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بهبود امنیت پلاتون در شبکه های خودرویی Improve the security of the platoon in vehicular networking 1 1 با رشد روزافزون خودروها و افزایش شبکه های حمل و نقل و به تبع آن نیاز به یک سیستم امن­تر و کارآمدتر، شبکه­های خودرویی مورد توجه واقع شده­اند. در همین راستا برای مدیریت بهینه و ساده­تر سیستم­های حمل و نقل، دسته­بندی خودروها به پلاتون پیشنهاد شده است. هرچند شبکه­های خودرویی و پروتکل ارتباطی استاندارد IEEE 802.11p ابزار کلیدی برای توسعه برنامه­های کاربردی پلاتون هستند، اما همکاری بین خودروها در این پروتکل براساس یک ساختار ارتباطی قابل اعتماد نیست؛ و ظهور ناگهانی یک حمله مخرب می­تواند باعث به خطر افتادن صحت جریان ترافیک داده­ها گردد. هرچند برای مقابله با مشکلات مطرح شده الگوریتم رای گیری پیشنهاد شده بود اما به دلیل نیاز خودروها به دریافت اطلاعات از حداقل دو خودرو جهت انجام رای گیری، در اغلب توپولوژی­ها از کارایی لازم برخوردار نبود؛ و سبب کاهش اثر حمله دست­کاری برای خودرو قربانی نگردیده بود. در این پژوهش جهت رفع مشکلات الگوریتم رای گیری، راه­کار مناسبی با استفاده از تغییرات فاصله هر خودرو نسبت به خودرو رهبر و همچنین بهترین سرعت خودروی مخرب نسبت به سرعت رهبر پیشنهاد شده است. شبیه­سازی راه­کار پیشنهادی نشان می­دهد که این پژوهش نسبت به حملات دست­کاری در زمان 43/0 ثانیه و نسبت به حملات جعل پیام در زمان 48/0 ثانیه واکنش نشان می­دهد. همچنین مشکلات الگوریتم رای­گیری را برطرف کرده است. 2 The growth of vehicles and the increase in urban traffic have led to the need for safe, secure and efficient Intelligent Transportation Systems (ITS). Improving the efficiency of ITS needs an efficient communication system. To this end, vehicular networks have been considered as a communication infrastructure in ITS. One of the mechanisms to manage vehicles in ITS is to categorize vehicles into platoon. Platoon is proposed as a way to increase road capacity, improve safety, minimize travel time, increase fuel efficiency, reduce environmental impact, lower-traffic jams and facilitate driving. To create a platoon through autonomous cooperative driving, vehicles must be able to communicate wirelessly, which is possible through vehicular networks. Wireless communications in vehicular networks suffer from three major problems, which are: limited range of radiofrequency, high data transmission over the wireless network, as well as security problems. Using omnidirectional antennas and utilizing DSRC channels in the lower layers are the base solutions to overcome these issues but, Omnidirectional antennas spread the vulnerability to all nodes within the signal propagation range and congestion challenges in the DSRC channel intensify the collision of packets. Such issues decrease the platoon's stability and security. Recent studies on vehicular networks security have focused on presenting solutions to reduce vulnerabilities at the level of communications. However, while the security of communications has been extensively explored in previous work, the security of platoons has recently been considered by researchers. In vehicular networks, an attack can pose a threat to the security and privacy of the platoon, so the security of the platoon has received a great deal of attention.  To improve the security of the platoon, the SP-VLC solution is proposed. This method uses asymmetric encryption and the transmission of information through Visible Light Communications (VLC) to overcome security challenges. In intra-platoon attacks, the attacker is a member of the platoon and has access to all keys and encrypted information, so this solution is not effective against platoon’s internal attacks. Moreover, the SP-VLC solution uses only the data flow topology of predecessor-following to transfer data. Another solution which fixes internal attack problem of SP-VLC solution is voting method. But this solution does not work well for the "predecessor-following" information flow topology because vehicles in the voting process need to receive information from at least two vehicles, while in the mentioned topology, vehicles receive information only from the vehicle in front.   Therefore, the voting solution does not have the required efficiency in "predecessor-following" topology. To overcome the challenges of the voting method, a new solution has been proposed that takes advantage of changes in the distance of each vehicle from the leader vehicle and the best speed of the malicious vehicle compared to the leader speed before the attack. Also, in order to evaluate the efficiency of the proposed solution, evaluation in Bidirectional-leader (BDL) and Predecessor-leader following (PLF) topologies has been performed as a representative of other information flow topologies. The performance of the proposed solution has been evaluated using OMNeT ++, SUMO, and PLEXE tools. To measure the efficiency of the proposed method, the parameters of position error, speed error and control effort have been used. The simulation of the proposed solution shows that this study responds to spoofing attack in 0.43 seconds and to message falsification attack in 0.48 seconds. 3 24 2020/11/24 1399/9/4 2021/12/20 1400/9/29 تیمور درزاده teymoor dorzadeh سیستان و بلوچستان ایران teymoor_drzfb@pgs.usb.ac.ir نیک محمد بلوچ زهی NikMohammad Balouchzahi سیستان و بلوچستان ایران Balouchzahi@ece.usb.ac.ir احمد بختیاری شهری Ahmad Bakhtiyarishehri سیستان و بلوچستان ایران Bakhtiyari@ece.usb.ac.ir platoon vehicular networking security پلاتون شبکه های خودرویی امنیت [1] E. Coelingh and S. Solyom, "All aboard the robotic road train," Ieee Spectrum, vol. 49, no. 11, pp. 34-39, 2012.##[2] A. K. Saha and D. B. Johnson, "Modeling Mobility for Vehicular Ad Hoc Networks," Proceedings of the 1st ACM International Workshop on Vehicular Ad hoc Networks , 2004, pp. 91-92.##[3] Ucar, Seyhan, Ergen, Sinem Coleri and Ozkasap, Oznur, "Security vulnerabilities of IEEE802.11 p and visible light communication based platoon," in 2016 IEEE Vehicular Networking Conference (VNC), 2016.##[4] Ucar, Seyhan, Ergen, Sinem Coleri and Ozkasap, Oznur, "IEEE 802.11 p and visible light hybrid communication based secure autonomous platoon," IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8667-8681, 2018.##[5] M. S. Al-Kahtani, "Survey on security attacks in Vehicular Ad hoc Networks (VANETs)," in International Conference on Signal Processing and Communication Systems, 2012.##[6] A. Petrillo, A. Pescapé and S. Santini, "A collaborative approach for improving the security of vehicular scenarios: The case of platooning," Computer Communications, vol. 122, pp. 59-75, 2018.##[7] DeBruhl, Bruce, Weerakkody, Sean, Sinopoli, Bruno and Tague, Patrick, "Is your commute driving you crazy? a study of misbehavior in vehicular platoons," in Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks, 2015.##[8] Kim, Yeongkwun and Kim, Injoo, "Security issues in vehicular networks," in The International Conference on Information Networking 2013 (ICOIN), 2013.##[9] A.-S. K. Pathan, Security of self-organizing networks: MANET, WSN, WMN, VANET, CRC press, 2016.##[10] Garip, Mevlut Turker, Gursoy, Mehmet Emre, Reiher, Peter and Gerla, Mario, "Congestion attacks to autonomous cars using vehicular botnets," in NDSS Workshop on Security of Emerging Networking Technologies (SENT), San Diego, CA, 2015.##[11] Engoulou, Richard Gilles, Bellaïche, Martine, Pierre, Samuel and Quintero, Alejandro, "VANET security surveys," Computer Communications, vol. 44, pp. 1-13, 2014.##[12] Alcaraz, Cristina, Lopez, Javier and Wolthusen, Stephen, "OCPP protocol: Security threats and challenges," IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2452-2459, 2017.##[13] Crepeau, Claude, Davis, Carlton R and Maheswaran, Muthucumaru, "A secure MANET routing protocol with resilience against byzantine behaviours of malicious or selfish nodes," in 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 2007.##[14] Hasrouny, Hamssa, Samhat, Abed Ellatif, Bassil, Carole and Laouiti, Anis, "VANet security challenges and solutions: A survey," Vehicular Communications, vol. 7, pp. 7-20, 2017.##[15] Jahanshahi, Niloofar and Ferrari, Riccardo MG, "Attack detection and estimation in cooperative vehicles platoons: A sliding mode observer approach," IFAC-PapersOnLine, vol. 21, no. 23, pp. 212-217, 2018.##[16] Boeira, Felipe, Asplund, Mikael and Barcellos, Marinho P, "Mitigating position falsification attacks in vehicular platooning," in 2018 IEEE Vehicular Networking Conference (VNC), 2018.##[17] Mousavinejad, Eman, Yang, Fuwen, Han, Qing-Long, Qiu, Quanwei and Vlacic, Ljubo, "Cyber attack detection in platoon-based vehicular networked control systems," in 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE), 2018.##[18] Merco, Roberto, Biron, Zoleikha Abdollahi and Pisu, Pierluigi, "Replay attack detection in a platoon of connected vehicles with cooperative adaptive cruise control," in 2018 Annual American Control Conference (ACC), 2018.##[19] Santini, Stefania, Salvi, Alessandro, Valente, Antonio Saverio, Pescapé, Antonio, Segata, Michele and Cigno, Renato Lo, "A consensus-based approach for platooning with intervehicular communications and its validation in realistic scenarios," IEEE Transactions on Vehicular Technology, vol. 66, no. 3, pp. 1985-1999, 2016.##[20] Yang, Zheng, Shengbo, Eben Li, Jianqiang, Wang, Dongpu, Cao and Keqiang, Li, "Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies," IEEE Transactions on Intelligent Transportation Systems , vol. 17, no. 1, pp. 14 - 26, 2016.##[21] S. E. Li, Y. Zheng, K. Li and J. Wang, "An Overview of Vehicular Platoon Control under the Four-Component," in 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 2015.##[22] Yang, Zheng, Shengbo, Eben Li, Jianqiang, Wang, Le, Yi Wang and Keqiang, Li, "Influence of information flow topology on closed-loop stability of vehicle platoon with rigid formation," in International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014.##[23] Yongcan, Cao, Wenwu, Yu, Wei, Ren and Guanrong, Chen, "An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination," IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 427 - 438, 2013.##[24] Jean-Pierre, Richard, "Time-delay systems:an overview of some recent advances and open problems," Automatica, 2003.##[25] D, Swaroop and J.K, Hedrick, "String stability of interconnected systems," IEEE Transactions on Automatic Control, vol. 41, no. 3, pp. 349 - 357, 1996.##[26] A. Salvi, S. Santini and A. S. Valente, "Design, analysis and performance evaluation of a third order distributed protocol for platooning in the presence of timevarying delays and switching topologies," Transportation Research Part C: Emerging Technologies, pp. 360-383, 2017.##[27] A. Botta, A. Pescape and G. Ventre, "Quality of service statistics over heterogeneous networks: Analysis and applications," European Journal of Operational Research, vol. 191, no. 3, pp. 1075-1088, 2008.##[28] R. P. Karrer, I. Matyasovszki, A. Botta and A. Pescape, "MagNets - experiences from deploying a joint research-operational next-generation wireless access network testbed," in 2007 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities, Lake Buena Vista, FL, USA, 2007.##[29] G. Chen and F. L. Lewis, "Leader-following control for multiple inertial agents," International Journal of Robust and Nonlinear Control, vol. 21, no. 8, pp. 925-942, 2011.##[30] M. Segata, S. Joerer, B. Bloessl, C. Sommer, F. Dressler and R. L. Cigno, "PLEXE: A Platooning Extension for Veins," 2014 IEEE Vehicular Networking Conference (VNC), 2014, pp. 53-60.##[31] C. Sommer, "Veins," 2006. [Online]. Available: https://veins.car2x.org/. [Accessed 2020].##[32] A. Varga and R. Hornig, "An overview of the OMNeT++ simulation environment," in SIMUTools 2008 - 1st International ICST Conference on Simulation Tools and Techniques for Communications, Networks and Systems, Belgium, 2008.##[1] E. Coelingh and S. Solyom, "All aboard the robotic road train," Ieee Spectrum, vol. 49, no. 11, pp. 34-39, 2012.##[2] A. K. Saha and D. B. Johnson, "Modeling Mobility for Vehicular Ad Hoc Networks," Proceedings of the 1st ACM International Workshop on Vehicular Ad hoc Networks , 2004, pp. 91-92.##[3] Ucar, Seyhan, Ergen, Sinem Coleri and Ozkasap, Oznur, "Security vulnerabilities of IEEE802.11 p and visible light communication based platoon," in 2016 IEEE Vehicular Networking Conference (VNC), 2016.##[4] Ucar, Seyhan, Ergen, Sinem Coleri and Ozkasap, Oznur, "IEEE 802.11 p and visible light hybrid communication based secure autonomous platoon," IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8667-8681, 2018.##[5] M. S. Al-Kahtani, "Survey on security attacks in Vehicular Ad hoc Networks (VANETs)," in International Conference on Signal Processing and Communication Systems, 2012.##[6] A. Petrillo, A. Pescapé and S. Santini, "A collaborative approach for improving the security of vehicular scenarios: The case of platooning," Computer Communications, vol. 122, pp. 59-75, 2018.##[7] DeBruhl, Bruce, Weerakkody, Sean, Sinopoli, Bruno and Tague, Patrick, "Is your commute driving you crazy? a study of misbehavior in vehicular platoons," in Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks, 2015.##[8] Kim, Yeongkwun and Kim, Injoo, "Security issues in vehicular networks," in The International Conference on Information Networking 2013 (ICOIN), 2013.##[9] A.-S. K. Pathan, Security of self-organizing networks: MANET, WSN, WMN, VANET, CRC press, 2016.##[10] Garip, Mevlut Turker, Gursoy, Mehmet Emre, Reiher, Peter and Gerla, Mario, "Congestion attacks to autonomous cars using vehicular botnets," in NDSS Workshop on Security of Emerging Networking Technologies (SENT), San Diego, CA, 2015.##[11] Engoulou, Richard Gilles, Bellaïche, Martine, Pierre, Samuel and Quintero, Alejandro, "VANET security surveys," Computer Communications, vol. 44, pp. 1-13, 2014.##[12] Alcaraz, Cristina, Lopez, Javier and Wolthusen, Stephen, "OCPP protocol: Security threats and challenges," IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2452-2459, 2017.##[13] Crepeau, Claude, Davis, Carlton R and Maheswaran, Muthucumaru, "A secure MANET routing protocol with resilience against byzantine behaviours of malicious or selfish nodes," in 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 2007.##[14] Hasrouny, Hamssa, Samhat, Abed Ellatif, Bassil, Carole and Laouiti, Anis, "VANet security challenges and solutions: A survey," Vehicular Communications, vol. 7, pp. 7-20, 2017.##[15] Jahanshahi, Niloofar and Ferrari, Riccardo MG, "Attack detection and estimation in cooperative vehicles platoons: A sliding mode observer approach," IFAC-PapersOnLine, vol. 21, no. 23, pp. 212-217, 2018.##[16] Boeira, Felipe, Asplund, Mikael and Barcellos, Marinho P, "Mitigating position falsification attacks in vehicular platooning," in 2018 IEEE Vehicular Networking Conference (VNC), 2018.##[17] Mousavinejad, Eman, Yang, Fuwen, Han, Qing-Long, Qiu, Quanwei and Vlacic, Ljubo, "Cyber attack detection in platoon-based vehicular networked control systems," in 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE), 2018.##[18] Merco, Roberto, Biron, Zoleikha Abdollahi and Pisu, Pierluigi, "Replay attack detection in a platoon of connected vehicles with cooperative adaptive cruise control," in 2018 Annual American Control Conference (ACC), 2018.##[19] Santini, Stefania, Salvi, Alessandro, Valente, Antonio Saverio, Pescapé, Antonio, Segata, Michele and Cigno, Renato Lo, "A consensus-based approach for platooning with intervehicular communications and its validation in realistic scenarios," IEEE Transactions on Vehicular Technology, vol. 66, no. 3, pp. 1985-1999, 2016.##[20] Yang, Zheng, Shengbo, Eben Li, Jianqiang, Wang, Dongpu, Cao and Keqiang, Li, "Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies," IEEE Transactions on Intelligent Transportation Systems , vol. 17, no. 1, pp. 14 - 26, 2016.##[21] S. E. Li, Y. Zheng, K. Li and J. Wang, "An Overview of Vehicular Platoon Control under the Four-Component," in 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 2015.##[22] Yang, Zheng, Shengbo, Eben Li, Jianqiang, Wang, Le, Yi Wang and Keqiang, Li, "Influence of information flow topology on closed-loop stability of vehicle platoon with rigid formation," in International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014.##[23] Yongcan, Cao, Wenwu, Yu, Wei, Ren and Guanrong, Chen, "An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination," IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 427 - 438, 2013.##[24] Jean-Pierre, Richard, "Time-delay systems:an overview of some recent advances and open problems," Automatica, 2003.##[25] D, Swaroop and J.K, Hedrick, "String stability of interconnected systems," IEEE Transactions on Automatic Control, vol. 41, no. 3, pp. 349 - 357, 1996.##[26] A. Salvi, S. Santini and A. S. Valente, "Design, analysis and performance evaluation of a third order distributed protocol for platooning in the presence of timevarying delays and switching topologies," Transportation Research Part C: Emerging Technologies, pp. 360-383, 2017.##[27] A. Botta, A. Pescape and G. Ventre, "Quality of service statistics over heterogeneous networks: Analysis and applications," European Journal of Operational Research, vol. 191, no. 3, pp. 1075-1088, 2008.##[28] R. P. Karrer, I. Matyasovszki, A. Botta and A. Pescape, "MagNets - experiences from deploying a joint research-operational next-generation wireless access network testbed," in 2007 3rd International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities, Lake Buena Vista, FL, USA, 2007.##[29] G. Chen and F. L. Lewis, "Leader-following control for multiple inertial agents," International Journal of Robust and Nonlinear Control, vol. 21, no. 8, pp. 925-942, 2011.##[30] M. Segata, S. Joerer, B. Bloessl, C. Sommer, F. Dressler and R. L. Cigno, "PLEXE: A Platooning Extension for Veins," 2014 IEEE Vehicular Networking Conference (VNC), 2014, pp. 53-60.##[31] C. Sommer, "Veins," 2006. [Online]. Available: https://veins.car2x.org/. [Accessed 2020].##[32] A. Varga and R. Hornig, "An overview of the OMNeT++ simulation environment," in SIMUTools 2008 - 1st International ICST Conference on Simulation Tools and Techniques for Communications, Networks and Systems, Belgium, 2008.## ##

ICTF : زمانبندی وظایف مبتنی بر الگوریتم رقابت استعماری در محیط محاسبات مه ICTF: Imperialist Competitive Algorithm-based Task Scheduling in Fog Computing 1 1 محاسبات مه برای حل چالش های متعدد محیط محاسبات ابری مانند زمان تاخیر بالا، ظرفیت کم و نقص شبکه ارایه گردیده است. در محیط محاسبات مه، دستگاه‌های اینترنت اشیاء  بعنوان یک کاشین محاسباتی کوچک با قابلیت پردازش و ارسال و دریافت اطلاعات، زیر ساخت یک مه را تشکیل می‌دهند. در محیط  مه، پردازش کارها و وظایف و ذخیره داده‌های اینترنت اشیاء بجای ارسال برای سرورهای دور در مراکز داده ابری به صورت محلی در دستگاه‌های اینترنت اشیاء صورت می­پذیرد که این قابلیت منجر به ارایه پاسخ­ سریع‌تر و با تاخیر کمتر و افزایش کیفیت ارایه خدمات در محیط مه می­گردد. بنابراین می‌توان گفت که محاسبات مه بهترین انتخاب برای فعال کردن اینترنت اشیاء در راستای ارایه خدمات کارآمد و امن برای بسیاری از کاربران در لبه شبکه محسوب می‌شود. در محاسبات مه، مدیریت منابع و زمانبندی کار با در نظر گرفتن محدودیت‌های انرژی، زمان، تاخیر  چالش بزرگی محسوب می‌شود. در این مقاله راهکار زمانبندی وظایف مبتنی بر الگوریتم رقابت استعماری برای محاسبات مه ارائه شده است. در راهکار پیشنهادی جمعیت اولیه بطور تصادفی شامل وظایف و ماشین ها شکل می­گیرد و تابع ارزیابی بر اساس معیارهای انرژی، زمان و هزینه تعریف شده و با به کارگیری دو عملگر جذب و انقلاب، الگوریتم زمانبندی وظایف مبتنی بر رقابت استعماری(ICTF) ارایه می­گردد. نتایج شبیه‌سازی نشان می­دهد که ICTF در معیارهای Makespan ، بهره‌وری منابع، مصرف انرژی و انرژی باقیمانده نسبت به سایر روش‌های مشابه کارایی بالاتری دارد. 2 Fog computing address numerous cloud computing challenges such as high latency, low capacity and network failure.  The cloud computing infrastructure includes a large number of IoT devices with the ability to process in the cloud environment.  In fog computing, processing and storage provide on IoT devices locally instead of remote servers, therefore, fog computing is the best choice to enable IoT in order to provide efficient, faster and secure services for many users on the edge of the network.  Fog computing have a variety of challenges. One of these important challenges is resource management and task scheduling such that solving this problem has a great impact on system efficiency and service quality. In this paper, we present a task scheduling approach based on the imperialist competition algorithm namely, Imperialist Competitive Algorithm-based Task Scheduling in Fog Computing (ICTF). In the proposed method, we consider the search space as a directional graph. Assume that each task that contains a set of tasks is a graph with a root node and an end leaf node whose middle nodes are the task set. Each path in this graph that starts at the root node and ends at the leaf is a solution represented by a string. This solution is modeled as a country. Therefore, in the proposed method, the concept of country includes the tasks of a job along with the fog nodes that are assigned to these tasks. The initial population consists of a random number of these solutions. ICTF presents a cost function consisting of three important criteria for assessing the initial population of countries and determining the imperialists and colonies countries include energy, execution time and execution cost. The assimilation operation is performed on two different members of countries, namely the imperialists and colonies country, and two new types of members are created called children. The countries participating in this process are among the best countries and are selected using the cost function. In this process, the best offspring produced are passed on to the next generation, and this operation continues until the final population of the countries is obtained. The assimilation operator has different models and in this article we use the two-point assimilation operator. The name of the revolution operator used in this algorithm is the inverse of the task. This operator randomly selects two tasks belonging to a fog node and moves them together. The above operation is repeated until the population converges and reaches the final answer. We show that our proposed approach is more efficient in terms of makespan, resource utilization, energy consumption and remaining energy compared to the similar approach. 25 38 2020/11/242020/12/7 1399/9/17 2021/12/202021/05/30 1400/3/9 حسین مومنی Hossein Momeni دانشگاه گلستان ایران h.momeni@gu.ac.ir علی یاوری Ali Yavari دانشگاه اراک ایران yavari@ustmb.ac.ir Fog computing resource management task scheduling imperialist competitive algorithm محاسبات مه مدیریت منابع زمانبندی وظایف الگوریتم رقابت استعماری [1] D. Tychalas and H. Karatza, "A scheduling algorithm for a fog computing system with bag-of-tasks jobs: Simulation and performance evaluation," Simul. Model. Pract. Theory, vol. 98, no. 101982, p. 101982, 2020.##[2] M. Yang, H. Ma, S. Wei, Y. Zeng, Y. Chen, and Y. Hu, "A multi-objective task scheduling method for fog computing in cyber-physical-social services," IEEE Access, vol. 8, pp. 65085-65095, 2020.##[3] S. Wang, T. Zhao, and S. Pang, "Task scheduling algorithm based on improved firework algorithm in fog computing," IEEE Access, vol. 8, pp. 32385-32394, 2020.##[4] M. Ghobaei‐Arani, A. Souri, F. Safara, and M. Norouzi, "An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing," Trans. emerg. telecommun. technol., vol. 31, no. 2, 2020.##[5] F. Murtaza, A. Akhunzada, S. ul Islam, J. Boudjadar, and R. Buyya, "QoS-aware service provisioning in fog computing," J. Netw. Comput. Appl., vol. 165, no. 102674, p. 102674, 2020.##[6] J. C. Guevara, R. da S. Torres, and N. L. S. da Fonseca, "On the classification of fog computing applications: A machine learning perspective," J. Netw. Comput. Appl., vol. 159, no. 102596, p. 102596, 2020.##[7] A. Bose, T. Biswas, and P. Kuila, "A novel genetic algorithm-based scheduling for multi-core systems," in Smart Innovations in Communication and Computational Sciences, Singapore: Springer Singapore, 2019, pp. 45-54.##[8] B. Jamil, M. Shojafar, I. Ahmed, A. Ullah, K. Munir, and H. Ijaz, "A job scheduling algorithm for delay and performance optimization in fog computing," Concurr. Comput., vol. 32, no. 7, 2020.##[9] M. Etemadi, M. Ghobaei-Arani, and A. Shahidinejad, "Resource provisioning for IoT services in the fog computing environment: An autonomic approach," Comput. Commun., vol. 161, pp. 109-131, 2020.##[10] M. S. Aslanpour, S. S. Gill, and A. N. Toosi, "Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research," Internet of Things, vol. 12, no. 100273, p. 100273, 2020.##[11] P. Kanani and M. Padole, "Exploring and optimizing the fog computing in different dimensions," Procedia Comput. Sci., vol. 171, pp. 2694-2703, 2020.##[12] M. H. 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Norouzi, "An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing," Trans. emerg. telecommun. technol., vol. 31, no. 2, 2020.##[5] F. Murtaza, A. Akhunzada, S. ul Islam, J. Boudjadar, and R. Buyya, "QoS-aware service provisioning in fog computing," J. Netw. Comput. Appl., vol. 165, no. 102674, p. 102674, 2020.##[6] J. C. Guevara, R. da S. Torres, and N. L. S. da Fonseca, "On the classification of fog computing applications: A machine learning perspective," J. Netw. Comput. Appl., vol. 159, no. 102596, p. 102596, 2020.##[7] A. Bose, T. Biswas, and P. Kuila, "A novel genetic algorithm-based scheduling for multi-core systems," in Smart Innovations in Communication and Computational Sciences, Singapore: Springer Singapore, 2019, pp. 45-54.##[8] B. Jamil, M. Shojafar, I. Ahmed, A. Ullah, K. Munir, and H. Ijaz, "A job scheduling algorithm for delay and performance optimization in fog computing," Concurr. 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Dang, "Efficient fog node resource allocation algorithm based on taboo genetic algorithm," in Advances in Intelligent Systems and Computing, Singapore: Springer Singapore, 2021, pp. 1565-1573.##[24] S. Javanmardi, M. Shojafar, V. Persico, and A. Pescapè, "FPFTS: A joint fuzzy particle swarm optimization mobility‐aware approach to fog task scheduling algorithm for Internet of Things devices," Softw. Pract. Exp., no. spe.2867, 2020.##[25] Z. Tang, L. Qi, Z. Cheng, K. Li, S. U. Khan, and K. Li, "An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment," J. Grid Comput., vol. 14, no. 1, pp. 55-74, 2016.##[26] P. Hosseinioun, M. Kheirabadi, S. R. Kamel Tabbakh, and R. Ghaemi, "A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm," J. Parallel Distrib. Comput., vol. 143, pp. 88-96, 2020.##[27] Q. Huang, S. Su, J. Li, P. Xu, K. Shuang, and X. 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خوشه بندی گروهی طیفی لاپلاسی-p نیمه نظارتی برای داده های با ابعاد بالا Ensembling semi-supervised p-spectral clustering for high dimensional data 1 1 با توجه به افزایش روزافزون اطلاعات و تحلیل دقیق آنها مسأله خوشه ­بندی که برای آشکارسازی الگوهای پنهان موجود در داده ­ها مورد استفاده قرار می­ گیرد، همچنان از اهمیت بالایی برخوردار است. از طرفی خوشه ­بندی داده ­های با ابعاد بالا با استفاده از روش­های سنتی پیشین دارای محدودیت ­های زیادی است. در مقاله حاضر، یک روش خوشه­ بندی گروهی نیمه­ نظارتی برای مجموعه ­ای از داده­ های پزشکی با ابعاد بالا پیشنهاد می ­شود. در فرموله­ سازی مسأله خوشه­ بندی اطلاعات نظارتی اندکی به عنوان دانش پیشین با استفاده از اطلاعات مربوط به تشابه و یا عدم تشابه (بصورت تعدادی زوج محدودیت­ های دوبه­ دو) در نظر گرفته می­شود. در ابتدا با استفاده از خاصیت تراگذری زوج محدودیت­ های دوبه ­دو را بر روی تمام داده ­ها تعمیم می ­دهیم. سپس با تقسیم فضای ویژگی به صورت تصادفی به چندین زیرفضای نابرابر ابعاد داده ­ها را کاهش می­ دهیم. خوشه­ بندی طیفی نیمه­ نظارتی مبتنی بر گراف لاپلاسی- p در هر زیر فضا بطور مستقل انجام می ­شود. سپس با استفاده از نتایج هر کدام یک ماتریس مجاورت، حاصل از تجمیع نتایج هر کدام (مبتنی بر یادگیری گروهی) ایجاد می ­شود. در نهایت با استفاده از چند عملگر جستجو روی زیرفضاها، بهترین زیرفضا، یعنی زیرفضایی که بهترین نتیجه خوشه­ بندی را دارد، می­ یابیم. نتایج آزمایشات متعدد بر روی چندین داده ­ی پزشکی با ابعاد بالا نشان می­ دهد که رویکرد پیشنهادی، عملکرد و کارآیی بهتری نسبت به روش­های پیشین دارد. 2 Due to the increasing information and the detailed analysis of them, the clustering problems that detect the hidden patterns lie in the data are still of great importance. On the other hand, clustering of high-dimensional data using previous traditional methods has many limitations. In this study, a semi-supervised ensemble clustering method is proposed for a set of high-dimensional medical data. In the proposed method of this study, little information is available as prior knowledge using the information on similarity or dissimilarity (as a number of pairwise constraints). Initially using the transitive property, we generalize the pairwise constraints to all data. Then we divide the feature space into a number of sub-spaces, and to find the optimal clustering solution, the feature space is divided into an unequal number of sub-spaces randomly. A semi-supervised spectral clustering based on the p-Laplacian graph is performed at each sub-space independently. Specifically, to increase the accuracy of spectral clustering, we have used the spectral clustering method based on the p-Laplacian graph. The p-Laplacian graph is a nonlinear generalization of the Laplacian graph. The results of any clustering solutions are compared with the pairwise constraints and according to the level of matching, a degree of confidence is assigned to each clustering solution. Based on these degrees of confidence, an ensemble adjacency matrix is formed, which is the result of considering the results of all clustering solutions for each sub-space. This ensemble adjacency matrix is used in the final spectral clustering algorithm to find the clustering solution of the whole sub-space. Since the sub-spaces are generated randomly with an unequal number of features, clustering results are strongly influenced by different initial values. Therefore, it is necessary to find the optimal sub-space set. To this end, a search algorithm is designed to find the optimal sub-space set. The search process is initialized by forming several sets (we call each set an environment) consisting of several numbers of sub-spaces. An optimal environment is the one that has the best clustering results. The search algorithm utilized three search operators to find the optimal environment. The search operators search all the environments and the consequent sub-spaces both locally and globally. These operators combine two environments and/or replace an environment with a newly generated one. Each search operator tries to find the best possible environment in the entire search space or in a local space. We evaluate the performance of our proposed clustering schema on 20 cancer gene datasets. The normalized mutual information (NMI) criterion and the adjusted rand index (ARI) are used to evaluate the performance evaluation. We first examine the effect of a different number of pairwise constraints. As expected, with increasing the number of pairwise constraints, the efficiency of the proposed method also increases. For example, the NMI value increases from 0.6 to 0.9 on the Khan-2001 dataset, when the number of pairwise constraints increases from 20 to 100. More number of pairwise constraints means more information is available, which helps to improve the performance of the clustering algorithm. Furthermore, we examine the effect of the number of random subspaces. It is observed that increasing the number of random subspaces has a positive effect on clustering performance with respect to the NMI value. In most datasets, when the number of sub-spaces reaches 20, the performance of the proposed method does not change much and is stable. Examining the effect of sampling rate for random subspace generation shows that the proposed method has the best performance in most cancer datasets, such as Armstrong-2002-v3, and Bredel-2005 datasets, when the random subspace generation rate is 0.5, and by deviating the rate from 0.5, the level of satisfaction decreases. Then, the results of the proposed idea are compared with the results of the method proposed in the reference [21] according to ARI and we see that our proposed method has performed better in 12 data sets out of 20 data sets than the method proposed in the reference [21]. Finally, the proposed idea is compared with some metric learning approaches with respect to NMI. We have observed that the proposed method obtained the best results compared to other compared methods on 11 datasets out of 20 datasets. It also achieved the second-best result on 6 out of 20 datasets. For example, the value NMI obtained in the proposed method is 0.1042 more than the reference [21] and it is 0.1846 more than RCA and it is 0.4 more than ITML and also it is 0.468 more than DCA on the Bredel-2005 dataset. Utilizing ensemble clustering methods besides the confidence factor improves the ability of the proposed algorithm to achieve better results. Also, utilizing the transitive operators as well as the selection of random subspaces of unequal sizes play an important role in achieving better performance for the proposed algorithm. Using the p-Laplacian spectral clustering method produces a better, more balanced, and normal volume of clusters compared to the standard spectral clustering. Another effective approach to the performance of the proposed method is to use search operators to find the best subspace, which leads to better results.   39 58 2020/11/242020/12/72020/12/16 1399/9/26 2021/12/202021/05/302022/10/8 1401/7/16 صدیقه صفری Sedigheh Safari دانشگاه شهید باهنر کرمان ایران s.safari@eng.uk.ac.ir فاطمه افسری Fatemeh Afsari دانشگاه شهید باهنر کرمان ایران afsari.f@gmail.com Clustering Subspace Learning Ensemble Learning Semi-supervised Learning Pairwise Constraints خوشه بندی یادگیری زیرفضا یادگیری گروهی یادگیری نیمه نظارتی زوج محدودیت های دوبه دو [1] C. Chrysouli and A. Tefas, "Spectral clustering and semi-supervised learning using evolving similarity graphs," Applied Soft Computing, vol. 34, pp. 625-637, 2015.##[2] W. Hu, C. Chen, F. Ye, Z. Zheng, and G. Ling, "Nonnegative Spectral Clustering for Large-Scale Semi-supervised Learning," in International Conference on Database Systems for Advanced Applications, 2019: Springer, pp. 287-291.##[3] E. Hancer, B. 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روش تکاملی بهبود انتخاب الگوریتم در سیستم های توصیه گر فیلترینگ مشارکتی An evolutionary approach for automating the selection of optimum Algorithm in Collaborative Filtering Recommender Systems 1 1 سیستم­ توصیه ­گر می­تواند به عنوان نرم ­افزاری که به افراد مناسب­ترین آیتم ­ها را پیشنهاد می­کنند، تعریف شود. این سیستم­ها به عنوان یک مشاور کار می کنند تا افراد را در یافتن محصولات مورد علاقه­ شان راهنمایی کنند. امروزه برای پیاده ­سازی سیستم­های توصیه ­گر، الگوریتم های متفاوتی به­ کار برده می­شود؛ دسته ­ای از این الگوریتم­ها، الگوریتم­های فیلترینگ مشارکتی نام دارند که در آن­ها از شباهت کاربران یا شباهت روابط ایجاد شده توسط کاربران میان آیتم­ها برای تعیین پیشنهادها استفاده می­شود. روش­های فیلترینگ مشارکتی، می توانند آیتم­ هایی موردپسند کاربر اما با محتوای کاملا متفاوت نسبت به سلایق پیشین او پیشنهاد دهند که  این پیشنهادها براساس علایق کاربران مشابه به کاربر هدف تولید شده ­است. در روش­های فیلترینگ مشارکتی برای ایجاد مدل یا محاسبه شباهت بین کاربران، معیارها و توابع فاصله متفاوتی استفاده شده­ است و روش بهینه که بهترین لیست از پیشنهادها را تولید کند، همواره یکسان نیست و متناسب با داده­های موجود، این الگوریتم در میان الگوریتم­های فیلترینگ مشارکتی، تغییر می کند؛ به همین دلیل، انتخاب روش مناسب برای ایجاد یک سیستم توصیه­ گر، به چالشی برای طراحان این سیستم تبدیل شده است. در این مقاله روشی مبتنی بر الگوریتم ژنتیک برای برای اجتماع نتایج روش­های همسایه­ محور و انتخاب بهترین پیشنهادها از بین پیشنهادهای تولید شده توسط روشهای مختلف با معیارهای فاصله متفاوت  برای یک سیستم توصیه­ گر با هدف پیشنهاد N آیتم برتر ارائه شده است. در پیاده سازی این روش­ها ، علاوه بر محاسبه شباهت مستقیم کاربران، تعیین اطمینان غیر مستقیم کاربران نیز مد نظر قرار گرفته است تا اطلاعات موجود از ارتباط بین علایق کاربران افزایش یابد. روش پیشنهادی برای هر مجموعه داده،­ یک ترکیب از روش­های فیلترینگ مشارکتی ایجاد می­کند که علاوه بر در نظرگرفتن محدودیت­های زمانی در تولید آن، دقت مناسبی دارد.   این روش با روش­های فیلترینگ مشارکتی همسایه­ محور به­ صورت مجزا و همینطور سیستم­های مشابه با استفاده از دیتاست Movielens 100k  و 1M  Movielens  و  Hetrec2011 مقایسه شده است .آزمایش­ها برتری و توانایی تولید پیشنهادهای دقیق­تر به کاربران با این روش را نشان می­دهد. 2 Recommender system can be defined as a software that suggests the most appropriate and closest item to the user's taste. They work as a counselor, behaving in such a way to guide people in the discovery of products of interest. Nowadays A great number of recommendation methods are used to implement a recommender system, a group of these algorithms are called collaborative filtering. These methods use the similarity between users or the similarity between items according to their user rating patterns for generating recommendations. Collaborative filtering algorithms can recommend the user, interesting items which are not similar to items she has rated before. These recommendations are generated according to the preferences of users with similar taste to the target user.  Different similarity functions and metrics have been used to create the model or compute the similarity in collaborative filtering methods. The best method which generates the most relevant items is not always the same and it may change according to the available data of users and items, because each approach has particularities and depends on the context to be applied. Thus, it becomes a hard task for system designers to manually select an appropriate method among the techniques. This article proposes an approach based on genetic algorithm for rank aggregation of memory based collaborative filtering methods and chooses the most relevant recommendations generated by different similarity techniques to create a Top-N recommender system. In order to implement these techniques, in addition to computing the similarity between users, inferred trust is also computed to increase the amount of available information about relations between user interests. The final method proposes a combination of collaborative filtering techniques for each data set, which in addition to considering time limits, has an acceptable precision for making recommendations. The proposed method has been compared against memory based collaborative filtering methods and similar methods. Experiments were performed using 1M MovieLens and 100k MovieLens and HetRec2011 data sets. The results show that the methodology proposed in this paper performs better and has a higher precision in generating recommendations for users than any of similar algorithms. 59 78 2020/11/242020/12/72020/12/162021/01/30 1399/11/11 2021/12/202021/05/302022/10/82023/02/22 1401/12/3 مژده رباطی انارکی mojdeh robati anaraki دانشگاه الزهرا ایران Robati.m7@gmail.com نوشین ریاحی nooshin riahi دانشگاه الزهرا ایران nriahi@alzahra.ac.ir recommender systems collaborative filtering genetic algorithm similarity metrics rank aggregation inferred trust. سیستم توصیه گر فیلترینگ مشارکتی الگوریتم ژنتیک توابع فاصله اجتماع نتایج اطمینان استنتاج شده. [1] Y. Koren, "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Mode,," Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008, 2008.##[2] F. Ricci, L. Rokach and B. Shapira, "Recommender Systems Handbook," Springer, 2011.##[3] F. Isinkaye, Y. Folajimi and B. Ojokoh, "Recommendation systems: Principles, methods and evaluation," Egyptian Informatics Journal 16, 261-273 ,2015.##[4] E. Q. Silva, C. G.Camilo-Junior, L. MarioL.Pascoal and C. Thierson, "An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering," IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 2014, pp. 959-966, 2014.##[5] B. B. Sinha and R. Dhanalakshmi, "Evolution of recommender system over the time," Soft Comput 23, 12169-12188, 2019.##[6] S. Gupta and S. Nagpal, "Trust Aware Recommender Systems: A Survey on Implicit Trust Generation Techniques," (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (4) , 3594-3599, 2015.##[7] M. Papagelis, D. Plexousakis and T. K. Kutsuras, "Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences," iTrust 2005, LNCS 3477, pp. 224 - 239, 2005..##[8] J. Bobadilla, F. Ortega, A. Hernando and A. Gutiérrez, "Recommender systems survey," Knowledge-Based Systems 46 109-132,2013.##[9] T. Dunning, "Accurate methods for the statistics of surprise and coincidence," Computational Linguistics Volume 19, Number 1 pages 61-74,1993.##[10] T. K, Paradarami, NathanielD, Bastian, J. and Wightman, "A hybrid recommender system using artificial neural networks," Expert Systems With Applications 83 (2017).##[11] Z. Kang, C. Peng and Q. Cheng, "Top-N Recommender System via Matrix Completion," Association for the Advancement of Artificial 2016.##[12] U. Kużelewska, "Clustering Algorithms in Hybrid Recommender System on MovieLens Data," UDIES IN LOGIC, GRAMMAR AND RHETORIC 37 (50) 2014##[13] M. Hosseini, M. Nasrollahi and A. Baghaei, "A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering,". JSDP 2018; 15 (2) :119-132.##[14] P. Massa and B. Bhattacharjee, "Using Trust in Recommender Systems:An Experimental Analysis," iTrust,Springer-Verlag Berlin Heidelberg , LNCS 2995, pp. 221-235,2004.##[15] W. Yuan, L. Shu, H. Chao, D. Guan, Y. Lee and S. Lee, "itars: trustaware recommender system using implicit trust networks,," Communications,IET, 4(14):17091721, 2010.##[16] SAMUEL, O. E. L, D. VICTOR, L. ANISIO, M. LUIZ and P. GISELE L, "Is Rank Aggregation Effective in Recommender Systems? An Experimental Analysis," ACM Transactions on Intelligent Systems and Technology 11(2), 2019.##[17] M. T. Ribeiro, Nivio Ziviani,, Edleno Silva De Moura and , Itamar Hata,, "Multiobjective Pareto-Efficient Approaches for Recommender Systems," ACM Trans. Intell. Syst. Technol. 5, 4, Article 53 , 2014.##[18] S. Oliveira, V. Diniz, A. Lacerda and G. L. Pappa., "Evolutionary rank aggregation for recommender systems.," IEEE Congress on Evolutionary Computation (CEC). 255-262, 2016.##[19] s. Mirjalili, "Evolutionary Algorithms and Neural Networks," Studies in Computational Intelligence 780,Springer International Publishing AG, part of Springer Nature, 2019.##[20] B. Alhijawi and Y. Kilani, "A collaborative filtering recommender system using genetic algorithm," Information Processing & Management 57(6):102310, 2020.##[21] M. Bhusal and A. Shakya, "Collaborative Filtering Recommender System Using Genetic Algorithm," Proceedings of IOE Graduate Conference, Volume: 6, 2019.##[22] J. Xiao, M. Luo, J.-M. Chen and J.-J. Li, "An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior," Springer International Publishing Switzerland , ICIC 2015, Part III, LNAI 9227, pp. 453-460,, 2015.##[23] F. H. d. Olmo and E. Gaudioso, "Evaluation of recommender systems: A new approach," Expert Systems with Applications 35 790-804 ,2008.##[24] G. Takacs, I. Pilaszy, B. Nemeth and D. Tikk, "Scalable Collaborative Filtering Approaches for Large Recommender Systems," Journal of Machine Learning Research 10 623-656,, 2009.##[25] S. Oliveira, V. Diniz, A. Lacerda, L. Merschmanm and G. L. Pappa., "Is Rank Aggregation Effective in Recommender Systems? An Experimental Analysis," ACM Trans. Intell.Syst. Technol. 1, 1, Article 1 ,2019.##[26] E. Q. d. Silva, C. G. Camilo, L. M. L. Pascoal and T. C. Rosa, "An evolutionary approach for combining results of recommender systems techniques based on Collaborative Filtering," Expert Systems With Applications 53 204-218,2016.##[1] Y. Koren, "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Mode,," Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008, 2008.##[2] F. Ricci, L. Rokach and B. Shapira, "Recommender Systems Handbook," Springer, 2011.##[3] F. Isinkaye, Y. Folajimi and B. Ojokoh, "Recommendation systems: Principles, methods and evaluation," Egyptian Informatics Journal 16, 261-273 ,2015.##[4] E. Q. Silva, C. G.Camilo-Junior, L. MarioL.Pascoal and C. Thierson, "An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering," IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 2014, pp. 959-966, 2014.##[5] B. B. Sinha and R. Dhanalakshmi, "Evolution of recommender system over the time," Soft Comput 23, 12169-12188, 2019.##[6] S. Gupta and S. Nagpal, "Trust Aware Recommender Systems: A Survey on Implicit Trust Generation Techniques," (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (4) , 3594-3599, 2015.##[7] M. Papagelis, D. Plexousakis and T. K. Kutsuras, "Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences," iTrust 2005, LNCS 3477, pp. 224 - 239, 2005..##[8] J. Bobadilla, F. Ortega, A. Hernando and A. Gutiérrez, "Recommender systems survey," Knowledge-Based Systems 46 109-132,2013.##[9] T. Dunning, "Accurate methods for the statistics of surprise and coincidence," Computational Linguistics Volume 19, Number 1 pages 61-74,1993.##[10] T. K, Paradarami, NathanielD, Bastian, J. and Wightman, "A hybrid recommender system using artificial neural networks," Expert Systems With Applications 83 (2017).##[11] Z. Kang, C. Peng and Q. Cheng, "Top-N Recommender System via Matrix Completion," Association for the Advancement of Artificial 2016.##[12] U. Kużelewska, "Clustering Algorithms in Hybrid Recommender System on MovieLens Data," UDIES IN LOGIC, GRAMMAR AND RHETORIC 37 (50) 2014##[13]حسینی منیره، نصرالهی مقصود، بقائی علی. یک سامانه توصیه‎گر ترکیبی با استفاده از اعتماد و خوشه‎بندی دوجهته به‎منظور افزایش کارایی پالایش‎گروهی. پردازش علائم و داده‌ها. ۱۳۹۷; ۱۵ (۲) :۱۱۹-۱۳۲##[13] M. Hosseini, M. Nasrollahi and A. Baghaei, "A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering,". JSDP 2018; 15 (2) :119-132.##[14] P. Massa and B. Bhattacharjee, "Using Trust in Recommender Systems:An Experimental Analysis," iTrust,Springer-Verlag Berlin Heidelberg , LNCS 2995, pp. 221-235,2004.##[15] W. Yuan, L. Shu, H. Chao, D. Guan, Y. Lee and S. Lee, "itars: trustaware recommender system using implicit trust networks,," Communications,IET, 4(14):17091721, 2010.##[16] SAMUEL, O. E. L, D. VICTOR, L. ANISIO, M. LUIZ and P. GISELE L, "Is Rank Aggregation Effective in Recommender Systems? An Experimental Analysis," ACM Transactions on Intelligent Systems and Technology 11(2), 2019.##[17] M. T. Ribeiro, Nivio Ziviani,, Edleno Silva De Moura and , Itamar Hata,, "Multiobjective Pareto-Efficient Approaches for Recommender Systems," ACM Trans. Intell. Syst. Technol. 5, 4, Article 53 , 2014.##[18] S. Oliveira, V. Diniz, A. Lacerda and G. L. Pappa., "Evolutionary rank aggregation for recommender systems.," IEEE Congress on Evolutionary Computation (CEC). 255-262, 2016.##[19] s. Mirjalili, "Evolutionary Algorithms and Neural Networks," Studies in Computational Intelligence 780,Springer International Publishing AG, part of Springer Nature, 2019.##[20] B. Alhijawi and Y. Kilani, "A collaborative filtering recommender system using genetic algorithm," Information Processing & Management 57(6):102310, 2020.##[21] M. Bhusal and A. Shakya, "Collaborative Filtering Recommender System Using Genetic Algorithm," Proceedings of IOE Graduate Conference, Volume: 6, 2019.##[22] J. Xiao, M. Luo, J.-M. Chen and J.-J. Li, "An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior," Springer International Publishing Switzerland , ICIC 2015, Part III, LNAI 9227, pp. 453-460,, 2015.##[23] F. H. d. Olmo and E. Gaudioso, "Evaluation of recommender systems: A new approach," Expert Systems with Applications 35 790-804 ,2008.##[24] G. Takacs, I. Pilaszy, B. Nemeth and D. Tikk, "Scalable Collaborative Filtering Approaches for Large Recommender Systems," Journal of Machine Learning Research 10 623-656,, 2009.##[25] S. Oliveira, V. Diniz, A. Lacerda, L. Merschmanm and G. L. Pappa., "Is Rank Aggregation Effective in Recommender Systems? An Experimental Analysis," ACM Trans. Intell.Syst. Technol. 1, 1, Article 1 ,2019.##[26] E. Q. d. Silva, C. G. Camilo, L. M. L. Pascoal and T. C. Rosa, "An evolutionary approach for combining results of recommender systems techniques based on Collaborative Filtering," Expert Systems With Applications 53 204-218,2016.## ##

فشرده سازی تصاویر ابرطیفی با استفاده از برازش خم، بازه‌بندی و هموارسازی Hyperspectral Data Compression by Using Subintervals Curve Fitting, and Smoothing Filter 1 1 تصاویر ابرطیفی به دلیل اکتساب همزمان داده­ها در بیش از صدها باند طیفی باریک و نزدیک به هم­، همبستگی بین باندی و حجم بسیار بالایی دارند لذا نیاز به فشرده سازی دارند. یکی از روشهای با­ ­اتلاف روش مبتنی بر برازش خم است که از امضای طیفی تصویر به جهت کاهش ویژگی استفاده می­کند و نتایج بسیار خوبی را در مقابل با روش­های قبلی مانند PCA به همراه داشته است، اما در فشرده­سازی با استفاده از این روش، منحنی امضای طیفی تقریب زده شده در برخی نقاط دارای اعوجاج است که در این مقاله سعی شده تا با استفاده از پیدا کردن نقاط دارای اعوجاج و بازه­بندی امضای طیفی و برازش خم روی هر بازه و یا استفاده از یک فیلتر هموار­ساز Savitsky–Golay و یا با ترکیب هر دو پیشنهاد، اعوجاج را از بین برده و نیز میزان PSNR را افزایش داد تا کیفیت تصویر باز­یابی شده به تصویر اصلی خیلی نزدیک گردد. 2 Hyperspectral images due to simultaneous acquisition of data in more than hundreds narrow and close spectral bands, have a very high correlation bandwidth. Hence, in order to store in less storage space, higher transmission speed and less bandwidth, they need compression. Various lossless and lossy methods for compression are exist, that can be in the spatial domain or in the spectrum domain. But, regard to the importance of spectral information of hyperspectral images in remote sensing, this compression should be done by this condition that the spectral information of this kind of images is well preserved. Compression methods can be based on either the predictive function or using of a codebook, to compress information. Data compression can also be done based on transformation coding, which these transformations can be cosine functions (DCTs), wavelet functions (DWTs), or principal component analysis (PCAs). Of course, PCA-based compression is one of the most effective ways to eliminate image correlations and reduce their volume. Another extension is the method of using curve fitting, which is applied exclusively to compress hyperspectral images due to its effect on the image spectrum. This method uses the spectral signature of the each pixel of image to reduce the feature by finding the closest approximation function to express the curve and storing its coefficients as a new feature for reconstruction compressed data. By replacing these coefficients in the equation of approximation, spectrum reflection curve for each pixel can be reconstructed. This method has very good results in comparison with previous methods such as PCA, but in compression using this method, the SRC curve has been approximated in some points with distortion. In this paper, we tried to eliminate these distortions, by finding points which have distortion and Breakdown the SCR. On the other hand, by using the Savitsky-Golay smoothing filter we can also reduce distortion and increase the PSNR. Another way to eliminate or reduce this distortion described in this article is as follow: At the first the spectral signature of each pixel of the intended data is smoothed by a Savitsky-Golay smoothing filter and then by using a particular method is divided into adjoining adjacent spaces and then a curve is plotted for each slice of data. By choosing the best degree and window length for smoothing and selecting the best degree of numerator and denominator of function, the coefficients of the selected rational function are considered as new features of the image. By using the proposed method, in addition to eliminating the distortion, the PSNR level is became much higher and the reconstructed image quality is very close to the original image. 79 98 2020/11/242020/12/72020/12/162021/01/302018/10/2 1397/7/10 2021/12/202021/05/302022/10/82023/02/222023/02/22 1401/12/3 سید ابوالفضل حسینی Seyed Abolfazl Hosseini دانشگاه آزاد اسلامی ایران abolfazl.hosseini@modares.ac.ir مرسده بیت اللهی Mersedeh Beitollahi دانشگاه آزاد اسلامی ایران mersedeh_beitollahi@yahoo.com Compression Curve Fitting Hyperspectral Least Square Savitsky-Golay Filter تصاویر ابرطیفی فشرده‌سازی برازش خم حداقل مربعات خطا فیلتر هموار‌سازSavitsky – Golay [1] Keshavarz, A. (2008), Classification of Hyperspectral Images Using Spatial Information, PhD. Desertation, Faculty of electrical and computer engineering, Tarbiat Modares University, Tehran, Iran.##[2] Chang, C. I. (2013). Hyperspectral data processing: algorithm design and analysis. John Wiley & Sons.‏##[3] Dhawan, S. (2011). A review of image compression and comparison of its algorithms. International Journal of electronics & Communication technology, 2(1), 22-26.##[4] Blanes, I., Magli, E., & Serra-Sagrista, J. (2014). A tutorial on image compression for optical space imaging systems. IEEE Geoscience and Remote Sensing Magazine, 2(3), 8-26.##[5] Li, F., Lukin, V., Ieremeiev, O., & Okarma, K. (2022). Quality Control for the BPG Lossy Compression of Three-Channel Remote Sensing Images. Remote Sensing, 14(8), 1824.##[6] Miguel, A. C., Ladner, R. E., Riskin, E. A., Hauck, S., Barney, D. K., Askew, A. R., & Chang, A. (2006). Predictive coding of hyperspectral images. In Hyperspectral Data Compression (pp. 197-231). Springer, Boston, MA.##[7] Christophe, E. (2011). Hyperspectral data compression tradeoff. In Optical remote sensing. Springer, Berlin, Heidelberg.‏ p. 9-29.##[8] Sujithra, D. S., Manickam, T., & Sudheer, D. S. (2013). Compression of hyperspectral image using discrete wavelet transform and Walsh Hadamard transform. Int. J. Adv. Res. Electron. Commun. Eng.(IJARECE), 2, 314-319.‏##[9] Hosseini, S. A., & Ghassemian, H. (2016). Rational function approximation for feature reduction in hyperspectral data. Remote Sensing Letters, 7(2), 101-110.##[10] Hosseini, S. A., & Ghassemian, H. (2016). Hyperspectral data feature extraction using rational function curve fitting. International Journal of Pattern Recognition and Artificial Intelligence, 30(01), 1650001.‏##[11] Fang, L., & Gossard, D. C. (1995). Multidimensional curve fitting to unorganized data points by nonlinear minimization. Computer-Aided Design, 27(1), 48-58.‏##[12] Boyd, J. P. (1992). Defeating the Runge phenomenon for equispaced polynomial interpolation via Tikhonov regularization. Applied Mathematics Letters, 5(6), 57-59.‏##[13] Epperson, J. F. (1987). On the Runge example. The American Mathematical Monthly, 94(4), 329-341.##[14] Amindavar, H., & Ritcey, J. A. (1994). Padé approximations of probability density functions. IEEE Transactions on Aerospace and Electronic Systems, 30(2), 416-424.##[15] Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639.##[16] Steinier, J., Termonia, Y., & Deltour, J. (1972). Smoothing and differentiation of data by simplified least square procedure. Analytical chemistry, 44(11), 1906-1909.##[17] Madden, H. (1978). Comments on smoothing and differentiation of data by simplified least square procedure. Analytical Chemistry, 50(9), 1383-86.‏##[18] Ruffin, C., & King, R. L. (1999, June). The analysis of hyperspectral data using Savitzky-Golay filtering-theoretical basis. 1. In IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293) (Vol. 2, pp. 756-758). IEEE.##[19] King, R. L., Ruffin, C., LaMastus, F. E., & Shaw, D. R. (1999, June). The analysis of hyperspectral data using Savitzky-Golay filtering-practical issues. 2. In IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293) (Vol. 1, pp. 398-400). IEEE.‏##[20]] Beitollahi, M., & Hosseini, S. A. (2017, May). Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression. In 2017 Iranian Conference on Electrical Engineering (ICEE) (pp. 1967-1972). IEEE.##[21] Universidad-del-Pais-Vasco. Hyperspectral Remote Sensing Scenes [Online].##[23] Available: http://www.ehu.es/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes.##[24] ] Landgrebe, D. A. (2003). Signal theory methods in multispectral remote sensing (Vol. 24). John Wiley & Sons.‏##[25] Beitollahi, M., & Hosseini, S. A. (2018, May). Using savitsky-golay smoothing filter in hyperspectral data compression by curve fitting. In Electrical Engineering (ICEE), Iranian Conference on (pp. 452-457). IEEE.##[26] Beitollahi, M., & Hosseini, S. A. (2018, July). Hyperspectral Data Compression by Using Rational Function Curve Fitting in Spectral Signature Subintervals. In 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) (pp. 1-6). IEEE.##[27] Kamandar, M., & Ghassemian, H. (2012). Linear feature extraction for hyperspectral images based on information theoretic learning. IEEE Geoscience and Remote Sensing Letters, 10(4), 702-706.‏##[28] Beitollahi, M., & Hosseini, S. A. (2016, July). Using curve fitting for spectral reflectance curves intervals in order to hyperspectral data compression. In 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) (pp. 1-5). IEEE.##[1] Keshavarz, A. (2008), Classification of Hyperspectral Images Using Spatial Information, PhD. Desertation, Faculty of electrical and computer engineering, Tarbiat Modares University, Tehran, Iran.##[2] Chang, C. I. (2013). Hyperspectral data processing: algorithm design and analysis. John Wiley & Sons.‏##[3] Dhawan, S. (2011). A review of image compression and comparison of its algorithms. International Journal of electronics & Communication technology, 2(1), 22-26.##[4] Blanes, I., Magli, E., & Serra-Sagrista, J. (2014). A tutorial on image compression for optical space imaging systems. IEEE Geoscience and Remote Sensing Magazine, 2(3), 8-26.##[5] Li, F., Lukin, V., Ieremeiev, O., & Okarma, K. (2022). Quality Control for the BPG Lossy Compression of Three-Channel Remote Sensing Images. Remote Sensing, 14(8), 1824.##[6] Miguel, A. C., Ladner, R. E., Riskin, E. A., Hauck, S., Barney, D. K., Askew, A. R., & Chang, A. (2006). Predictive coding of hyperspectral images. In Hyperspectral Data Compression (pp. 197-231). Springer, Boston, MA.##[7] Christophe, E. (2011). Hyperspectral data compression tradeoff. In Optical remote sensing. Springer, Berlin, Heidelberg.‏ p. 9-29.##[8] Sujithra, D. S., Manickam, T., & Sudheer, D. S. (2013). Compression of hyperspectral image using discrete wavelet transform and Walsh Hadamard transform. Int. J. Adv. Res. Electron. Commun. Eng.(IJARECE), 2, 314-319.‏##[9] Hosseini, S. A., & Ghassemian, H. (2016). Rational function approximation for feature reduction in hyperspectral data. Remote Sensing Letters, 7(2), 101-110.##[10] Hosseini, S. A., & Ghassemian, H. (2016). Hyperspectral data feature extraction using rational function curve fitting. International Journal of Pattern Recognition and Artificial Intelligence, 30(01), 1650001.‏##[11] Fang, L., & Gossard, D. C. (1995). Multidimensional curve fitting to unorganized data points by nonlinear minimization. Computer-Aided Design, 27(1), 48-58.‏##[12] Boyd, J. P. (1992). Defeating the Runge phenomenon for equispaced polynomial interpolation via Tikhonov regularization. Applied Mathematics Letters, 5(6), 57-59.‏##[13] Epperson, J. F. (1987). On the Runge example. The American Mathematical Monthly, 94(4), 329-341.##[14] Amindavar, H., & Ritcey, J. A. (1994). Padé approximations of probability density functions. IEEE Transactions on Aerospace and Electronic Systems, 30(2), 416-424.##[15] Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639.##[16] Steinier, J., Termonia, Y., & Deltour, J. (1972). Smoothing and differentiation of data by simplified least square procedure. Analytical chemistry, 44(11), 1906-1909.##[17] Madden, H. (1978). Comments on smoothing and differentiation of data by simplified least square procedure. Analytical Chemistry, 50(9), 1383-86.‏##[18] Ruffin, C., & King, R. L. (1999, June). The analysis of hyperspectral data using Savitzky-Golay filtering-theoretical basis. 1. In IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293) (Vol. 2, pp. 756-758). IEEE.##[19] King, R. L., Ruffin, C., LaMastus, F. E., & Shaw, D. R. (1999, June). The analysis of hyperspectral data using Savitzky-Golay filtering-practical issues. 2. In IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293) (Vol. 1, pp. 398-400). IEEE.‏##[20]] Beitollahi, M., & Hosseini, S. A. (2017, May). Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression. In 2017 Iranian Conference on Electrical Engineering (ICEE) (pp. 1967-1972). IEEE.##[21] Universidad-del-Pais-Vasco. Hyperspectral Remote Sensing Scenes [Online].##[23] Available: http://www.ehu.es/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes.##[24] ] Landgrebe, D. A. (2003). Signal theory methods in multispectral remote sensing (Vol. 24). John Wiley & Sons.‏##[25] Beitollahi, M., & Hosseini, S. A. (2018, May). Using savitsky-golay smoothing filter in hyperspectral data compression by curve fitting. In Electrical Engineering (ICEE), Iranian Conference on (pp. 452-457). IEEE.##[26] Beitollahi, M., & Hosseini, S. A. (2018, July). Hyperspectral Data Compression by Using Rational Function Curve Fitting in Spectral Signature Subintervals. In 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) (pp. 1-6). IEEE.##[27] Kamandar, M., & Ghassemian, H. (2012). Linear feature extraction for hyperspectral images based on information theoretic learning. IEEE Geoscience and Remote Sensing Letters, 10(4), 702-706.‏##[28] Beitollahi, M., & Hosseini, S. A. (2016, July). Using curve fitting for spectral reflectance curves intervals in order to hyperspectral data compression. In 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) (pp. 1-5). IEEE.## ##

استفاده از الگوریتم آبکاری فلزات برای بهبود اجماع خوشه‌بندی Using Simulated Annealing algorithm to improve ensemble clustering 1 1 خوشه‌بندی داده‌ها یکی از وظایف اصلی داده‌کاوی است که وظیفه کاوش الگوهای پنهان در داده‌های بدون برچسب را بر عهده دارد. به خاطر پیچیدگی مسئله و ضعف روش‌های خوشه‌بندی پایه، امروزه اکثر مطالعات به سمت روش‌های اجماع خوشه‌بندی هدایت شده است. اگر چه برای بیشتر مجموعه داده‌ها، الگوریتم‌های خوشه‌بندی منفردی وجود دارد که نتایج قابل قبولی به­دست می‌دهند، اما توانایی یک الگوریتم خوشه‌بندی منفرد محدود است. در واقع هدف اصلی اجماع خوشه‌بندی جستجوی نتایج بهتر و پایدارتر، با استفاده از ترکیب اطلاعات و نتایج حاصل از چندین خوشه‌بندی اولیه است. در این مقاله، روشی مبتنی بر اجماع خوشه‌بندی پیشنهاد خواهد شد که مانند بیشتر روش‌های انباشت شواهد دارای دو گام است: 1- ساختن ماتریس مشارکت همزمان و 2- تعیین افراز‌های نهایی از ماتریس مشارکت پیشنهادی. در روش پیشنهادی، برای ساخت ماتریس مشارکت همزمان، علاوه‌بر هم خوشه بودن نمونه‌ها از بعضی اطلاعات دیگر هم استفاده خواهد شد. این اطلاعات می‌توانند مربوط به میزان شباهت نمونه‌ها، اندازه خوشه‌های اولیه، میزان پایداری خوشه‌های اولیه و غیره باشد. در این مقاله مسئله خوشه‌بندی به­صورت یک مسئله بهینه‌سازی صریح توسط مدل آمیخته گوسی تعریف می‌شود و که با استفاده از الگوریتم آبکاری فلزات حل می‌شود. همچنین روشی تکاملی مبتنی بر آبکاری فلزات برای تعیین افراز نهایی از ماتریس مشارکت همزمان پیشنهادی ارایه خواهد شد. مهم‌ترین بخش روش تکاملی، تعیین تابع هدفی است که تضمین کند افراز نهایی از کیفیت بالایی برخوردار خواهد بود. نتایج تجربی نشان می­دهد روش پیشنهادی از نظر معیارهای مختلف ارزیابی کیفیت خوشه­بندی از سایر روش­های مشابه بهتر می­باشد.   2 Data clustering is one of the main tasks of data mining, which is responsible for exploring hidden patterns in unlabeled data. Due to the complexity of the problem and the weakness of the basic clustering methods, today most of the studies are directed towards clustering ensemble methods. Although for most datasets, there are individual clustering algorithms that provide acceptable results, but the ability of a single clustering algorithm is limited. In fact, the main purpose of clustering ensemble is to search for better and more stable results, using the combination of information and results obtained from several initial clustering. In this paper, a clustering ensemble-based method will be proposed, which, like most evidence accumulation methods, has two steps: 1- building a simultaneous participation matrix and 2- determining the final output from the proposed participation matrix. In the proposed method, some other information will be used in addition to the clustering of the samples to construct the simultaneous participation matrix. This information can be related to the degree of similarity of the samples, the size of the initial clusters, the degree of stability of the initial clusters, etc. In this paper, the clustering problem is defined as an explicit optimization problem by the mixed Gaussian model and is solved using the simulated annealing algorithm. Also, an evolutionary method based on simulated annealing will be presented to determine the final output from the proposed simultaneous participation matrix. The most important part of the evolutionary method is to determine the objective function that guarantees the final output will be of high quality. The experimental results show that the proposed method is better than other similar methods in terms of different clustering quality evaluation criteria.   99 122 2020/11/242020/12/72020/12/162021/01/302018/10/22021/03/25 1400/1/5 2021/12/202021/05/302022/10/82023/02/222023/02/222023/06/2 1402/3/12 فروزان رشیدی Froozan Rashidi دانشکده مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، کهگیلویه و بویراحمد، ایران ایران f_rashidi@gmail.com صمد نجاتیان Samad Nejatian دانشکده مهندسی برق، واحد یاسوج، دانشگاه آزاد اسلامی، کهگیلویه و بویراحمد، ایران، ایران samad.nej.2007@gmail.com حمید پروین Hamid Parvin دانشگاه آزاد اسلامی واحد نورآباد ممسنی، فارس، ایران ایران parvin@iust.ac.ir وحیده رضایی vahideh Rezaei دانشکده ریاضی، واحد یاسوج، دانشگاه آزاد اسلامی، کهگیلویه و بویراحمد، ایران، ایران vahidehrezaie@gmail.com کرم اله باقری فرد Karamolah Bagheri Fard دانشکده مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، کهگیلویه و بویراحمد، ایران ایران k.bagheri@iauyasooj.ac.ir Clustering ensemble Gaussian mixture model simulated annealing algorithm simultaneous participation matrix stability objective function. اجماع خوشه‌بندی مدل آمیخته گوسی الگوریتم آبکاری فلزات ماتریس مشارکت همزمان پایداری تابع هدف. 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Elisseeff, ''An introduction to variable and feature selection,'' J. Mach. Learn. Res., vol. 3, pp. 1157_1182, Jan. 2003.##[19] G. Chandrashekar and F. Sahin, ''A survey on feature selection methods,''Comput. Electr. Eng., vol. 40, no. 1, pp. 16_28, Jan. 2014.##[20] E. Hancer, B. Xue, and M. Zhang, ''A survey on feature selection approaches for clustering,'' Artif. Intell. Rev., pp. 1_27, doi: 10.1007/s10462-019-09800-w.##[21] B. Kim, H. Lee, and P. Kang, ''Integrating cluster validity indices based on data envelopment analysis,'' Appl. Soft Comput., vol. 64, pp. 94_108, Mar. 2018.##[22] D. Huang, X. Cai, and C.-D. Wang, ''Unsupervised feature selection with multi-subspace randomization and collaboration,'' Knowl. Based Syst., vol. 182, Oct. 2019, Art. no. 104856.##[23] H. Zhang, R. Zhang, F. Nie, and X. Li, ''An ef_cient framework for unsupervised feature selection,'' Neurocomputing, vol. 366, pp. 194_207, Nov. 2019.##[24] D. Huang, C.-D. Wang, J.-S. Wu, J.-H. Lai, and C.-K. 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Fahy, S. Yang, M. Gongora, "Ant Colony stream clustering: a fast density clustering algorithm for dynamic data streams". IEEE Trans Cybern, vol. 49, no. 6, pp. 2215-2228, 2019.##[32] M. Mojarad, S. Nejatian, H. Parvin, M. Mohammadpoor, "A fuzzy clustering ensemble based on cluster clustering and iterative fusion of base clusters". Appl Intell, vol. 49, no.7, pp. 2567-2581, 2019.##[33] T. Lai, R. Chen, C. Yang, Q. Li, H. Fujita, A. Sadri, H. Wang, "Efficient robust model fitting for multistructure data using global greedy search". IEEE Trans Cybern, vol. 50, no. 7, pp. 3294-3306, 2020.##[34] Y. Yang, J. Jiang, Adaptive bi-weighting toward automatic initialization and model selection for HMM-based hybrid meta-clustering ensembles. IEEE Trans. Cybern, vol. 49, no. 5, pp. 1657-1668, 2018a.##[35] Y. Yang, J. Jiang., Bi-weighted ensemble via HMM-based approaches for temporal data clustering. Pattern Recogn. vol. 76, pp. 391-403. 2018b.##[36] A. Banerjee, A.K. Pujari, C. Rani Panigrahi, B. 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تعیین دامنه برای بکارگیری مجموعه سه پیمانه ای {2^n-1, 2n, 2^n+1} Set the Domain for Using 3-moduli set {2^n-1, 2^n, 2^n +1} 1 1 سیستم عددی مانده ­ای[1] به دلیل انجام عملیات جمع، تفریق و ضرب در کانال­های موازی باعث بهبود سرعت محاسبات می­گردد. برای استفاده از این سیستم به انجام عملیات تبدیل از دودویی به مانده­ای و مانده­ای به دودویی نیاز است. وجود سربار محاسبات تبدیل می­تواند باعث کاهش کارایی در به­ کارگیری از این سیستم گردد، مگراینکه تعداد عملیات مانده ­ای متوالی به قدری زیاد باشد که زمان سربار تبدیلات را پوشش دهد. در این مقاله با بررسی مجموعه سه پیمانه ای {2n -1, 2n, 2n+1}   مشخص شد که به ازای چه تعداد عملیات متوالی جمع یا ضرب، استفاده از عملیات مانده­ ای منجر به سرعت بیشتر می­گردد. نتایج نشان­ می­دهند که در صورت استفاده از  جمع­ کننده با انتشاررقم نقلی[2] ،  در پیمانه­ های با عرض بیشتر از 8 بیت ( n>8 ) اگر تعداد عملیات متوالی حداقل4 باشد، باعث تسریع در محاسبات می­گردد. به همین ترتیب در عمل ضرب و جمع­کننده­ی پیشوندی تعداد توالی به ۲ کاهش می­یابد. [1] Residue Number System [2] Ripple Adder 2 In special purpose circuits, the amount of energy consumed and the speed of operation are the main challenges. There are wide researches and methods to improve the performance of these types of circuits. One of these methods is to use a Residue Number System (RNS). In the RNS, there are a number of modules (channels) as a set to represent the number and perform parallel arithmetic operations. The most famous set is the 3-modlui set {2n-1, 2n, 2n +1}. The form of modules to the power of 2 makes it easier to perform binary computational operations. To use this system, you need to perform conversion operations from binary to residue (forward conversion) and residue to binary (reverse conversion). The greater the number of modules (channels) in the set, the higher the degree of parallelism of computational operations. In contrast, more complex forward and reverse conversion circuits are required. The overhead of conversion computing can reduce the efficiency of using this system, unless the number of consecutive operations is large enough to cover the conversion overhead time. In this paper, based on 3-moduli set {2n-1, 2n, 2n +1} evaluation, it was determined that for how many consecutive addition or multiplication operations, the use of RNS operations leads to greater speed. In this paper, we evaluate the carry propagation adder as the most popular adder and parallel prefix adder as the high speed adder. Also, the parallel block multiplier circuit was used to evaluate the multiplication operations. First, modular adder/multiplier, binary, and forward and reverse conversion circuits were implemented and synthesized. We used Synopsys Design Compiler, K-2015.06 version and 45nm technology. The results show that if the carry propagation adder is used, in modules with a width of more than 8 bits (n≥8), if the number of consecutive operations is at least 4, it will speed up the calculations. Likewise, in the multiplication operation and parallel prefix addition, the number of sequences is reduced to two. 123 132 2020/11/242020/12/72020/12/162021/01/302018/10/22021/03/252021/03/2 1399/12/12 2021/12/202021/05/302022/10/82023/02/222023/02/222023/06/22023/02/22 1401/12/3 حمیدرضا احمدی فر HamidReza Ahmadifar دانشگاه گیلان ایران ahmadifar@guilan.ac.ir زهرا حکیمی Zahra Hakimi دانشگاه گیلان ایران zahra.hakimi_h@yahoo.com Residue Number System Residue Operations Carry Ripple Adder Parallel Prefix Adder Parallel Multiplier سیستم عددی مانده ای عملیات مانده ای جمع کننده با انتشار رقم نقلی جمع کننده پیشوندی موازی ضرب کننده موازی بلوکی [1] Hakimi, Z., Ahmadifar, H.," Delimitation for Using Residue Number Systems", 4th Conference on Electrical and Computer Engineering Technology, E-TECH2019, ITRC, Tehran, 1398.##[2] Omondi, A., Premkumar, B., "Residue Number Systems - Theory and Implementation", Imperial College Press (ICP), 2007.##[3] Belghadr, A., Jaberipur, G., "FIR Filter Realization via Deferred End-Around Carry Modular Addition", IEEE TCAS I, Vol. 65, pp. 2878 - 2888, 2018.##[4] Xiao, L., Xiang-Gen, X., "Robust Polynomial Reconstruction via Chinese Remainder Theorem in the Presence of Small Degree Residue Errors", IEEE TCAS II: Vol. 65 , Issue: 11, pp. 1778 - 1782, 2018.##[5] Sousa, L., Antao, S., Martins, P., "Combining Residue Arithmetic to Design Efficient Cryptographic Circuits and Systems", IEEE Circuits and Systems Magazine, Vol.16, Issue:4, pp. 6-32, 2016.##[6] Tay, T., Chang, C.-H., "A non-iterative multiple residue digit error detection and correction algorithm in RRNS", IEEE Transactions on Computers, Vol. 65 , Issue: 2, pp. 396 - 408, 2016.##[7] Koren I, "Computer Arithmetic Algorithms", 2d Edition, A.K. Peters Ltd, 2002.##[8] Hiasat, A, "An Efficient Reverse Converter for the Three-Moduli Set { 2^(n+1)-1,2^n,2^n-1 }", IEEE Transactions on Circuits and Systems II, Vol. 64, Issue: 8 pp. 962 - 966, 2017.##[9] Ahmadifar, A., and G. Jaberipur, "Improved modulo-2^q±3 multipliers," in Proc. Of the 17th CSI International Symposium on Computer Architecture and Digital Systems (CADS2013), Tehran, Iran, pp. 31-35.##[10] Patronik, P., Piestrak, S.J., "Hardware/Software Approach to Designing Low-Power RNS-Enhanced Arithmetic Units," IEEE TCAS I, Vol. 64, 2017.##[11] Ahmadifar, H., Jaberipur, G., "A New Residue Number System with 5-Moduli Set: {22q,2q±3,2q±1}", The Computer Journal, Vol. 58 , Issue: 7, pp.1548 - 1565, 2015.##[12] Wang Y, Song X, Aboulhamid M, Shen H, " Adder Based Residue to Binary Number Converters for {2^n- 1,2^n,2^n + 1}", IEEE Transactions on Signal Processing, Vol. 50, No. 7, July 2002.##[13] Jaberipur, G., B. Parhami, and S. Nejati, "On Building General Modular Adders from Standard Binary Arithmetic Components," Proc. 45th Asilomar Conf. Signals, Systems, and Computers, 6-9 Nov., Pacific Grove, CA, USA, pp. 154-159, 2011.##[14] Kalamatianos L, Nikolos D, Efstathiou C, T. Vergos H, Kalamatianos J, "High-Speed Parallel-Prefix Modulo 2^n-1 Adders," IEEE Trans. Computers, Vol. 49, No. 7, special issue on computer arithmetic, pp. 673-680, July 2000.##[15] Efstathiou C., H. T. Vergos, and D. Nikolos, "Fast Parallel-Prefix 2^n+1 Adder", IEEE Trans. on Computers, Vol. 53, No. 9, pp. 1211-1216, September 2004.##[16] Jaberipur G, Alavi H, "Comment on "Fast Parallel Prefix Modulo 2^n+1 Adder", IEEE Trans. on Computers, Vol. 64, No. 1, pp. 293-294, January 2015.##[17] Cardirilli, G.C., Nunzio, L.D., Fazzolari, R., Nannarelli, A., et al," Design Space Exploration Based Methodology for Residue Number System Digital Filters Implementation", IEEE Trans. On Emerging Topics in Computing, Early Access,25 May, 2020.##[1] ز. حکیمی، ح. احمدی¬فر، مرزبندی برای بهکارگیری سیستمهای عددی مانده¬ای، چهارمین کنفرانس تکنولوژی در مهندسی برق و کامپیوتر، پژوهشگاه ارتباطات و فناوری اطلاعات، تهران، ایران، خرداد ۱۳۹۸.##[1] Hakimi, Z., Ahmadifar, H.," Delimitation for Using Residue Number Systems", 4th Conference on Electrical and Computer Engineering Technology, E-TECH2019, ITRC, Tehran, 1398.##[2] Omondi, A., Premkumar, B., "Residue Number Systems - Theory and Implementation", Imperial College Press (ICP), 2007.##[3] Belghadr, A., Jaberipur, G., "FIR Filter Realization via Deferred End-Around Carry Modular Addition", IEEE TCAS I, Vol. 65, pp. 2878 - 2888, 2018.##[4] Xiao, L., Xiang-Gen, X., "Robust Polynomial Reconstruction via Chinese Remainder Theorem in the Presence of Small Degree Residue Errors", IEEE TCAS II: Vol. 65 , Issue: 11, pp. 1778 - 1782, 2018.##[5] Sousa, L., Antao, S., Martins, P., "Combining Residue Arithmetic to Design Efficient Cryptographic Circuits and Systems", IEEE Circuits and Systems Magazine, Vol.16, Issue:4, pp. 6-32, 2016.##[6] Tay, T., Chang, C.-H., "A non-iterative multiple residue digit error detection and correction algorithm in RRNS", IEEE Transactions on Computers, Vol. 65 , Issue: 2, pp. 396 - 408, 2016.##[7] Koren I, "Computer Arithmetic Algorithms", 2d Edition, A.K. Peters Ltd, 2002.##[8] Hiasat, A, "An Efficient Reverse Converter for the Three-Moduli Set { 2^(n+1)-1,2^n,2^n-1 }", IEEE Transactions on Circuits and Systems II, Vol. 64, Issue: 8 pp. 962 - 966, 2017.##[9] Ahmadifar, A., and G. Jaberipur, "Improved modulo-2^q±3 multipliers," in Proc. Of the 17th CSI International Symposium on Computer Architecture and Digital Systems (CADS2013), Tehran, Iran, pp. 31-35.##[10] Patronik, P., Piestrak, S.J., "Hardware/Software Approach to Designing Low-Power RNS-Enhanced Arithmetic Units," IEEE TCAS I, Vol. 64, 2017.##[11] Ahmadifar, H., Jaberipur, G., "A New Residue Number System with 5-Moduli Set: {22q,2q±3,2q±1}", The Computer Journal, Vol. 58 , Issue: 7, pp.1548 - 1565, 2015.##[12] Wang Y, Song X, Aboulhamid M, Shen H, " Adder Based Residue to Binary Number Converters for {2^n- 1,2^n,2^n + 1}", IEEE Transactions on Signal Processing, Vol. 50, No. 7, July 2002.##[13] Jaberipur, G., B. Parhami, and S. 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ارائه یک مدل فراابتکاری تشخیص نفوذ به کمک انتخاب ویژگی مبتنی بر بهینه سازی گرگ خاکستری بهبودیافته و جنگل تصادفی Propose a meta-heuristic model of intrusion detection using feature selection based on improved gray wolf optimization and random forest 1 1 پیشرفت سریع در زمینه های اینترنت و ارتباطات منجر به رشد چشمگیر شبکه های کامپیوتری، اندازه‌ی شبکه و تبادل داده شده است و همین امر زمینه را برای حملات مختلف فراهم کرده است. سیستم‌های تشخیص نفوذ نقش مهمی در امنیت شبکه‌های اینترنتی بر عهده دارند که با بازرسی ترافیک‌های شبکه از محرمانگی، یکپارچگی و در دسترس بودن شبکه محافظت می کند. مدل های تشخیص نفوذ در زمینه امنیت شبکه، مدل های پیش بینی کننده‌ای هستند که در جهت پیش‌بینی داده‌های ترافیکی نفوذ در شبکه‌ها به کار می‌روند و یکی از پرکاربردترین مدل ها در سیستم های تشخیص نفوذ مدل های مبتنی بر یادگیری ماشین می‌باشد. عدم توازن و تعادل بین دقت تشخیص و نرخ هشدار کاذب یکی از چالش های بزرگ در این زمینه محسوب می‌شود. در این مقاله برای افزایش قدرت جستجو از الگوریتم‌های فراابتکاری و جهت افزایش قدرت محاسباتی و کلاس بندی از تکنیک یادگیری ماشین استفاده می‌گردد. از این رو در این پژوهش مدلی کارا مبتنی بر الگوریتم‌های گرگ خاکستری باینری بهبودیافته و جنگل تصادفی، جهت شناسایی بهترین مجموعه ویژگی‌های ترافیک برای تشخیص و پیشگیری از حملات اینترنتی ارائه می‌گردد. جهت پیدا کردن بهترین زیر مجموعه از الگوریتم گرگ خاکستری و برای ارزیابی هر زیرمجموعه از جنگل تصادفی استفاده می‌شود. همچنین به‌منظور بهبود عملکرد گرگ خاکستری، این الگوریتم بهبود داده می‌شود. دقت حاصل شده برای طبقه بندی صحیح در روش پیشنهادی در مجموعه داده‌ها NSL-KDD در روش گرگ خاکستری سنتی و بهبودیافته به ترتیب برابر با 97.14 و 98.97 درصد است که در مقایسه با روش های دیگر دارای دقت بالاتری می‌باشد. 2 Rapid development in the Internet and communications have led to dramatic growth in computer networks, network size, and data exchange, and this can pose harmful threats to the network. Intrusion detection systems play an important role in the security of Internet networks, which protects the privacy, integrity, and availability of the network by inspecting network traffic. Intrusion detection models in the field of network security are predictive models that are used to predict malicious data in networks and one of the most widely used models in intrusion detection systems is based on machine learning. The imbalance between the accuracy of detection and false alarm rate is one of the most important challenges in this regard. In this paper, meta-heuristic algorithms are used to increase searchability and machine learning method is used to increase computational power and classification. Therefore, in this study, an efficient model based on the gray wolf algorithm and random forest algorithm to identify the best set of traffic features to identify and prevent cyberattacks is presented. The gray wolf algorithm is used to find the best feature subset and the random forest is used to evaluate each subset. This algorithm has also been improved to increase gray wolf performance. The accuracy obtained for correct classification in the proposed method in the NSL-KDD data set. as shown in the result, the detection accuracy of the traditional and improved gray wolf method is obtained 97.14% and 98.97%, respectively, which is outperformed other methods. 133 144 2020/11/242020/12/72020/12/162021/01/302018/10/22021/03/252021/03/22021/03/23 1400/1/3 2021/12/202021/05/302022/10/82023/02/222023/02/222023/06/22023/02/222022/07/31 1401/5/9 شهریار محمدی Shahriar mohammadi خواجه نصیرالدین طوسی ایران mohammadi@kntu.ac.ir احمد خلعتبری Ahmad khalatbary خواجه نصیرالدین طوسی ایران Khalatbary@gmail.com مهدی باباگلی Mehdi Babagoli خواجه نصیرالدین طوسی ایران mehdi.babagoli@email.kntu.ac.ir Intrusion detection system Feature Selection improved Gray Wolf Optimization Algorithm Random Forest Machine Learning تشخیص نفوذ انتخاب ویژگی الگوریتم گرگ خاکستری بهبودیافته جنگل تصادفی یادگیری ماشین [1] I. Manzoor and N. Kumar, "A feature reduced intrusion detection system using ANN classifier," Expert Systems with Applications, vol. 88, pp. 249-257, 2017.##[2] A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019.##[3] T. A. Alamiedy, M. Anbar, Z. N. Alqattan, and Q. M. Alzubi, "Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm," Journal of Ambient Intelligence and Humanized Computing, pp. 1-22, 2019.##[4] E.-G. Talbi, "Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics," 2020.##[5] D. Molina, J. Poyatos, J. Del Ser, S. García, A. Hussain, and F. Herrera, "Comprehensive Taxonomies of Nature-and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations," Cognitive Computation, vol. 12, no. 5, pp. 897-939, 2020.##[6] X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, "An adaptive ensemble machine learning model for intrusion detection," IEEE Access, vol. 7, pp. 82512-82521, 2019.##[7] J. M. Fossaceca, T. A. Mazzuchi, and S. Sarkani, "MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection," Expert Systems with Applications, vol. 42, no. 8, pp. 4062-4080, 2015.##[8] K. M. Prasad, A. R. M. Reddy, and K. V. Rao, "BIFAD: Bio-inspired anomaly based HTTP-flood attack detection," Wireless Personal Communications, vol. 97, no. 1, pp. 281-308, 2017.##[9] A. A. Aburomman and M. B. I. Reaz, "A novel SVM-kNN-PSO ensemble method for intrusion detection system," Applied Soft Computing, vol. 38, pp. 360-372, 2016.##[10] D. Arivudainambi, V. K. KA, and S. S. Chakkaravarthy, "LION IDS: A meta-heuristics approach to detect DDoS attacks against Software-Defined Networks," Neural Computing and Applications, vol. 31, no. 5, pp. 1491-1501, 2019.##[11] S. Velliangiri and H. M. Pandey, "Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms," Future Generation Computer Systems, vol. 110, pp. 80-90, 2020.##[12] A. J. Wilson and S. Giriprasad, "A Feature Selection Algorithm for Intrusion Detection System Based On New Meta-Heuristic Optimization," Journal of Soft Computing and Engineering Applications, vol. 1, no. 1, 2020.##[13] T. Khorram and N. A. Baykan, "Feature selection in network intrusion detection using metaheuristic algorithms," International Journal of Advanced Research, Ideas and Innovations in Technology, vol. 4, no. 4, 2018.##[14] Q. Al-Tashi, S. J. Abdulkadir, H. M. Rais, S. Mirjalili, and H. Alhussian, "Approaches to multi-objective feature selection: A systematic literature review," IEEE Access, vol. 8, pp. 125076-125096, 2020.##[15] J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: A new perspective," Neurocomputing, vol. 300, pp. 70-79, 2018.##[16] M. Di Mauro, G. Galatro, G. Fortino, and A. Liotta, "Supervised feature selection techniques in network intrusion detection: A critical review," Engineering Applications of Artificial Intelligence, vol. 101, p. 104216, 2021.##[17] R. Purushothaman, S. Rajagopalan, and G. Dhandapani, "Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering," Applied Soft Computing, vol. 96, p. 106651, 2020.##[18] E. Emary, H. M. Zawbaa, and C. Grosan, "Experienced gray wolf optimization through reinforcement learning and neural networks," IEEE transactions on neural networks and learning systems, vol. 29, no. 3, pp. 681-694, 2017.##[19] A. Thakkar and R. Lohiya, "Attack classification using feature selection techniques: a comparative study," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 1249-1266, 2021.##[20] R. Ahmadi, G. Ekbatanifard, and P. Bayat, "A Modified Grey Wolf Optimizer Based Data Clustering Algorithm," Applied Artificial Intelligence, vol. 35, no. 1, pp. 63-79, 2021.##[21] A. N. Singh, J. Mrudula, R. Pandey, and S. Das, "A Comparative Study of Four Genetic Algorithm-Based Crossover Operators for Solving Travelling Salesman Problem," in Intelligent Algorithms for Analysis and Control of Dynamical Systems: Springer, 2021, pp. 33-40.##[22] G. S. Kushwah and V. Ranga, "Optimized extreme learning machine for detecting DDoS attacks in cloud computing," Computers & Security, p. 102260, 2021.##[23] K. Singh, L. Kaur, and R. Maini, "Comparison of Principle Component Analysis and Stacked Autoencoder on NSL-KDD Dataset," in Computational Methods and Data Engineering: Springer, 2021, pp. 223-241.##[24] S. Gavel, A. S. Raghuvanshi, and S. Tiwari, "Distributed intrusion detection scheme using dual-axis dimensionality reduction for Internet of things (IoT)," The Journal of Supercomputing, pp. 1-24, 2021.##[25] M. C. Belavagi and B. Muniyal, "Performance evaluation of supervised machine learning algorithms for intrusion detection," Procedia Computer Science, vol. 89, pp. 117-123, 2016.##[26] S. Shakya, "Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection," 2021.##[27] O. Almomani, "A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System," CMC-COMPUTERS MATERIALS & CONTINUA, vol. 68, no. 1, pp. 409-429, 2021.##[1] I. Manzoor and N. Kumar, "A feature reduced intrusion detection system using ANN classifier," Expert Systems with Applications, vol. 88, pp. 249-257, 2017.##[2] A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019.##[3] T. A. Alamiedy, M. Anbar, Z. N. Alqattan, and Q. M. Alzubi, "Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm," Journal of Ambient Intelligence and Humanized Computing, pp. 1-22, 2019.##[4] E.-G. Talbi, "Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics," 2020.##[5] D. Molina, J. Poyatos, J. Del Ser, S. García, A. Hussain, and F. Herrera, "Comprehensive Taxonomies of Nature-and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations," Cognitive Computation, vol. 12, no. 5, pp. 897-939, 2020.##[6] X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, "An adaptive ensemble machine learning model for intrusion detection," IEEE Access, vol. 7, pp. 82512-82521, 2019.##[7] J. M. Fossaceca, T. A. Mazzuchi, and S. Sarkani, "MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection," Expert Systems with Applications, vol. 42, no. 8, pp. 4062-4080, 2015.##[8] K. M. Prasad, A. R. M. Reddy, and K. V. Rao, "BIFAD: Bio-inspired anomaly based HTTP-flood attack detection," Wireless Personal Communications, vol. 97, no. 1, pp. 281-308, 2017.##[9] A. A. Aburomman and M. B. I. Reaz, "A novel SVM-kNN-PSO ensemble method for intrusion detection system," Applied Soft Computing, vol. 38, pp. 360-372, 2016.##[10] D. Arivudainambi, V. K. KA, and S. S. Chakkaravarthy, "LION IDS: A meta-heuristics approach to detect DDoS attacks against Software-Defined Networks," Neural Computing and Applications, vol. 31, no. 5, pp. 1491-1501, 2019.##[11] S. Velliangiri and H. M. Pandey, "Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms," Future Generation Computer Systems, vol. 110, pp. 80-90, 2020.##[12] A. J. Wilson and S. Giriprasad, "A Feature Selection Algorithm for Intrusion Detection System Based On New Meta-Heuristic Optimization," Journal of Soft Computing and Engineering Applications, vol. 1, no. 1, 2020.##[13] T. Khorram and N. A. Baykan, "Feature selection in network intrusion detection using metaheuristic algorithms," International Journal of Advanced Research, Ideas and Innovations in Technology, vol. 4, no. 4, 2018.##[14] Q. Al-Tashi, S. J. Abdulkadir, H. M. Rais, S. Mirjalili, and H. Alhussian, "Approaches to multi-objective feature selection: A systematic literature review," IEEE Access, vol. 8, pp. 125076-125096, 2020.##[15] J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: A new perspective," Neurocomputing, vol. 300, pp. 70-79, 2018.##[16] M. Di Mauro, G. Galatro, G. Fortino, and A. Liotta, "Supervised feature selection techniques in network intrusion detection: A critical review," Engineering Applications of Artificial Intelligence, vol. 101, p. 104216, 2021.##[17] R. Purushothaman, S. Rajagopalan, and G. Dhandapani, "Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering," Applied Soft Computing, vol. 96, p. 106651, 2020.##[18] E. Emary, H. M. Zawbaa, and C. Grosan, "Experienced gray wolf optimization through reinforcement learning and neural networks," IEEE transactions on neural networks and learning systems, vol. 29, no. 3, pp. 681-694, 2017.##[19] A. Thakkar and R. Lohiya, "Attack classification using feature selection techniques: a comparative study," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 1249-1266, 2021.##[20] R. Ahmadi, G. Ekbatanifard, and P. Bayat, "A Modified Grey Wolf Optimizer Based Data Clustering Algorithm," Applied Artificial Intelligence, vol. 35, no. 1, pp. 63-79, 2021.##[21] A. N. Singh, J. Mrudula, R. Pandey, and S. Das, "A Comparative Study of Four Genetic Algorithm-Based Crossover Operators for Solving Travelling Salesman Problem," in Intelligent Algorithms for Analysis and Control of Dynamical Systems: Springer, 2021, pp. 33-40.##[22] G. S. Kushwah and V. Ranga, "Optimized extreme learning machine for detecting DDoS attacks in cloud computing," Computers & Security, p. 102260, 2021.##[23] K. Singh, L. Kaur, and R. Maini, "Comparison of Principle Component Analysis and Stacked Autoencoder on NSL-KDD Dataset," in Computational Methods and Data Engineering: Springer, 2021, pp. 223-241.##[24] S. Gavel, A. S. Raghuvanshi, and S. Tiwari, "Distributed intrusion detection scheme using dual-axis dimensionality reduction for Internet of things (IoT)," The Journal of Supercomputing, pp. 1-24, 2021.##[25] M. C. Belavagi and B. Muniyal, "Performance evaluation of supervised machine learning algorithms for intrusion detection," Procedia Computer Science, vol. 89, pp. 117-123, 2016.##[26] S. Shakya, "Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection," 2021.##[27] O. Almomani, "A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System," CMC-COMPUTERS MATERIALS & CONTINUA, vol. 68, no. 1, pp. 409-429, 2021.## ##

تحلیل احساس پست های شبکه های اجتماعی در بحران کرونا با استفاده از خوشه بندی دو مرحله ای Sentiment Analysis of Social Media Posts in the Corona Crisis using two-stage Clustering 1 1 در بحران کرونا با طیف وسیعی از افکار، احساسات و نگرش ها در شبکه های اجتماعی مواجه ایم. دستیابی به درک جامعی از نگرش های جامعه نیازمند پردازش این داده‌هاست. هدف این پژوهش شناسایی ویژگی پیام هایی است که منجر به قطبیت های احساسی مختلف در شبکه های اجتماعی می شوند. در این پژوهش از پست های فارسی توییتر، اینستاگرام، تلگرام و کانال های خبری و تکنیک‌های پردازش زبان طبیعی استفاده شده است. در روش پیشنهادی این پژوهش، خوشه بندی دو مرحله ای مبتنی بر شبکه عصبی خود سازمانده و K-میانگین استفاده شده است. نتایج نشان دادند پست های حوزه سلامت و فرهنگ با قطبیت منفی، به احساساتی مانند ترس، تنفر، غم و خشم منجر شده است. پیام های مربوط به عملکرد هیجانی و نادرست مردم با احساس غم، ترس و استرس همراه است و امید در جامعه را کاهش داده است. 2 In the Corona crisis, we face a wide range of thoughts, feelings, attitudes, and behaviors on social media. This data contains valuable information for responding to the crisis by the people and administrators. The goal of this study is to identify the characteristics of messages that lead to different emotional polarities. This study aims to investigate the information posted by Twitter, Instagram, and Telegram users and news related to the COVID-19 pandemic in Iran. The data extracted from social networks are focused on the period of January 21, to April 29, 2020, which were shared in Iran and in Persian. It should be noted that the data set and their labels were published by the Cognitive Sciences and Technologies Council (CSTC) in Iran. In this work, the content of each post was pre-processed. Pre-processing was performed by removing stop words, normalizing the words, tokenizing, and stemming. The emotion labels were based on plutchik’s model and included joy, trust, fear, surprise, sadness, anticipation, anger, disgust, stress, and other emotions. In this study, clustering algorithms were used to analyze social media posts. We applied a two-stage clustering method. The proposed clustering algorithm was a combination of self-organized neural network and K-means algorithms. According to our proposed algorithm, the data were clustered through SOM at first, the results of which provided the initial cluster centers for the K-means algorithm. Implementations were built in Python version 3.7 and MATLAB R2015a. Hazm Tools was used for pre-processing data, and clustering was done in MATLAB. The Davies-Bouldin clustering evaluation was applied to find the optimal number of clusters. This measure was calculated for the number of clusters in the range of 2-50 in the two-stage clustering method. The results showed that the optimal number of clusters was ten. Analysis of the results showed that posts related to health and culture with negative polarity led to negative emotions such as fear, hatred, sadness, and anger. Messages about people's emotional and improper functioning have led to feelings of sadness, fear, and stress, and reduced hope in society. The results revealed a strong correlation between anger and disgust. Also, a positive correlation between fear, stress, and sadness was observed. In order to reduce the negative feelings and to create a sense of trust in the authorities, we suggest clarifying about the corona pandemic 145 158 2020/11/242020/12/72020/12/162021/01/302018/10/22021/03/252021/03/22021/03/232020/12/24 1399/10/4 2021/12/202021/05/302022/10/82023/02/222023/02/222023/06/22023/02/222022/07/312022/01/8 1400/10/18 سمیرا عباسی Samira Abasi دانشگاه صنعتی همدان ایران samira.abbasi@gmail.com فاطمه امیری Fatemeh Amiri دانشگاه صنعتی همدان ایران fateme.amiri@gmail.com : COVID-19 Social media Sentiment analysis Clustering کرونا شبکه های اجتماعی تحلیل احساسات خوشه بندی [1] A. Abd-Alrazaq, D. Alhuwail, M. Househ, M. Hamdi, and Z. Shah, "Top concerns of tweeters during the COVID-19 pandemic: infoveillance study," J Med Internet Res, vol. 22, no. 4, p. e19016, 2020.##[2] M. Smith, D. A. Broniatowski, M. J. Paul, and M. Dredze, "Towards real-time measurement of public epidemic awareness: Monitoring influenza awareness through twitter," in AAAI spring symposium on observational studies through social media and other human-generated content, California, USA, 2016.##[3] P. Hitlin and K. Olmstead, "The science people see on social media. Pew Research Center," ed, pp. 142-148, 2018.##[4] A. R. Ahmad and H. R. Murad, "The impact of social media on panic during the COVID-19 pandemic in Iraqi Kurdistan: online questionnaire study," J Med Internet Res, vol. 22, no. 5, p. e19556, 2020.##[5] J. Zarocostas, "How to fight an infodemic," Lancet, vol. 395, no. 10225, p. 676, 2020.##[6] B. Ghanem, P. Rosso, and F. Rangel, "An emotional analysis of false information in social media and news articles," ACM T Internet Techn, vol. 20, no. 2, pp. 1-18, 2020.##[7] K. P. Murphy, "Naive bayes classifiers," University of British Columbia, vol. 18, p. 60, 2006.##[8] M. Salathé and S. Khandelwal, "Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control," PLoS Comput Biol, vol. 7, no. 10, p. e1002199, 2011.##[9] Y. Lu, X. Hu, F. Wang, S. Kumar, H. Liu, and R. Maciejewski, "Visualizing social media sentiment in disaster scenarios," 24th International Conference on World Wide Web, 2015, USA, pp. 1211-1215, 2015.##[10] K. Chakraborty, S. Bhattacharyya, and R. Bag, "A Survey of Sentiment Analysis from Social Media Data," IEEE Trans Comput Soc Syst, vol. 7, no. 2, pp. 450-464, 2020.##[11] K. Rudra, S. Ghosh, N. Ganguly, P. Goyal, and S. Ghosh, "Extracting situational information from microblogs during disaster events: a classification-summarization approach," 24th ACM International on Conference on Information and Knowledge Management, 2015, Melbourne, Australia, pp. 583-592, 2015.##[12] S. E. Vieweg, "Situational awareness in mass emergency: A behavioral and linguistic analysis of microblogged communications," University of Colorado at Boulder, 2012.##[13] Q. Zhang, F.-Y. Wang, D. Zeng, and T. Wang, "Understanding crowd-powered search groups: a social network perspective," PloS one, vol. 7, no. 6, p. e39749, 2012.##[14] B. Takahashi, E. C. Tandoc Jr, and C. Carmichael, "Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines," Comput Hum Behav, vol. 50, pp. 392-398, 2015.##[15] N. Bhuvana and I. A. Aram, "Facebook and Whatsapp as disaster management tools during the Chennai (India) floods of 2015," Int J Disast Risk Re, vol. 39, p. 101135, 2019.##[16] L. Li et al., "Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo," IEEE Trans Comput Soc Syst, vol. 7, no. 2, pp. 556-562, 2020.##[17] A. Mosam, S. Goldstein, A. Erzse, A. Tugendhaft, and K. Hofman, "Building trust during COVID 19: Value-driven and ethical priority-setting," S Afr Med J, vol. 110, no. 6, pp. 0-0, 2020.##[18] A. Oksanen, M. Kaakinen, R. Latikka, I. Savolainen, N. Savela, and A. Koivula, "Regulation and Trust: 3-Month Follow-up Study on COVID-19 Mortality in 25 European Countries," JMIR Public Health and Surveillance, vol. 6, no. 2, p. e19218, 2020.##[19] X. Ji, S. A. Chun, and J. Geller, "Monitoring public health concerns using twitter sentiment classifications," IEEE International Conference on Healthcare Informatics, 2013, Washington, DC, USA, pp. 335-344: IEEE, 2013.##[20] X. Ye, S. Li, X. Yang, and C. Qin, "Use of social media for the detection and analysis of infectious diseases in China," ISPRS Int J Geo-Inf, vol. 5, no. 9, p. 156, 2016.##[21] L. Nemes and A. Kiss, "Social media sentiment analysis based on COVID-19," J Inf Telecommun, pp. 1-15, 2020.##[22] C. E. Lopez, M. Vasu, and C. Gallemore, "Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset," arXiv preprint arXiv:2003.10359, 2020.##[23] D. Pastor-Escuredo and C. Tarazona, "Characterizing information leaders in Twitter during COVID-19 crisis," arXiv preprint arXiv:2005.07266, 2020.##[24] R. Kouzy et al., "Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter," Cureus, vol. 12, no. 3, 2020.##[25] L. Singh et al., "A first look at COVID-19 information and misinformation sharing on Twitter," arXiv preprint arXiv:2003.13907, 2020.##[26] F. Pierri and S. Ceri, "False news on social media: a data-driven survey," ACM Sigmod Record, vol. 48, no. 2, pp. 18-27, 2019.##[27] N. Ruchansky, S. Seo, and Y. Liu, "Csi: A hybrid deep model for fake news detection," ACM on Conference on Information and Knowledge Management, 2017, Singapore, pp. 797-806, 2017.##[28] K. Popat, S. Mukherjee, A. Yates, and G. Weikum, "Declare: Debunking fake news and false claims using evidence-aware deep learning," arXiv preprint arXiv:1809.06416, 2018.##[29] V. K. Vijayan, K. Bindu, and L. Parameswaran, "A comprehensive study of text classification algorithms," International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, Udupi, Karnataka, India, pp. 1109-1113: IEEE, 2017.##[30] B. Liu, E. Blasch, Y. Chen, D. Shen, and G. Chen, "Scalable sentiment classification for big data analysis using naive bayes classifier," IEEE international conference on big data, 2013, Santa Clara, CA, USA, pp. 99-104: IEEE, 2013.##[31] E. Boiy, P. Hens, K. Deschacht, and M.-F. Moens, "Automatic Sentiment Analysis in On-line Text," in ELPUB, 2007, pp. 349-360, 2007.##[32] K. M. Leung, "Naive bayesian classifier," Polytechnic University Department of Computer Science/Finance and Risk Engineering, vol. 2007, pp. 123-156, 2007.##[33] W. Ramadhan, S. A. Novianty, and S. C. Setianingsih, "Sentiment analysis using multinomial logistic regression," International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), 2017, Piscataway, New Jersey, pp. 46-49: IEEE, 2017.##[34] K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, "Text classification algorithms: A survey," Information, vol. 10, no. 4, p. 150, 2019.##[35] R. Plutchik, "A general psychoevolutionary theory of emotion," in Theories of emotion: Elsevier, 1980, pp. 3-33.##[36] S. Wu, Y. Liu, J. Wang, and Q. Li, "Sentiment analysis method based on Kmeans and online transfer learning," Comput. Mater. Continua, vol. 60, no. 3, pp. 1207-1222, 2019.##[37] Y.-C. Liu, M. Liu, and X.-L. Wang, Application of self-organizing maps in text clustering: a review. chapter, 2012.##[38] V. Khachidze, T. Wang, S. Siddiqui, V. Liu, S. Cappuccio, and A. Lim, "Contemporary research on E-business technology and strategy," in Conference proceedings iCETS, 2012, p. 43: Springer.##[39] Y. P. Raykov, A. Boukouvalas, F. Baig, and M. A. Little, "What to do when k-means clustering fails: a simple yet principled alternative algorithm," PloS one, vol. 11, no. 9, p. e0162259, 2016.##[40] Y. Kou, H. Cui, and L. Xu, "The Application of SOM and K-Means Algorithms in Public Security Performance Analysis and Forecasting," International Conference on E-business Technology and Strategy, 2012, Tianjin, China, pp. 73-84: Springer, 2012.##[41] J. C. Bezdek, R. Ehrlich, and W. Full, "FCM: The fuzzy c-means clustering algorithm," Computers & geosciences, vol. 10, no. 2-3, pp. 191-203, 1984.##[1] A. Abd-Alrazaq, D. Alhuwail, M. Househ, M. Hamdi, and Z. Shah, "Top concerns of tweeters during the COVID-19 pandemic: infoveillance study," J Med Internet Res, vol. 22, no. 4, p. e19016, 2020.##[2] M. Smith, D. A. Broniatowski, M. J. Paul, and M. Dredze, "Towards real-time measurement of public epidemic awareness: Monitoring influenza awareness through twitter," in AAAI spring symposium on observational studies through social media and other human-generated content, California, USA, 2016.##[3] P. Hitlin and K. Olmstead, "The science people see on social media. Pew Research Center," ed, pp. 142-148, 2018.##[4] A. R. Ahmad and H. R. Murad, "The impact of social media on panic during the COVID-19 pandemic in Iraqi Kurdistan: online questionnaire study," J Med Internet Res, vol. 22, no. 5, p. e19556, 2020.##[5] J. Zarocostas, "How to fight an infodemic," Lancet, vol. 395, no. 10225, p. 676, 2020.##[6] B. Ghanem, P. Rosso, and F. Rangel, "An emotional analysis of false information in social media and news articles," ACM T Internet Techn, vol. 20, no. 2, pp. 1-18, 2020.##[7] K. P. Murphy, "Naive bayes classifiers," University of British Columbia, vol. 18, p. 60, 2006.##[8] M. Salathé and S. Khandelwal, "Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control," PLoS Comput Biol, vol. 7, no. 10, p. e1002199, 2011.##[9] Y. Lu, X. Hu, F. Wang, S. Kumar, H. Liu, and R. Maciejewski, "Visualizing social media sentiment in disaster scenarios," 24th International Conference on World Wide Web, 2015, USA, pp. 1211-1215, 2015.##[10] K. Chakraborty, S. Bhattacharyya, and R. Bag, "A Survey of Sentiment Analysis from Social Media Data," IEEE Trans Comput Soc Syst, vol. 7, no. 2, pp. 450-464, 2020.##[11] K. Rudra, S. Ghosh, N. Ganguly, P. Goyal, and S. Ghosh, "Extracting situational information from microblogs during disaster events: a classification-summarization approach," 24th ACM International on Conference on Information and Knowledge Management, 2015, Melbourne, Australia, pp. 583-592, 2015.##[12] S. E. Vieweg, "Situational awareness in mass emergency: A behavioral and linguistic analysis of microblogged communications," University of Colorado at Boulder, 2012.##[13] Q. Zhang, F.-Y. Wang, D. Zeng, and T. Wang, "Understanding crowd-powered search groups: a social network perspective," PloS one, vol. 7, no. 6, p. e39749, 2012.##[14] B. Takahashi, E. C. Tandoc Jr, and C. Carmichael, "Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines," Comput Hum Behav, vol. 50, pp. 392-398, 2015.##[15] N. Bhuvana and I. A. Aram, "Facebook and Whatsapp as disaster management tools during the Chennai (India) floods of 2015," Int J Disast Risk Re, vol. 39, p. 101135, 2019.##[16] L. Li et al., "Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo," IEEE Trans Comput Soc Syst, vol. 7, no. 2, pp. 556-562, 2020.##[17] A. Mosam, S. Goldstein, A. Erzse, A. Tugendhaft, and K. Hofman, "Building trust during COVID 19: Value-driven and ethical priority-setting," S Afr Med J, vol. 110, no. 6, pp. 0-0, 2020.##[18] A. Oksanen, M. Kaakinen, R. Latikka, I. Savolainen, N. Savela, and A. Koivula, "Regulation and Trust: 3-Month Follow-up Study on COVID-19 Mortality in 25 European Countries," JMIR Public Health and Surveillance, vol. 6, no. 2, p. e19218, 2020.##[19] X. Ji, S. A. Chun, and J. Geller, "Monitoring public health concerns using twitter sentiment classifications," IEEE International Conference on Healthcare Informatics, 2013, Washington, DC, USA, pp. 335-344: IEEE, 2013.##[20] X. Ye, S. Li, X. Yang, and C. Qin, "Use of social media for the detection and analysis of infectious diseases in China," ISPRS Int J Geo-Inf, vol. 5, no. 9, p. 156, 2016.##[21] L. Nemes and A. Kiss, "Social media sentiment analysis based on COVID-19," J Inf Telecommun, pp. 1-15, 2020.##[22] C. E. Lopez, M. Vasu, and C. Gallemore, "Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset," arXiv preprint arXiv:2003.10359, 2020.##[23] D. Pastor-Escuredo and C. Tarazona, "Characterizing information leaders in Twitter during COVID-19 crisis," arXiv preprint arXiv:2005.07266, 2020.##[24] R. Kouzy et al., "Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter," Cureus, vol. 12, no. 3, 2020.##[25] L. Singh et al., "A first look at COVID-19 information and misinformation sharing on Twitter," arXiv preprint arXiv:2003.13907, 2020.##[26] F. Pierri and S. Ceri, "False news on social media: a data-driven survey," ACM Sigmod Record, vol. 48, no. 2, pp. 18-27, 2019.##[27] N. Ruchansky, S. Seo, and Y. Liu, "Csi: A hybrid deep model for fake news detection," ACM on Conference on Information and Knowledge Management, 2017, Singapore, pp. 797-806, 2017.##[28] K. Popat, S. Mukherjee, A. Yates, and G. Weikum, "Declare: Debunking fake news and false claims using evidence-aware deep learning," arXiv preprint arXiv:1809.06416, 2018.##[29] V. K. Vijayan, K. Bindu, and L. Parameswaran, "A comprehensive study of text classification algorithms," International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, Udupi, Karnataka, India, pp. 1109-1113: IEEE, 2017.##[30] B. Liu, E. Blasch, Y. Chen, D. Shen, and G. Chen, "Scalable sentiment classification for big data analysis using naive bayes classifier," IEEE international conference on big data, 2013, Santa Clara, CA, USA, pp. 99-104: IEEE, 2013.##[31] E. Boiy, P. Hens, K. Deschacht, and M.-F. Moens, "Automatic Sentiment Analysis in On-line Text," in ELPUB, 2007, pp. 349-360, 2007.##[32] K. M. Leung, "Naive bayesian classifier," Polytechnic University Department of Computer Science/Finance and Risk Engineering, vol. 2007, pp. 123-156, 2007.##[33] W. Ramadhan, S. A. Novianty, and S. C. Setianingsih, "Sentiment analysis using multinomial logistic regression," International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), 2017, Piscataway, New Jersey, pp. 46-49: IEEE, 2017.##[34] K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, "Text classification algorithms: A survey," Information, vol. 10, no. 4, p. 150, 2019.##[35] R. Plutchik, "A general psychoevolutionary theory of emotion," in Theories of emotion: Elsevier, 1980, pp. 3-33.##[36] S. Wu, Y. Liu, J. Wang, and Q. Li, "Sentiment analysis method based on Kmeans and online transfer learning," Comput. Mater. Continua, vol. 60, no. 3, pp. 1207-1222, 2019.##[37] Y.-C. Liu, M. Liu, and X.-L. Wang, Application of self-organizing maps in text clustering: a review. chapter, 2012.##[38] V. Khachidze, T. Wang, S. Siddiqui, V. Liu, S. Cappuccio, and A. Lim, "Contemporary research on E-business technology and strategy," in Conference proceedings iCETS, 2012, p. 43: Springer.##[39] Y. P. Raykov, A. Boukouvalas, F. Baig, and M. A. Little, "What to do when k-means clustering fails: a simple yet principled alternative algorithm," PloS one, vol. 11, no. 9, p. e0162259, 2016.##[40] Y. Kou, H. Cui, and L. Xu, "The Application of SOM and K-Means Algorithms in Public Security Performance Analysis and Forecasting," International Conference on E-business Technology and Strategy, 2012, Tianjin, China, pp. 73-84: Springer, 2012.##[41] J. C. Bezdek, R. Ehrlich, and W. Full, "FCM: The fuzzy c-means clustering algorithm," Computers & geosciences, vol. 10, no. 2-3, pp. 191-203, 1984.## ##

یک سیستم تشخیص ناهنجاری برای شبکه‌های حسگر بی‌سیم بدن An Intrusion Detection System for Wireless Body Area Networks 1 1 شبکه‌های‌ حسگر بی‌سیم به دلیل کاربردهای متنوعی که دارند همواره مورد توجه قرارگرفته‌اند. در تقسیم‌بندی شبکه‌های حسگر بی‌سیم، شبکه‌های حسگر بی‌سیم بدن به دلیل کاربردهای حساس پزشکی از اهمیت ویژه‌ای برخوردارند. هرگونه حمله به شبکه‌های حسگر بی‌سیم بدن می‌تواند خسارت‌های جانی جبران‌ناپذیری برای بیمار به همراه داشته باشد. یکی از روش‌های تأمین امنیت استفاده از سیستم‌های تشخیص نفوذ به‌عنوان یک دفاع خط دوم می‌باشد. در این مقاله یک سیستم تشخیص نفوذ مبتنی بر ناهنجاری با استفاده از روش‌های ترکیبی ارائه‌شده است. در سیستم تشخیص نفوذ پیشنهادی ابتدا با استفاده از الگوریتم ژنتیک ویژگی‌هایی از داده‌های جمع‌آوری‌شده انتخاب می‌شوند که موجب به دست آمدن بالاترین نرخ تشخیص شوند. سپس با استفاده از روش‌های ماشین بردار پشتیبان و k نزدیک‌ترین همسایه طبقه‌بندی داده‌ها به‌منظور کشف ترافیک ناهنجار از ترافیک داده‌های نرمال انجام می‌شود. نتایج شبیه‌سازی برای حمله جلوگیری از سرویس نشان می‌دهد که استفاده از سیستم پیشنهادی با استفاده از روش طبقه‌بندی k نزدیک‌ترین همسایه می‌تواند بازده ای معادل 90% داشته باشد. 2 Wireless Body Area Network (WBAN) is a pioneer trend in healthcare technology. Since any cyber-attack on a WBAN could jeopardize the patient's health, securing the WBAN plays a crucial role in healthcare applications. An intrusion detection system (IDS), as a second-line defense, is one of the security methods in computer networks. In this paper, a new IDS has been presented which is able to detect denial of service (DoS) attacks in a WBAN. In the proposed IDS, a genetic algorithm is used to select features of collected data, in a way that increases the performance of the IDS and as a result the WBAN. Then, using support vector machine and k nearest neighbor techniques, the data classification is performed to detect DoS traffic from regular data traffic. Simulation results indicate that the proposed IDS has effective performance with a 90% detection rate. 159 170 2020/11/242020/12/72020/12/162021/01/302018/10/22021/03/252021/03/22021/03/232020/12/242020/01/6 1398/10/16 2021/12/202021/05/302022/10/82023/02/222023/02/222023/06/22023/02/222022/07/312022/01/82023/02/22 1401/12/3 پیام محمودی نصر Payam Mahmoudi-Nasr دانشگاه مازندران ایران p.mahmoudi@umz.ac.ir علیرضا رحمانی Alireza Rahmani موسسه صنعتی مازندران ایران Alireza.rahmani96@yahoo.com Anomaly detection cyber security DoS attack Genetic algorithm WBAN. شبکه حسگر بی‌سیم بدن تشخیص ناهنجاری حمله جلوگیری از سرویس الگوریتم ژنتیک چندهدفه. [1] M. Ghamari, B. Janko, R. Sherratt, W. Harwin, R. Piechockic, and C. Soltanpur, "A survey on wireless body area networks for ehealthcare systems in residential environments," Sensors, vol. 16, no. 6, p. 831, 2016.##[2] M. Contaldo, B. Banerjee, D. Ruffieux, J. Chabloz, E. Le Roux, and C. C. 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Ma, "An energy-efficient routing protocol for reliable data transmission in wireless body area networks," Sensors, vol. 19, no. 19, p. 4238, 2019.##[17] S. R. Nabavi, N. Osati Eraghi, and J. Akbari Torkestani, "Temperature-Aware Routing in Wireless Body Area Network Based on Meta-Heuristic Clustering Method," Journal of Communication Engineering, 2021.##[18] N. Bilandi, H. K. Verma, and R. Dhir, "PSOBAN: a novel particle swarm optimization based protocol for wireless body area networks," SN Applied Sciences, vol. 1, no. 11, pp. 1-14, 2019.##[19] B. Vahedian and P. Mahmoudi-Nasr12, "Toward Energy-efficient Communication Protocol in Wireless Body Area Network: A Dynamic Scheduling Policy Approach."##[20] M. S. Hajar, M. O. Al-Kadri, and H. K. 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یک مدل جدید فازی نوع-2 بازگشتی غیرخطی جهت شناسایی رفتار سیستم‌های دینامیکی غیرخطی A New Nonlinear Recurrent Type-2 Fuzzy Model to Identify the Behavior of Nonlinear Dynamic Systems 1 1 در این مقاله یک شبکه عصبی فازی نوع-2 بازگشتی جدید جهت شناسایی سیستم­های دینامیکی غیرخطی ارائه می­گردد. ساختار شبکه عصبی فازی نوع-2 جدید با قسمت "آنگاه" غیرخطی، دارای 8 لایه می­باشد. در لایه­های 0، 1 و 2 عملیات فازی سازی انجام شده و حدود بالا و پایین درجه عضویت تعیین می­شود. در لایه­های 3 و 4 عملیات نرمال­سازی و وزن­دهی انجام می­گردد. در لایه 5، توابع غیرخطی مثلثاتی وجود دارند که در واقع قسمت "آنگاه" سیستم فازی را تشکیل داده و فیدبک بازگشتی از لایه خروجی به این لایه وارد می­شود. در انتها در لایه­های 6 و 7 عملیات فازی­زدایی و محاسبه خروجی انجام می­گیرد. جهت بررسی و ارزیابی عملکرد شبکه در شناسایی سیستم، اطلاعات ورودی-خروجی دو سیستم فیزیکی (یک موتور DC و یک بازوی ربات منعطف) به شبکه عصبی فازی نوع-2 بازگشتی اعمال شده است. این پژوهش کاملا آزمایشگاهی و عملی بوده و به عبارتی بهره­برداری از تکنیک­های هوش مصنوعی در کار عملیاتی است. از نوآوری­های این مقاله علاوه بر ارائه شبکه عصبی جدید، تولید سیگنال مناسب جهت تحریک سیستم، استخراج داده از سیستم­های عملی، پیش پردازش داده (حذف داده پرت، تخمین داده ناموجود و نرمال­سازی داده­ها) می­باشد. در شبیه­سازی، معیار مجذور میانگین مربعات خطا نشان می­دهد که روش پیشنهادی با اختلاف فراوانی از سایر روش­ها، عملکرد مناسب­تر دارد.   2 In this paper, a new recurrent type-2 fuzzy neural network for nonlinear dynamic systems identification is presented. The structure of the new type-2 fuzzy neural network with the non-linear "then" part has 8 layers. In layers 0, 1 and 2, the fuzzification operation is performed and the upper and lower limits of the membership degree are determined. Normalization and weighting operations are performed in layers 3 and 4. In layer 5, there are non-linear trigonometric functions, which actually form the "then" part of the fuzzy system, and return feedback from the output layer enters this layer. Finally, in the 6th and 7th layers, the de-fuzzification operation and the output calculation are performed. In order to check and evaluate the performance of the network in system identification, the input-output information of two physical systems (a DC motor and a flexible robot arm) has been applied to the type-2 recurrent fuzzy neural network. This research is completely experimental and practical, in other words, it is the use of artificial intelligence techniques in operational work. Among the innovations of this article, in addition to presenting a new neural network, is generating a suitable signal to stimulate the system, extracting data from practical systems, data pre-processing (removing outliers, estimating missing data, and normalizing data). In the simulation, the root mean square error criterion shows that the proposed method has a better performance than other methods.   171 180 2020/11/242020/12/72020/12/162021/01/302018/10/22021/03/252021/03/22021/03/232020/12/242020/01/62019/08/21 1398/5/30 2021/12/202021/05/302022/10/82023/02/222023/02/222023/06/22023/02/222022/07/312022/01/82023/02/222023/02/22 1401/12/3 جعفر طاوسی Jafar Tavoosi دانشگاه ایلام ایران j.tavoosi@ilam.ac.ir سجاد یوسفی Sajjad Yousefi دانشگاه فنی و حرفه ای ایران syosefi_1980@yahoo.com Recurrent Type-2 Fuzzy Neural Network System Identification Nonlinear Consequent Part شبکه عصبی فازی نوع- 2 بازگشتی شناسایی سیستم قسمت آنگاه غیرخطی [1] A. Rabbani, A. Karimpor, Identification and Modeling of Gas Turbine and Response Investigation of the Model to the Frequency Variation of Grid Power. Journal of Control. Vol. 12, no. 3, pp.77-87, 2018. Doi: 10.29252/joc.12.3.77.##[2] W. Greblicki and M. Pawlak, "The Weighted Nearest Neighbor Estimate for Hammerstein System Identification," in IEEE Transactions on Automatic Control, vol. 64, no. 4, pp. 1550-1565, April 2019, doi: 10.1109/TAC.2018.2866463.##[3] M. Lin, C. Cheng, Z. Peng, X. Dong, Y. Qu, and G. Meng, Nonlinear dynamical system identification using the sparse regression and separable least squares methods, Journal of Sound and Vibration, vol. 505, pp. 116141, 2021. ##https://doi.org/10.1016/j.jsv.2021.116141##[4] E. Ghorbani, O. Buyukozturk, and Y. J. Cha, Hybrid output-only structural system identification using random decrement and Kalman filter, Mechanical Systems and Signal Processing, vol. 144, pp. 106977, 2020. ##https://doi.org/10.1016/j.ymssp.2020.106977##[5] E. Yazid, C. Y. Ng, Identification of time-varying linear and nonlinear impulse response functions using parametric Volterra model from model test data with application to a moored floating structure, Ocean Engineering, vol. 219, pp. 108370, 2021. ##https://doi.org/10.1016/j.oceaneng.2020.108370##[6] M. Jalanko, Y. Sanchez, V. Mahalec, P. Mhaskar, Adaptive system identification of industrial ethylene splitter: A comparison of subspace identification and artificial neural networks, Computers & Chemical Engineering, Vol. 147, 2021. ##https://doi.org/10.1016/j.compchemeng.2021.107240##[7] H. L. Lyu, W. Wang, X. P. Liu, System identification of fuzzy relation matrix models by semi-tensor product operations, Fuzzy Sets and Systems, vol. 440, pp. 77-89, 2021. ##https://doi.org/10.1016/j.fss.2021.06.004##[8] L. Xu, B. Song & M. Cao "An improved particle swarm optimization algorithm with adaptive weighted delay velocity", Systems Science & Control Engineering, vol. 9, no. 1, pp. 188-197, 2021. ##https://doi.org/10.1080/21642583.2021.1891153##[9] J. Tavoosi, A. Suratgar, and M. Menhaj, "Stable ANFIS2 for Nonlinear System Identification". Neurocomputing, vol. 182, pp. 235-246, 2016. ##https://doi.org/10.1016/j.neucom.2015.12.030##[10] J. Tavoosi, A. Suratgar, and M. Menhaj, "Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN". Engineering Applications of Artificial Intelligence, vol. 54, pp. 26-38, 2016. ##https://doi.org/10.1016/j.engappai.2016.04.006##[11] J. Tavoosi, A. Mohammadzadeh, K. Jermsittiparsert, A review on type-2 fuzzy neural networks for system identification, Soft Computing, vol. 25, no. 10, pp. 7197-7212, 2021. ##https://doi.org/10.1007/s00500-021-05686-5##[12] H. Wang, C. Luo, and X. Wang, "Synchronization and identification of nonlinear systems by using a novel self-evolving interval type-2 fuzzy LSTM-neural network". Engineering Applications of Artificial Intelligence, vol. 81, pp.79-93, 2019. ##https://doi.org/10.1016/j.engappai.2019.02.002##[13] A. M. El-Nagar, "Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure". ISA Transactions, vol. 72, pp.205-217, 2018. ##https://doi.org/10.1016/j.isatra.2017.10.012##[14] C. M. Lin, T. L. Le and Huynh, "Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control". Neurocomputing, vol. 275, pp.2239-2250, 2019. ##https://doi.org/10.1016/j.neucom.2017.11.009##[15] Y. -Y. Lin, J. -Y. Chang, N. R. Pal and C. -T. Lin, "A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) With Self-Evolving Structure and Parameters," in IEEE Transactions on Fuzzy Systems, vol. 21, no. 3, pp. 492-509, June 2013, doi: 10.1109/TFUZZ.2013.2255613.##[16] H. Moodi and D. Bustan, "Wind turbine control using T-S systems with nonlinear consequent parts". Energy, vol. 172, pp.922-931, 2019. ##https://doi.org/10.1016/j.energy.2019.01.133##[17] J. Dong, Y. Wang and G. -H. Yang, "Control Synthesis of Continuous-Time T-S Fuzzy Systems with Local Nonlinear Models," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 5, pp. 1245-1258, Oct. 2009, doi: 10.1109/TSMCB.2009.2014961.##[18] H. Moodi, M. Farrokhi, "Robust observer-based controller design for Takagi-Sugeno systems with nonlinear consequent parts". Fuzzy Sets Systems. vol. 273, no. 15, p. 141-154, 2015. https://doi.org /10.1016/j.fss .2015.01.007.##[19] P. Agand, M. A. Shoorehdeli, A. Khaki-Sedigh. "Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification". Engineering Applications of Artificial Intelligence, vol. 65, p. 1-11, 2017. ##https://doi.org/10.1016/j.engappai.2017.07.009##[20] Y. -Y. Lin, S. -H. Liao, J. -Y. Chang and C. -T. Lin, "Simplified Interval Type-2 Fuzzy Neural Networks," in IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 5, pp. 959-969, May 2014, doi: 10.1109/TNNLS.2013.2284603.##[21] J. Tavoosi, A. A. Suratgar, M. B. Menhaj, A. Mosavi, A. Mohammadzadeh, and E. Ranjbar, "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction," Sustainability, vol. 13, no. 6, pp. 3301, Mar. 2021, doi: 10.3390/su13063301. [Online]. Available: http://dx.doi.org/10.3390/su13063301.##[1] A. Rabbani, A. Karimpor, Identification and Modeling of Gas Turbine and Response Investigation of the Model to the Frequency Variation of Grid Power. Journal of Control. Vol. 12, no. 3, pp.77-87, 2018. Doi: 10.29252/joc.12.3.77.##[2] W. Greblicki and M. Pawlak, "The Weighted Nearest Neighbor Estimate for Hammerstein System Identification," in IEEE Transactions on Automatic Control, vol. 64, no. 4, pp. 1550-1565, April 2019, doi: 10.1109/TAC.2018.2866463.##[3] M. Lin, C. Cheng, Z. Peng, X. Dong, Y. Qu, and G. Meng, Nonlinear dynamical system identification using the sparse regression and separable least squares methods, Journal of Sound and Vibration, vol. 505, pp. 116141, 2021. ##https://doi.org/10.1016/j.jsv.2021.116141##[4] E. Ghorbani, O. Buyukozturk, and Y. J. Cha, Hybrid output-only structural system identification using random decrement and Kalman filter, Mechanical Systems and Signal Processing, vol. 144, pp. 106977, 2020. ##https://doi.org/10.1016/j.ymssp.2020.106977##[5] E. Yazid, C. Y. Ng, Identification of time-varying linear and nonlinear impulse response functions using parametric Volterra model from model test data with application to a moored floating structure, Ocean Engineering, vol. 219, pp. 108370, 2021. ##https://doi.org/10.1016/j.oceaneng.2020.108370##[6] M. Jalanko, Y. Sanchez, V. Mahalec, P. Mhaskar, Adaptive system identification of industrial ethylene splitter: A comparison of subspace identification and artificial neural networks, Computers & Chemical Engineering, Vol. 147, 2021. ##https://doi.org/10.1016/j.compchemeng.2021.107240##[7] H. L. Lyu, W. Wang, X. P. Liu, System identification of fuzzy relation matrix models by semi-tensor product operations, Fuzzy Sets and Systems, vol. 440, pp. 77-89, 2021. ##https://doi.org/10.1016/j.fss.2021.06.004##[8] L. Xu, B. Song & M. Cao "An improved particle swarm optimization algorithm with adaptive weighted delay velocity", Systems Science & Control Engineering, vol. 9, no. 1, pp. 188-197, 2021. ##https://doi.org/10.1080/21642583.2021.1891153##[9] J. Tavoosi, A. Suratgar, and M. Menhaj, "Stable ANFIS2 for Nonlinear System Identification". Neurocomputing, vol. 182, pp. 235-246, 2016. ##https://doi.org/10.1016/j.neucom.2015.12.030##[10] J. Tavoosi, A. Suratgar, and M. Menhaj, "Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN". Engineering Applications of Artificial Intelligence, vol. 54, pp. 26-38, 2016. ##https://doi.org/10.1016/j.engappai.2016.04.006##[11] J. Tavoosi, A. Mohammadzadeh, K. Jermsittiparsert, A review on type-2 fuzzy neural networks for system identification, Soft Computing, vol. 25, no. 10, pp. 7197-7212, 2021. ##https://doi.org/10.1007/s00500-021-05686-5##[12] H. Wang, C. Luo, and X. Wang, "Synchronization and identification of nonlinear systems by using a novel self-evolving interval type-2 fuzzy LSTM-neural network". Engineering Applications of Artificial Intelligence, vol. 81, pp.79-93, 2019. ##https://doi.org/10.1016/j.engappai.2019.02.002##[13] A. M. El-Nagar, "Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure". ISA Transactions, vol. 72, pp.205-217, 2018. ##https://doi.org/10.1016/j.isatra.2017.10.012##[14] C. M. Lin, T. L. Le and Huynh, "Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control". Neurocomputing, vol. 275, pp.2239-2250, 2019. ##https://doi.org/10.1016/j.neucom.2017.11.009##[15] Y. -Y. Lin, J. -Y. Chang, N. R. Pal and C. -T. Lin, "A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) With Self-Evolving Structure and Parameters," in IEEE Transactions on Fuzzy Systems, vol. 21, no. 3, pp. 492-509, June 2013, doi: 10.1109/TFUZZ.2013.2255613.##[16] H. Moodi and D. Bustan, "Wind turbine control using T-S systems with nonlinear consequent parts". Energy, vol. 172, pp.922-931, 2019. ##https://doi.org/10.1016/j.energy.2019.01.133##[17] J. Dong, Y. Wang and G. -H. Yang, "Control Synthesis of Continuous-Time T-S Fuzzy Systems with Local Nonlinear Models," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 5, pp. 1245-1258, Oct. 2009, doi: 10.1109/TSMCB.2009.2014961.##[18] H. Moodi, M. Farrokhi, "Robust observer-based controller design for Takagi-Sugeno systems with nonlinear consequent parts". Fuzzy Sets Systems. vol. 273, no. 15, p. 141-154, 2015. https://doi.org /10.1016/j.fss .2015.01.007.##[19] P. Agand, M. A. Shoorehdeli, A. Khaki-Sedigh. "Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification". Engineering Applications of Artificial Intelligence, vol. 65, p. 1-11, 2017. ##https://doi.org/10.1016/j.engappai.2017.07.009##[20] Y. -Y. Lin, S. -H. Liao, J. -Y. Chang and C. -T. Lin, "Simplified Interval Type-2 Fuzzy Neural Networks," in IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 5, pp. 959-969, May 2014, doi: 10.1109/TNNLS.2013.2284603.##[21] J. Tavoosi, A. A. Suratgar, M. B. Menhaj, A. Mosavi, A. Mohammadzadeh, and E. Ranjbar, "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction," Sustainability, vol. 13, no. 6, pp. 3301, Mar. 2021, doi: 10.3390/su13063301. [Online]. Available: http://dx.doi.org/10.3390/su13063301.## ##

به‌کارگیری نظریه ساختار بلاغی برای بهبود بازنمایی متن با شبکه‌های عصبی عمیق Using RST-based deep neural networks to improve text representation 1 1 یافتن یک بازنمایی معنایی غنی با ابعاد کم برای متون طولانی یکی از چالش­های اساسی در فعالیت­های مختلف پردازش زبان طبیعی به شمار می­رود. این بازنمایی باید اطلاعات معنایی و نحوی متن را در برگرفته و همچنین بر حسب وظیفه مد نظر ارتباط و تشابه متون را در ابعاد کم مدل‌سازی کند. در این مقاله تلاش بر آن است تا با بهره‌گیری از نظریه ساختار بلاغی و شبکه‌های عصبی عمیق چالش­های مطرح شده مرتفع گردد. نظریه ساختار بلاغی با ارائه یک ساختار سلسله مراتبی به توصیف اهمیت عبارات موجود در متن و روابط بین آن‌ها می‌پردازد. در اینجا تأثیر به‌کارگیری این ساختار درختی بر دو وظیفه بازیابی اطلاعات و تحلیل احساسات بررسی شده‌است. در وظیفه بازیابی اطلاعات، جهت مدلسازی وابستگی معنایی بین مستندات، یادگیری بازنمایی سند توسط شبکه‌های عصبی بازگشتی عمیق دوقلو صورت پذیرفت. بطوریکه ذخیره و بازیابی مستندات متنی تسهیل گردد. این شبکه از دو زیرشبکه بازگشتی عمیق تشکیل شده­است. این شبکه­های بازگشتی، مبتنی بر ساختار درختی حاصل از تجزیه متن توسط نظریه ساختار بلاغی می‌باشند. این متدلوژی بر روی دو مجموعه داده خبری شامل اخبار بی­بی­سی و همچنین زیرمجموعه­ای از دادگان رویترز مورد ارزیابی قرار گرفت. نتایج نشان می­دهد بازنمایی ارائه شده توسط این ساختار، کارآیی بالاتری از بازنمایی­های سنتی مبتنی بر سبد کلمه دارد. این رویکرد کارایی را به میزان ۶٪ بر روی مجموعه داده بی‌بی‌سی و ۳٪ بر روی مجموعه داده رویترز نسبت به بهترین روش کلاسیک بهبود داده‌است. در وظیفه تحلیل احساسات، در ابتدا به کمک شبکه عصبی بازگشتی عمیق مبتنی بر درخت ساختار بلاغی به ایجاد بازنمایی و در نهایت دسته‌بندی احساسات نظرات افراد پرداخته شد. سپس سایر اطلاعات موجود در درخت جهت بهبود مدل مورد استفاده قرار گرفت. این اطلاعات شامل آگاهی از اهمیت هر بخش از متن با استفاده از درخت ساختار بلاغی می­باشد. با تشخیص بخش­های مرکزی متن و اعمال مکانیزم توجه بر آن در شبکه عمیق بازگشتی بازنمایی غنی‌تری برای متن ایجاد می­گردد. این بازنمایی کارایی مدل تحلیل احساسات را بر روی دادگان اینترنتی نظرات بینندگان فیلم در مقایسه با روش­های پایه به میزان ۳٪ افزایش داده است. نتایج حاصل از این بررسی، بهبود بازنمایی متن با استفاده از شبکه­های عمیق مبتنی بر نظریه ساختار بلاغی را نشان می­دهد. بهبود بازنمایی به کمک ساختاردهی متن غیر ساختار یافته بر روی زبان­های دیگر از جمله زبان فارسی می­تواند مورد راستی آزمایی قرار بگیرد.   2 Finding a highly informative, low-dimensional representation for texts, specifically long texts, is one of the main challenges for natural language processing (NLP) tasks. For texts longer than sentences or a paragraph, finding a good representation beyond the bag-of-words model without losing word order is still a challenge. This representation should capture the semantic and syntactic information of the text while retaining relevance for large-scale similarity search and accurate text classification. We propose the utilization of Rhetorical Structure Theory (RST) to consider the text structure in the representation. RST creates a tree-structure format for the text document and model the importance and relationship between sentences or phrases. In this paper, we examine the effect of using this structure on two different NLP tasks. In information retrieval, to embed document relevance in distributed representation, we use a Siamese neural network to jointly learn document representations. Our Siamese network consists of two sub-networks of recursive neural networks (RNN) built over the RST tree. For this task, we use a subset of Reuters’s news corpus and BBC news dataset. The results show that our approach outperforms conventional text representations like tf_idf, LDA, LSA and word vector averaging. The proposed representation beats the best conventional method by %6 and %3 in precision at k retrieved documents on BBC and Reuters datasets, respectively. In the sentiment analysis task, first, we use an rst-based recursive neural network to represent movie reviews and classify the polarity of people’s opinions. Then we propose to use the nucleus-satellite information of a node in the rst-tree to build an attention mechanism by deep RNN to generate better discourse representations. We test the effectiveness of our approach on  sentiment analysis task, and we prove that considering the importance of the text span improves sentiment analysis performance by %3 on the internet movie review database. In this paper, we improve the text representation by the rst-based deep neural network. We can evaluate this approach on the other languages to show the effectiveness of using the structure format of the text.   181 197 2020/11/242020/12/72020/12/162021/01/302018/10/22021/03/252021/03/22021/03/232020/12/242020/01/62019/08/212019/04/28 1398/2/8 2021/12/202021/05/302022/10/82023/02/222023/02/222023/06/22023/02/222022/07/312022/01/82023/02/222023/02/222021/12/6 1400/9/15 عرفانه غروی Erfaneh Gharavi دانشگاه تهران ایران e.gharavi@ut.ac.ir هادی ویسی Hadi Veisi دانشگاه تهران ایران h.veisi@ut.ac.ir Document embedding Semantic representation Rhetorical Structure Theory Deep Neural network Attention Mechanism بازنمایی متن نظریه ساختار بلاغی شبکه های عصبی عمیق شبکه های دوقلو مکانیزم توجه [1] R. Socher, C. D. C. Manning, and A. Y. A. Ng, "Learning continuous phrase representations and syntactic parsing with recursive neural networks," Proc. NIPS-2010 Deep Learn. Unsupervised Featur. Learn. Work., pp. 1-9, 2010.##[2] R. Socher, C. Manning, B. Huval, and A. Ng, "Semantic compositionality through recursive matrix-vector spaces," in EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 1201-1211.##[3] Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, "A Neural Probabilistic Language Model," J. Mach. Learn. Res., vol. 3, pp. 1137-1155, 2003.##[4] K. S. Tai, R. Socher, and C. D. Manning, "Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks," Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process., pp. 1556-1566, 2015.##[5] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A Convolutional Neural Network for Modelling Sentences," Acl. pp. 655-665, 2014.##[6] Q. V. Le and T. Mikolov, "Distributed Representations of Sentences and Documents," vol. 32, pp. 1188-1196, 2014.##[7] J. Märkle-Huß, S. Feuerriegel, and H. Prendinger, "Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures," in Proceedings of the 50th Hawaii International Conference on System Sciences, 2017, pp. 1142-1151.##[8] A. Hogenboom, F. Frasincar, F. de Jong, and U. Kaymak, "Using rhetorical structure in sentiment analysis," Commun. ACM, vol. 58, no. 7, pp. 69-77, 2015.##[9] D. Marcu, "Discourse Trees are Good Indicators of Importance in Text," in Advances in Automatic Text Summarization, 1999, pp. 123-136.##[10] W. C. Mann and S. A. Thompson, "Rhetorical Structure Theory: Toward a functional theory of text organization," Text, vol. 8, no. 3, pp. 243-281, 1988.##[11] D. Noel, Towards a functional characterization of the news of the BBC World Service. 1986.##[12] B. A. Fox, Discourse Structure and Anaphora: Written and Conversational English. Cambridge University Press, 1993.##[13] R. Salakhutdinov and G. Hinton, "Semantic hashing," Int. J. Approx. Reason., vol. 50, no. 7, pp. 969-978, 2009.##[14] Q. Wang, D. Zhang, and L. Si, "Semantic hashing using tags and topic modeling," in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13, 2013, p. 213.##[15] M. A. Livermore, F. Dadgostari, M. Guim, P. Beling, and D. Rockmore, "Law Search as Prediction," Virginia Public Law Leg. Theory Res. Pap., no. 2018-61, 2018.##[16] P. Huang et al., "Learning Deep Structured Semantic Models for Web Search using Clickthrough Data," 22nd ACM Int. Conf. Conf. Inf. Knowl. Manag., pp. 2333-2338, 2013.##[17] J. Mueller, "Siamese Recurrent Architectures for Learning Sentence Similarity," Proc. 30th Conf. Artif. Intell. (AAAI 2016), no. 2012, pp. 2786-2792, 2016.##[18] C. Lioma, B. Larsen, and W. Lu, "Rhetorical Relations for Information Retrieval," in Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012, pp. 931-940.##[19] Y. Ji and N. Smith, "Imported from Neural Discourse Structure for Text Categorization. (arXiv:1702.01829v1 [cs.CL]) http://arxiv.org/abs/1702.01829," Preprint, 2017.##[20] W. Yin, H. Schütze, B. Xiang, and B. Zhou, "Abcnn: Attention-based convolutional neural network for modeling sentence pairs," arXiv Prepr. arXiv1512.05193, 2015.##[21] and A. I. Zhiguo Wang, Haitao Mi, "Semi-supervised clustering for short text via deep representation learning," in The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2016.##[22] S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, "Generating sentences from a continuous space," arXiv Prepr. arXiv1511.06349, 2015.##[23] P. Bhatia, Y. Ji, and J. Eisenstein, "Better Document-level Sentiment Analysis from RST Discourse Parsing," Emnlp, no. September, pp. 2212-2218, 2015.##[24] M. Taboada, K. Voll, and J. Brooke, "Extracting sentiment as a function of discourse structure and topicality," Tech. Rep., vol. 20, pp. 1-22, 2008.##[25] Y. Liu and M. Lapata, "Learning Structured Text Representations," arXiv Prepr. arXiv1705.09207, 2017.##[26] M. Kraus and S. Feuerriegel, "Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees," arXiv Prepr. arXiv1704.05228, 2017.##[27] C. D. Manning, P. Ragahvan, and H. Schutze, An Introduction to Information Retrieval, no. c. 2009.##[28] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality arXiv : 1310 . 4546v1 [ cs . CL ] 16 Oct 2013," arXiv Prepr. arXiv1310.4546, pp. 1-9, 2013.##[29] J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global Vectors for Word Representation," Proc. 2014 Conf. Empir. Methods Nat. Lang. Process., pp. 1532-1543, 2014.##[30] R. Socher, "Recursive Deep Learning for Natural Language Processing and Computer Vision," PhD thesis, no. August, 2014.##[31] Y. Ji and J. Eisenstein, "Representation Learning for Text-level Discourse Parsing," Proc. 52nd Annu. Meet. Assoc. Comput. Linguist., pp. 13-24, 2014.##[32] G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Inf. Process. Manag., vol. 24, no. 5, pp. 513-523, 1988.##[33] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet Allocation David," J. Mach. Learn. Res., vol. 3, no. Jan, pp. 993-1022, 2003.##[34] S. T. Dumais, G. W. Furnas, T. K. Landauer, S. Deerwester, and R. Harshman, "Using latent semantic analysis to improve access to textual information," in Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '88, 1988, pp. 281-285.##[35] C. Goller and A. Kuchler, "Learning task-dependent distributed representations by backpropagation through structure," Proceedings of International Conference on Neural Networks (ICNN'96), vol. 1. pp. 347-352, 1996.##[36] M. Morey, P. Muller, and N. Asher, "How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT," Emnlp, pp. 1330-1335, 2017.##[1] R. Socher, C. D. C. Manning, and A. Y. A. Ng, "Learning continuous phrase representations and syntactic parsing with recursive neural networks," Proc. NIPS-2010 Deep Learn. Unsupervised Featur. Learn. Work., pp. 1-9, 2010.##[2] R. Socher, C. Manning, B. Huval, and A. Ng, "Semantic compositionality through recursive matrix-vector spaces," in EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 1201-1211.##[3] Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, "A Neural Probabilistic Language Model," J. Mach. Learn. Res., vol. 3, pp. 1137-1155, 2003.##[4] K. S. Tai, R. Socher, and C. D. Manning, "Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks," Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process., pp. 1556-1566, 2015.##[5] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A Convolutional Neural Network for Modelling Sentences," Acl. pp. 655-665, 2014.##[6] Q. V. Le and T. Mikolov, "Distributed Representations of Sentences and Documents," vol. 32, pp. 1188-1196, 2014.##[7] J. Märkle-Huß, S. Feuerriegel, and H. Prendinger, "Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures," in Proceedings of the 50th Hawaii International Conference on System Sciences, 2017, pp. 1142-1151.##[8] A. Hogenboom, F. Frasincar, F. de Jong, and U. Kaymak, "Using rhetorical structure in sentiment analysis," Commun. ACM, vol. 58, no. 7, pp. 69-77, 2015.##[9] D. Marcu, "Discourse Trees are Good Indicators of Importance in Text," in Advances in Automatic Text Summarization, 1999, pp. 123-136.##[10] W. C. Mann and S. A. Thompson, "Rhetorical Structure Theory: Toward a functional theory of text organization," Text, vol. 8, no. 3, pp. 243-281, 1988.##[11] D. Noel, Towards a functional characterization of the news of the BBC World Service. 1986.##[12] B. A. Fox, Discourse Structure and Anaphora: Written and Conversational English. Cambridge University Press, 1993.##[13] R. Salakhutdinov and G. Hinton, "Semantic hashing," Int. J. Approx. Reason., vol. 50, no. 7, pp. 969-978, 2009.##[14] Q. Wang, D. Zhang, and L. Si, "Semantic hashing using tags and topic modeling," in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13, 2013, p. 213.##[15] M. A. Livermore, F. Dadgostari, M. Guim, P. Beling, and D. Rockmore, "Law Search as Prediction," Virginia Public Law Leg. Theory Res. Pap., no. 2018-61, 2018.##[16] P. Huang et al., "Learning Deep Structured Semantic Models for Web Search using Clickthrough Data," 22nd ACM Int. Conf. Conf. Inf. Knowl. Manag., pp. 2333-2338, 2013.##[17] J. Mueller, "Siamese Recurrent Architectures for Learning Sentence Similarity," Proc. 30th Conf. Artif. Intell. (AAAI 2016), no. 2012, pp. 2786-2792, 2016.##[18] C. Lioma, B. Larsen, and W. Lu, "Rhetorical Relations for Information Retrieval," in Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012, pp. 931-940.##[19] Y. Ji and N. Smith, "Imported from Neural Discourse Structure for Text Categorization. (arXiv:1702.01829v1 [cs.CL]) http://arxiv.org/abs/1702.01829," Preprint, 2017.##[20] W. Yin, H. Schütze, B. Xiang, and B. Zhou, "Abcnn: Attention-based convolutional neural network for modeling sentence pairs," arXiv Prepr. arXiv1512.05193, 2015.##[21] and A. I. Zhiguo Wang, Haitao Mi, "Semi-supervised clustering for short text via deep representation learning," in The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2016.##[22] S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, "Generating sentences from a continuous space," arXiv Prepr. arXiv1511.06349, 2015.##[23] P. Bhatia, Y. Ji, and J. Eisenstein, "Better Document-level Sentiment Analysis from RST Discourse Parsing," Emnlp, no. September, pp. 2212-2218, 2015.##[24] M. Taboada, K. Voll, and J. Brooke, "Extracting sentiment as a function of discourse structure and topicality," Tech. Rep., vol. 20, pp. 1-22, 2008.##[25] Y. Liu and M. Lapata, "Learning Structured Text Representations," arXiv Prepr. arXiv1705.09207, 2017.##[26] M. Kraus and S. Feuerriegel, "Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees," arXiv Prepr. arXiv1704.05228, 2017.##[27] C. D. Manning, P. Ragahvan, and H. Schutze, An Introduction to Information Retrieval, no. c. 2009.##[28] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality arXiv : 1310 . 4546v1 [ cs . CL ] 16 Oct 2013," arXiv Prepr. arXiv1310.4546, pp. 1-9, 2013.##[29] J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global Vectors for Word Representation," Proc. 2014 Conf. Empir. Methods Nat. Lang. Process., pp. 1532-1543, 2014.##[30] R. Socher, "Recursive Deep Learning for Natural Language Processing and Computer Vision," PhD thesis, no. August, 2014.##[31] Y. Ji and J. Eisenstein, "Representation Learning for Text-level Discourse Parsing," Proc. 52nd Annu. Meet. Assoc. Comput. Linguist., pp. 13-24, 2014.##[32] G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Inf. Process. Manag., vol. 24, no. 5, pp. 513-523, 1988.##[33] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet Allocation David," J. Mach. Learn. Res., vol. 3, no. Jan, pp. 993-1022, 2003.##[34] S. T. Dumais, G. W. Furnas, T. K. Landauer, S. Deerwester, and R. Harshman, "Using latent semantic analysis to improve access to textual information," in Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '88, 1988, pp. 281-285.##[35] C. Goller and A. Kuchler, "Learning task-dependent distributed representations by backpropagation through structure," Proceedings of International Conference on Neural Networks (ICNN'96), vol. 1. pp. 347-352, 1996.##[36] M. Morey, P. Muller, and N. Asher, "How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT," Emnlp, pp. 1330-1335, 2017.## ##