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الگوریتم‌های اعتماد در اینترنت اشیا: بررسی، تحلیل و ارائه معیارهای ارزیابی Trust Management in Internet of Things: Review, Analysis and Establishment of Evaluation Criteria 1 1 در فضایی که ارتباط بین انسان‌­ها و اشیا و نیز اشیا با یکدیگر پیچیده است و انتظار اجرای برنامه‌­های کاربردی فراوانی روی بستر شبکه می­‌رود، یک رویکرد برای حرکت به سمت هوشمندی با حفظ امنیت، پیاده­‌سازی سامانه‌های مدیریت اعتماد است. اعتماد همه مفاهیم امنیت، محرمانگی، حریم خصوصی و قابلیت اطمینان را تحت تأثیر قرار می‌­دهد. تعداد زیاد اشیا در شبکه، قابلیت‌­ها و کاربردهای متنوع، پویایی بسیار بالا و همچنین حضور اشیای‌ مخرب، مدیریت اعتماد در شبکه اینترنت اشیا را به یک چالش جدی مبدل کرده است؛ به‌طوری که راه‌کارهای قدیمی پیاده‌سازی اعتماد، در  این شبکه قابل استفاده نیست. در این مقاله علاوه‌بر این‌که تحلیل جامعی روی مدل­‌های محاسبه مستقیم، غیرمستقیم و ترکیبی اعتماد انجام می‌شود، انواع حملات و روش­‌های مقابله با آن­ها، روش‌­های ارزیابی مدل‌های ارائه‌شده و تأثیر محدودیت‌­های اشیا بر مدل­‌های محاسبه اعتماد، بررسی می‌شود. حیطه این بررسی، دو حوزه مدیریت اعتماد و اینترنت اشیای اجتماعی است. به­‌طور خلاصه، مطالعات صورت‌گرفته در این حوزه از چهار دیدگاه مرور و مقایسه می‌شوند: (1) مدل‌های محاسبه اعتماد، (2) راه‌کارهای مقابله با حملات اعتماد، (3) تأثیر محدودیت­‌های عناصر اینترنت اشیا و (4) روش‌­های ارزیابی الگوریتم‌­های اعتماد؛ تا به این ترتیب بتوان با تحلیلی مناسب، به نقاط  قوت و ضعف الگوریتم­‌های موجود در مقالات مطرح پی برد و متر و معیاری برای بحث ارزیابی اعتماد در اینترنت اشیا ارائه کرد. در این راستا، روش‌­های ارزیابی (متریک‌­های) کمی‌ای ارایه می‌شود که هدف آنها کشف معایب و مزایای مدل­‌های تخمین اعتماد تحت شرایط مختلف است. 2 In the complex Internet of Things (IoT) paradigm that things interact with each other as well as with human beings, one approach is to implement trust management systems in order to provide security for smart network applications. Trust, in general, overlaps with concepts such as privacy, security, and reliability. However, the high number of objects in IoT, along with its dynamic nature and existence of malicious entities, make IoT trust management quite challenging. These attributes rule out the possibility of using traditional best practices for IoT networks. Trust management algorithms have been implemented for a variety of applications in IoT environments. These algorithms are usually utilized to enhance the quality of received services in the presence of malicious entities. Such algorithms and methods have been proposed to secure IoT networks in different contexts, including traffic routing, smart cities, vehicular ad-hoc networks, healthcare ecosystems, and object authentication. In this paper, first, different state of the art trust computation methods are numerically evaluated to estimate trust in a common testbed. Finding the best approach to assign a precise value to the trust level of an object is a crucial matter. Therefore, the principal parameters that make trust computation methods different are extracted and then, the existing trust calculation approaches built upon them are categorized. Type of relationship, direct trust, indirect trust, combination of trust values, trust updating process, data storage, and social relationships are considered as the parameters to analyze trust computation models with. Type of relationship between trustor and trustee can be different. Either of them can be object or human. Moreover, trust is usually a combination of direct experiences and recommenders’ feedback. There are different update methods too. Trust estimation can be updated after each transaction, a definite time interval, or both of them. Depending on the storage and accessibility of data, algorithms can be built to be centralized, decentralized or semi-centralized. Moreover, social parameters can be involved in trust assessment, which is the subject of trust management in Social IoT. After analyzing each of these parameters’ effect on trust assessment, in the next part of the article, trust-related attacks are studied. Every method that can make trust management models resistant to attacks is explained.  We introduce relevant attacks and their countermeasures in direct, indirect, and hybrid trust calculation algorithms. More importantly, we study the methods of trust model evaluation and the effect of limited resources on the performance of trust calculation algorithms. In short, we conduct a comparative survey in which trust-related IoT works are studied from four perspectives: (1) Trust calculation principles, (2) Attack resistance, (3) The effect of resource limitation on model performance, and (4) Trust management evaluation framework. Through this, we find the advantages and disadvantages of existing algorithms and make a measure for the evaluation of IoT trust management systems. We provide comparative tables to show the differences between IoT trust models. A major contribution of this paper is establishing quantitative metrics to assess trust estimation models and reveal their strengths and weaknesses under different conditions. 3 28 2020/03/10 1398/12/20 2021/03/8 1399/12/18 مریم ابراهیمی Maryam Ebrahimi پژوهشگاه ارتباطات و فناوری اطلاعات، آموزشکده فنی و حرفه‌ای سما تهران، دانشگاه آزاد اسلامی ایران m.ebrahimi@itrc.ac.ir محمد حسام تدین Mohammad Hesam Tadayon پژوهشگاه ارتباطات و فناوری اطلاعات ایران tadayon@itrc.ac.ir محمد صیادحقیقی Mohammad Sayad Haghighi دانشکده مهندسی برق و کامپیوتر، دانشگاه تهران ایران sayad@ut.ac.ir Trust Internet of Things Social Internet of Things Trust Evaluation اعتماد اینترنت اشیا اینترنت اشیای اجتماعی ارزیابی اعتماد [1] J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, "Internet of Things (IoT): A vision, architectural elements, and future directions," Future Generation Comput.Syst., vol. 29, pp. 1645-1660,9, 2013.##[2] H. Sundmaeker, P. Guillemin, P. Friess and S. Woelfflé, "Vision and challenges for realising the Internet of Things," in Cluster of European Research Projects on the Internet of Things-CERP IoT, 2010.##[3] X. Li, R. Lu, X. Liang, X. Shen, J. Chen and X. Lin, "Smart community: an internet of things application," IEEE Communications Magazine, vol. 49, no. 11, pp. 68-75, 2011.##[4] M. K. Geir, "Reflections on trust in devices: an informal survey of human trust in an internet-of-things context," Wireless Personal Communications, vol. 61, no. 3, pp. 495-510, 2011.##[5] T. Eder, D. Nachtmann and D. Schreckling, "Trust and Reputation in the Internet of Things," Universit ̈at Passau, Tech. Rep., 2013.##[6] Z. Yan, P. Zhang and A. V. Vasilakos, "A survey on trust management for Internet of Things," Journal of Network and Computer Applications , vol. 42, pp. 120-134, June 2014.##[7] 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.##[8] V. Suryani and and others, "A survey on trust in Internet of Things," in Information Technology and Electrical Engineering (ICITEE), 2016 8th International Conference on, 2016.##[9] W. Abdelghani, C. A. Zayani, I. Amous and F. Sèdes, "Trust management in social internet of things: a survey," in Conference on e-Business, e-Services and e-Society, 2016.##[10] C. Perera, A. Zaslavsky, P. Christen and D. Georgakopoulos, "Context aware computing for the internet of things: A survey," vol. 16, no. 1, pp. 414-454, 2014.##[11] J. Guo, R. Chen and J. J. Tsai, "A survey of trust computation models for service management in internet of things systems," Computer Communications, vol. 97, pp. 1-14, 2017.##[12] Elizabeth J.Chang, Farookh Khadeer Hussain and Tharam S. Dillon , "Fuzzy Nature of Trust and Dynamic Trust Modeling in Service Oriented Environments," in Proceedings of the 2005 workshop on Secure web services. ACM, 2005.##[13] M. Blaze, J. Feigenbaum and J. Lacy, "Decentralized trust management," in Security and Privacy, 1996. Proceedings, 1996 IEEE Symposium on, 1996.##[14] A. Josang, C. Keser and T. Dimitrakos, "Can we manage trust?," in iTrust, 2005.##[15] S. Chaeikar, M. Alizadeh, M. Tadayon and A. Jolfaei, "An intelligent cryptographic key management model for secure communications in distributed industrial intelligent systems," International Journal of Intelligent Systems, DOI:10.1002/int.22435, 25 April 2021.##[16] D.Chen, G.Chang, D.Sun, J.Li, J.Jia and X.Wang, "TRM-IoT:A trust management model based on fuzzy reputation for Internet of things," Comput. Sci. Inf. Syst., vol. 8, pp. 1207-1228, 2011.##[17] F. Bao and I.-R. Chen, "Dynamic trust management for internet of things applications," in Proceedings of the 2012 international workshop on Self-aware internet of things, San Jose, California, USA, 2012.##[18] S. Yosra Ben, O. Alexis, Z. Djamal and L. Maryline, "Trust management system design for the Internet of Things: A context-aware and multi-service approach," Computer and Security, vol. 39, pp. 351-365, 2013.##[19] C. Ray, F. Bao and J. Guo, "Trust management for SOA-based IoT and its application to service composition," IEEE Transactions on Services Computing, vol. 9, no. 3, pp. 482-495, 2016.##[20] H. Al-Hamadi, R. Chen and J.-H. Cho, "Trust management of smart service communities," IEEE Access, vol. 7, pp. 26362-26378, 2019.##[21] M. Nitti, R. Gira, L. Atzori, A. Iera and G. Morabito, "A subjective model for trustworthiness evaluation in the social Internet of Things," in Personal Indoor and Mobile Radio Communications (PIMRC), 2012 IEEE 23rd International Symposium on, 2012.##[22] B. Fenye, R. Chen and J. Guo, "Scalable, adaptive and survivable trust management for community of interest based internet of things systems," in Autonomous Decentralized Systems (ISADS), 2013 IEEE Eleventh International Symposium on , 2013.##[23] M. Nitti, R. Girau and L. Atzori, "Trustworthiness Management in the Social Internet of Things," Knowledge and Data Engineering, IEEE Transactions on, vol. 26, pp. 1253-1266, 2014.##[24] Chen, Ray, Fenye Bao and Jia Guo, "Trust-based service management for social internet of things systems," IEEE transactions on dependable and secure computing, vol. 13, no. 6, pp. 684-696, 2016.##[25] R. Sherif Emad Abdel, A. Ayman and E. Mohamad Abou, "CBSTM-IoT: Context-based social trust model for the Internet of Things," in Selected Topics in Mobile & Wireless Networking (MoWNeT), 2016 International Conference on, 2016.##[26] D. Airehrour, J. Gutierrez and S. K. Ray, "SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things," Future Generation Computer Systems, vol. 93, pp. 860-876, 2019.##[27] A. Tandon and P. Srivastava, "Trust-based Enhanced Secure Routing against Rank and Sybil Attacks in IoT," in 2019 Twelfth International Conference on Contemporary Computing (IC3), 2019.##[28] X. Anita, L. M. Martin and M. A. Bhagyaveni, "Two-way acknowledgment-based trust framework for wireless sensor networks," International Journal of Distributed Sensor Networks, vol. 9, no. 5, p. 952905, 2013.##[29] S. Bhattacharya and A. Ghosh, "Entropy Trust based Approach against IP spoofing attacks in network," International Journal of Computer Applications, vol. 67, no. 23, 2013.##[30] R. A. Raje and A. V. Sakhare, "Routing in wireless sensor network using fuzzy based trust model," in 2014 Fourth International Conference on Communication Systems and Network Technologies, 2014.##[31] Y. Gao and W. Liu, "BeTrust: a dynamic trust model based on bayesian inference and tsallis entropy for medical sensor networks," Journal of Sensors, vol. 2014, 2014.##[32] T. Yang, X. Xiangyang, L. Peng, L. Tonghui and P. Leina, "A secure routing of wireless sensor networks based on trust evaluation model," Procedia computer science, vol. 131, pp. 1156-1163, 2018.##[33] O. AlFarraj, A. AlZubi and A. Tolba, "Trust-based neighbor selection using activation function for secure routing in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, pp. 1-11, 2018.##[34] M. Selvi, K. Thangaramya, S. Ganapathy, K. Kulothunganx, H. K. Nehemiah and A. Kannan, "An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks," Wireless Personal Communications, vol. 105, no. 4, pp. 1475-1490, 2019.##[35] W. Fang, W. Zhang, W. Chen, Y. Liu and C. Tang, "TMSRS: trust management-based secure routing scheme in industrial wireless sensor network with fog computing," Wireless Networks, pp. 1-14, 2019.##[36] A. Alnasser and H. Sun, "A fuzzy logic trust model for secure routing in smart grid networks," IEEE access, vol. 5, pp. 17896-17903, 2017.##[37] Sh. M. Antesar, P. Keshav Dahal, S. K. Bista and I. U. Awan, "Recommendation based trust model with an effective defence scheme for MANETs," IEEE Transactions on Mobile Computing, vol. 14, no. 10, pp. 2101-2115, 2015.##[38] V. V. Sarbhukan and L. Ragha, "Establishing Secure Routing Path Using Trust to Enhance Security in MANET," Wireless Personal Communications, vol. 110, no. 1, pp. 245-255, 2020.##[39] L. Wenjia, S. Houbing and Z. Feng, "Policy-based secure and trustworthy sensing for internet of things in smart cities," IEEE Internet of Things Journal, 27 jun 2017.##[40] X. Xu, X. Liu, Z. Xu, F. Dai, . X. Zhang and L. Qi, "Trust-oriented IoT service placement for smart cities in edge computing," vol. 7, no. 5, pp. 4084-4091, 2019.##[41] J. Guo, I.-R. Chen, D.-C. Wang, J. J. Tsai and H. Al-Hamadi, "Trust-based iot cloud participatory sensing of air quality," Wireless Personal Communications, vol. 105, no. 4, pp. 1461-1474, 2019.##[42] G. C. Karmakar, R. Das and J. Kamruzzaman, "IoT Sensor Numerical Data Trust Model Using Temporal Correlation," IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2573-2581, 2019.##[43] Z. Liu, J. Weng, J. Ma, J. Guo, B. Feng, Z. Jiang and K. Wei, "TCEMD: A Trust Cascading-Based Emergency Message Dissemination Model in VANETs," IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4028-4048, 2019.##[44] A. Alnasser, H. Sun and J. Jiang, "Recommendation-based Trust Model for Vehicle-to-Everything (V2X)," IEEE Internet of Things Journal, vol. 7, no. 1, pp. 440-450, 2019.##[45] J. Guo, X. Li, Z. Liu, J. Ma, C. Yang, J. Zhang and D. Wu, "TROVE: A Context Awareness Trust Model for VANETs Using Reinforcement Learning," IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6647-6662, 2020.##[46] 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 of Things Journal, vol. 7, no. 4, pp. 3310-3322, 2020.##[47] S. Nasiri, F. Sadoughi, M. H. Tadayon and A. Dehnad, "Security and privacy mechanisms of Internet of things in healthcare and non-healthcare industry," Journal of Health Administration, vol. 22, no. 4, 2019.##[48] S. Nasiri, F. Sadoughi, M. H. Tadayon and A. Dehnad, "Security requirements of internet of things-based healthcare system: a survey study," Acta Informatica Medica, vol. 27, no. 4, p. 253, 2019.##[49] K. Kang, , Z. Pang, , L. Da Xu, , L. Ma, and C.Wang, "An interactive trust model for application market of the internet of things," IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1516-1526, 2014.##[50] H. Al-Hamadi and I.-R. Chen, "Trust-based decision making for health IoT systems," IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1408-1419, 2017.##[51] M. T. Hammi, B. Hammi, P. Bellot and A. Serhrouchni, "Bubbles of Trust: A decentralized blockchain-based authentication system for IoT," Computers and Security, vol. 78, pp. 126-142, 2018.##[52] N. Ghosh, S. Chandra, V. Sachidananda and Y. Elovici, "SoftAuthZ: A Context-Aware, Behavior-Based Authorization Framework for Home IoT," IEEE Internet of Things Journal, vol. 6, no. 6, pp. 10773-10785, 2019.##[53] S. Janbabaei, H. Gharaee and N. Mohammadzadeh, "The Lightweight Authentication Scheme with Capabilities of Anonymity and Trust in Internet of Things (IoT)," Signal and Data Processing, vol. 15, no. 4, pp. 111-122, 2019.##[54] L. Jun, S. Zhiqi and M. Chunyan, "Using Blockchain Technology to Build Trust in Sharing LoRaWAN IoT," in Proceedings of the 2nd International Conference on Crowd Science and Engineering, 2017.##[55] B. Wen, Z. Luo and Y. Wen, "Evidence and Trust: IoT Collaborative Security Mechanism," in 2018 Eighth International Conference on Information Science and Technology (ICIST), 2018.##[56] M. Zhaofeng, W. Lingyun, W. Xiaochang, W. Zhen and Z. Weizhe, "Blockchain-Enabled Decentralized Trust Management and Secure Usage Control of IoT Big Data," IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4000-4015, 2019.##[57] S. Guo, Y. Dai, S. Xu, X. Qiu and F. Qi, "Trusted cloud-edge network resource management: Drl-driven service function chain orchestration for IoT," IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6010-6022, 2020.##[58] B. Jafarian, N. Yazdani and M. Sayad Haghighi, "Discrimination-aware trust management for social internet of things," Computer Networks, vol. 178, p. 107254, 2020.##[59] B. Fenye and R. Chen, "Trust management for the internet of things and its application to service composition," in World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2012 IEEE International Symposium on a, 2012.##[60] L. Qinghua, S. Zhu and G. Cao, "Routing in socially selfish delay tolerant networks," in NFOCOM, 2010 Proceedings IEEE, 2010.##[61] L. Atzori, A. Iera and G. Morabito, "SIoT: Giving a Social Structure to the Internet of Things," EEE Communications Letters, vol. 15, no. 11, pp. 1193 - 1195, 13 10 2011.##[62] Y.L. Sun, Z. Han, W. Yu and K.J.R. Liu, "A trust evaluation framework in distributed networks: Vulnerability analysis and defense against attacks," in INFOCOM 2006. 25th IEEE International Conference on Computer Communications, 2006.##[63] F. Bao, I. Chen and J. Guo, "Scalable, adaptive and survivable trust management for community of interest based Internet of Things systems," in Autonomous Decentralized Systems (ISADS), 2013 IEEE Eleventh International Symposium on, 2013.##[64] Z. Zibin, Y. Zhang and M. R. Lyu, "Investigating QoS of real-world web services," IEEE Transactions on Services Computing, vol. 1, pp. 32-39, Jan 2014.##[65] X. Li and L. Liu, "Peertrust: Supporting reputation-based trust for peer-to-peer electronic communities," IEEE transactions on Knowledge and Data Engineering, vol. 7, Jul 2004.##[66] K. Sepandar D, M. T. Schlosser and H. G. Molina, "The eigentrust algorithm for reputation management in p2p networks," in Proceedings of the 12th international conference on World Wide Web, 2003.##[1] J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, "Internet of Things (IoT): A vision, architectural elements, and future directions," Future Generation Comput.Syst., vol. 29, pp. 1645-1660,9, 2013.##[2] H. Sundmaeker, P. Guillemin, P. Friess and S. Woelfflé, "Vision and challenges for realising the Internet of Things," in Cluster of European Research Projects on the Internet of Things-CERP IoT, 2010.##[3] X. Li, R. Lu, X. Liang, X. Shen, J. Chen and X. Lin, "Smart community: an internet of things application," IEEE Communications Magazine, vol. 49, no. 11, pp. 68-75, 2011.##[4] M. K. Geir, "Reflections on trust in devices: an informal survey of human trust in an internet-of-things context," Wireless Personal Communications, vol. 61, no. 3, pp. 495-510, 2011.##[5] T. Eder, D. Nachtmann and D. Schreckling, "Trust and Reputation in the Internet of Things," Universit ̈at Passau, Tech. Rep., 2013.##[6] Z. Yan, P. Zhang and A. V. Vasilakos, "A survey on trust management for Internet of Things," Journal of Network and Computer Applications , vol. 42, pp. 120-134, June 2014.##[7] 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.##[8] V. Suryani and and others, "A survey on trust in Internet of Things," in Information Technology and Electrical Engineering (ICITEE), 2016 8th International Conference on, 2016.##[9] W. Abdelghani, C. A. Zayani, I. Amous and F. Sèdes, "Trust management in social internet of things: a survey," in Conference on e-Business, e-Services and e-Society, 2016.##[10] C. Perera, A. Zaslavsky, P. Christen and D. Georgakopoulos, "Context aware computing for the internet of things: A survey," vol. 16, no. 1, pp. 414-454, 2014.##[11] J. Guo, R. Chen and J. J. Tsai, "A survey of trust computation models for service management in internet of things systems," Computer Communications, vol. 97, pp. 1-14, 2017.##[12] Elizabeth J.Chang, Farookh Khadeer Hussain and Tharam S. Dillon , "Fuzzy Nature of Trust and Dynamic Trust Modeling in Service Oriented Environments," in Proceedings of the 2005 workshop on Secure web services. ACM, 2005.##[13] M. Blaze, J. Feigenbaum and J. Lacy, "Decentralized trust management," in Security and Privacy, 1996. Proceedings, 1996 IEEE Symposium on, 1996.##[14] A. Josang, C. Keser and T. Dimitrakos, "Can we manage trust?," in iTrust, 2005.##[15] S. Chaeikar, M. Alizadeh, M. Tadayon and A. Jolfaei, "An intelligent cryptographic key management model for secure communications in distributed industrial intelligent systems," International Journal of Intelligent Systems, DOI:10.1002/int.22435, 25 April 2021.##[16] D.Chen, G.Chang, D.Sun, J.Li, J.Jia and X.Wang, "TRM-IoT:A trust management model based on fuzzy reputation for Internet of things," Comput. Sci. Inf. Syst., vol. 8, pp. 1207-1228, 2011.##[17] F. Bao and I.-R. Chen, "Dynamic trust management for internet of things applications," in Proceedings of the 2012 international workshop on Self-aware internet of things, San Jose, California, USA, 2012.##[18] S. Yosra Ben, O. Alexis, Z. Djamal and L. Maryline, "Trust management system design for the Internet of Things: A context-aware and multi-service approach," Computer and Security, vol. 39, pp. 351-365, 2013.##[19] C. Ray, F. Bao and J. Guo, "Trust management for SOA-based IoT and its application to service composition," IEEE Transactions on Services Computing, vol. 9, no. 3, pp. 482-495, 2016.##[20] H. Al-Hamadi, R. Chen and J.-H. Cho, "Trust management of smart service communities," IEEE Access, vol. 7, pp. 26362-26378, 2019.##[21] M. Nitti, R. Gira, L. Atzori, A. Iera and G. Morabito, "A subjective model for trustworthiness evaluation in the social Internet of Things," in Personal Indoor and Mobile Radio Communications (PIMRC), 2012 IEEE 23rd International Symposium on, 2012.##[22] B. Fenye, R. Chen and J. Guo, "Scalable, adaptive and survivable trust management for community of interest based internet of things systems," in Autonomous Decentralized Systems (ISADS), 2013 IEEE Eleventh International Symposium on , 2013.##[23] M. Nitti, R. Girau and L. Atzori, "Trustworthiness Management in the Social Internet of Things," Knowledge and Data Engineering, IEEE Transactions on, vol. 26, pp. 1253-1266, 2014.##[24] Chen, Ray, Fenye Bao and Jia Guo, "Trust-based service management for social internet of things systems," IEEE transactions on dependable and secure computing, vol. 13, no. 6, pp. 684-696, 2016.##[25] R. Sherif Emad Abdel, A. Ayman and E. Mohamad Abou, "CBSTM-IoT: Context-based social trust model for the Internet of Things," in Selected Topics in Mobile & Wireless Networking (MoWNeT), 2016 International Conference on, 2016.##[26] D. Airehrour, J. Gutierrez and S. K. Ray, "SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things," Future Generation Computer Systems, vol. 93, pp. 860-876, 2019.##[27] A. Tandon and P. Srivastava, "Trust-based Enhanced Secure Routing against Rank and Sybil Attacks in IoT," in 2019 Twelfth International Conference on Contemporary Computing (IC3), 2019.##[28] X. Anita, L. M. Martin and M. A. Bhagyaveni, "Two-way acknowledgment-based trust framework for wireless sensor networks," International Journal of Distributed Sensor Networks, vol. 9, no. 5, p. 952905, 2013.##[29] S. Bhattacharya and A. Ghosh, "Entropy Trust based Approach against IP spoofing attacks in network," International Journal of Computer Applications, vol. 67, no. 23, 2013.##[30] R. A. Raje and A. V. Sakhare, "Routing in wireless sensor network using fuzzy based trust model," in 2014 Fourth International Conference on Communication Systems and Network Technologies, 2014.##[31] Y. Gao and W. Liu, "BeTrust: a dynamic trust model based on bayesian inference and tsallis entropy for medical sensor networks," Journal of Sensors, vol. 2014, 2014.##[32] T. Yang, X. Xiangyang, L. Peng, L. Tonghui and P. Leina, "A secure routing of wireless sensor networks based on trust evaluation model," Procedia computer science, vol. 131, pp. 1156-1163, 2018.##[33] O. AlFarraj, A. AlZubi and A. Tolba, "Trust-based neighbor selection using activation function for secure routing in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, pp. 1-11, 2018.##[34] M. Selvi, K. Thangaramya, S. Ganapathy, K. Kulothunganx, H. K. Nehemiah and A. Kannan, "An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks," Wireless Personal Communications, vol. 105, no. 4, pp. 1475-1490, 2019.##[35] W. Fang, W. Zhang, W. Chen, Y. Liu and C. Tang, "TMSRS: trust management-based secure routing scheme in industrial wireless sensor network with fog computing," Wireless Networks, pp. 1-14, 2019.##[36] A. Alnasser and H. Sun, "A fuzzy logic trust model for secure routing in smart grid networks," IEEE access, vol. 5, pp. 17896-17903, 2017.##[37] Sh. M. Antesar, P. Keshav Dahal, S. K. Bista and I. U. 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استخراج ویژگی مبتنی بر تفکیک‌پذیری بیشتر رده‌ها با استفاده از طبقه‌‌‌بندهای کمکی Feature extraction based on the more resolution of the classes using auxiliary classifiers 1 1 طبقه‌­بندی یک روش یادگیری ماشین است که برای پیش­‌گویی برچسب یک نمونه خاص با کمترین خطا استفاده می‌­شود. در این مقاله، از توانایی پیش‌­گویی برچسب به‌کمک طبقه‌­بند برای ایجاد ویژگی جدید استفاده شده­ است. امروزه روش‌­های استخراج ویژگی زیادی مانند PCA و ICA وجود دارند که در زمینه­‌های مختلف به­‌طور وسیع استفاده می‌شوند و از هزینه بالای انتقال به فضای دیگر رنج می‌برند. در روش پیشنهادی، هدف این است که به‌کمک ویژگی جدید، قدرت تفکیک‌­پذیری بیشتری بین رده‌­های مختلف ایجاد شود و داده‌های درون رده‌­ها به یکدیگر نزدیک‌­تر و تمایز بیشتری بین داده‌های رده‌­های مختلف به وجود آید تا کارایی طبقه‌­بندها افزایش یابد. ابتدا به‌کمک یک یا چند طبقه‌­بند، برچسب پیشنهادی برای مجموعه‌­داده­ اولیه تعیین و به‌عنوان ویژگی جدید به مجموعه‌داده اولیه اضافه می­‌شود. ایجاد مدل به‌کمک مجموعه‌داده جدید انجام می­‌شود. ویژگی جدید برای مجموعه‌داده آموزش و آزمون به‌صورت جداگانه به‌دست آورده می­‌شود. آزمایش‌­ها بر روی بیست مجموعه­‌داده استاندارد انجام شده و نتایج روش پیشنهادی با نتایج دو روش بیان‌شده در کارهای مرتبط نیز مقایسه شده است. نتایج نشان می­‌دهد که روش پیشنهادی به‌طور قابل توجهی باعث بهبود دقت رده‌بندی شده است. در بخش دوم آزمایش‌ها، برای بررسی میزان مؤثر‌بودن روش پیشنهادی، قدرت تفکیک‌­پذیری ویژگی جدید بر اساس دو معیار بهره اطلاعاتی و شاخص جینی بررسی شده ­است. نتایج نشان می­‌دهد که ویژگی به‌دست‌‌آمده در روش پیشنهادی در بیشتر موارد دارای بهره اطلاعاتی بیشتر و شاخص جینی کمتری است، زیرا بی‌­نظمی کمتری دارد. در ادامه، جهت جلوگیری از افزایش ابعاد داده، ویژگی استخراج‌شده با بیش‌ترین بار اطلاعاتی، جایگزین ویژگی با کم‌ترین بار اطلاعاتی شده­ است. نتایج این مرحله نیز بیان‌گر افزایش میزان کارایی است. 2 Classification is a machine learning method used to predict a particular sample’s label with the least error. The present study was conducted using label prediction ability with the help of a classifier to create a new feature. Today, there are several feature-extraction methods like principal component analysis (PCA) and independent component analysis (ICA) that are widely used in different fields; however, they all suffer from the high cost of transferring to another space. The purpose of the proposed method was to create a higher distinction between various classes using the new feature in a way that, make the data in the classes closer to each other. As a result, for increasing the efficiency of classifiers, more differentiation is created between the data of various classes. Firstly, the suggested labels for the primary data set were determined using one or more classifiers and added to the primary data set as a new feature. The model was created using a new data set. The new feature for training and testing data sets was provided separately. The tests were performed on 20 standard data sets and the results of the proposed method were compared with those of the two methods described in the related studies. The outputs indicated that the proposed method has significantly improved the classification accuracy. In the second part of the tests, the resolution of the new feature was examined according to two criteria, namely Information Gain and Gini Index, for examining the effectiveness of the proposed method. The results showed that the feature obtained in the proposed method has higher Information Gain and lower Gini Index in most cases, as it has less irregularity. To prevent the increase in data dimensions, the feature with the least Information Gain was replaced with the feature extracted with the most Information Gain. The results of this step showed an increase in efficiency as well. 29 44 2020/03/102019/03/14 1397/12/23 2021/03/82020/08/18 1399/5/28 حمیدرضا غفاری Hamid Reza Ghaffari دانشگاه آزاد اسلامی فردوس ایران hghaffaripaper@ferdowsiau.ac.ir آتنا جلالی مجاهد Atena Jalali Mojahed دانشگاه آزاد اسلامی فردوس ایران st.ajalalia@ferdowsiau.ac.ir Feature extraction classification information gain Gini index استخراج ویژگی طبقه‌بندی بهره اطلاعاتی شاخص جینی [1] S. J.Russell and P. Norvig, Artificial intelligence: a modern approach. Malaysia, Pearson Education Limited, 2016.##[2] R. P. Duin and D. M. J. Tax, "Statistical pattern recognition", In Handbook of Pattern Recognition and Computer Vision, pp. 3-24, 2005.##[3] Guyon, S. Gunn, M.Nikravesh, L. A. Zadeh, and editors, Feature extraction: foundations and applications, Vol. 207. Springer, 2008.##[4] T. Cover and P. Hart, "Nearest neighbor pattern classification," IEEE transactions on information theory, vol. 13, no. 1, pp. 21-27, 1967.##[5] C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995.##[6] J. Showe-Taylor and N. Christianini, Support vector machines and other kernel-based learning methods, 2000.##[7] J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014.##[8] B. W. Silverman and M. C. Jones, "An important contribution to nonparametric discriminant analysis and density estimation," International statistical review/revue Internationale de statistique, pp. 233-238, 1989.##[9] R. C.Barros, M. P. Basgalupp, A. C. De Carvalho, and A. A. Freitas, "A survey of evolutionary algorithms for decision-tree induction," IEEETransactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no 3, pp. 291-312, 2012.##[10] L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.##[11] M.Woźniak, M.GrañaandE. Corchado, "A survey of multiple classifier systems as hybrid systems," Information Fusion, vol. 16, pp. 3-17, 2014.##[12] H. Hotelling, "Analysis of a Complex of Statistical Variables into Principal Components," Journal of Educational Psychology, vol. 24, no. 6, pp. 417-441, 1933.##[13] P. Comon, "Independent component analysis, a new concept?" Signal Processing, vo. 36, no. 3, pp. 287-314, 1994.##[14] K. Fukunaga, "Introduction to Statistical Pattern Recognition," San Diego: Academic Press Inc, 1990.##[15] C. F. Tsai and C. Y Lin, "A triangle area based nearest neighbors approach to intrusion detection," Pattern recognition. vol. 43, no. 1, pp. 222-229, 2010.##[16] W. C.Lin, S. W. Ke and C. F. Tsai, "CANN: An intrusion detection system based on combining cluster centers and nearest neighbors", Knowledge-based systems, no. 78, pp. 13-21, 2015.##[17] X. Wang, C. Zhang and K. Zheng, "Intrusion detection algorithm based on density, cluster centers, and nearest neighbors", China Communications, vol. 13, no. 7, pp. 24-31, 2016.##[18] A. Asuncion and D. J. Newman, UCI Machine Learning Repository, University of California, 2007. https://archive.ics.uci.edu/ml/index.php##[19] C. W. Hsua and C. J. Lin, "A comparison of methods for multiclass support vector machines", IEEE transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.##[20] T. T. Wong, "Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation", Pattern Recognition, vol. 48, no. 9, pp. 2839-2846, 2015.##[21] J. T. Townsend, "Theoretical analysis of an alphabetic confusion matrix", Perception & Psychophysics, vol. 9, no. 1, pp. 40-50, 1971.##[22] M. Dash and H. Liu, "Consistency-based search in feature selection", Artificial intelligence, vol. 151, no. 1-2, pp. 155-176, 2003.##[23] J. R. Quinlan, "Induction of decision trees", Machine learning, vol. 1, no. 1, pp. 81-106, 1986.##[24] L. Breiman, "Classification and regression trees".Routledge, 2017.##[25] L. E. Raileanu and K. Stoffel, "Theoretical comparison between the gini index and information gain criteria", Annals of Mathematics and Artificial Intelligence, vol. 41, no. 1, pp.77-93, 2004.##[1] S. J.Russell and P. Norvig, Artificial intelligence: a modern approach. Malaysia, Pearson Education Limited, 2016.##[2] R. P. Duin and D. M. J. Tax, "Statistical pattern recognition", In Handbook of Pattern Recognition and Computer Vision, pp. 3-24, 2005.##[3] Guyon, S. Gunn, M.Nikravesh, L. A. Zadeh, and editors, Feature extraction: foundations and applications, Vol. 207. Springer, 2008.##[4] T. Cover and P. Hart, "Nearest neighbor pattern classification," IEEE transactions on information theory, vol. 13, no. 1, pp. 21-27, 1967.##[5] C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995.##[6] J. Showe-Taylor and N. Christianini, Support vector machines and other kernel-based learning methods, 2000.##[7] J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014.##[8] B. W. Silverman and M. C. Jones, "An important contribution to nonparametric discriminant analysis and density estimation," International statistical review/revue Internationale de statistique, pp. 233-238, 1989.##[9] R. C.Barros, M. P. Basgalupp, A. C. De Carvalho, and A. A. Freitas, "A survey of evolutionary algorithms for decision-tree induction," IEEETransactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no 3, pp. 291-312, 2012.##[10] L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.##[11] M.Woźniak, M.GrañaandE. Corchado, "A survey of multiple classifier systems as hybrid systems," Information Fusion, vol. 16, pp. 3-17, 2014.##[12] H. Hotelling, "Analysis of a Complex of Statistical Variables into Principal Components," Journal of Educational Psychology, vol. 24, no. 6, pp. 417-441, 1933.##[13] P. Comon, "Independent component analysis, a new concept?" Signal Processing, vo. 36, no. 3, pp. 287-314, 1994.##[14] K. Fukunaga, "Introduction to Statistical Pattern Recognition," San Diego: Academic Press Inc, 1990.##[15] C. F. Tsai and C. Y Lin, "A triangle area based nearest neighbors approach to intrusion detection," Pattern recognition. vol. 43, no. 1, pp. 222-229, 2010.##[16] W. C.Lin, S. W. Ke and C. F. Tsai, "CANN: An intrusion detection system based on combining cluster centers and nearest neighbors", Knowledge-based systems, no. 78, pp. 13-21, 2015.##[17] X. Wang, C. Zhang and K. Zheng, "Intrusion detection algorithm based on density, cluster centers, and nearest neighbors", China Communications, vol. 13, no. 7, pp. 24-31, 2016.##[18] A. Asuncion and D. J. Newman, UCI Machine Learning Repository, University of California, 2007. https://archive.ics.uci.edu/ml/index.php##[19] C. W. Hsua and C. J. Lin, "A comparison of methods for multiclass support vector machines", IEEE transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.##[20] T. T. Wong, "Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation", Pattern Recognition, vol. 48, no. 9, pp. 2839-2846, 2015.##[21] J. T. Townsend, "Theoretical analysis of an alphabetic confusion matrix", Perception & Psychophysics, vol. 9, no. 1, pp. 40-50, 1971.##[22] M. Dash and H. Liu, "Consistency-based search in feature selection", Artificial intelligence, vol. 151, no. 1-2, pp. 155-176, 2003.##[23] J. R. Quinlan, "Induction of decision trees", Machine learning, vol. 1, no. 1, pp. 81-106, 1986.##[24] L. Breiman, "Classification and regression trees".Routledge, 2017.##[25] L. E. Raileanu and K. Stoffel, "Theoretical comparison between the gini index and information gain criteria", Annals of Mathematics and Artificial Intelligence, vol. 41, no. 1, pp.77-93, 2004.## ##

جهت‌یابی زمان‌حقیقی منابع صوت زیر آب با استفاده از واحد پردازنده گرافیکی Real-Time DOA Estimation of Underwater Sound Sources Using GPU 1 1 جهت­‌یابی منابع صوت به‌کمک روش‌­های مبتنی بر آرایه فازی، اهمیت فراوانی در حوزه‌­های مختلف از جمله سونار، بینایی ربات و تشخیص عیوب مکانیکی دارد. روش­‌های شکل­‌دهی‌­پرتو وفقی، از جمله الگوریتم کمینه‌­واریانس بدون‌­اعوجاج از قدرت تفکیک بالایی نسبت به روش‌­های غیروفقی برخوردار هستند؛ اما این برتری در ازای پیچیدگی محاسباتی این الگوریتم­‌ها به‌دست آمده است. این مسأله باعث می‌­شود در کاربردهایی که نیاز به جهت­‌یابی زمان­‌حقیقی منبع صوت دارند، به‌ندرت از این الگوریتم‌­ها استفاده شود. از سوی دیگر، یک ویژگی­ مهم روش‌­های شکل‌­دهی‌­پرتو وفقی از جمله کمینه‌واریانس، پتانسیل بالای این الگوریتم‌­ها برای موازی­‌سازی است. هدف این مقاله، پیاده­‌سازی موازی الگوریتم کمینه‌­واریانس با به کارگیری واحد پردازنده گرافیکی (GPU) به جای واحد پردازنده مرکزی (CPU)، به‌منظور افزایش سرعت اجرا و رسیدن به حالت زمان‌­حقیقی است. برای دست‌­یابی به این هدف از مدل برنامه­‌نویسی کودا  برای پیاده‌­سازی الگوریتم بر روی پردازنده گرافیکی استفاده شده است. به‌منظور بررسی عملکرد پیاده‌­سازی موازی الگوریتم کمینه­‌واریانس، دو مدل GPU متفاوت و همچنین CPU ­به‌کاربرده شده است. صحت عملکرد پیاده­‌سازی­‌های مختلف در این مقاله به‌وسیله داده‌­های واقعی سونار و همچنین داده­‌های شبیه­‌سازی تأیید شد. نتایج نشان می­‌دهد که می‌­توان با استفاده از یک آرایه 64 حس‌گره، جهت منابع صوت زیر آب را با استفاده از الگوریتم کمینه­‌واریانس به‌صورت زمان­‌حقیقی و با قدرت تفکیک بالا تخمین زد.   2 Direction of Arrival (DOA) estimation of sound sources using phased array-based methods has a lot of importance in various fields, including sonar, robot vision, and mechanical defect detection. Adaptive beamforming methods, such as the MVDR (Minimum Variance Distortionless Response) algorithm, have high resolution compared to non-adaptive methods (Delay and Sum algorithm); but this advantage is achieved in return for the computational complexity of these algorithms. This makes it hard to use these algorithms in applications that require real-time sound source DOA estimation. On the other hand, an important feature of the adaptive beamforming methods including MVDR is the high potential of these algorithms for parallelization. The purpose of this paper is the parallel implementation of the MVDR algorithm by employing Graphical Processor Unit (GPU) instead of Central Processor Unit (CPU) to increase the execution speed and achieve the real-time mode. For this purpose, the CUDA (Compute Unified Device Architecture) programming model has been used to implement the algorithm on the GPU. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers to use a CUDA-enabled GPU for parallel processing. In order to investigate the performance of parallel implementation of the MVDR algorithm, two different GPUs, as well as CPUs, have been used. The performance validity of various implementations in this paper was confirmed by real sonar data as well as simulation data. The results show that using an array of 64 sensors, it is possible to estimate the DOA of underwater sound sources in real-time mode and with high resolution using the MVDR algorithm. 45 56 2020/03/102019/03/142019/03/31 1398/1/11 2021/03/82020/08/182020/04/21 1399/2/2 احسان ایمانی فر Ehsan Imani far دانشکده مهندسی پزشکی-بیوالکتریک، دانشگاه صنعتی امیرکبیر ایران ehsanimanif@gmail.com امیر اخوان Amir Akhavan دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی اصفهان ایران aakhavan@cc.iut.ac.ir علی اصغر آبنیکی ali asghar abniki مرکز پژوهشی مهندسی دریا، دانشکده مهندسی مکانیک، دانشگاه صنعتی شریف ایران aliasghar_abniki@yahoo.com DOA estimation of sound sources MVDR algorithm Parallel processing GPU CUDA جهت‌یابی منابع صوت الگوریتم کمینه‌واریانس پردازش موازی واحد پردازنده گرافیکی مدل برنامه‌نویسی کودا [1] S. H. Yazdani, H. R. Abutalebi, "Adaptive and Smart Beamforming in Ad-hoc Microphone Arrays by Clustering and Ranking of the Microphones," JSDP. 2015; 12 (3) :57-68.##[2] S. Majidian, F. Haddadi, "Direction of Arrival (DOA) Estimation Using Kronecker Subspace," JSDP. 2018; 15 (1) :29-40.##[3] J. Benesty, J. Chen, and Y. Huang, "Conventional Beamforming Techniques," in Microphone Array Signal Processing, vol. 1, Berlin, Germany: Springer, 2008.##[4] F. Vignon, and M. R. Burcher, "Capon beamforming in medical ultrasound imaging with focused beams," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 55, no. 3, pp. 619-628, 2008.##[5] F. Yan, M. Jin, and X. Qiao, "Low-Complexity DOA Estimation Based on Compressed MUSIC and Its Performance Analysis," IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 1915-1930, 2013.##[6] X. Wu, W. Zhu, and J. Yan, "Direction of Arrival Estimation for Off-Grid Signals Based on Sparse Bayesian Learning," IEEE Sensors Journal, vol. 16, no. 7, 2016.##[7] J. Dai, and H. Cheung, "Sparse Bayesian Learning Approach for Outlier-Resistant Direction-of-Arrival Estimation," IEEE Transactions on Signal Processing, vol. 66, no. 3, pp. 744-756, 2018.##[8] J. Capon, "High-resolution frequency-wavenumber spectrum analysis," Proceedings of the IEEE, vol. 57, no. 8, August 1969.##[9] J. I. Buskenes, J. P. Asen, C. I. C. Nilsen, and A. Austeng, "An Optimized GPU Implementation of the MVDR Beamformer for Active Sonar Imaging," IEEE J. Ocean. Eng., vol. 40, no. 2, pp. 441-451, 2014.##[10] M. Sasso, and C. Cohen-Bacrie, "Medical Ultrasound Imaging Using the Fully Adaptive Beamformer," Philips Research, no. 4, pp. 489-492, 2005.##[11] H. Sun, M. Lu, and T. D. Abhayapala, "Ultrasound imaging using modal domain frequency smoothed MVDR beamforming," in 2017, 11th Int. Conf. Signal Process. Commun. Syst. ICSPCS 2017 - Proc., no. 1, pp. 1-5, Janua 2017.##[12] I. K. Holfort, F. Gran, and J. A. Jøensen, "Broadband minimum variance beamforming for ultrasound imaging," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, no. 2. pp. 314-325, 2009.##[13] J. P. Åsen, J. I. Buskenes, C. I. C. Nilsen, A. Austeng, and S. Holm, "Implementing capon beamforming on a GPU for real-time cardiac ultrasound imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 61, no. 1, pp. 76-85, 2014.##[14] Y. S. Yoon, M. G. Amin, and F. Ahmad, "MVDR beamforming for through-the-wall radar imaging," IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 347-366, 2011.##[15] C. I. Nilsen and I. Hafizovic, "Beamspace adaptive beamforming for ultrasound imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 56, no. 10, pp. 2187-2197, 2009.##[16] A. M. Deylami, and B. M. Asl, "A Fast and Robust Beamspace Adaptive Beamformer for Medical Ultrasound Imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2017.##[17] J. F. Synnevåg, S. Holm, and A. Austeng, "A Low Complexity Data-dependent Beamformer," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 57, no. 2, 2010.##[18] K. Kim, S. Park, J. Kim, S. Park, and M. Bae, "A Fast Minimum Variance Beamforming Method Using Principal Component Analysis," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 61, no. 6, pp. 930-945, 2014.##[19] A. Jakobsson, S. L. Marple, Jr., and P. Stoica, "Computationally Efficient Two-Dimensional Capon Spectrum Analysis," IEEE Trans. Signal Process., vol. 48, no. 9, pp. 2651-2661, 2000.##[20] Q. Huang, L. Zhang, and Y. Fang, "Performance analysis of Low-complexity MVDR beamformer in spherical harmonics domain," Signal Processing, vol. 153, pp. 153-163, 2018.##[21] B. M. Asl and A. Mahloojifar, "A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 59, no. 4, pp. 660-667, 2012.##[22] H. Sadeghi, A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks," JSDP. 2018; 14 (4) :19-30.##[23] D. B. Kirk, and W. W. Hwu, Programming Massively Parallel Processors _ A Hands-On Approach. Burlington, MA: Elsevier, 2010.##[24] R. Paridar, M. Mozaffarzadeh, M. Nasiriavanaki, and M. Orooji, "Double Minimum Variance Beamforming Method to Enhance Photoacoustic Imaging," Journal of the Optical Society of America A, pp. 1-9, 2018.##[25] A. Hakam, R. M. Shubair, and E. Salahat, "Enhanced DOA Estimation Algorithms Using MVDR and MUSIC," in 2013 International Conference on Current Trends in Information Technology (CTIT), Dubai, United Arab Emirates, 2013.##[26] Y. Fathi, A. Mahloojifar, and B. M. Asl, "Real-time implementation of adaptive beamformer in ultrasound imaging using parallel processing with GPU," Journal of Iran Audio Engineering Society, vol. 1, no. 1, 2013.##[27] Nvidia, "cuBLAS," docs.nvidia.com, Oct. 30, 2018. [Online]. Available:https://docs.nvidia.com/cuda/cublas/index.html. [Accessed: Sept. 19, 2018].##[1] یزدانی سیدحمید، ابوطالبی حمیدرضا. شکل‌دهی وفقی و هوشمند پرتو در آرایه‌های میکروفونی Ad-hoc با استفاده از خوشه‌بندی و رتبه‌بندی میکروفون‌ها. پردازش علائم و داده‌ها. ۱۳۹۴; ۱۲ (۳) :۵۷-۶۸##[1] S. H. Yazdani, H. R. Abutalebi, "Adaptive and Smart Beamforming in Ad-hoc Microphone Arrays by Clustering and Ranking of the Microphones," JSDP. 2015; 12 (3) :57-68.##[2] مجیدیان سینا، حدادی فرزان. تخمین جهت منابع با استفاده از زیرفضای کرونکر. پردازش علائم و داده‌ها. ۱۳۹۷; ۱۵ (۱) :۲۹-۴۰##[2] S. Majidian, F. Haddadi, "Direction of Arrival (DOA) Estimation Using Kronecker Subspace," JSDP. 2018; 15 (1) :29-40.##[3] J. Benesty, J. Chen, and Y. Huang, "Conventional Beamforming Techniques," in Microphone Array Signal Processing, vol. 1, Berlin, Germany: Springer, 2008.##[4] F. Vignon, and M. R. Burcher, "Capon beamforming in medical ultrasound imaging with focused beams," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 55, no. 3, pp. 619-628, 2008.##[5] F. Yan, M. Jin, and X. Qiao, "Low-Complexity DOA Estimation Based on Compressed MUSIC and Its Performance Analysis," IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 1915-1930, 2013.##[6] X. Wu, W. Zhu, and J. Yan, "Direction of Arrival Estimation for Off-Grid Signals Based on Sparse Bayesian Learning," IEEE Sensors Journal, vol. 16, no. 7, 2016.##[7] J. Dai, and H. Cheung, "Sparse Bayesian Learning Approach for Outlier-Resistant Direction-of-Arrival Estimation," IEEE Transactions on Signal Processing, vol. 66, no. 3, pp. 744-756, 2018.##[8] J. Capon, "High-resolution frequency-wavenumber spectrum analysis," Proceedings of the IEEE, vol. 57, no. 8, August 1969.##[9] J. I. Buskenes, J. P. Asen, C. I. C. Nilsen, and A. Austeng, "An Optimized GPU Implementation of the MVDR Beamformer for Active Sonar Imaging," IEEE J. Ocean. Eng., vol. 40, no. 2, pp. 441-451, 2014.##[10] M. Sasso, and C. Cohen-Bacrie, "Medical Ultrasound Imaging Using the Fully Adaptive Beamformer," Philips Research, no. 4, pp. 489-492, 2005.##[11] H. Sun, M. Lu, and T. D. Abhayapala, "Ultrasound imaging using modal domain frequency smoothed MVDR beamforming," in 2017, 11th Int. Conf. Signal Process. Commun. Syst. ICSPCS 2017 - Proc., no. 1, pp. 1-5, Janua 2017.##[12] I. K. Holfort, F. Gran, and J. A. Jøensen, "Broadband minimum variance beamforming for ultrasound imaging," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, no. 2. pp. 314-325, 2009.##[13] J. P. Åsen, J. I. Buskenes, C. I. C. Nilsen, A. Austeng, and S. Holm, "Implementing capon beamforming on a GPU for real-time cardiac ultrasound imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 61, no. 1, pp. 76-85, 2014.##[14] Y. S. Yoon, M. G. Amin, and F. Ahmad, "MVDR beamforming for through-the-wall radar imaging," IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 347-366, 2011.##[15] C. I. Nilsen and I. Hafizovic, "Beamspace adaptive beamforming for ultrasound imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 56, no. 10, pp. 2187-2197, 2009.##[16] A. M. Deylami, and B. M. Asl, "A Fast and Robust Beamspace Adaptive Beamformer for Medical Ultrasound Imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2017.##[17] J. F. Synnevåg, S. Holm, and A. Austeng, "A Low Complexity Data-dependent Beamformer," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 57, no. 2, 2010.##[18] K. Kim, S. Park, J. Kim, S. Park, and M. Bae, "A Fast Minimum Variance Beamforming Method Using Principal Component Analysis," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 61, no. 6, pp. 930-945, 2014.##[19] A. Jakobsson, S. L. Marple, Jr., and P. Stoica, "Computationally Efficient Two-Dimensional Capon Spectrum Analysis," IEEE Trans. Signal Process., vol. 48, no. 9, pp. 2651-2661, 2000.##[20] Q. Huang, L. Zhang, and Y. Fang, "Performance analysis of Low-complexity MVDR beamformer in spherical harmonics domain," Signal Processing, vol. 153, pp. 153-163, 2018.##[21] B. M. Asl and A. Mahloojifar, "A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 59, no. 4, pp. 660-667, 2012.##[22] صادقی حامد، اخوان بی‌تقصیر امیر. آشکارسازی سیگنال بر اساس پردازش موازی مبتنی بر جی‌پی‌یو در شبکه‌های حس‌گری صوتی دارای زیرساخت. پردازش علائم و داده‌ها. ۱۳۹۶; ۱۴ (۴) :۱۹-۳۰##[22] H. Sadeghi, A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks," JSDP. 2018; 14 (4) :19-30.##[23] D. B. Kirk, and W. W. Hwu, Programming Massively Parallel Processors _ A Hands-On Approach. Burlington, MA: Elsevier, 2010.##[24] R. Paridar, M. Mozaffarzadeh, M. Nasiriavanaki, and M. Orooji, "Double Minimum Variance Beamforming Method to Enhance Photoacoustic Imaging," Journal of the Optical Society of America A, pp. 1-9, 2018.##[25] A. Hakam, R. M. Shubair, and E. Salahat, "Enhanced DOA Estimation Algorithms Using MVDR and MUSIC," in 2013 International Conference on Current Trends in Information Technology (CTIT), Dubai, United Arab Emirates, 2013.##[26] فتحی. یاسر، محلوجی‌فر. علی، محمدزاده اصل. بابک، "پیاده‌سازی بلادرنگ شکل‌دهی‌پرتو وفقی در تصویربرداری فراصدای پزشکی با استفاده از پردازش موازی به کمک واحدهای پردازنده ترسیمی (جی پی یو)"، مجله انجمن مهندسی صوتیات ایران، جلد 1، شماره 1، 1392.##[26] Y. Fathi, A. Mahloojifar, and B. M. Asl, "Real-time implementation of adaptive beamformer in ultrasound imaging using parallel processing with GPU," Journal of Iran Audio Engineering Society, vol. 1, no. 1, 2013.##[27] Nvidia, "cuBLAS," docs.nvidia.com, Oct. 30, 2018. [Online]. Available:https://docs.nvidia.com/cuda/cublas/index.html. [Accessed: Sept. 19, 2018].## ##

یافتن گره‌های پرنفوذ جهت انتشار رفتار اعتماد در شبکه‌های اجتماعی Identifying Influential Nodes to Diffuse the Trusting Behavior in Social Networks 1 1 اعتماد یکی از مهم‌ترین موضوعات مطرح‌شده در حوزه شبکه‌های اجتماعی است. اغلب اوقات نحوه اعتماد همه کاربران در این شبکه‌ها به‌­صورت یکسان در نظر گرفته می‌­شود. درحالی‌که کاربران شبکه‌های اجتماعی می‌توانند نظرات متفاوتی در نحوه اعتماد به سایرین داشته باشند و ممکن است فاکتورهای مختلفی را در اعتماد به سایر اعضا در نظر بگیرند. همچنین کاربران می‌­توانند بر روی نظرات یکدیگر اثر گذاشته و باعث تغییر رفتار دیگر کاربران در اعتماد‌کردن به افراد شوند. به همین دلیل، سازوکار و شیوه تأثیرگذاری کاربران بر روی نظر و رفتار اعتماد به یکدیگر و شرایط تغییر حالات رفتاری، از اهمیت زیادی برخوردار خواهد بود. هدف ما در این مقاله در مرحله نخست در‌نظر‌گرفتن رفتار متفاوت کاربران شبکه‌های اجتماعی در نحوه اعتماد‌کردن آن‌ها به سایر کاربران است. برای این منظور سه حالت رفتاری مهم در نحوه اعتماد کاربران در نظر گرفته ‌شده است. در هرکدام از این حالت‌ها ویژگی‌های مختلف رفتاری و عملکردی کاربران مبنای محاسبه اعتماد هستند که بر مبنای باور ذهنی افراد شکل می‌­گیرند. در مرحله بعدی نحوه انتشار رفتار اعتماد کاربران در شبکه اجتماعی هدف و نوآوری این مقاله است. برای این منظور، سه سناریوی مختلف برای اثرگذاری و انتشار رفتار گره‌ها در نظر گرفته­ شده است. به‌دنبال آن به بیشینه‌سازی اثر و یافتن افراد پرنفوذ جهت انتشار رفتار اعتماد در شبکه‌های اجتماعی پرداخته ‌شده است. برای این منظور بر روی ساختار شبکه اجتماعی افراد تمرکز شده و تأثیرگذارترین افراد با توجه به سناریوهای مختلف انتشار تعیین ‌شده‌اند. نتایج به‌دست‌آمده نشان‌دهنده وجود اختلاف در میزان اعتماد در هرکدام از حالت­‌های مختلف رفتاری است که دارای تطابق بیشتری با واقعیت است. با بررسی آزمایش‌­ها به این نتیجه می‌­رسیم که در مدل پیشنهادی،  انتخاب گره شروع از هر اجتماع با 14/48 درصد دارای تأثیر بهتری در بهبود رفتار، دامنه و سرعت انتشار و گره با بیش‌ترین درجه با 03/37 درصد نسبت به روش‌های متداول دارای جواب بهتری در تغییر رفتار است. 2 Trust is one of the most important cornerstones in social networks' discussions. mostly the way that users of these networks trust each other are considered identical, while these users can have different approaches and considerations in trusting others. Meanwhile, users can impress each other and change their trusting patterns in other users. As a result, the mechanism and manner of impressing opinion trust behavior and conditions of behavioral modes changing have a place of importance to be considered. The question is that, how we can consider different behavior of users and their impression in trusting others? In the first step, the main purpose of this paper is to spotlight social networks' different user behavior in trusting other users. For this purpose, the three most important behavioral modes in  users trust are considered. In each of these modes, behavioral and functional characteristics of users are the basis of calculating trust, which is based on mental beliefs of them. These modes are named as optimistic, moderate, and pessimistic trusting modes. In optimistic mode, we suppose that users think positively and consider low level of activities and signs in trusting others. Here, negative interactions have little impact on users mind. In moderate mode, we suppose that users are not as optimistic as mode A and consider all the interactions and signs when they want to trust others. Here, any negative action can destroy the trust of users and has a greater impact on users. Finally, in pessimistic mode, we suppose that users are pessimistic and hardly trust someone. In this mode, the interactions that happened more recently have more value than those happened in the past. In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined.   Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models. 57 74 2020/03/102019/03/142019/03/312019/02/4 1397/11/15 2021/03/82020/08/182020/04/212021/01/10 1399/10/21 مینا عباسپور اورنگی mina Abbaspour orangi دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر ایران Mina.Abbaspour@aut.ac.ir علیرضا هاشمی گلپایگانی Alireza Hashemi golpayegani دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر ایران Sa.Hashemi@aut.ac.ir Trusting behavior Trust diffusion Influential nodes Social networks Social network analysis رفتار اعتماد انتشار اعتماد گره‌های پرنفوذ شبکه‌های اجتماعی تحلیل شبکه‌ای [1] J. Barnes, "Class and committees in a Norwegian island parish," in Human relations, 7, no.1, 1954, pp. 39-58.##[2] J. A. Golbeck, "Computing and applying trust in web-based social networks," Doctoral dissertation, 2005.##[3] E. Uslaner, "The moral foundation of trust the moral foundations of trust * Eric M . Uslaner Department of Government and Politics University of Maryland - College Park College Park , MD 20742 Prepared for the Symposium , ' Trust in the Knowledge Society ,' University of Jyvaskyla , Jyvaskala , Finland , 20 September , 2002 and for presentation at Nuffield College , Oxford," no. October, 2017.##[4] S. Nepal, W. Sherchan and C. Paris, "Strust: a trust model for social networks", in Trust, Security, and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on. IEEE, 2011, pp. 841-846.##[5] S. A. Hashemi Golpayegani, L. Esmaeili, S. Mardani, S. M. Mutallebi, "A survey of trust in social commerce" Latest Trends of E-Systems: concept, development and applications, Apple Academic Press, Vol. 1, 2015, pp. 3-40.##[6] S. Nepal, S. K. Bista, and C. Paris, "Behavior-based propagation of trust in social networks with restricted and anonymous participation," Comput. Intell., vol. 31, no. 4, pp. 642-668, 2015.##[7] D. Centola, "The spread of behavior in an online social network experiment", in science 329, no 5996, 2010, pp. 1194-1197.##[8] D. Centola, M. Eguı, and M. W. Macy, "Cascade dynamics of complex propagation," Physica A: Statistical Mechanics and its Applications, vol. 374, pp. 449-456, 2007.##[9] D. Chen, L. Lü, M. S. Shang, Y. C. Zhang, and T. Zhou, "Identifying influential nodes in complex networks," Physica a: Statistical mechanics and its applications, vol. 391, no. 4, pp. 1777-1787, 2012.##[10] S. Ahmed and C. I. Ezeife, "Discovering influential nodes from trust network," In Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 121-128, 2013.##[11] I. G. Amnieh and M. 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یک الگوریتم آگاه از انرژی نوین جهت تشخیص مؤثر حفره پوششی در شبکه‌های حس‌گر بی‌سیم A Novel Efficient Energy-Aware Coverage Hole Detection Algorithm in Wireless Sensor Networks 1 1 مهم‌ترین مانع بر سر افزایش سطح پوشش در شبکه­‌های بی‌سیم، مصرف انرژی حس‌گرها است که باعث اتمام انرژی آن‌ها می­‌شود. این رخداد به‌عنوان یک مسأله بحرانی با نام حفره پوششی شناخته‌ می­‌شود و در آن منطقه خاصی تحت پوشش هیچ حس‌گری نیست. درنتیجه رخدادهای واقعی در آن نقاط قابل‌شناسایی و ردیابی نخواهند بود. در این راستا،‌ این پژوهش یک الگوریتم نوین مبتنی بر سامانه مدیریت اعتماد اعتبار در شبکه‌های حس‌گر بی‌سیم را جهت بیشینه‌سازی میزان پوشش‌دهی ‌شبکه با تنظیم هوشمندانه شعاع حس‌گری گره‌ها معرفی کرده است. این الگوریتم میزان حفره پوششی را با استفاده از ماشین بردار پشتیبان و ضمن مصرف کم انرژی در شبکه، به‌طور چشم‌گیری کاهش می­‌دهد. به عبارتی، با جمع‌آوری اطلاعات در بازه زمانی‌های مختلف و تعاملات بین گره‌ها، میزان رضایت‌مندی آن‌ها ارزیابی و بر این اساس پاداش یا جریمه‌ای برای آن‌ها در نظر گرفته می‌شود. ارزیابی‌ها نشان می‌دهند که طول عمر شبکه و اندازه شعاع حس‌گری گره‌ها به‌لحاظ تعداد گره‌های حس‌گر در محیطی با در‌نظر‌گرفتن شعاع متحرک گره‌ها، گره‌های متحرک و آتوماتای سلولی در مقایسه با محیطی با در‌نظر‌گرفتن شعاع ثابت گره‌ها، گره‌های متحرک و بدون آتوماتای سلولی به‌ترتیب معادل 42/12% و 4/20% بهبود یافته‌اند. 2 One of the most widely researched areas in wireless sensor networks is the issue of coverage. Coverage is directly related to the degree of quality, how and how long the sensors are able to detect predetermined targets in areas. Standard coverage for calculating network Quality of Service (QoS) greatly affects network performance. The most important factor in maximum coverage in wireless sensor networks is to consider the energy consumption constraints. Sensor nodes typically use a battery to supply power, which in most cases is not rechargeable; therefore, it is very important to carry out operations to reduce energy consumption and increase the life of the sensor node. Full network coverage is such that all areas of interest must be monitored and covered by diffused sensors. Now, if a certain area is not covered by any sensor, it is known as a cover hole and the real events in the cover points will not be recognizable and traceable. The aim of this study is to identify cover holes to reduce energy consumption in wireless sensor networks; which is applied by presenting a new model of credit management system. In this system, the sensor radius of the nodes is adjusted for maximum coverage in the network. In the simulation presented, decisions are made by gathering information at different time intervals and interactions among nodes; According to this decision, the evaluation is applied for the level of satisfaction of the nodes and based on the level of satisfaction of the nodes, rewards and fines are considered for the nodes and finally, using the support vector machine tool, cover holes in the sensor network are identified. Thus, the effect of identifying holes in reducing energy consumption, performance and quality of services provided in the network is expressed. Evaluations show that the longevity of the network and the size of the sensor radius of the nodes have improved 12.42% and 20.4%, respectively in terms of the number of sensor nodes in the environment with respect to the moving radius of the nodes, mobile nodes and cellular automata compared to the environment considering the constant radius of the nodes, mobile nodes and without automata. 75 96 2020/03/102019/03/142019/03/312019/02/42019/05/6 1398/2/16 2021/03/82020/08/182020/04/212021/01/102020/08/19 1399/5/29 مریم یوسف پور Maryam Yousefpour دانشکده مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی ایران Maryam.yousefpour@yahoo.com بهرنگ برکتین Behrang Barekatain مرکز تحقیقات کلان داده، واحد نجف‌آباد، دانشگاه آزاد اسلامی ایران behrang_barekatain@yahoo.com Coverage Cavities Wireless Network Sensing Radius Trust Management Energy Consumption Support Vector Machine حفره‌های پوششی شعاع حس‌گری شبکه بی‌سیم مدیریت اعتماد مصرف انرژی ماشین بردار پشتیبان [1] I. 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ترکیب یک روش خوشه‌بندی تجمعی و یک معیار شباهت جدید برای مدل‌سازی رفتار وراثتی بیماری‌ها Combining an Ensemble Clustering Method and a New Similarity Criterion for Modeling the Hereditary Behavior of Diseases 1 1 امروزه تئوری‌های بسیاری در مورد علل بروز بیماری‌های وراثتی وجود دارد، اما پزشکان معتقدند که دو فاکتور ژنتیک و محیط ‌زیست هم‌زمان باهم نقش مهمی در بروز و پیشرفت این بیماری‌ها ایفا می‌کنند، هرچند که چگونگی این اثرگذاری هنوز به‌طور دقیق مشخص نیست. برای اینکه بتوان ژن‌های مؤثر در بروز بیماری‌ها را تشخیص داد، باید ارتباط بین سلول‌ها/بافت­‌ها را به‌دست آورد. تعامل بین سلول‌ها یا بافت‌های مختلف را می‌توان، با بیان ژن بین آنها نشان داد. با نمونه‌­برداری از کروموزوم‌ها، اطلاعات مفیدی در مورد نوع بیماری و چگونگی انتقال آن استخراج می‌­شود. با بررسی این اطلاعات می‌­توان اختلالاتی را که منجر به تغییرات به‌شدت پرتکراری شده‌­اند شناسایی کرد. در این مقاله تشخیص ارتباط‌های بین سلولی و بین بافتی در بیماری‌های مختلف با توجه به مشخصات ساختار توپولوژیکی گراف و یک روش خوشه‌بندی تجمعی بهبودیافته انجام شده است. روش پیشنهای دو مرحله دارد؛ در مرحله نخست چندین مدل خوشه‌بندی به‌منظور تشخیص ارتباط‌های اولیه بین سلول‌ها یا بافت‌ها در جهت تولید نتایج بهتر نسبت به الگوریتم‌های انفرادی، ترکیب می‌شوند. در مرحله دوم تشابه بین سلول‌ها یا بافت‌ها در هر خوشه با استفاده از یک معیار شباهت مبتنی بر ساختار توپولوژیکی گراف محاسبه و درنهایت از بیشینه شباهت­‌های بین سلول­‌ها یا بافت‌­ها در هر خوشه برای کشف ارتباطات بین بیماری‌­ها استفاده می‌­شود. به‌منظور ارزیابی عملکرد روش پیشنهادی از چندین مجموعه‌داده UCI و همچنین مجموعه‌داده فانتوم پنج استفاده شده است. نتایج روش پیشنهادی روی مجموعه داده فانتوم پنج، ضریب سیلوئت 901/0 را در 18 خوشه برای سلول­‌ها و 762/0 در 13 خوشه برای بافت­‌ها را گزارش می­‌کند. 2 Background: There are many theories about the causes of hereditary diseases, but physician believe that both the genetic and environmental factors simultaneously play an important role in the development and progression of these diseases, although the extent to which this effect is not yet clear. In order to detect effective genes in the development of diseases, it is necessary to achieve the relationship between cells/tissues. Objective: In fact, inter-cell or inter-tissue communications indicate the hereditary relationships between patients. Detecting these communications help to identify common parts of the body that are influenced by various diseases. The interaction between different cells/tissues can be demonstrated by expressing the gene between them. By sampling chromosomes, useful information is obtained about the type of disease and how it is transmitted. By examining this information, you can identify disorders that have led to highly altered changes. In previous research, various clustering methods have been used to discover the links between diseases based on gene expression data. However, ensembl clustering approaches have not yet been used for this purpose. Method: In this paper, the recognition of intercellular and inter-tissue interactions in various diseases have been done according to the characteristics of the topological structure of the graph and an improved ensembl clustering method. The proposed clustering algorithm uses an agreed similarity function to measure the similarity between objects. The proposed method has two stages; in the first step, several clustering models are combined to identify the initial relationships between cells or tissues in order to produce better results than individual algorithms. In the second stage, the similarity between cells or tissues in each cluster is calculated by using a similarity criterion based on the topological structure of the graph. Eventually, the maximum similarity between cells or tissues in each cluster is used to discover the relationship between diseases. In addition, an algorithm for improving the uncertainty of objects is evaluated by allocating them to other clusters in order to enhance the quality of the final clusters. Results: To evaluate the performance of the proposed method, several UCI datasets and the FANTOM5 dataset have been used. The results of the proposed method on the phantom data set 5 report a silhouette of 0.901 in 18 clusters for cells and 0.762 in 13 clusters for tissues. Conclusion: The conducted evaluations have confirmed the power of the proposed clustering algorithm in terms of accuracy. Clustering of cells or tissues has increased the accuracy and concentration of the topological similarity criterion of the graph in the range of similarity of cells or tissues. 97 114 2020/03/102019/03/142019/03/312019/02/42019/05/62019/02/26 1397/12/7 2021/03/82020/08/182020/04/212021/01/102020/08/192020/08/18 1399/5/28 موسی مجرد musa mojarad دانشگاه آزاد اسلامی، واحد فیروزآباد ایران mosa.mojarad@gmail.com حمید پروین hamid parvin دانشگاه آزاد اسلامی، واحد نورآباد ممسنی ایران parvin@iust.ac.ir صمد نجاتیان samad nejatiyan دانشگاه آزاد اسلامی، واحد یاسوج ایران samad.nej.2007@gmail.com کرم الله باقری فرد Karam allah Bagheri Fard دانشگاه آزاد اسلامی، واحد یاسوج ایران k.bagheri@iauyasooj.ac.ir Intercellular communication Improved clustering Graph topological structure FANTOM5 dataset ارتباط بین سلولی خوشه‌بندی بهبودیافته ساختار توپولوژیکی گراف مجموعه‌داده فانتوم 5 [1] I. H. Park, N. Arora, H. Huo, N. Maherali, T. Ahfeldt, A. Shimamura,G. Q. 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ارزیابی کیفیت تصاویر بدون مرجع ناآگاه از خرابی با استفاده از ویژگی‌های آماری Non-distortion-specific no-reference Image Quality Assessment using Statistical Features 1 1 ارزیابی کیفیت تصاویر بدون مرجع ناآگاه از خرابی، یکی از چالش‌های موجود در زمینه پردازش تصاویر دیجیتال است، چون در این زمینه هیچ تصویر مرجع، نوع خرابی، نمرات و امتیازدهی ناظر انسانی برای کمک و یادگیری در ارزیابی موجود نیست؛ در‌حالی‌که در برنامه‌های مختلف مانند تصویربرداری کاربرد دارد. هدف این مقاله استفاده از خواص و ویژگی آماری موجود در تصاویر و مدل‌سازی آن با مدل توزیع q-Gaussian، به‌منظور ارزیابی کیفیت تصویر است. توزیع  q-Gaussianیکی از گزینه‌هایی است که مرزهای تصمیم‌گیری انعطاف‌پذیری با اشکال مختلف گوسی ایجاد می‌کند که قابلیت تعمیم‌پذیری بیشتری در ناهنجاری‌ها به نسبت توزیع‌های دیگر داشته و همچنین برای مدل‌کردن خواص آماری تصویر، عملکرد بهتری در ارزیابی کیفیت بدون مرجع دارد. 2 Objective Image Quality Assessment (IQA) algorithms are divided into three categories according to the availability of the reference image and the amount of information available in the assessment, namely, algorithms with Full Reference (FR), Reduced Reference (RR), and No Reference (NR). If the original high-quality image exists in the evaluation algorithm at the same time to compare with the test image, and both images are identical in content, the evaluation is called FR assessment. If only a few features extracted from the high-quality image are used to compare with the test image, it is called RR assessment. In NR assessment algorithms, there is no feature or reference image to compare with the test image. Algorithms in NR are divided into two subcategories, Distortion-Specific (DS) and Non-Distortion-Specific (NDS). In first one, algorithms predict the quality of an image by knowing the type of distortion which is effective when distortion information or type is available. However, information about the type of distortion is not available in most applications which limits the use of these algorithms. The NDS algorithms can be applied to different types of distortion and are designed to be all-purpose. The NDS algorithms are divided into two subcategories namely, Opinion Aware (OA) and Opinion Unaware (OU). In the OA model, the images are evaluated and scored by the human factor, and each image with its corresponding human score is mapped by the learning system, while the OU model does not have the score of a human observer and is evaluated completely blind. The proposed algorithm is this paper is for NR model and NDS and opinion unaware. Image quality is well correlated with features of local structure, contrast, and color. By modeling, these features with distributions such as Gaussian or Gaussian families can be used to detect image degradation. The proposed method consists of two stages of training and testing. Five NSS features that are actually extracted from the MSCN coefficient. The q-Gaussian distribution model is used for image distribution. The q-Gaussian distribution is one of the options that create flexible decision boundaries with different Gaussian shapes that are more generalizable in anomalies than other distributions. The learning phase is performed only once to extract the features of images and to be considered as a model in the system to compare with the images that are to be entered as test images. To evaluate the performance, the proposed method is compared with IL-NIQE which is similar to the proposed method in terms of behavioral mechanism and it uses natural scene statistics. Performance metrics such as PLCC, KROCC, RMSE, SROCC and some datasets, LIVE, CSIQ, and TID2013 have been used for evaluation. The proposed method performs better than the compared technique. The proposed method can show better performance due to the adjustable parameter of q. 115 134 2020/03/102019/03/142019/03/312019/02/42019/05/62019/02/262019/07/21 1398/4/30 2021/03/82020/08/182020/04/212021/01/102020/08/192020/08/182020/04/29 1399/2/10 مائده دریا نورد Maedeh Daryanavard گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی لاهیجان ایران shahbahrami@guilan.ac.ir اسدالله شاه بهرامی Asadollah Shahbahrami گروه مهندسی کامپیوتر، دانشکده فنی دانشگاه گیلان ایران shahbahrami@guilan.ac.ir Image quality assessment No-reference assessment Non-distortion-specific assessment q-Gaussian distribution ارزیابی کیفیت تصویر ارزیابی بدون مرجع ارزیابی ناآگاه از خرابی توزیع q-Gaussian [1] A. Attar, A. Shahbahrami, R. Moradi rad, "Image quality assessment using edge based features", Multimedia tools and applications, Vol. 75, pp. 7407-7422, 2015.##[2] M. Saad, A. C. Bovik, and C. 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ارائه یک روش تشخیص نفوذ هوشمند مبتنی بر رفتار بر روی ماشین‌های مجازی An intelligent behavior-based intrusion detection method for virtual machines 1 1 امروزه، فناوری مجازی­سازی به‌طور گسترده‌­ای در جهت راه‌­اندازی چندین سامانه مجازی بر روی یک سامانه فیزیکی در حال توسعه است که ابرهای محاسباتی نمونه بارز به‌کارگیری این فناوری هستند. سامانه‌‌های تشخیص نفوذ نقش کلیدی در محافظت از منابع ابر بر روی ماشین­‌های مجازی دارند. با افزایش سرعت و پیچیدگی این ماشین­‌ها، سامانه‌های تشخیص نفوذ نیز باید توانایی و دقت تشخیص خود را بالا ببرند تا توانایی شناسایی انواع مختلف حملات در زمان مناسب را کسب کنند. در این راستا، استفاده از رویکردهای مبتنی بر رفتار به‌دلیل مقیاس­پذیری بالا در شبکه‌­های بزرگ مورد توجه قرار گرفته­‌اند. در این مقاله، یک روش جدید برای تشخیص نفوذ در شبکه مبتنی بر رفتار ارائه شده است. برای این منظور، روش پیشنهادی ابتدا داده‎‎‌های استخراج‌شده را از طریق مفهوم جریان داده به‌صورت گراف پراکندگی ترافیک مدل‌­سازی کرده و سپس، با استفاده از یک الگوریتم بهبود ‌یافته مبتنی بر مدل مارکوف خوشه‌بندی می‌کند. در ادامه، با تحلیل خوشه‌های تولید‌شده بر اساس معیارهای آماری مدلی برای تشخیص نفوذ ارائه می‌دهد. کارایی روش پیشنهادی بر روی مجموعه‌دادهDARPA 99  به‌عنوان یک مجموعه‌داده استاندارد و جامع برای ارزیابی سامانه‌های تشخیص نفوذ مورد آزمایش و ارزیابی قرار گرفت و با نتایج حاصل از هفت روش مطرح دیگر مقایسه شد. نتایج مقایسه نشان می­‌دهد که روش پیشنهادی در مقایسه با روش‌­های­ دیگر دارای قابلیت‌­های بهتری بوده و می‌تواند حملات را با دقت بالایی تشخیص دهد. 2 In recent years, the speed and complexity of computer networks have grown significantly. At the same time, network-based anomalies and attacks have increased. Nowadays, intrusion detection and prevention is considered as a main strategy in satisfying the security of computer systems and communication networks, and the detection of these attacks with high accuracy and the least error is very important, especially in the field of network management. Today, virtualization technology is widely developing in order to set up multiple virtual systems on a physical system. Computational clouds are the most hallmark of this technology. Intrusion detection systems play a key role in protecting cloud resources on virtual machines. An intrusion detection system has the task of monitoring events within a computer system and the communication networks, and detects unauthorized and abnormal behaviors to deal with them. The proposed systems for intrusion detection mainly use data mining, machine learning and statistical analysis of data. Therefore, it is natural that in some cases they lead to the production of false alarms. Consequently, it is essential to improve the accuracy and high detection capability of these systems. Regarding the increasing speed and complexity of these machines, it is necessary to increase the ability and accuracy of intrusion detection systems for identifying different types of attacks at a right time. In this regard, the use of behavior-based approaches has attracted more attention due to their high scalability in the large networks. The methods for intrusion detection that utilize network traffic graph clustering do not have the accuracy and appropriateness with the speed of data transfer in the current computer networks. Thus, the solutions can be improved by choosing an appropriate strategy for clustering. In this paper, a new behavior-based method for detecting intrusion in computer networks is presented. To this end, the network data was modeled through the flow of data as a traffic dispersion graph and then clustered using an improved Markov-based algorithm. Then, by analyzing a set of statistical criteria, the produced clusters, a penetration detection model was constructed. A set of modified statistical criteria was defined and utilized for analyzing the constructed clusters. The proposed model was examined and evaluated on the DARPA 99 dataset. In addition, the results of the proposed method were compared with seven other methods which work based on machine learning techniques. The results show that in the proposed method, the error detection rate is significantly reduced and the accuracy rate of the method is increased compared to seven other intrusion detection approaches. The reason for this performance improvement can be attributed to the good performance of Markov's improved clustering algorithm, which has produced more accurate results on flow-based data. Also, defining and applying appropriate criteria to determine the threshold limits is effective in obtaining accurate results. In addition, the results demonstrate that the proposed model has better capabilities than the methods which are not use graph clustering and can detect attacks with high accuracy. 135 146 2020/03/102019/03/142019/03/312019/02/42019/05/62019/02/262019/07/212019/03/8 1397/12/17 2021/03/82020/08/182020/04/212021/01/102020/08/192020/08/182020/04/292019/11/10 1398/8/19 فاطمه میرجلیلی Fatemeh Mirjalili گروه علوم کامپیوتر، دانشکده ریاضی، آمار و علوم کامپیوتر، دانشگاه تبریز ایران f.mirjalili93@ms.tabrizu.ac.ir جعفر رزم آرا Jafar Razmara گروه علوم کامپیوتر، دانشکده ریاضی، آمار و علوم کامپیوتر، دانشگاه تبریز ایران razmaraj@gmail.com behavior-based intrusion detection traffic dispersion graph data flow graph clustering optimized Markov clustering تشخیص نفوذ مبتنی بر رفتار گراف پراکندگی ترافیک جریان داده خوشه‌بندی گراف‌ خوشه‌بندی بهبود‌یافته مارکوف [1] R. Rastgar, A. Isazadeh, J. Karimpour, "Flow-based intrusion detection based on traffic distribution graph", In 13th International Conf. on Iranian Cryptography Society, 2016.##[2] M. Zabihi, M. Vafaei Jahan, "An optimized accurate algorithm based on Markov clustering for web robots detection", 7th International Conf. on Iranian Operation Research, 2014.##[3] S. Anwar, J. M. Zain, M. F. Zolkipli, Z. Inayat, S. Khan, B. Anthony, V. Chang, "From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions", Algorithms, vol. 10(39), 2017.##[4] U.H. Rao, U. Nayak, "Intrusion Detection and Prevention Systems", The InfoSec Handbook. Apress, Berkeley, CA. 2014.##[5] E. Viegas, A. O. Santin, A. Franca, R. Jasinski, V. A. Pedroni and L. S. Oliveira, "Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems"-, IEEE Transactions on Computers, vol. 66 (1), pp. 163-177, 2017.##[6] A. Sperotto, "Flow-based intrusion detection", Ph.D. Dissertation, University of Twente, 2010.##[7] L. Hellemons, L. Hendriks, R. Hofstede, A. Sperotto, R. Sadre, and A. Pras, "Sshcure: a flow-based ssh intrusion detection system", Dependable Networks and Services, LNCS vol. 7279, pp.86-97, 2012.##[8] M. Iliofotou, P. Pappu, M. Faloutsos, M. Mitzenmacher, S. Singh, and G. Varghese, "Network traffic analysis using traffic dispersion graphs (tdgs): techniques and hardware implementation", UCR Technical Report, 2007.##[9] D. Q. Le, T. Jeong, H. E. Roman, and J. W.-K. Hong, "Traffic dispersion graph based anomaly detection", In Proc. of the Second Sym. on Information and Communication Technology, pp.36-41, ACM, 2011.##[10] D. Q. Le, T. Jeong, H. E. Roman, and J. W. Hong, "Trafic dispersion graph based anomaly detection", In Proc. of the Second Sym. on Information and Communication Technology, pp. 36-41, ACM, 2011##[11] P. Manandhar and Z. Aung, "Towards practical anomaly-based intrusion detection by outlier mining on tcp packets", Database and Expert Systems Applications, LNCS vol. 8645, pp. 164-173, 2014.##[12] H. A. Kholidy, F. Baiardi, "CIDD: A Cloud Intrusion Detection Dataset for Cloud Computing and Masquerade Attacks", 9th International Conference on Information Technology - New Generations, Las Vegas, NV, USA, 2012.##[13] R. Lippmann, J.W. Haines, D. J. Fried, J. Korba, and K. Das, "The 1999 darpa off-line intrusion detection evaluation", Computer networks, vol. 34(4), pp. 579-595, 2000.##[14] S. M. Dongen, "Graph Clustering by Flow Simulation", PhD Dissertation, University of Utrecht, 2000.##[15] G. P. Guptaa, M. Kularivaa, "A Framework for Fast and Efficient Cyber Security Network Intrusion Detection using Apache Spark", 6th Int. Conf. on Advances in Computing & Communications, 2016, 6-8 September 2016. Procedia Computer Science 93, pp. 824 - 831.##[1] راستگار، رویا، عیسی‌زاده، آیاز، کریم‌پور، جابر، "تشخیص نفوذ مبتنی بر جریان، بر‌اساس خوشه‌بندی گراف پراکندگی ترافیک"، سیزدهمین کنفرانس بین‌المللی انجمن رمز ایران،١٣٩۵.##[1] R. Rastgar, A. Isazadeh, J. Karimpour, "Flow-based intrusion detection based on traffic distribution graph", In 13th International Conf. on Iranian Cryptography Society, 2016.##[2] ضبیحی، مهدیه، وفایی جهان، مجید، "ارائه الگوریتمی بهینه و دقیق مبتنی بر خوشه‌بندی مارکوف برای شناسایی روبات‌های وب"، هفتمین کنفرانس بین‌المللی انجمن ایرانی تحقیق در عملیات، ١٣٩٣.##[2] M. Zabihi, M. Vafaei Jahan, "An optimized accurate algorithm based on Markov clustering for web robots detection", 7th International Conf. on Iranian Operation Research, 2014.##[3] S. Anwar, J. M. Zain, M. F. Zolkipli, Z. Inayat, S. Khan, B. Anthony, V. Chang, "From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions", Algorithms, vol. 10(39), 2017.##[4] U.H. Rao, U. Nayak, "Intrusion Detection and Prevention Systems", The InfoSec Handbook. Apress, Berkeley, CA. 2014.##[5] E. Viegas, A. O. Santin, A. Franca, R. Jasinski, V. A. Pedroni and L. S. Oliveira, "Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems"-, IEEE Transactions on Computers, vol. 66 (1), pp. 163-177, 2017.##[6] A. Sperotto, "Flow-based intrusion detection", Ph.D. Dissertation, University of Twente, 2010.##[7] L. Hellemons, L. Hendriks, R. Hofstede, A. Sperotto, R. Sadre, and A. Pras, "Sshcure: a flow-based ssh intrusion detection system", Dependable Networks and Services, LNCS vol. 7279, pp.86-97, 2012.##[8] M. Iliofotou, P. Pappu, M. Faloutsos, M. Mitzenmacher, S. Singh, and G. Varghese, "Network traffic analysis using traffic dispersion graphs (tdgs): techniques and hardware implementation", UCR Technical Report, 2007.##[9] D. Q. Le, T. Jeong, H. E. Roman, and J. W.-K. Hong, "Traffic dispersion graph based anomaly detection", In Proc. of the Second Sym. on Information and Communication Technology, pp.36-41, ACM, 2011.##[10] D. Q. Le, T. Jeong, H. E. Roman, and J. W. Hong, "Trafic dispersion graph based anomaly detection", In Proc. of the Second Sym. on Information and Communication Technology, pp. 36-41, ACM, 2011##[11] P. Manandhar and Z. Aung, "Towards practical anomaly-based intrusion detection by outlier mining on tcp packets", Database and Expert Systems Applications, LNCS vol. 8645, pp. 164-173, 2014.##[12] H. A. Kholidy, F. Baiardi, "CIDD: A Cloud Intrusion Detection Dataset for Cloud Computing and Masquerade Attacks", 9th International Conference on Information Technology - New Generations, Las Vegas, NV, USA, 2012.##[13] R. Lippmann, J.W. Haines, D. J. Fried, J. Korba, and K. Das, "The 1999 darpa off-line intrusion detection evaluation", Computer networks, vol. 34(4), pp. 579-595, 2000.##[14] S. M. Dongen, "Graph Clustering by Flow Simulation", PhD Dissertation, University of Utrecht, 2000.##[15] G. P. Guptaa, M. Kularivaa, "A Framework for Fast and Efficient Cyber Security Network Intrusion Detection using Apache Spark", 6th Int. Conf. on Advances in Computing & Communications, 2016, 6-8 September 2016. Procedia Computer Science 93, pp. 824 - 831.## ##

موزاییک تصاویر طبیعی بر اساس حذف نقاط کلیدی زائد در الگوریتم SIFT و الگوریتم RANSAC تطبیقی Natural Image Mosaicing based on Redundant Keypoint Elimination Method in SIFT algorithm and Adaptive RANSAC method 1 1 موزاییک تصویر به ترکیب دو یا چند تصویر که قسمت­‌های هم‌پوشان دارند، به تصویری بزرگ‌تر و جامع‌­تر اطلاق می­‌شود. تبدیل ویژگیِ مقیاس‌­نابسته (SIFT) یکی از متداول­‌ترینِ شناساگرها است که در‌قبل در موزاییک تصویر مورداستفاده قرارگرفته است. از ایرادهای الگوریتمSIFT  کلاسیک تعداد زیاد نقاط کلیدی تکراری و زمان اجرای بالای آن به‌دلیل ابعاد بالای توصیف‌گر SIFT کلاسیک است، که باعث کاهش کارایی این الگوریتم می‌­شود. در این مقاله، برای بالا‌بردن کیفیت موزاییک تصویر، از  الگوریتم RKEM-SIFT که نسخه بهبودیافته SIFT است جهت شناسایی نقاط کلیدی استفاده‌ شده و سپس، برای بهبود سرعت الگوریتم از توصیف‌گر ۶۴‌بُعدی SIFT استفاده ‌شده است. پنجره کوچک‌ترِ این توصیف‌گر نسبت به توصیف‌گر ۱۲۸‌بُعدی SIFT باعث می‌شود دقّت تطبیق افزایش و زمان اجرا کاهش پیدا کند. در ادامه، برای حذف تطبیق­‌های نادرست، از الگوریتم اجتماع نمونه تصادفی (RANSAC) استفاده‌ شده که مقدار آستانه پیشنهادی آن به‌صورت وفقی بر اساس میانه فواصل بین نقاط تطبیق و مدل تطبیق آن­ها محاسبه‌ شده است. برای هر نقطه تطبیق اگر فاصله بین آن نقطه و تطبیق‌یافته آن بر اساس مدل از مقدار آستانه پیشنهادی کمتر باشد، نقطه تطبیق درست تشخیص و حفظ و در غیر این صورت تطبیق نادرست تشخیص داده‌شده و حذف می‌­شود؛ درنهایت روش جدیدی نیز در این مقاله جهت ترکیب تصویر پیشنهاد شده است. روش پیشنهادی ترکیب تصویر بر اساس تابع وزنی گوسی است که میانگین این تابع گوسی به‌صورت میانگین داده‌­های محدوده مشترک و هم‌پوشان دو تصویر، در ­نظر گرفته ­شده است. در قسمت آزمایش‌­ها، روش پیشنهادی موزاییک تصاویر طبیعی که شامل استفاده از الگوریتم RKEM-SIFT، RANSAC وفقی پیشنهادی و الگوریتم ترکیب تصویر پیشنهادی است، بر روی پایگاه­‌های تصاویر استاندارد و همین‌طور پایگاه تصاویر ایجادشده پیاده­‌سازی شده و با روش­‌های SURF و تطبیق دوطرفه سریع، SURF-LM و SIFT-RANSAC مورد مقایسه قرارگرفته است. نتایج آزمایش‌­ها حاکی از برتری روش­ پیشنهادی با توجه به معیارهای خطای میانگین مربعی و دقّت دارد که نسبت به بهترین روش مقایسه‌شده (SURF و تطبیق دوطرفه سریع) کاهش 7/6 ٪  بیشینه خطا،  09/30 ٪ ریشه میانگین مربعات خطا و 68/37 ٪ میانه خطا را باعث شده است. 2 Image mosaicing refers to stitching two or more images which have overlapping regions to a larger and more comprehensive image. The image mosaicing process is widely used in scene stabilization, change detection, video compression, and image compression. Image mosaicing methods can be divided into two categories, direct methods and feature-based methods, which feature-based methods are more accurate. Scale Invariant Feature transform (SIFT (is one of the most common feature-based methods in the image mosaicing. However, one of the big defects of SIFT algorithm is the large number of duplicate key points and being time-consuming due to the high dimensions of classical SIFT descriptor. In this paper, to solve these problems, a new four-step approach for image mosaicing is proposed. At first, Redundant Keypoint Elimination-SIFT (RKEM-SIFT) algorithm which has been proposed in [1] is used to identify keypoints of reference and sensed images and to improve the mosaicing process. In the second stage, for each keypoint of the image, 64-D SIFT descriptor is computed. In this descriptor, unlike the 128-D SIFT descriptor, a smaller window is used which improves the accuracy of matching and reduces the running time. In the third stage, the proposed improved RANdom SAmple Consensus (RANSAC) algorithm is used to determine the adaptive threshold in the RANSAC algorithm to remove the mismatches and to improve the image mosaicing. Determining the appropriate threshold value in RANSAC is so important, because if an appropriate value is not chosen for this algorithm, the mismatches are not removed, and eventually there will be a serious impact on the outcome of the image mosaicing process. In this method, the threshold value is based on the median value of distances between matching points and their transformed model. Image blending in the mosaicing process is the final step which blends the pixels intensity in the overlapped region to avoid seams. The suggested method of blending is to combine the images based on the average of the data in the overlapped region of two images. The proposed blending method reduces artifacts in the image for better performance of the mosaicing process. Another advantage of this proposed method is the possibility to combine more than two images that are suitable for creating panoramic images. The simulation results of the proposed image mosaicing technique, which includes the RKEM-SIFT algorithm as feature detector, 64-D SIFT descriptor, proposed adaptive RANSAC algorithm, and proposed image blending algorithm. The proposed method is implemented on standard image databases, created image databases, and has been compared with SURF- fast bidirectional matching, SURF-LM and SIFT-RANSAC methods. The results of the experiments show the superiority of the proposed method according to the criteria of mean square error and accuracy, which compared to the best compared method (SURF-fast bidirectional matching) reduces 6.7% maximum error, 30.09% root mean square error and 37.68% caused the median error. 147 162 2020/03/102019/03/142019/03/312019/02/42019/05/62019/02/262019/07/212019/03/82019/05/2 1398/2/12 2021/03/82020/08/182020/04/212021/01/102020/08/192020/08/182020/04/292019/11/102020/06/1 1399/3/12 زهرا حسین نژاد Zahra Hossein-Nejad گروه مهندسی برق، واحد سیرجان، دانشگاه آزاد اسلامی ایران hoseinnejad.zahra@yahoo.com مهدی نصری Mehdi Nasri گروه مهندسی برق، واحد خمینی‌شهر، دانشگاه آزاد اسلامی ایران nasri_me@iaukhsh.ac.ir Image mosaicing Image registration SIFT موزاییک تصویر انطباق تصویر الگوریتم SIFT. [1] Z. Hossein-Nejad and M. Nasri, "RKEM: Redundant Keypoint Elimination Method in Image Registration," IET Image Processing, vol. 11, pp. 273-284, 2017.##[2] S. Laaroussi, A. Baataoui, A. Halli, and S. J. P. C. S. Khalid, "A dynamic mosaicking method based on histogram equalization for an improved seamline," vol. 127, pp. 344-352, 2018.##[3] D. Vaghela and P. Naina, "A review of image mosaicing techniques," arXiv preprint arXiv:1405.2539, 2014.##[4] T. Koch, X. Zhuo, P. Reinartz, F. J. I. A. o. P. Fraundorfer, Remote Sensing, and S. I. Sciences, "A new paradigm for matching uav-and aerial images," vol. 3, pp. 83-90, 2016.##[5] A. Pandey and U. C. Pati, "Panorama Generation Using Feature-Based Mosaicing and Modified Graph-Cut Blending," in Soft Computing: Theories and Applications, ed: Springer, 2018, pp. 477-488.##[6] U. B. Patel, H. N. J. I. J. o. E. Mewada, and I. Technology, "Review of image mosaic based on feature technique," vol. 2, 2013.##[7] P. M. Jain and V. K. Shandliya, "A review paper on various approaches for image mosaicing," International Journal of Computational Engineering Research, vol. 3, pp. 106-109, 2013.##[8] D. Bheda, M. Joshi, and V. Agrawal, "A study on features extraction techniques for image mosaicing," International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, pp. 3432-3437, 2014.##[9] Z. Hossein-nejad and M. Nasri, "Image registration based on SIFT features and adaptive RANSAC transform," in Communication and Signal Processing (ICCSP), 2016 International Conference on, 2016, pp. 1087-1091.##[10] Z. Hossein-Nejad and M. Nasri, "A Review on Image Registration Methods, Concepts and applications," Journal of Machine Vision and Image Processing, pp. 39-67, 2017.##[11] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, K. J. M. T. Satori, et al., "Image mosaicing using voronoi diagram," vol. 76, pp. 8803-8829, 2017.##[12] Z. Hossein-Nejad and M. Nasri, "A-RANSAC: Adaptive random sample consensus method in multimodal retinal image registration," Biomedical Signal Processing and Control, vol. 45, pp. 325-338, 2018.##[13] F. ZohourParvaz, E. Fatemizadeh, and H. Behnam, "Speed improvement in graph-cuts-based registration for non-rigid image registration of brain magnetic resonance images," Signal and Data Processing, vol. 13, pp. 79-92, 2017.##[14] A. Torkian and P. Moallem, "Multi-frame Super Resolution for Improving Vehicle Licence Plate Recognition," Signal and Data Processing, vol. 16, pp. 61-76, 2019.##[15] Z. Ghassabi, J. Shanbehzadeh, A. Mohammadzadeh, and S. S. Ostadzadeh, "Colour retinal fundus image registration by selecting stable extremum points in the scale-invariant feature transform detector," IET Image Processing, vol. 9, pp. 889-900, 2015.##[16] N. Cheng, J. Wang, A. Xu, H. Zhu, X. Wang, Y. Lan, et al., "Liver Image Mosaicing System Based on Scale Invariant Feature Transform and Point Set Matching Method," vol. 8, 2014.##[17] S. J. I. J. o. E. Michahial and I. Technology, "Automatic Image Mosaicing Using Sift, Ransac and Homography," vol. 3, pp. 247-251, 2014.##[18] D. G. Lowe, "Object recognition from local scale-invariant features," in iccv, 1999, pp. 1150-1157.##[19] Z. Hossein-Nejad and M. Nasri, "A New Method in Image Matching Based on Spatial Relationships in Multi-Sensor Remote Sensing Images," Iranian Remote Sensing & GIS, pp. 73-94, 2018.##[20] M. Brown and D. G. J. I. j. o. c. v. Lowe, "Automatic panoramic image stitching using invariant features," vol. 74, pp. 59-73, 2007.##[21] Z.-L. Yang and B.-L. Guo, "Image mosaic based on SIFT," in 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2008, pp. 1422-1425.##[22] J. He, Y.-s. Li, H. Lu, and Z.-m. J. O.-E. E. Ren, "Research of UAV aerial image mosaic based on SIFT," vol. 2, 2011.##[23] Y. Wang, J. Shen, W. Liao, and L. Zhou, "Automatic fundus images mosaic based on SIFT feature," in 2010 3rd International Congress on Image and Signal Processing, 2010, pp. 2747-2751.##[24] C. Gao, X. Zhang, Y.-L. Wang, and H. J. J. Y. J. o. C. A. Wang, "Automatic stitching approach of aerial image sequence based on SIFT features," vol. 27, pp. 2789-2792, 2007.##[25] L.-n. Li and N. Geng, "Algorithm for sequence image automatic mosaic based on SIFT feature," in 2010 WASE International Conference on Information Engineering, 2010, pp. 203-206.##[26] M. Liu and D. Wen, "Automatic seamless image mosaic method based on SIFT features," in Second International Conference on Photonics and Optical Engineering, 2017, p. 1025636.##[27] G. Kaur, P. J. I. J. o. S. Agrawal, and Technology, "Optimisation of image fusion using feature matching based on SIFT and RANSAC," vol. 9, pp. 1-7, 2016.##[28] A. Laraqui, A. Saaidi, K. J. M. T. Satori, and Applications, "MSIP: Multi-scale image pre-processing method applied in image mosaic," vol. 77, pp. 7517-7537, 2018.##[29] P. Kang and H. Ma, "An automatic airborne image mosaicing method based on the SIFT feature matching," in Multimedia Technology (ICMT), 2011 International Conference on, 2011, pp. 155-159.##[30] R. Monali, S. Moonka, A. Priya, and S. S. Tripathy, "Effects of noise and relative overlap on image mosaicing using SURF features," in Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE International Conference on, 2016, pp. 773-777.##[31] Y. Murali and M. J. I. Madanapalle, "Image mosaic using speeded up robust feature detection," vol. 1, 2012.##[32] G. Venugopala, G. Padmajadevi, M. J. I. J. o. R. Tech, I. T. i. Computing, and Communication, "Image stitching using speeded up robust features," vol. 3, pp. 3514-3519, 2015.##[33] Z. Yang, D. Shen, and P.-T. J. P. o. Yap, "Image mosaicking using SURF features of line segments," vol. 12, p. e0173627, 2017.##[34] X.-h. Yang and M. Wang, "Seamless image stitching method based on ASIFT," Comput Eng, vol. 39, pp. 241-244, 2013.##[35] Z. Hossein-Nejad and M. Nasri, "Retinal Image Registration based on Auto-Adaptive SIFT and Redundant Keypoint Elimination Method," in 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 1294-1297.##[36] H. Moradi-Gharghani and M. Nasri, "A new block-based copy-move forgery detection method in digital images," in 2016 International Conference on Communication and Signal Processing (ICCSP), 2016, pp. 1208-1212.##[37] N. Y. Khan, B. McCane, and G. Wyvill, "SIFT and SURF performance evaluation against various image deformations on benchmark dataset," in 2011 International Conference on Digital Image Computing: Techniques and Applications, 2011, pp. 501-506.##[38] M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, pp. 381-395, 1981.##[39] A. Li, S. Zhou, and R. Wang, "An improved method for eliminating ghosting in image stitching," in 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2017, pp. 415-418.##[40] F. Tian and P. Shi, "Image mosaic using orb descriptor and improved blending algorithm," in Image and Signal Processing (CISP), 2014 7th International Congress on, 2014, pp. 693-698.##[41] W. Aguilar, Y. Frauel, F. Escolano, M. E. Martinez-Perez, A. Espinosa-Romero, and M. A. Lozano, "A robust graph transformation matching for non-rigid registration," Image and Vision Computing, vol. 27, pp. 897-910, 2009.##[42] Z. Hossein-Nejad and M. Nasri, "An adaptive image registration method based on SIFT features and RANSAC transform," Computers & Electrical Engineering, vol. 62, pp. 524-537, 2017.##[43] H. Tang, A. Pan, Y. Yang, K. Yang, Y. Luo, S. Zhang, et al., "Retinal image registration based on robust non-rigid point matching method," vol. 8, pp. 240-249, 2018.##[44] Q. Zhang, Y. Wang, and L. Wang, "Registration of images with affine geometric distortion based on Maximally Stable Extremal Regions and phase congruency," Image and Vision Computing, vol. 36, pp. 23-39, 2015.##[45] G.-Q. Gao, Q. Zhang, S. J. M. Zhang, and Control, "Pose detection of parallel robot based on improved RANSAC algorithm," p. 0020294019847712, 2019.##[46] W. Zhang, X. Li, J. Yu, M. Kumar, Y. J. E. J. o. I. Mao, and V. Processing, "Remote sensing image mosaic technology based on SURF algorithm in agriculture," vol. 2018, p. 85, 2018.##[47] Y. Zhen, Z. Sun, J. Li, and Y. Peng, "An airborne remote sensing image mosaic algorithm based on feature points," in 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2016, pp. 202-205.##[1] Z. Hossein-Nejad and M. Nasri, "RKEM: Redundant Keypoint Elimination Method in Image Registration," IET Image Processing, vol. 11, pp. 273-284, 2017.##[2] S. Laaroussi, A. Baataoui, A. Halli, and S. J. P. C. S. Khalid, "A dynamic mosaicking method based on histogram equalization for an improved seamline," vol. 127, pp. 344-352, 2018.##[3] D. Vaghela and P. Naina, "A review of image mosaicing techniques," arXiv preprint arXiv:1405.2539, 2014.##[4] T. Koch, X. Zhuo, P. Reinartz, F. J. I. A. o. P. Fraundorfer, Remote Sensing, and S. I. Sciences, "A new paradigm for matching uav-and aerial images," vol. 3, pp. 83-90, 2016.##[5] A. Pandey and U. C. Pati, "Panorama Generation Using Feature-Based Mosaicing and Modified Graph-Cut Blending," in Soft Computing: Theories and Applications, ed: Springer, 2018, pp. 477-488.##[6] U. B. Patel, H. N. J. I. J. o. E. Mewada, and I. Technology, "Review of image mosaic based on feature technique," vol. 2, 2013.##[7] P. M. Jain and V. K. Shandliya, "A review paper on various approaches for image mosaicing," International Journal of Computational Engineering Research, vol. 3, pp. 106-109, 2013.##[8] D. Bheda, M. Joshi, and V. Agrawal, "A study on features extraction techniques for image mosaicing," International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, pp. 3432-3437, 2014.##[9] Z. Hossein-nejad and M. Nasri, "Image registration based on SIFT features and adaptive RANSAC transform," in Communication and Signal Processing (ICCSP), 2016 International Conference on, 2016, pp. 1087-1091.##[10] ز. حسین نژاد، م. نصری "‌مروری بر روشهای انطباق تصویر، مفاهیم و کاربردهای آن" مجله ماشین بینایی و پردازش تصویر,. دوره5 ، شماره 2، صفحات39-67 ، 1397.##[10] Z. Hossein-Nejad and M. Nasri, "A Review on Image Registration Methods, Concepts and applications," Journal of Machine Vision and Image Processing, pp. 39-67, 2017.##[11] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, K. J. M. T. Satori, et al., "Image mosaicing using voronoi diagram," vol. 76, pp. 8803-8829, 2017.##[12] Z. Hossein-Nejad and M. Nasri, "A-RANSAC: Adaptive random sample consensus method in multimodal retinal image registration," Biomedical Signal Processing and Control, vol. 45, pp. 325-338, 2018.##[13] ف. ظهورپرواز، ع.فاطمی زاده،ح بهنام "بهبود سرعت انطباق مبتنی بر روش برش گراف جهت انطباق غیرصلب تصاویر تشدید مغناطیسی مغز"مجله پردازش علائم و داده ها دوره 13، شماره 4، صفحات 79-92، 1395##[13] F. ZohourParvaz, E. Fatemizadeh, and H. Behnam, "Speed improvement in graph-cuts-based registration for non-rigid image registration of brain magnetic resonance images," Signal and Data Processing, vol. 13, pp. 79-92, 2017.##[14] ا. ترکیان، پ. معلم، " ابر تفکیک پذیری رشته تصاویر خودرو به منظور بهبود بازشناسی پلاک" پردازش و علائم داده ها ، دوره 16، شماره،2 صفحات 61-76، 1398##[14] A. Torkian and P. Moallem, "Multi-frame Super Resolution for Improving Vehicle Licence Plate Recognition," Signal and Data Processing, vol. 16, pp. 61-76, 2019.##[15] Z. Ghassabi, J. Shanbehzadeh, A. Mohammadzadeh, and S. S. Ostadzadeh, "Colour retinal fundus image registration by selecting stable extremum points in the scale-invariant feature transform detector," IET Image Processing, vol. 9, pp. 889-900, 2015.##[16] N. Cheng, J. Wang, A. Xu, H. Zhu, X. Wang, Y. Lan, et al., "Liver Image Mosaicing System Based on Scale Invariant Feature Transform and Point Set Matching Method," vol. 8, 2014.##[17] S. J. I. J. o. E. Michahial and I. Technology, "Automatic Image Mosaicing Using Sift, Ransac and Homography," vol. 3, pp. 247-251, 2014.##[18] D. G. Lowe, "Object recognition from local scale-invariant features," in iccv, 1999, pp. 1150-1157.##[19] ز.حسین نژاد ، م. نصری "روشی جدید در تناظریابی مبتنی بر روابط فضایی در تصاویر چندسنجنده سنجش از دوری," سنجش از دور و GIS ایران، دوره 9، شماره 4، صفحات 73-93 ،1396##[19] Z. Hossein-Nejad and M. Nasri, "A New Method in Image Matching Based on Spatial Relationships in Multi-Sensor Remote Sensing Images," Iranian Remote Sensing & GIS, pp. 73-94, 2018.##[20] M. Brown and D. G. J. I. j. o. c. v. Lowe, "Automatic panoramic image stitching using invariant features," vol. 74, pp. 59-73, 2007.##[21] Z.-L. Yang and B.-L. Guo, "Image mosaic based on SIFT," in 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2008, pp. 1422-1425.##[22] J. He, Y.-s. Li, H. Lu, and Z.-m. J. O.-E. E. Ren, "Research of UAV aerial image mosaic based on SIFT," vol. 2, 2011.##[23] Y. Wang, J. Shen, W. Liao, and L. Zhou, "Automatic fundus images mosaic based on SIFT feature," in 2010 3rd International Congress on Image and Signal Processing, 2010, pp. 2747-2751.##[24] C. Gao, X. Zhang, Y.-L. Wang, and H. J. J. Y. J. o. C. A. Wang, "Automatic stitching approach of aerial image sequence based on SIFT features," vol. 27, pp. 2789-2792, 2007.##[25] L.-n. Li and N. Geng, "Algorithm for sequence image automatic mosaic based on SIFT feature," in 2010 WASE International Conference on Information Engineering, 2010, pp. 203-206.##[26] M. Liu and D. Wen, "Automatic seamless image mosaic method based on SIFT features," in Second International Conference on Photonics and Optical Engineering, 2017, p. 1025636.##[27] G. Kaur, P. J. I. J. o. S. Agrawal, and Technology, "Optimisation of image fusion using feature matching based on SIFT and RANSAC," vol. 9, pp. 1-7, 2016.##[28] A. Laraqui, A. Saaidi, K. J. M. T. Satori, and Applications, "MSIP: Multi-scale image pre-processing method applied in image mosaic," vol. 77, pp. 7517-7537, 2018.##[29] P. Kang and H. Ma, "An automatic airborne image mosaicing method based on the SIFT feature matching," in Multimedia Technology (ICMT), 2011 International Conference on, 2011, pp. 155-159.##[30] R. Monali, S. Moonka, A. Priya, and S. S. Tripathy, "Effects of noise and relative overlap on image mosaicing using SURF features," in Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE International Conference on, 2016, pp. 773-777.##[31] Y. Murali and M. J. I. Madanapalle, "Image mosaic using speeded up robust feature detection," vol. 1, 2012.##[32] G. Venugopala, G. Padmajadevi, M. J. I. J. o. R. Tech, I. T. i. Computing, and Communication, "Image stitching using speeded up robust features," vol. 3, pp. 3514-3519, 2015.##[33] Z. Yang, D. Shen, and P.-T. J. P. o. Yap, "Image mosaicking using SURF features of line segments," vol. 12, p. e0173627, 2017.##[34] X.-h. Yang and M. Wang, "Seamless image stitching method based on ASIFT," Comput Eng, vol. 39, pp. 241-244, 2013.##[35] Z. Hossein-Nejad and M. Nasri, "Retinal Image Registration based on Auto-Adaptive SIFT and Redundant Keypoint Elimination Method," in 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 1294-1297.##[36] H. Moradi-Gharghani and M. Nasri, "A new block-based copy-move forgery detection method in digital images," in 2016 International Conference on Communication and Signal Processing (ICCSP), 2016, pp. 1208-1212.##[37] N. Y. Khan, B. McCane, and G. Wyvill, "SIFT and SURF performance evaluation against various image deformations on benchmark dataset," in 2011 International Conference on Digital Image Computing: Techniques and Applications, 2011, pp. 501-506.##[38] M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, pp. 381-395, 1981.##[39] A. Li, S. Zhou, and R. Wang, "An improved method for eliminating ghosting in image stitching," in 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2017, pp. 415-418.##[40] F. Tian and P. Shi, "Image mosaic using orb descriptor and improved blending algorithm," in Image and Signal Processing (CISP), 2014 7th International Congress on, 2014, pp. 693-698.##[41] W. Aguilar, Y. Frauel, F. Escolano, M. E. Martinez-Perez, A. Espinosa-Romero, and M. A. Lozano, "A robust graph transformation matching for non-rigid registration," Image and Vision Computing, vol. 27, pp. 897-910, 2009.##[42] Z. Hossein-Nejad and M. Nasri, "An adaptive image registration method based on SIFT features and RANSAC transform," Computers & Electrical Engineering, vol. 62, pp. 524-537, 2017.##[43] H. Tang, A. Pan, Y. Yang, K. Yang, Y. Luo, S. Zhang, et al., "Retinal image registration based on robust non-rigid point matching method," vol. 8, pp. 240-249, 2018.##[44] Q. Zhang, Y. Wang, and L. Wang, "Registration of images with affine geometric distortion based on Maximally Stable Extremal Regions and phase congruency," Image and Vision Computing, vol. 36, pp. 23-39, 2015.##[45] G.-Q. Gao, Q. Zhang, S. J. M. Zhang, and Control, "Pose detection of parallel robot based on improved RANSAC algorithm," p. 0020294019847712, 2019.##[46] W. Zhang, X. Li, J. Yu, M. Kumar, Y. J. E. J. o. I. Mao, and V. Processing, "Remote sensing image mosaic technology based on SURF algorithm in agriculture," vol. 2018, p. 85, 2018.##[47] Y. Zhen, Z. Sun, J. Li, and Y. Peng, "An airborne remote sensing image mosaic algorithm based on feature points," in 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2016, pp. 202-205.## ##

انتخاب ویژگی برای تشخیص آریتمی‌های قلبی با استفاده از بهینه‌سازی ازدحام ذرات دودویی چند‌هدفه Features selection for cardiac arrhythmia diagnosis using multiple objective binary particle swarm optimization 1 1 سیگنال الکتروکاردیوگرام، یکی از مهم­ترین ابزار برای طبقه­‌بندی انواع مختلف آریتمی­‌های قلبی است. به‌طورمعمول سیگنال­‌های ECG، حاوی نوفه‌های متفاوتی هستند. در این مقاله، تکنیک تجزیه مد تجربی گروهی که در آن هر تابع مد ذاتی (IMF)، شامل تنها یک مؤلفه فرکانسی است، برای حذف نوفه به کار رفته است. با کمک پنجره­‌بندی مناسب بر روی کمپلکس QRS متشکل از جمع سه IMF نخست، حذف نوفه با کمترین اعوجاج انجام شده و با استفاده از تبدیل موجک گسسته، نوفه‌های باقی‌مانده نیز از بین رفته، سپس با به­‌کارگیری تجزیه بسته موجک، از سیگنال ویژگی استخراج شده است که ویژگی‌­های بهینه، با روش بهینه‌سازی ازدحام ذرات دودویی چند‌هدفه انتخاب شده­اند؛ در‌نهایت از شبکه عصبی پس‌انتشار، برای طبقه­‌بندی استفاده شده که مقدار دقت 12/99 درصد برای 17 عدد سیگنال دریافت‌‌شده از پایگاه داده MIT-BIH، به‌دست آمده است. 2 Any heart activity disorder may lead an irregularity in is rhythm, or cardiac arrhythmia. An ECG signal is one of the major tools for classifying different types of cardiac arrhythmias. ECG signals usually contain various noises. To have a better signal processing, it is essential to remove noises in a way that a signal structure never becomes subject to distortion. After the step of noise removal, selection of an appropriate method is of paramount importance for feature extraction. Optimal features can be selected to improve efficiency and reduce calculations. This article used the ensemble empirical mode decomposition (EEMD) in which any intrinsic mode function (IMF) contains only a single frequency component for noise removal. The noise removal operation with the least distortion is possible using an appropriate windowing on a QRS complex containing sum of the first three IMFs. Later, the remaining noises can be removed using discrete wavelet transform (DWT). The results of using the EEMD-DWT combined method were compared with EMD and DWT combination. After the noise removal step, feature extraction was performed through a wavelet packet decomposition. It is capable of signal decomposition at all frequencies. Multiple objective binary particle swarm optimization (MOBPSO) method was used to select optimal features and the effect of this method on the results was examined. Finally, the back propagation neural network (BPNN) and a support vector machine based on particle swarm optimization were used for classification. This article used 17 signals received from the MIT-BIH database. The acquired data belong to 6 different types of classes. After pre-processing, feature extraction, feature selection, and classification on the input data, it is observed that the proposed technique of EEMD-DWT is an appropriate method for noise removal and MOBPSO is a suitable method for the selection of best features. The BPNN classifier managed to classify cardiac arrhythmias with a higher accuracy and the values for accuracy, sensitivity, specificity, and positive predictive value were 99.12%, 97.08%, 99.38%, and 97.12%, respectively. 163 176 2020/03/102019/03/142019/03/312019/02/42019/05/62019/02/262019/07/212019/03/82019/05/22019/02/14 1397/11/25 2021/03/82020/08/182020/04/212021/01/102020/08/192020/08/182020/04/292019/11/102020/06/12020/08/18 1399/5/28 مهسا واقفی Mahsa Vaghefi گروه مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی ایران mahsavaghefi@gmail.com فاطمه جمشیدی Fatemeh Jamshidi گروه مهندسی برق، دانشکده مهندسی، دانشگاه فسا ایران Jamshidi@fasau.ac.ir Back Propagation Neural Network Discrete Wavelet Transform Electrocardiogram Ensemble Empirical Mode Decomposition Multiple Objective Binary Particle Swarm Optimization بهینه‌سازی ازدحام ذرات دودویی چند‌هدفه؛ تجزیه بسته موجک؛ تجزیه مد تجربی گروهی؛ شبکه عصبی پس‌انتشار [1] A. Guyton, H. Hall, "Medical Physiology", Translated by F. Shadan, Tehran, Chehr Publication, Vol. I, 2010.##[2]. Md. A. Kabir and C. Shahnaz, "Denoising of ECG Signals Based on Noise Reduction Algorithms in EMD and Wavelet Domains," Biomedical Signal Processing and Control, vol. 7, no. 5, pp. 481- 489, 2012.##[3]. N. Li and P. Li, "An Improved Algorithm Based on EMD-Wavelet for ECG Signal De-noising," In: International Joint Conference on Computational Sciences and Optimization, Sanya, pp. 825-827, 2009.##[4]. K. M. Chang, "Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition," Sensors, vol. 10, no. 6, pp. 6063-6080, 2010.##[5]. S. Poungponsri and X. H. Yu, "An Adaptive Filtering Approach for Electrocardiogram (ECG) Signal Noise Reduction Using Neural Networks," Neurocomputing, vol. 117, pp. 206- 213, 2013.##[6]. H. Y. Lin, S. Y. Liang, Y. L. Ho, Y. H. Lin and H. P. Ma, "Discrete-Wavelet-Transform-Based Noise Removal and Feature Extraction for ECG Signals," IRBM, vol. 35, no. 6, pp. 351-361, 2014.##[7]. V. Joshi, A. R. Verma and Y. Singh, "De-noising of ECG Signal Using Adaptive Filter Based on MPSO," Procedia Computer Science, vol. 57, pp. 395-402, 2015.##[8]. S. Karpagachelvi, M. Arthanari and M. Sivakumar, "ECG Feature Extraction Techniques - A Survey Approach," International Journal of Computer Science and Information Security, vol. 8, no. 1, 2010, arXiv: 1005.0957.##[9]. S. Coumo, G. De. Pietro, R. Farina, A. Galletti and G. Sannino, "A Revised Scheme for Real Time ECG Signal Denoising Based on Recursive Filtering", Biomedical Signal Processing and Control, vol. 27, pp. 134-144, 2016.##[10]. M. Korurek and A. Nizam, "Clustering MIT-BIH arrhythmias with ant colony optimization using time domain and PCA compressed wavelet coefficients," Digital Signal Processing, vol. 20, no. 4, pp. 1050-1060, 2010.##[11]. N. Acir, "Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm," Neural Computing & Applications, vol. 14, no. 4, pp. 299-309, 2005.##[12]. P. de Chazal, M. O'Dwyer and R. B. Reilly, "Automatic classification of heartbeats using ECG morphology and heartbeat interval features", IEEE Transactions on Biomedical Engineering, vol. 51, no. 7, pp. 1196-1206, 2004.##[13]. M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt and L. Sornmo, "Clustering ECG complexes using hermite functions and self-organizing maps," IEEE Transactions on Biomedical Engineering, vol. 47, no. 7, pp. 838-848, 2000.##[14]. L. Senhadji, J. J. Bellanger, G. Carrault and G. Passariello, "Comparing wavelet transforms for recognizing cardiac patterns," IEEE Engineering in Medicine and Biology Magazine, vol. 14, no. 2, pp. 167-173, 1995.##[15]. R. Rodriguez, A. Mexicano, J. Bila, S. Cervantes and R. Ponce, "Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis," Journal of Applied Research and Technology, vol. 13, no. 2, pp. 261-269, 2015.##[16]. A. Ghaheri, S. Shoar, M. Naderan and S. S. Hoseini, "The Applications of Genetic Algorithms in Medicine," Oman Medical Journal, vol. 30, no. 6, pp. 406-416, 2015.##[17]. Y. Kutlua and D. Kuntalp, "Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients," Computer Methods and Programs in Biomedicine, vol. 105, no. 3, pp. 257-267, 2012.##[18]. P. Kora and K. S. R. krishna, "Hybrid Firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block," International Journal of the Cardiovascular Academy, vol. 2, no. 1, pp. 44-48, 2016.##[19]. J. S. Wang, C. W. Lin and Y. T. C. Yang, "A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition," Neurocomputing, vol. 116, pp.136-143, 2013.##[20]. H. Li, D. Yuan, X. Ma, D. Cui and L. Cao, "Genetic Algorithm for the Optimization of Features and Neural Networks in ECG Signals Classification," Scientific Reports, vol. 7, pp. 41011, 2017, doi: 10.1038/srep41011.##[21]. P. R. Bokde, "An ECG Beat Classification Using Adaptive Neuro-Fuzzy Inference System," International Research Journal of Advanced Engineering and Science, vol. 2. No. 2, pp. 354-358, 2017.##[22]. P. Bhardwaj, R. R. Choudhary and R. Dayama, "Analysis and Classification of Cardiac Arrhythmia Using ECG Signals," International Journal of Computer Applications, vol. 38, no. 1, pp. 37-40, 2012.##[23]. M. Rahbaripour and B. Mohammadzadeh Asl. "Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers". JSDP. 2018; 15 (1) :55-70##[24]. J. Rodriguez, A. Goni and A. Illarramendi, "Real-Time Classification of ECGs on a PDA," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 1m pp. 23-34, 2005.##[25]. S. Kiranyaz, T. Ince and M. Gabbouj, "Real-Time Patient-Specific ECG Classification by 1D Convolutional Neural Networks," IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 664-675, 2016.##[26]. Y. Zhang, Y. Zhang, B. Lo, W. Xu, "Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection," John Wiley & Sons, Ltd, 2019, DOI: 10.1111/exsy.12432.##[27]. G. B. Moody and R. G. Mark, "The Impact of the MIT-BIH Arrhythmia Database," IEEE Engineering in Medicine and Biology Society, vol. 20, no. 3, pp. 45-50, 2001.##[28]. P. Nguyen and J. M. Kim, "Adaptive ECG Denoising Using Genetic Algorithm-Based Thresholding and Ensemble Emprical Mode Decomposition," Information Sciences, vol. 373, pp. 499-511, 2016.##[29]. S. G. Chang, B. Yu and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532-1546, 2000.##[30]. Y. Kutlu and D. Kuntalp, "Feature Extraction for ECG Heartbeats Using Higher Order Statistics of WPD Coefficients," Computer Methods and Programs in Biomedicine, vol. 105, no. 3, pp. 257-267, 2012.##[31]. S. Mirjalili and A. Lewis, "S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization," Swarm and Evolutionary Computation, vol. 9, pp. 1-14, 2013.##[32]. R. Parikh, A. Mathai, S. Parikh, G. C. Sekhar and R. Thomas, "Understanding and using sensitivity, specificity and predictive values," Indian Journal of Ophthalmology, vol. 56, no. 1, pp. 45-50, 2008.##[33]. S. S. Mehta and N. S. Lingayat, "SVM-Based Algorithm for Recognition of QRS Complexes in Electrocardiogram," IRBM, vol. 29, pp. 310-317, 2008.##[34]. J. Saeedi, S. M. Ahadi and K. Faez, "Robust voice activity detection directed by noise classification. Signal," Image and Video Processing, vol. 9, no. 3, pp. 561-572, 2013.##[35]. S. Dutta, A. Chatterjee and S. Munshi, "Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification," Medical Engineering & Physics, vol. 32, no. 10, pp. 1161-1169, 2010.##[36]. W. Jatmiko, W. P. Nulad, I. E. Matul, I. M. A. Setiawan and P. Mursanto, "Heart Beat Classification Using Wavelet Feature Based on Neural Network," WSEAS Transactions on Systems, vol. 10, no. 1, pp. 17-26, 2011.##[37]. A. Ebahimzadeh, B. Shakiba and A. Khazaee, "Detection of electrocardiogram signals using an efficient method," Applied Soft Computing, vol. 22, pp. 108-117, 2014.##[1]. گایتون آ، هال ج ا، 1389، "فیزیولوژی پزشکی"، ترجمه شادان ف، تهران، انتشارات چهر، جلد اول.##[1] A. Guyton, H. Hall, "Medical Physiology", Translated by F. Shadan, Tehran, Chehr Publication, Vol. I, 2010.##[2]. Md. A. Kabir and C. Shahnaz, "Denoising of ECG Signals Based on Noise Reduction Algorithms in EMD and Wavelet Domains," Biomedical Signal Processing and Control, vol. 7, no. 5, pp. 481- 489, 2012.##[3]. N. Li and P. Li, "An Improved Algorithm Based on EMD-Wavelet for ECG Signal De-noising," In: International Joint Conference on Computational Sciences and Optimization, Sanya, pp. 825-827, 2009.##[4]. K. M. Chang, "Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition," Sensors, vol. 10, no. 6, pp. 6063-6080, 2010.##[5]. S. Poungponsri and X. H. Yu, "An Adaptive Filtering Approach for Electrocardiogram (ECG) Signal Noise Reduction Using Neural Networks," Neurocomputing, vol. 117, pp. 206- 213, 2013.##[6]. H. Y. Lin, S. Y. Liang, Y. L. Ho, Y. H. Lin and H. P. Ma, "Discrete-Wavelet-Transform-Based Noise Removal and Feature Extraction for ECG Signals," IRBM, vol. 35, no. 6, pp. 351-361, 2014.##[7]. V. Joshi, A. R. Verma and Y. Singh, "De-noising of ECG Signal Using Adaptive Filter Based on MPSO," Procedia Computer Science, vol. 57, pp. 395-402, 2015.##[8]. S. Karpagachelvi, M. Arthanari and M. Sivakumar, "ECG Feature Extraction Techniques - A Survey Approach," International Journal of Computer Science and Information Security, vol. 8, no. 1, 2010, arXiv: 1005.0957.##[9]. S. Coumo, G. De. Pietro, R. Farina, A. Galletti and G. Sannino, "A Revised Scheme for Real Time ECG Signal Denoising Based on Recursive Filtering", Biomedical Signal Processing and Control, vol. 27, pp. 134-144, 2016.##[10]. M. Korurek and A. Nizam, "Clustering MIT-BIH arrhythmias with ant colony optimization using time domain and PCA compressed wavelet coefficients," Digital Signal Processing, vol. 20, no. 4, pp. 1050-1060, 2010.##[11]. N. Acir, "Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm," Neural Computing & Applications, vol. 14, no. 4, pp. 299-309, 2005.##[12]. P. de Chazal, M. O'Dwyer and R. B. Reilly, "Automatic classification of heartbeats using ECG morphology and heartbeat interval features", IEEE Transactions on Biomedical Engineering, vol. 51, no. 7, pp. 1196-1206, 2004.##[13]. M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt and L. Sornmo, "Clustering ECG complexes using hermite functions and self-organizing maps," IEEE Transactions on Biomedical Engineering, vol. 47, no. 7, pp. 838-848, 2000.##[14]. L. Senhadji, J. J. Bellanger, G. Carrault and G. Passariello, "Comparing wavelet transforms for recognizing cardiac patterns," IEEE Engineering in Medicine and Biology Magazine, vol. 14, no. 2, pp. 167-173, 1995.##[15]. R. Rodriguez, A. Mexicano, J. Bila, S. Cervantes and R. Ponce, "Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis," Journal of Applied Research and Technology, vol. 13, no. 2, pp. 261-269, 2015.##[16]. A. Ghaheri, S. Shoar, M. Naderan and S. S. Hoseini, "The Applications of Genetic Algorithms in Medicine," Oman Medical Journal, vol. 30, no. 6, pp. 406-416, 2015.##[17]. Y. Kutlua and D. Kuntalp, "Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients," Computer Methods and Programs in Biomedicine, vol. 105, no. 3, pp. 257-267, 2012.##[18]. P. Kora and K. S. R. krishna, "Hybrid Firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block," International Journal of the Cardiovascular Academy, vol. 2, no. 1, pp. 44-48, 2016.##[19]. J. S. Wang, C. W. Lin and Y. T. C. Yang, "A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition," Neurocomputing, vol. 116, pp.136-143, 2013.##[20]. H. Li, D. Yuan, X. Ma, D. Cui and L. Cao, "Genetic Algorithm for the Optimization of Features and Neural Networks in ECG Signals Classification," Scientific Reports, vol. 7, pp. 41011, 2017, doi: 10.1038/srep41011.##[21]. P. R. Bokde, "An ECG Beat Classification Using Adaptive Neuro-Fuzzy Inference System," International Research Journal of Advanced Engineering and Science, vol. 2. No. 2, pp. 354-358, 2017.##[22]. P. Bhardwaj, R. R. Choudhary and R. Dayama, "Analysis and Classification of Cardiac Arrhythmia Using ECG Signals," International Journal of Computer Applications, vol. 38, no. 1, pp. 37-40, 2012.##[23]. رهبری پور، م. و محمدزاده اصل، ب. 1397. تشخیص آریتمی انقباضات زودرس بطنی در سیگنال الکتریکی قلب با استفاده ازترکیب طبقه بندها. پردازش علایم و داده ها, 15(1), 55-70.##[23]. M. Rahbaripour and B. Mohammadzadeh Asl. "Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers". JSDP. 2018; 15 (1) :55-70##[24]. J. Rodriguez, A. Goni and A. Illarramendi, "Real-Time Classification of ECGs on a PDA," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 1m pp. 23-34, 2005.##[25]. S. Kiranyaz, T. Ince and M. Gabbouj, "Real-Time Patient-Specific ECG Classification by 1D Convolutional Neural Networks," IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 664-675, 2016.##[26]. Y. Zhang, Y. Zhang, B. Lo, W. Xu, "Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection," John Wiley & Sons, Ltd, 2019, DOI: 10.1111/exsy.12432.##[27]. G. B. Moody and R. G. Mark, "The Impact of the MIT-BIH Arrhythmia Database," IEEE Engineering in Medicine and Biology Society, vol. 20, no. 3, pp. 45-50, 2001.##[28]. P. Nguyen and J. M. Kim, "Adaptive ECG Denoising Using Genetic Algorithm-Based Thresholding and Ensemble Emprical Mode Decomposition," Information Sciences, vol. 373, pp. 499-511, 2016.##[29]. S. G. Chang, B. Yu and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532-1546, 2000.##[30]. Y. Kutlu and D. Kuntalp, "Feature Extraction for ECG Heartbeats Using Higher Order Statistics of WPD Coefficients," Computer Methods and Programs in Biomedicine, vol. 105, no. 3, pp. 257-267, 2012.##[31]. S. Mirjalili and A. Lewis, "S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization," Swarm and Evolutionary Computation, vol. 9, pp. 1-14, 2013.##[32]. R. Parikh, A. Mathai, S. Parikh, G. C. Sekhar and R. Thomas, "Understanding and using sensitivity, specificity and predictive values," Indian Journal of Ophthalmology, vol. 56, no. 1, pp. 45-50, 2008.##[33]. S. S. Mehta and N. S. Lingayat, "SVM-Based Algorithm for Recognition of QRS Complexes in Electrocardiogram," IRBM, vol. 29, pp. 310-317, 2008.##[34]. J. Saeedi, S. M. Ahadi and K. Faez, "Robust voice activity detection directed by noise classification. Signal," Image and Video Processing, vol. 9, no. 3, pp. 561-572, 2013.##[35]. S. Dutta, A. Chatterjee and S. Munshi, "Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification," Medical Engineering & Physics, vol. 32, no. 10, pp. 1161-1169, 2010.##[36]. W. Jatmiko, W. P. Nulad, I. E. Matul, I. M. A. Setiawan and P. Mursanto, "Heart Beat Classification Using Wavelet Feature Based on Neural Network," WSEAS Transactions on Systems, vol. 10, no. 1, pp. 17-26, 2011.##[37]. A. Ebahimzadeh, B. Shakiba and A. Khazaee, "Detection of electrocardiogram signals using an efficient method," Applied Soft Computing, vol. 22, pp. 108-117, 2014.## ##