] A. M. Metelli, M. Mutti and M. Restelli, "Configurable Markov decision processes," In International Conference on Machine Learning, pp. 3491-3500, PMLR, 2018.##[11] L. Zhou, P. Yang, C. Chen and Y. Gao, "Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer," IEEE transactions on cybernetics, vol. 47, no. 5, pp. 1238-1250, 2017.##[12] L. Canese, G.C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Re and S. Spano, "Multi-Agent Reinforcement Learning: A Review of Challenges and Applications," Applied Sciences, p. 4948, 2021.##[13] J. Tahmoresnezhad and S. Hashemi, "Exploiting kernel-based feature weighting and instance clustering to transfer knowledge across domains," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 25, no. 1, pp. 292-307, 2017.##[14] J. Tahmoresnezhad and S. Hashemi, "Visual domain adaptation via transfer feature learning," Knowledge and Information Systems, vol. 50, no. 2, pp. 585-605, 2017.##[15] Y. Hu, Y. Gao and B. An, "Accelerating multiagent reinforcement learning by equilibrium transfer," IEEE transactions on cybernetics, vol. 45, no. 7, pp. 1289-1302, 2015.##[16] C. J. C. H. Watkins, "Learning from delayed rewards," (Doctoral dissertation, King's College, Cambridge), 1989.##[17] Y. M. D. Hauwere, P. Vrancx and A. Nowé, "Learning multi-agent state space representations," In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 715-722, 2010.##[18] Y. Hu, Y. Gao and B. An, "Multiagent reinforcement learning with unshared value functions," IEEE transactions on cybernetics, vol. 45, no. 4, pp. 647-662, 2015.##[19] D. Abel, Y. Jinnai, S. Y. Guo, G. Konidaris and M. Littman, "Policy and Value Transfer in Lifelong Reinforcement Learning," In International Conference on Machine Learning, pp. 20-29, 2018.##[20] Y. Hu, Y. Gao and B. An, "Learning in multi-agent systems with sparse interactions by knowledge transfer and game abstraction," In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 753-761, 2015.##[21] P. Mannion, "Knowledge-based multi-objective multi-agent reinforcement learning (Doctoral dissertation), 2017.##[22] T. Kujirai and T. Yokota, "Breaking Deadlocks in Multi-agent Reinforcement Learning with Sparse Interaction," In Pacific Rim Internatioal Conference on Artifical Intelligence, pp. 746-759, 2019.##[23] Y. Liu, Y. Hu, Y.Gao, Y.Chen and C.Fan, "Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns," In IJCAI, pp. 457-463, 2019.##[24] F. S. Melo and M. Veloso, "Learning of coordination: Exploiting sparse interactions in multiagent systems," In Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 773-780, 2009.##[25] P. Vrancx, Y. M. D. Hauwere and A. Nowé, "Transfer Learning for Multi-agent Coordination," In ICAART (2) , pp. 263-272, 2011.##[26] Melo, Francisco S., and Manuela Veloso. "Decentralized MDPs with sparse interactions," Artificial Intelligence 175.11 (2011).##[27] Sherafati F, Tahmoresnezhad J. Image Classification via Sparse Representation and Subspace Alignment. JSDP 2020; 17 (2) :58-47.##[28] Zandifar M, Tahmoresnezhad J. Sample-oriented Domain Adaptation for Image Classification. JSDP 2019; 16 (3) :148-129.## ##