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کشف دست‌انداز به‌کمک محاسبات نرم Pothole Detection by Soft Computing 1 1 وجود دست­انداز در سطح جاده‌ها معضل بزرگی در حمل‌و‌نقل جاده‌ای است و نادیده‌گرفتن آن منجر به افزایش تصادفات، افزایش مصرف سوخت خودرو و هدر‌رفت وقت و انرژی خواهد شد؛ از‌این‌رو حل مسأله کشف دست‌انداز، مورد توجه پژوهش‌گران قرار گرفته و الگوریتم‌های مختلفی برای حل آن ارائه شده است. در این مقاله، برای افزایش دقت، یک روش پردازش داده مبتنی بر محاسبات نرم برای کشف دست‌انداز پیشنهاد شده است. در روش پیشنهادی، از ترکیب سیستم فازی و الگوریتم­های تکاملی استفاده شده است. این روش، از الگوریتم­های ژنتیک و جستجوی هارمونی برای تنظیم توابع عضویت سیستم فازی استفاده می­کند. به‌منظور بررسی الگوریتم پیشنهادی،  عملکرد این روش برای کشف دست­انداز­های واقع در خیابان غفاری شهر بیرجند، مورد ارزیابی قرارگرفته است. نتایج حاصل حاکی از عملکرد  موفقیت‌آمیز روش پیشنهادی، در مقایسه با سایر روش­ها، از نظر دقت است؛ به‌گونه‌ای که دقت  الگوریتم فازی ژنتیک 98 درصد و الگوریتم فازی هارمونی 99 درصد به‌دست می‌آید. 2 Potholes on roads are regarded as serious problems in the transportation domain, and ignoring them lead to an increase in accidents, traffic, vehicle fuel consumption, and waste of time and energy. As a result, pothole detection has attracted researchers’ attention, and different methods have been presented for it up to now. Data analysis methods such as machine learning and soft computing have been widely used for detection purposes. They rely on a dataset and propose a system that can detect a special event in similar datasets. Their effectiveness can be measured by evaluating their accuracy in detecting the event. Image processing involves a wide range of analytics that are used to extract specific information from images. The majority of image processing programs require massive computational power. The major part of previous research is based on image processing. They utilize dedicated cameras which are embedded in vehicles to take images and analyze them through massive image processing programs. This scheme requires dedicated hardware that is not typically available on vehicles. In this paper, a new scheme is proposed, which uses accelerometer and GPS sensors. These types of sensors are available in today’s smartphones as well as modern vehicles. The data generated by these sensors is processed via soft computing to increase the accuracy of pothole detection. The proposed algorithm uses a combination of a fuzzy system and evolutionary algorithms. Fuzzy systems have been widely used to model the real-world problems that are described by uncertainty and ambiguity. Evolutionary algorithms (e.g., genetic algorithms) try to imitate evolutionary science in solving hard problems. Genetic algorithm and harmony search are used to adjust membership functions of the proposed fuzzy system. For evaluation, a case study has been conducted with regard to detect potholes on Ghaffari Street in Birjand. To this end, a real dataset has been collected and used for implementing the proposed method. Experimental results show the high accuracy of the proposed algorithm in comparison to other solutions. They reveal that the accuracy of the proposed genetic fuzzy algorithm is 98 percent and for the proposed harmony fuzzy algorithm is 99 percent. 1 12 2019/12/19 1398/9/28 2021/12/6 1400/9/15 شکوه شیخ زاده Shokooh Sheikhzade دانشگاه آزاد اسلامی واحد بیرجند ایران sheikhzade.69@gmail.com حامد وحدت نژاد Hamed Vahdat-Nejad دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند ایران vahdatnejad@birjand.ac.ir رمضان هاونگی Ramazan Havangi دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند ایران havangi@birjand.ac.ir Soft computing Pothole detection Fuzzy system Genetic algorithm Harmony search. محاسبات نرم کشف دست‌انداز سیستم فازی الگوریتم ژنتیک جستجوی هارمونی [1] M. Kherad, Traffic assignment of Tehran urban network by Genetic fuzzy algorithm, Master thesis, University of Birjand, 2016.##[2] A. Jafari, H. Shahbazi, A review on fuzzy reasoning algorithms, 4th conference of new idea on electrical engineering, 2014.##[3] A. Sajjadzadeh, Feeature selection by harmony search algorithm, Master thesis, University of Birjand, 2013.##[4] R. Sahraeian, I. Rastgar, "Developing harmony search algorithm for solving optimization problems: a case study in parallel machine production scheduling problem", Journal of Industrial Engineering Research in Production Systems, Vol 1, 2013.##[5] G. Alessandroni, LC. Klopfenstein, S. Delpriori, M. Dromedari, G. Luchetti, BD. Paolini, A. Seraghiti, E. Lattanzi, V. Freschi, A. Carini and A. Bogliolo, "SmartRoadSense: collaborative road surface condition monitoring," The Eighth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM, 2014, pp. 210-215.##[6] S. Balakuntala and S. Venkatesh, "An intelligent system to detect, avoid and maintain potholes: A graph theoretic approach," arXiv preprint arXiv: 1305.5522, 2013.##[7] E. Salari and X. Yu, "Pavement distress detection and classification using a genetic algorithm," IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-5, 2011.##[8] R. Fan, U. Ozgunalp, I. Pitas "Pothole Detection Based on Disparity Transformation and Road Surface Modeling," IEEE Transactions on Image Processing, pp. 897-908, 2019.##[9] A. Dhiman, R. Klette, "Pothole Detection Using Computer Vision and Learning," IEEE Transactions on Intelligent Transportation Systems, pp. 3536 - 3550, 2019.##[10] X. Ankalikar, T. Bhatia, S. Chowdhary and M. Roja, "Fuzzy controller based pothole detection system," Tania Bhatia et al, Int.J.Computer Technology & Applications, vol. 5, no. 5, pp.1763-1767, 2016.##[11] A. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs and L. Selavo, "Real time pothole detection using Android smartphones with accelerometers," International Conference on Distributed Computing in Sensor Systems and Workshops, 2011, pp. 1-6.##[12] H. Verbruggen and P. Bruijn, "Fuzzy control and conventional control: What is (and can be) the real contribution of fuzzy systems?," Fuzzy Sets and Systems, vol. 90, pp. 151-160, 1997.##[13] O. Cordón, "Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases", World Scientific, vol. 19, 2001.##[14] R. Yager, Ronald, and A. Lotfi Zadeh, "An introduction to fuzzy logic applications in intelligent systems", Springer Science & Business Media, vol. 165, 2012.##[15] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, "Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence", America: Prentice-Hall, 1997.##[16] A. P. Engelbrecht, "Computational intelligence: an introduction,", John Wiley & Sons, 3 ed, 2007.##[17] K. S. Lee, Z.W. Geem, S. H. Lee and K. W. Bae, "The harmony search heuristic algorithm for discrete structural optimization," Engineering Optimization, vol. 37, no. 7, pp. 663-684, 2005.##[18] M. Mahdavi, M. Fesanghary and E. Damangir, "An improved harmony search algorithm for solving optimization problems," Applied Mathematics Computation, vol. 188, no. 2, pp. 1567-1579, 2007.##[19] C. Zanchettin and T. B. Ludermir, "A methodology to train and improve artificial neural network's weights and connections," International Joint Conference on IJCNN, 2006, pp. 5267-5274.##[20] Z. W. Geem, Music-Inspired Harmony Search Algorithm," Springer -Verlag Berlin Heidelberg, 2009.##[1] مهدی خرد، تخصیص ترافیک شبکه شهری تهران با استفاده از الگوریتم فازی ژنتیک، پایان نامه کارشناسی ارشد مهندسی فناوری اطلاعات، دانشگاه بیرجند، 1395.##[1] M. Kherad, Traffic assignment of Tehran urban network by Genetic fuzzy algorithm, Master thesis, University of Birjand, 2016.##[2] عالمه جعفری سوق و حامد شهبازی، «مروری بر الگوریتم¬های استنتاج فازی»، چهارمین کنفرانس ملی ایده¬های نو در مهندسی برق،20و21 آبان، 1394.##[2] A. Jafari, H. Shahbazi, A review on fuzzy reasoning algorithms, 4th conference of new idea on electrical engineering, 2014.##[3] علی سجاد زاده، انتخاب ویژگی با الگوریتم جستجوی هارمونی، پایان نامه کارشناسی ارشد مهندسی برق- کامپیوتر، دانشگاه بیرجند، 1392.##[3] A. Sajjadzadeh, Feeature selection by harmony search algorithm, Master thesis, University of Birjand, 2013.##[4] ایمان رستگار و راشد صحرائیان، «توسعه روش جستجوی هماهنگی در حل مسائل بهینه‌سازی: مطالعه موردی در زمان‌بندی تولید ماشین‌های موازی»، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، صفحه 57-71، 1392.##[4] R. Sahraeian, I. Rastgar, "Developing harmony search algorithm for solving optimization problems: a case study in parallel machine production scheduling problem", Journal of Industrial Engineering Research in Production Systems, Vol 1, 2013.##[5] G. Alessandroni, LC. Klopfenstein, S. Delpriori, M. Dromedari, G. Luchetti, BD. Paolini, A. Seraghiti, E. Lattanzi, V. Freschi, A. Carini and A. Bogliolo, "SmartRoadSense: collaborative road surface condition monitoring," The Eighth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM, 2014, pp. 210-215.##[6] S. Balakuntala and S. Venkatesh, "An intelligent system to detect, avoid and maintain potholes: A graph theoretic approach," arXiv preprint arXiv: 1305.5522, 2013.##[7] E. Salari and X. Yu, "Pavement distress detection and classification using a genetic algorithm," IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-5, 2011.##[8] R. Fan, U. Ozgunalp, I. Pitas "Pothole Detection Based on Disparity Transformation and Road Surface Modeling," IEEE Transactions on Image Processing, pp. 897-908, 2019.##[9] A. Dhiman, R. Klette, "Pothole Detection Using Computer Vision and Learning," IEEE Transactions on Intelligent Transportation Systems, pp. 3536 - 3550, 2019.##[10] X. Ankalikar, T. Bhatia, S. Chowdhary and M. Roja, "Fuzzy controller based pothole detection system," Tania Bhatia et al, Int.J.Computer Technology & Applications, vol. 5, no. 5, pp.1763-1767, 2016.##[11] A. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs and L. Selavo, "Real time pothole detection using Android smartphones with accelerometers," International Conference on Distributed Computing in Sensor Systems and Workshops, 2011, pp. 1-6.##[12] H. Verbruggen and P. Bruijn, "Fuzzy control and conventional control: What is (and can be) the real contribution of fuzzy systems?," Fuzzy Sets and Systems, vol. 90, pp. 151-160, 1997.##[13] O. Cordón, "Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases", World Scientific, vol. 19, 2001.##[14] R. Yager, Ronald, and A. Lotfi Zadeh, "An introduction to fuzzy logic applications in intelligent systems", Springer Science & Business Media, vol. 165, 2012.##[15] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, "Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence", America: Prentice-Hall, 1997.##[16] A. P. Engelbrecht, "Computational intelligence: an introduction,", John Wiley & Sons, 3 ed, 2007.##[17] K. S. Lee, Z.W. Geem, S. H. Lee and K. W. Bae, "The harmony search heuristic algorithm for discrete structural optimization," Engineering Optimization, vol. 37, no. 7, pp. 663-684, 2005.##[18] M. Mahdavi, M. Fesanghary and E. Damangir, "An improved harmony search algorithm for solving optimization problems," Applied Mathematics Computation, vol. 188, no. 2, pp. 1567-1579, 2007.##[19] C. Zanchettin and T. B. Ludermir, "A methodology to train and improve artificial neural network's weights and connections," International Joint Conference on IJCNN, 2006, pp. 5267-5274.##[20] Z. W. Geem, Music-Inspired Harmony Search Algorithm," Springer -Verlag Berlin Heidelberg, 2009.## ##

افزایش کارایی سیستم‌های مدولاسیون چندحاملی OFDM با تکنیک کاهش PAPR در ارسال دنباله جزئی با استفاده از الگوریتم بهینه‌سازی انتخابات Increasing the Performance of OFDM Systems by PAPR Reduction in PTS Technique using Election Optimization Algorithm 1 1 مدولاسیون چندحاملی تسهیم با تقسیم فرکانسی متعامد (OFDM) یک فناوری سودمند در ارتباطات بی‌سیم است که امکان ارسال اطلاعات با نرخ بالا را در کانال‌های محو‌شونده چندمسیری فراهم می‌سازد. در سیستم‌های OFDM، برای مدولاسیون سمبل‌ها از تعداد زیادی زیرحامل استفاده که موجب می‌شود، سیگنال ارسالی در حوزه زمان تغییرات دینامیکی زیادی داشته باشد که موجب بروز PAPR می‌‌شود. در این پژوهش، از روش ارسال دنباله جزئی (PTS) برای کاهش PAPR در سیستم‌های OFDM استفاده شده است. یکی از مشکلات اساسی روش PTS، پیچیدگی محاسباتی بسیار بالای آن است؛ زیرا این روش نیازمند یک جستجوی جامع بین تمام ترکیبات ممکنِ فاکتورهای فاز است. پیچیدگی محاسباتی نیز با افزایش تعداد فاکتورهای فاز و زیربلوک‌ها افزایش می‌یابد. در این پژوهش، برای غلبه بر مسأله پیچیدگی محاسباتی روش PTS، روش بهینه‌سازی مبتنی بر الگوریتم انتخابات (EA) بهبود یافته پیشنهاد می‌شود که به‌اختصار EA-PTS نامیده می‌شود. الگوریتم پیشنهادی EA-PTS با جستجوی فاکتورهای فاز بهینه، پیچیدگی محاسباتی کمتری را به سیستم تحمیل کرده و مقدار PAPR را به میزان قابل قبولی کاهش می‌دهد. روش پیشنهادیEA-PTS با روش‌های PTS بهینه (O-PTS)، روش GA-PTS و ICA-PTS مقایسه شده است. نتایج آزمایش‌ها نشان می‌دهند که روش EA-PTS کارایی بهتری در کاهش همزمان PAPR و پیچیدگی محاسباتی دارد. 2 Orthogonal Frequency Division Multiplexing (OFDM) is a useful technology in wireless communications that provides high-rate data transmission in multipath fading channels. The advantages of OFDM systems are the high spectral efficiency and strong resistance to frequency selective fading. In OFDM systems, a large number of sub-carriers are used to modulate the symbols causing the time-domain OFDM signal to have a large dynamic range, or a high peak-to-average power ratio (PAPR). When the signals are applied to a nonlinear power amplifier, the OFDM systems’ performance is degraded by the high PAPR. In recent years, several works have been done to reduce the PAPR of OFDM systems. One of the most well-known methods is a partial transmit sequence (PTS). Regardless of the PTS advantages, it suffers from a high computational complexity. Because it requires an exhaustive search over all possible combinations of phase factors. The computational complexity of the PTS increases with increasing the number of phase factors and sub-blocks. There are several approaches to overcome the computation complexity issue of the PTS technique. The majority of these methods mainly employed swarm intelligence and evolutionary optimization algorithms to resolve the PTS shortcoming. These methods report encouraging results, however, their performance is far from the ideal state. This highlights that improving the performance of PTS is an open problem and there is room for more improvement. As an element of research, we propose an optimization approach based on the election algorithm (EA) to overcome the computational complexity of the PTS technique. To realize this goal, we improve the EA algorithm by introducing a new version of positive advertisements operator. The new operator efficiently improves the search capability of the EA through balancing between the exploration and exploitation power of the algorithm. The proposed EA based PTS (EA-PTS) approach, by searching the optimal phase factors, imposes less computational complexity on the system and reduces the PAPR to an acceptable level. The proposed method is compared with the optimal PTS (O-PTS), genetic algorithm-based PTS (GA-PTS) and imperialism competition algorithm based PTS (ICA-PTS) techniques. Simulation results show that the proposed EA-PTS has better performance in simultaneously reducing the PAPR and computational complexity. 13 26 2019/12/192019/10/25 1398/8/3 2021/12/62020/02/8 1398/11/19 عباسعلی شریفی Abbas Ali Sharifi دانشکده فنی و مهندسی، گروه مهندسی برق، دانشگاه بناب ایران sharifi@ubonab.ac.ir حجت امامی Hojjat Emami دانشکده فنی و مهندسی، گروه مهندسی کامپیوتر، دانشگاه بناب ایران emami@ubonab.ac.ir OFDM PAPR Partial Transmit Sequence (PTS) Election Algorithm (EA) EA-PTS مدولاسیون چندحاملی OFDM کاهش PAPR ارسال دنباله جزئی (PTS) الگوریتم انتخابات (EA) الگوریتم EA-PTS [1] R. Prasad, "OFDM for wireless communications systems", Artech House, 2004.##[2] Y. Rahmatallah, S. Mohan, "Peak-to-average power ratio reduction in ofdm systems: A survey and taxonomy", IEEE communications surveys & tutorials, vol. 15, no. 4, pp. 1567-1592, 2013.##[3] F. Sandoval, G. Poitau, F. Gagnon, "Hybrid peak-to-average power ratio reduction techniques: Review and performance comparison", IEEE Access, vol. 5, pp. 27145-27161, 2017.##[4] K. Anoh, C. Tanriover, B. Adebisi, "On the optimization of iterative clipping and filtering for papr reduction in ofdm systems", IEEE Access, vol. 5, pp. 12004-12013, 2017.##[5] S. Peng, Z. Yuan, "A novel criterion for designing of nonlinear companding functions for peak-to-average power ratio reduction in multicarrier transmission systems", Wireless Networks, vol. 24, no. 2, pp. 581-595, 2018.##[6] I. Baig, V. Jeoti, A. A. Ikram, M. Ayaz, "Papr reduction in mobile wimax: a novel dst precoding based random interleaved ofdma uplink system", Wireless networks, vol. 20, no. 5, pp. 1213-1222, 2014.##[7] M. M. Hasan, "A novel cvm precoding scheme for papr reduction in ofdm transmissions", Wireless networks, vol. 20, no. 6, pp. 1573-1581, 2014.##[8] A. A. Sharifi, "A new post-coding approach for PAPR reduction in DC-biased optical OFDM systems", Optoelectronics Letters, vol. 15, no. 4, pp. 302-305, 2019.##[9] M. Jangalwa, V. Tokekar, "Performance analysis of selective mapping and clipping based multicarrier-cdma system with and without mimo technique", AEU-International Journal of Electronics and Communications, vol. 101, pp. 62-68, 2019.##[10] Y. A. Jawhar, L. Audah, M. A. Taher, K. N. Ramli, N. S. M. Shah, M. Musa, M. S. Ahmed, "A review of partial transmit sequence for papr reduction in the ofdm systems", IEEE Access, vol. 7, pp. 18021-18041, 2019.##[11] J.-K. Lain, S.-Y. Wu, P.-H. Yang, "Papr reduction of ofdm signals using pts: a real-valued genetic approach", EURASIP Journal on Wireless Communications and Networking, vol. 1, no. 126, 2011.##[12] M. H. Aghdam, A. A. Sharifi, "Papr reduction in ofdm systems: An efficient pts approach based on particle swarm optimization", ICT Express, 2018.##[13] Y. Wang, W. Chen, C. Tellambura, "A papr reduction method based on artificial bee colony algorithm for ofdm signals", IEEE transactions on wireless communications, vol. 9, no. 10, pp. 2994-2999, 2010.##[14] N. Taspinar, A. Kalinli, and M. Yildirim, "Partial transmit sequences for PAPR reduction using parallel tabu search algorithm in OFDM systems", IEEE Communications Letters, vol. 15, no. 9, pp.974-976, 2011.##[15] M. Singh, S. K. Patra, "Partial transmit sequence optimization using improved harmony search algorithm for papr reduction in ofdm", ETRI Journal, vol. 39, no. 6, pp. 782-793, 2017.##[16] J. Zhou, E. Dutkiewicz, R. P. Liu, X. Huang, G. Fang, Y. Liu, "A modified shuffled frog leaping algorithm for papr reduction in ofdm systems", IEEE Transactions on Broadcasting, vol. 61, no. 4, pp. 698-709, 2015.##[17] T. Zhang, S. Li, and X. Yu, "Global optimal firefly algorithm in peak-to-average power ratio reduction of orthogonal frequency division multiplexing systems", Sensor Letters, vol. 12, no. 2, pp. 281-286, 2014.##[18] R. S. Rao, P. Malathi, "A novel pts: grey wolf optimizer-based papr reduction technique in ofdm scheme for high-speed wireless applications", Soft Computing, vol. 23, no. 8, pp. 2701-2712, 2019.##[19] H. Emami and F. Derakhshan, "Election algorithm: a new socio-politically inspired strategy", AI Commun, vol. 28, no. 3, pp. 591-603, 2015.##[20] R. L. Haupt and S. E. Haupt, "Practical genetic algorithms", John Wiley & Sons, Inc., Publication, 1998.##[21] E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, pp. 4661-4667, 2007.##[22] S. Talatahari, B. F. Azar, R. Sheikholeslami, and A. H. Gandomi, "Imperialist competitive algorithm combined with chaos for global optimization", Commun. Nonlinear Sci. Numer. Simul, vol. 17, no. 3, pp. 1312-1319, 2012.##[23] M. Jamil and X. S. Yang,"A literature survey of benchmark functions for global optimisation problems",Int. J. Math. Model. Numer. Optim.vol. 4, no. 2, pp. 150-194, 2013.##[1] R. Prasad, "OFDM for wireless communications systems", Artech House, 2004.##[2] Y. Rahmatallah, S. Mohan, "Peak-to-average power ratio reduction in ofdm systems: A survey and taxonomy", IEEE communications surveys & tutorials, vol. 15, no. 4, pp. 1567-1592, 2013.##[3] F. Sandoval, G. Poitau, F. Gagnon, "Hybrid peak-to-average power ratio reduction techniques: Review and performance comparison", IEEE Access, vol. 5, pp. 27145-27161, 2017.##[4] K. Anoh, C. Tanriover, B. Adebisi, "On the optimization of iterative clipping and filtering for papr reduction in ofdm systems", IEEE Access, vol. 5, pp. 12004-12013, 2017.##[5] S. Peng, Z. Yuan, "A novel criterion for designing of nonlinear companding functions for peak-to-average power ratio reduction in multicarrier transmission systems", Wireless Networks, vol. 24, no. 2, pp. 581-595, 2018.##[6] I. Baig, V. Jeoti, A. A. Ikram, M. Ayaz, "Papr reduction in mobile wimax: a novel dst precoding based random interleaved ofdma uplink system", Wireless networks, vol. 20, no. 5, pp. 1213-1222, 2014.##[7] M. M. Hasan, "A novel cvm precoding scheme for papr reduction in ofdm transmissions", Wireless networks, vol. 20, no. 6, pp. 1573-1581, 2014.##[8] A. A. Sharifi, "A new post-coding approach for PAPR reduction in DC-biased optical OFDM systems", Optoelectronics Letters, vol. 15, no. 4, pp. 302-305, 2019.##[9] M. Jangalwa, V. Tokekar, "Performance analysis of selective mapping and clipping based multicarrier-cdma system with and without mimo technique", AEU-International Journal of Electronics and Communications, vol. 101, pp. 62-68, 2019.##[10] Y. A. Jawhar, L. Audah, M. A. Taher, K. N. Ramli, N. S. M. Shah, M. Musa, M. S. Ahmed, "A review of partial transmit sequence for papr reduction in the ofdm systems", IEEE Access, vol. 7, pp. 18021-18041, 2019.##[11] J.-K. Lain, S.-Y. Wu, P.-H. Yang, "Papr reduction of ofdm signals using pts: a real-valued genetic approach", EURASIP Journal on Wireless Communications and Networking, vol. 1, no. 126, 2011.##[12] M. H. Aghdam, A. A. Sharifi, "Papr reduction in ofdm systems: An efficient pts approach based on particle swarm optimization", ICT Express, 2018.##[13] Y. Wang, W. Chen, C. Tellambura, "A papr reduction method based on artificial bee colony algorithm for ofdm signals", IEEE transactions on wireless communications, vol. 9, no. 10, pp. 2994-2999, 2010.##[14] N. Taspinar, A. Kalinli, and M. Yildirim, "Partial transmit sequences for PAPR reduction using parallel tabu search algorithm in OFDM systems", IEEE Communications Letters, vol. 15, no. 9, pp.974-976, 2011.##[15] M. Singh, S. K. Patra, "Partial transmit sequence optimization using improved harmony search algorithm for papr reduction in ofdm", ETRI Journal, vol. 39, no. 6, pp. 782-793, 2017.##[16] J. Zhou, E. Dutkiewicz, R. P. Liu, X. Huang, G. Fang, Y. Liu, "A modified shuffled frog leaping algorithm for papr reduction in ofdm systems", IEEE Transactions on Broadcasting, vol. 61, no. 4, pp. 698-709, 2015.##[17] T. Zhang, S. Li, and X. Yu, "Global optimal firefly algorithm in peak-to-average power ratio reduction of orthogonal frequency division multiplexing systems", Sensor Letters, vol. 12, no. 2, pp. 281-286, 2014.##[18] R. S. Rao, P. Malathi, "A novel pts: grey wolf optimizer-based papr reduction technique in ofdm scheme for high-speed wireless applications", Soft Computing, vol. 23, no. 8, pp. 2701-2712, 2019.##[19] H. Emami and F. Derakhshan, "Election algorithm: a new socio-politically inspired strategy", AI Commun, vol. 28, no. 3, pp. 591-603, 2015.##[20] R. L. Haupt and S. E. Haupt, "Practical genetic algorithms", John Wiley & Sons, Inc., Publication, 1998.##[21] E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, pp. 4661-4667, 2007.##[22] S. Talatahari, B. F. Azar, R. Sheikholeslami, and A. H. Gandomi, "Imperialist competitive algorithm combined with chaos for global optimization", Commun. Nonlinear Sci. Numer. Simul, vol. 17, no. 3, pp. 1312-1319, 2012.##[23] M. Jamil and X. S. Yang,"A literature survey of benchmark functions for global optimisation problems",Int. J. Math. Model. Numer. Optim.vol. 4, no. 2, pp. 150-194, 2013.## ##

بهبود رده‌بندی داده‌های نامتوازن با استفاده از معیارهای شباهت فازی و خوشه‌بندی کاهشی Improving Imbalanced Data Classification Accuracy by using Fuzzy Similarity Measure and Subtractive Clustering 1 1 یکی از قسمت­های مهم در داده­کاوی و کشف دانش از پایگاه داده، رده­بندی است. در اغلب موارد داده­هایی که برای آموزش رده‌بندها به کار می­روند از توزیع مناسبی برخوردار نیستند. این توزیع نامناسب هنگامی رخ می­دهد که یک رده تعداد نمونه­های زیادی دارد؛ درحالی‌که به‌طور ذاتی نمونه‌های رده دیگر کم است. به‌طورکلی روش‌های حل این نوع مسائل به دو دسته نمونه‌گیری کاهشی و نمونه‌گیری افزایشی تقسیم می‌شود. در این مقاله یک روش نمونه‌گیری کاهشی با استفاده از ترکیب خوشه‌بندی و معیارهای شباهت فازی ارائه ‌شده است و عملکرد آن‌ها ازنظر کارآمدی در رده‌بندی داده‌های نامتوازن مورد تحلیل و بررسی قرارگرفته‌اند. بدین منظور در ابتدا خوشه‌بندی کاهشی انجام‌ شده و داده‌های رده اکثریت خوشه‌بندی، سپس با استفاده از معیارهای شباهت فازی نمونه‌های هر خوشه رتبه‌بندی و بر اساس این رتبه‌ها نمونه‌های مناسب انتخاب می‌شود؛ نمونه‌های انتخاب‌شده به همراه رده اقلیت مجموعه داده نهایی را تشکیل می‌دهند. در این پژوهش پیاده‌سازی در نرم‌افزار MATLAB، ارزیابی نتایج از طریق محاسبه معیار AUC و تحلیل نتایج با استفاده از آزمون‌های آماری استاندارد انجام‌ شده است. نتایج مطالعه نشان‌دهنده عملکرد بهتر روش پیشنهادی، نسبت به سایر روش‌های شناخته ‌شده است. 2 One of the biggest challenges in this field is classification problems which refers to the number of different samples in each class. If a data set includes two classes, imbalance distribution occurs when one class has a large number of samples while the other is represented by a small number of samples. In general, the methods of solving these problems are divided into two categories: under-sampling and over-sampling. In this research, it is focused on under-sampling and the advantages of this method will be analyzed by considering the efficiency of classifying imbalanced data and it’s supposed to provide a method for sampling a majority data class by using subtractive clustering and fuzzy similarity measure. For this purpose, at first the subtractive clustering is conducted and the majority data class is clustered. Then, using fuzzy similarity measure, samples of each cluster will be ranked and appropriate samples are selected based on these rankings. The selected samples with the minority class create the final dataset. In this research, MATLAB software is used for implementation, the results are evaluated by using AUC criterion and analyzing the results has been performed by standard statistical tools. The experimental results show that the proposed method is superior to other methods of under-sampling. 27 38 2019/12/192019/10/252019/12/2 1398/9/11 2021/12/62020/02/82020/08/18 1399/5/28 احسان یثربی نائینی Ehsan Yasrebi Naeini گروه برق و کامپیوتر، دانشگاه تربت حیدریه ایران e.yasrebi@torbath.ac.ir مهلا حاتمی mahla hatami دانشگاه شهید باهنر کرمان ایران hatami.mahla@gmail.com Imbalanced data Fuzzy similarity measure Under-sampling Subtractive clustering رده‌بندی داده‌های نامتوازن معیارهای شباهت فازی نمونه‌گیری خوشه‌بندی کاهشی [1] A. Braun, and et al, "Landslide Susceptibility Mapping in Tegucigalpa, Honduras, Using Data Mining Methods", in IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018-Volume 1. 2019. Springer.##[2] S.Fotouhi, S. Asadi, and M.W. Kattan, "A comprehensive data level analysis for cancer diagnosis on imbalanced data", Journal of biomedical informatics, 2019.##[3] N. Junsomboon, and T. Phienthrakul, "Combining over-sampling and under-sampling techniques for imbalance dataset", in Proceedings of the 9th International Conference on Machine Learning and Computing. 2017. ACM.##[4] S.A. Golder, B.A. Huberman, "Usage patterns of collaborative tagging systems", Journal of information science, vol. 32(2), pp. 198-208. 2006.##[5] Y. Sun, and et al., "Cost-sensitive boosting for classification of imbalanced data", Pattern Recognition, vol. 40(12), pp. 3358-3378, 2007.##[6] Z.-H. Zhou, X.-Y. Liu, "Training cost-sensitive neural networks with methods addressing the class imbalance problem", IEEE Transactions on Knowledge & Data Engineering, pp. 63-77. 2006.##[7] N.V. Chawla, and et al., "SMOTE: synthetic minority over-sampling technique", Journal of artificial intelligence research, vol. 16, pp. 321-357. 2002.##[8] E. Fernandes, and et al., "Ensemble of Classifiers based on MultiObjective Genetic Sampling for Imbalanced Data", IEEE Transactions on Knowledge and Data Engineering, 2019.##[9] A. Roy, et al. "A study on combining dynamic selection and data preprocessing for imbalance learning", Neurocomputing, pp. 179-192, 2002.##[10] W. Xie, G.Liang, Z. Dong, B. Tan, and B. Zhang, "Mathematical Problems in Engineering; An Improved Oversampling Algorithm Based on the Samples", Selection Strategy for Classifying Imbalanced Data. 2019.##[11] V.C. Silvia Cateni, M. Vannucci, "A method for resampling imbalanced datasets in binary classification tasks for real-world problems", Neurocomputing,Elsevier.##[12] T. M. Khoshgoftaar, A.F., D. J. Dittman and A. Napolitano, "Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data?" 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, , 2015, pp. 705-712.##[13] G.E. Batista, R.C. Prati, and M.C. Monard, "A study of the behavior of several methods for balancing machine learning training data", ACM SIGKDD explorations newsletter, vol. 6(1), pp. 20-29, 2004.##[14] P. Hart, "The condensed nearest neighbor rule (Corresp.)", IEEE transactions on information theory, vol. 14(3), pp. 515-516, 1968.##[15] I.Tomek, "Two modifications of CNN", IEEE Trans. Systems, Man and Cybernetics, vol.6, pp. 769-772, 1976.##[16] J. Laurikkala, "Improving identification of difficult small classes by balancing class distribution", in Conference on Artificial Intelligence in Medicine in Europe, Springer. 2001.##[17] S.-J.Yen, and Y.-S. Lee, "Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset", in Intelligent Control and Automation, Springer. pp. 731-740, 2006.##[18] M. Kubat, and S. Matwin, "Addressing the curse of imbalanced training sets: one-sided selection", in Icml. 1997. Nashville, USA.##[19] S. Gazzah , A.H., N. Essoukri Ben Amara, "A hybrid sampling method for imbalanced data", pp. 1-6, 2015.##[20] H. Han, W.-Y. Wang, and B.-H. Mao, "Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning", in International conference on intelligent computing, 2005, Springer.##[21] H. He, et al, "ADASYN: Adaptive synthetic sampling approach for imbalanced learning", in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008.##[22] G. Cohen, et al., "Learning from imbalanced data in surveillance of nosocomial infection", Artificial intelligence in medicine, vol. 37(1), pp. 7-18, 2006.##[23] S. Tang, and S.-p. Chen, "The generation mechanism of synthetic minority class examples", in 2008 International Conference on Information Technology and Applications in Biomedicine, IEEE, 2008,.##[24] J. Stefanowski, and S. Wilk, "Selective pre-processing of imbalanced data for improving classification performance", in International Conference on Data Warehousing and Knowledge Discovery, Springer, 2008.##[25] D.M.B. Tarigan, and D.P. Rini, "Particle Swarm Optimization-Based on Decision Tree of C4. 5 Algorithm for Upper Respiratory Tract Infections (URTI) Prediction", in Journal of Physics: Conference Series, IOP Publishing, 2019.##[26] D. Devi, and B. Purkayastha, "Redundancy-driven modified Tomek-link based undersampling: a solution to class imbalance", Pattern Recognition Letters, vol.93, pp. 3-12, 2017.##[27] K. Javed, R. Gouriveau, and N. Zerhouni, "A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering", IEEE transactions on cybernetics, vol.45(12), pp. 2626-2639, 2015.##[28] X.L. Xie, and G. Beni, "A validity measure for fuzzy clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.(8), pp. 841-847. 1991##[29] K.Bataineh, M. Naji, and M. Saqer, "A comparison study between various fuzzy clustering algorithms", Editorial Board, vol. 5, pp. 335, 2011.##[30] Y. Ding, and X. Fu, "Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm", Neurocomputing, vol.188, pp. 233-238, 2016.##[31] R.R.Yager, and D.P. Filev, "Generation of fuzzy rules by mountain clustering", Journal of Intelligent & Fuzzy Systems, vol. 2(3), pp. 209-219. 1994.##[32] S.L. Chiu, "Fuzzy model identification based on cluster estimation", Journal of Intelligent & Fuzzy Systems,vol. 2(3), pp. 267-278. 1994.##[33] D. W.Kim, et al., "A kernel-based subtractive clustering method", Pattern Recognition Letters, vol. 26(7), pp. 879-891, 2005.##[34] M. Y Chen, "A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering", Information Sciences, vol.220, pp. 180-195. 2013.##[35] S. Zeng, S. M. Chen,. M. O.Teng, "Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm", Information Sciences, vol.484, pp.350-366, 2019.##[36] I. Beg, and S. Ashraf, "Similarity measures for fuzzy sets", Appl. and Comput. Math, vol.8(2), pp. 192-202, 2009.##[37] L.T. Kóczy, and D. Tikk, "Fuzzy rendszerek", TypoTEX, Budapest, 2000.##[38] J. Williams, and N. Steele, "Difference, distance and similarity as a basis for fuzzy decision support based on prototypical decision classes", Fuzzy sets and systems, vol.131(1), pp. 35-46. 2002.##[39] S. Santini, and R. Jain, "Similarity is a geometer", Multimedia Tools and Applications, vol. 5(3), pp. 277-306, 1997.##[40] R. Zwick, E. Carlstein, and D.V. Budescu, "Measures of similarity among fuzzy concepts: A comparative analysis", International Journal of Approximate Reasoning, vol. 1(2), pp. 221-242,1987.##[41] S. García, et al., "A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization", Journal of Heuristics, vol.15(6), pp. 617-644, 2009.##[42] O.T. Yıldız, Ö. Aslan, and E. Alpaydın, "Multivariate statistical tests for comparing classification algorithms," in Learning and Intelligent Optimization, Springer, pp. 1-15, 2011.##[43] D.J. Sheskin, Handbook of parametric and nonparametric statistical procedures. 2003: Chapman and Hall/CRC.##[44] S.García, and et al., "Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power", Information Sciences, vol.180(10), pp. 2044-2064, 2010.##[1] A. Braun, and et al, "Landslide Susceptibility Mapping in Tegucigalpa, Honduras, Using Data Mining Methods", in IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018-Volume 1. 2019. Springer.##[2] S.Fotouhi, S. Asadi, and M.W. Kattan, "A comprehensive data level analysis for cancer diagnosis on imbalanced data", Journal of biomedical informatics, 2019.##[3] N. Junsomboon, and T. Phienthrakul, "Combining over-sampling and under-sampling techniques for imbalance dataset", in Proceedings of the 9th International Conference on Machine Learning and Computing. 2017. ACM.##[4] S.A. Golder, B.A. Huberman, "Usage patterns of collaborative tagging systems", Journal of information science, vol. 32(2), pp. 198-208. 2006.##[5] Y. Sun, and et al., "Cost-sensitive boosting for classification of imbalanced data", Pattern Recognition, vol. 40(12), pp. 3358-3378, 2007.##[6] Z.-H. Zhou, X.-Y. Liu, "Training cost-sensitive neural networks with methods addressing the class imbalance problem", IEEE Transactions on Knowledge & Data Engineering, pp. 63-77. 2006.##[7] N.V. Chawla, and et al., "SMOTE: synthetic minority over-sampling technique", Journal of artificial intelligence research, vol. 16, pp. 321-357. 2002.##[8] E. Fernandes, and et al., "Ensemble of Classifiers based on MultiObjective Genetic Sampling for Imbalanced Data", IEEE Transactions on Knowledge and Data Engineering, 2019.##[9] A. Roy, et al. "A study on combining dynamic selection and data preprocessing for imbalance learning", Neurocomputing, pp. 179-192, 2002.##[10] W. Xie, G.Liang, Z. Dong, B. Tan, and B. Zhang, "Mathematical Problems in Engineering; An Improved Oversampling Algorithm Based on the Samples", Selection Strategy for Classifying Imbalanced Data. 2019.##[11] V.C. Silvia Cateni, M. Vannucci, "A method for resampling imbalanced datasets in binary classification tasks for real-world problems", Neurocomputing,Elsevier.##[12] T. M. Khoshgoftaar, A.F., D. J. Dittman and A. Napolitano, "Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data?" 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, , 2015, pp. 705-712.##[13] G.E. Batista, R.C. Prati, and M.C. Monard, "A study of the behavior of several methods for balancing machine learning training data", ACM SIGKDD explorations newsletter, vol. 6(1), pp. 20-29, 2004.##[14] P. Hart, "The condensed nearest neighbor rule (Corresp.)", IEEE transactions on information theory, vol. 14(3), pp. 515-516, 1968.##[15] I.Tomek, "Two modifications of CNN", IEEE Trans. Systems, Man and Cybernetics, vol.6, pp. 769-772, 1976.##[16] J. Laurikkala, "Improving identification of difficult small classes by balancing class distribution", in Conference on Artificial Intelligence in Medicine in Europe, Springer. 2001.##[17] S.-J.Yen, and Y.-S. Lee, "Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset", in Intelligent Control and Automation, Springer. pp. 731-740, 2006.##[18] M. Kubat, and S. Matwin, "Addressing the curse of imbalanced training sets: one-sided selection", in Icml. 1997. Nashville, USA.##[19] S. Gazzah , A.H., N. Essoukri Ben Amara, "A hybrid sampling method for imbalanced data", pp. 1-6, 2015.##[20] H. Han, W.-Y. Wang, and B.-H. Mao, "Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning", in International conference on intelligent computing, 2005, Springer.##[21] H. He, et al, "ADASYN: Adaptive synthetic sampling approach for imbalanced learning", in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008.##[22] G. Cohen, et al., "Learning from imbalanced data in surveillance of nosocomial infection", Artificial intelligence in medicine, vol. 37(1), pp. 7-18, 2006.##[23] S. Tang, and S.-p. Chen, "The generation mechanism of synthetic minority class examples", in 2008 International Conference on Information Technology and Applications in Biomedicine, IEEE, 2008,.##[24] J. Stefanowski, and S. Wilk, "Selective pre-processing of imbalanced data for improving classification performance", in International Conference on Data Warehousing and Knowledge Discovery, Springer, 2008.##[25] D.M.B. Tarigan, and D.P. Rini, "Particle Swarm Optimization-Based on Decision Tree of C4. 5 Algorithm for Upper Respiratory Tract Infections (URTI) Prediction", in Journal of Physics: Conference Series, IOP Publishing, 2019.##[26] D. Devi, and B. Purkayastha, "Redundancy-driven modified Tomek-link based undersampling: a solution to class imbalance", Pattern Recognition Letters, vol.93, pp. 3-12, 2017.##[27] K. Javed, R. Gouriveau, and N. Zerhouni, "A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering", IEEE transactions on cybernetics, vol.45(12), pp. 2626-2639, 2015.##[28] X.L. Xie, and G. Beni, "A validity measure for fuzzy clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.(8), pp. 841-847. 1991##[29] K.Bataineh, M. Naji, and M. Saqer, "A comparison study between various fuzzy clustering algorithms", Editorial Board, vol. 5, pp. 335, 2011.##[30] Y. Ding, and X. Fu, "Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm", Neurocomputing, vol.188, pp. 233-238, 2016.##[31] R.R.Yager, and D.P. Filev, "Generation of fuzzy rules by mountain clustering", Journal of Intelligent & Fuzzy Systems, vol. 2(3), pp. 209-219. 1994.##[32] S.L. Chiu, "Fuzzy model identification based on cluster estimation", Journal of Intelligent & Fuzzy Systems,vol. 2(3), pp. 267-278. 1994.##[33] D. W.Kim, et al., "A kernel-based subtractive clustering method", Pattern Recognition Letters, vol. 26(7), pp. 879-891, 2005.##[34] M. Y Chen, "A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering", Information Sciences, vol.220, pp. 180-195. 2013.##[35] S. Zeng, S. M. Chen,. M. O.Teng, "Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm", Information Sciences, vol.484, pp.350-366, 2019.##[36] I. Beg, and S. Ashraf, "Similarity measures for fuzzy sets", Appl. and Comput. Math, vol.8(2), pp. 192-202, 2009.##[37] L.T. Kóczy, and D. Tikk, "Fuzzy rendszerek", TypoTEX, Budapest, 2000.##[38] J. Williams, and N. Steele, "Difference, distance and similarity as a basis for fuzzy decision support based on prototypical decision classes", Fuzzy sets and systems, vol.131(1), pp. 35-46. 2002.##[39] S. Santini, and R. Jain, "Similarity is a geometer", Multimedia Tools and Applications, vol. 5(3), pp. 277-306, 1997.##[40] R. Zwick, E. Carlstein, and D.V. Budescu, "Measures of similarity among fuzzy concepts: A comparative analysis", International Journal of Approximate Reasoning, vol. 1(2), pp. 221-242,1987.##[41] S. García, et al., "A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization", Journal of Heuristics, vol.15(6), pp. 617-644, 2009.##[42] O.T. Yıldız, Ö. Aslan, and E. Alpaydın, "Multivariate statistical tests for comparing classification algorithms," in Learning and Intelligent Optimization, Springer, pp. 1-15, 2011.##[43] D.J. Sheskin, Handbook of parametric and nonparametric statistical procedures. 2003: Chapman and Hall/CRC.##[44] S.García, and et al., "Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power", Information Sciences, vol.180(10), pp. 2044-2064, 2010.## ##

پرکردن داده‌های گمشده در داده‌های سری زمانی چندمتغیره Missing Data Imputation in Multivariate Time Series Data 1 1 داده­های سری زمانی چندمتغیره در زمینه‌­های مختلف مانند بیوانفورماتیک، زیست­‌شناسی، ژنتیک، نجوم، علوم جغرافیایی و امور مالی یافت می‌­شوند. بسیاری از این مجموعه‌داده­‌ها دارای داده گمشده هستند. جایگذاری داده­‌های گمشده سری زمانی چندمتغیره، یکی از مباحث چالش برانگیز است و قبل از فرایند یادگیری یا پیش­بینی سری­‌های زمانی باید با دقت مورد توجه و بررسی قرار گیرد. تحقیقات فراوانی در استفاده از روش‌های مختلف برای جایگذاری داده‌­های گمشده سری زمانی انجام شده ­است که به‌طورمعمول شامل روش‌­های تجزیه و تحلیل و مدل­‌سازی­‌های ساده در کاربردهای خاص و یا سری­‌های زمانی تک‌متغیره هستند. در این مقاله یک نسخه بهبود‌یافته از درون‌یابی معکوس فاصله وزن‌دار برای جایگذاری داده‌­های گمشده پیشنهاد شده‌ است. روش درون‌یابی معکوس فاصله وزن‌دار دو محدودیت اساسی دارد: 1) یافتن بهترین نقاط نزدیک­تر به داده‌­های گمشده 2) انتخاب توان تأثیر بهینه برای همسایگان داده گمشده. برای بهبود روش درون‌یابی، از خوشه­‌بندی k-means استفاده شده ‌است، تا همسایه‌­های با بیشترین شباهت به الگوی داده­ای انتخاب شوند. از آنجا که میزان تأثیر هر یک از همسایه‌­ها بر روی داده گمشده متفاوت است، از الگوریتم جستجوی فاخته برای تعیین توان تأثیر همسایگی استفاده می­شود. برای ارزیابی عملکرد روش پیشنهادی، از پنج معیار ارزیابی شناخته‌شده ‌استفاده می­شود. نتایج تجربی بر روی چهار مجموعه‌داده UCI با درصدهای مختلف گمشدگی مورد بررسی قرار گرفته و در‌مجموع الگوریتم پیشنهادی نسبت به سه روش مقایسه‌­ای دیگر عملکرد بهتر و به‌طور میانگین حدود 05/0 خطای RMSE، 04/0 خطای MAE، 003/0 خطای MSE و  5 درصد خطای MAPE داشته است. میزان همبستگی داده‌های واقعی و مقدار برآورد‌شده در روش پیشنهادی بسیار مطلوب و در حدود 99 درصد است. 2 Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of different techniques for time series missing data imputation, which usually include simple analytic methods and modeling in specific applications or univariate time series. In this paper, a hybrid approach to obtain missing data is proposed. An improved version of inverse distance weighting (IDW) interpolation is used to missing data imputation. The IDW interpolation method has two major limitations: 1) finding closest points to missing data 2) Choosing the optimal effect power for missing data neighbors. Clustering has been used to remove the first constraint and find closest points to the missing data. With the help of clustering, the search radius and the number of input points that are supposed to be used in interpolation calculations are limited and controlled, and it is possible to determine which points are used to determine the value of a missing data.Therefore, most similar data to the missing data are found. In this paper, the k-maens clustering method is used to find similar data. This method has been more accurate than other clustering methods in multivariate time series. Evolutionary algorithms are used to find the optimal effect power of each data point to remove the second constraint. Considering that each sample within each cluster has a different effect on the estimation of missing data, cuckoo search is used to find the effect on missing data. The cuckoo search algorithm is applied to the data of each cluster, and each data sample that has more similarity with the missing data has more influence, and each data sample that has less similarity has less influence and has less influence in determining the amount of missing data. Among evolutionary algorithms, evolutionary cuckoo search algorithm is used due to high convergence speed, much less probability of being trapped in local optimal points, and ability to quickly solve high dimensional optimization problems in multivariate time series problems. To evaluate the performance of the proposed method, RMS, MAE, , MSE and MAPE criteria are used. Experimental results are investigated on four UCI datasets with different percentages of missingness and in general, the proposed algorithm performs better than the other three comparative methods with an average RMSE error of 0.05, MAE error of 0.04, MSE error of 0.003, and MAPE error of 5. The correlation between the actual data and the estimated value in the proposed method is about 99%. 39 60 2019/12/192019/10/252019/12/22019/12/30 1398/10/9 2021/12/62020/02/82020/08/182020/10/13 1399/7/22 نگین دانشپور Negin Daneshpour دانشکده مهندسی کامپیوتر، دانشگاه تربیت دبیر شهید رجایی ایران ndaneshpour@sru.ac.ir سیده فاطمه میرابوالقاسمی Seyedeh fatemeh mirabolghasemi دانشکده مهندسی کامپیوتر، دانشگاه تربیت دبیر شهید رجایی ایران fmirabolghasemi@yahoo.com Missing Data imputation IDW Interpolation Cuckoo Search Algorithm k-means Clustering Multivariate Time Series جایگذاری داده‌های گمشده درون‌یابی IDW الگوریتم جستجوی فاخته خوشه‌بندی k‌-means سری‌های زمانی چندمتغیره [1] R. H. Shumway and D. S. Stoffer, "Time series analysis and its applications: with R examples", Springer Science & Business Media, Fourth edition, 2017.##[2] Ratanamahatana C., "Multimedia retrieval using time series representation and relevance feedback", in: Proceedings of 8th International Conference on Asian Digital Libraries, 2005, pp. 400-405.##[3] C.Ratanamahatana, V. Niennattrakul, "Clustering multimedia data using time series", in: Proceedings of the International Conference on Hybrid InformationTechnology, ICHIT '06, 2016, pp.372-379.##[4] M.S. Mahmoud, M.F. Emzir, "State estimation with asynchronous multi-rate multi-smart sensors", Information Sciences, vol.196, pp.15-27, 2012.##[5] S. Mohamed, T. Marwala, "Neural network based techniques for estimating missing data in databases", pp. 27-32, 2005.##[6] W. Qiao, Z. GAO, R.G. Harley, "Continuous on-line identification of nonlinear plants in power systems with missing sensor measurements", IEEE, pp. 1729-1734, 2005.##[7] J. Honaker, G. King, "What to do about missing values in time-series cross-section data", American Journal of Political Science, vol.54 (2), pp.561-581, 2010.##[8] J. Lin, E. Keogh, S. Lonardi, J. Lankford, D. Nystrom, "Visually mining and monitoring massive time series", in: Proceedings of 2004ACM SIGKDD International Conference on Knowledge Discovery and data Mining - KDD '04, 2004, 460-475.##[9] R.J.A. Little, D.B. Rubin, "Statistical analysis with missing data", 3rd Edition, 2014.##[10] M. Amiri, R. Jensen, "Missing data imputation using fuzzy-rough methods", Neurocomputing, vol.196, pp.15-27, 2016.##[11] C.K. Enders, "Applied Missing Data Analysis", Guilford Press. ISBN 978-1-60623-639-0 .2010.##[12] D. M. Kreindler, C. J. Lumsden, "The effects of the irregular sample and missing data in time series analysis", Nonlinear Dynamics Psychology and Life Sciences, vol.10(2), pp.187-214, 2012.##[13] C.De Boor, E. Mathématicien, "A practical guide to splines", Mathematical Sciences, vol. 27, 2005.##[14] D. Mondal, D. B. Percival, "Wavelet variance analysis for gappy time series", Annals Inst. Stat. Math, vol.62, pp. 943-966, 2010.##[15] K. Rehfeld, N. Marwan, J. Heitzig, "Comparison of correlation analysis techniques for irregularly sampled time series", Nonlinear Process. Geophys, vol.18, 2011.##[16] P.J. Garca-Laencina, J.-L Sancho-Gómez. "Pattern classification with missing data: a review", Neural Comput, vol.19, 2010.##[17] R. Mazumder, T. Hastie, R. Tibshirani, "Spectral regularization algorithms for learning large incomplete matrices", Machine learning research, vol.11, pp. 2287-2322, 2010.##[18] Y. Koren, R. Bell, C. Volinsky, "Matrix factorization techniques for recommender systems", Comput, vol.42, 2009.##[19] I. R. White, P. Royston, A. M. Wood, "Multiple imputation using chained equations: issues and guidance for practice", Stat. medicine, vol.30, pp. 377-399, 2011.##[20] B. J. Wells, K. M. Chagin, A. S. Nowacki, M. W. Kattan, "Strategies for handling missing data in electronic health record derived data," EGEMS 1, 2013.##[21] C. Lipton, C. Kale," Modeling Missing Data in Clinical Time Series with RNNs", Machine Learning for Healthcare, pp.56, 2016.##[22] Li. Li, J. Zhang, Y. Wang, "Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method", IEEE Transactions on Intelligent Transportation Systems, vol.20, pp. 2933 - 2943, 2019.##[23] A. McLinden, V. Fioletov, W. Shephard, N. Krotkov, "Space-based detection of missing sulfur dioxide sources of global air pollution", Nature Geoscience, vol.9, pp. 496-500, 2016.##[24] R. Mahmoudvand, P. Canas, "Missing value imputation in time series using Singular Spectrum Analysis", International Journal of Energy and Statistics, vol. 04,165005, 2016.##[25] N. Bokde, W. Beck, "A novel imputation methodology for time series based on pattern sequence forecasting", Pattern Recognition Letters, vol.116, pp.88-96, 2018.##[26] T.T. Hong Phan, E. Poisson Caillault, A. Lefebvre, A. Bigand, "Dynamic Time Warping-based imputation for univariate time series data", Pattern Recognition Letters, 2017.##[27] Z. Che, S. Purushotham, K. Cho, D. Sontag, Y. Liu."Recurrent Neural Networks for Multivariate Time Series with Missing Values", Scientific reports, vol.6085, pp.85-99, 2018.##[28] W.S. David, Wong. "Interpolation: Inverse‐Distance Weighting", The International Encyclopedia of Geography, pp.156-173, 2017.##[29] G. Mei, N. Xu & L. Xu, "Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search", pp.1389, 2016.##[30] J. Pratama, H. Pramoedyo, R. Fitriani, "comparison of inverse distance weighted and natural neighbor interpolation method at air temperature data in malang region", Cauchy, vol.5, pp.48-54, 2018.##[31] S. Aghabozorgi, A. SeyedShirkhorshidi, T. YingWah,"Time-seriesclustering-A decadereview", Information Systems, vol.53, pp.16-38, 2015.##[32] P. Roelofsen, "Time series clustering", Master thesis Business Analytic, Vrije Universiteit Amsterdam, 2018.##[33] Z. Bankó, J. Abonyi, "Correlation based dynamic time warping of multivariate time series", Expert Systems with Applications, vol.39, no.17, pp.12814-12823, 2012.##[34] Guanyu Wang, "A Comparative Study of Cuckoo Algorithm and Ant Colony Algorithm in Optimal Path Problems", MATEC Web of Conferences 232, 03003, 2018.##[35] M. Jalal, M. Goharzay, "Cuckoo search algorithm for applied structural and design optimization: float system for experimental setups", Computational Design and Engineering, vol.6, no.159-172, 2018.##[36] J. Tang, G. Zhang, Y. Wang, H. Wang, F. Liu, "A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation", Transportation Research Part C: Emerging Technologies, vol.51, no. 29-40, 2015.##[37] W.L. Junger, A.P. de Leon, "Imputation of missing data in time series for air pollutants", Atmospheric Environment, vol. 102, pp. 96-104.2015.##[38] L. Folguera, J. Zupan, D. Cicerone, J.F. Magallanes, "Self-organizing maps for imputation of missing data in incomplete data matrices", Chemometrics and Intelligent Laboratory Systems, vol. 143, pp.146-151,2015.##[39] T.T. Hong Phan, E. Poisson Caillault, A. Lefebvre, A. Bigand, "Dynamic Time Warping-based imputation for univariate time series data", Pattern Recognition Letters S0167-8655(17)30275-1, 2017.##[40] N. Bokde, M. W. Beck, F. Marttinez," A novel imputation methodology for time series based on pattern sequence forecasting", Pattern Recognition Letters, pp. 88-96, 2018.##[41] T.T. Hong Phan, A. Bigand, É. P. Caillault," A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series", Computational Intelligence and Soft Computing, pp. 1-15, 2018.##[42] J. Tang, G. Zhang, Y. Wang, H. Wang," A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation", Transportation Research, vol. 51, pp. 29-40, 2015.##[43] S. Sridevi, S. Rajaram, C. Parthiban, S. SibiArasan, C. Swadhikar, "Imputation for the Analysis of Missing Values and Prediction of Time Series Data", International Conference on Recent Trends in Information Technology,2011.##[44] C. O. Resende, A. Santana, F. Lobato, "Time series imputation using genetic programming and Lagrange interpolation", 5th Brazilian Conference on Intelligent Systems, 2016.##[45] Y. Jane Nancya, H.Nehemiah Khannaa, K.Arputharaj, "Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework", vol.112, pp. 63-79, 2017.##[46] S. Aghabozorgi, A. SeyedShirkhorshidi, T. YingWah,"Time-seriesclustering-A decadereview", Information Systems, vol.53, pp.16-38, 2015.##[47] P. Roelofsen, "Time series clustering", Master thesis Business Analytic, Vrije Universiteit Amsterdam, 2018.##[48] C. Cassisi, P. Montalto, M. Aliotta, A. Cannata, A. Pulvirenti, "Similarity measures and dimensionality reduction techniques for time series data mining", In A. Karahoca (Ed.), Advances in Data Mining Knowledge Discovery and Applications, Chapter 03, pp.71 - 96, 2012.##[49] Aach, J. and G. M. Church. "Aligning gene expression time series with time warping algorithms", Bioinformatics, vol.17, no.6, pp. 495-508, 2001.##[50] T. Gorecki, "Classification of time series using combination of dtw and lcss dissimilarity measures", Communications in Statistics, Simulation and Computation, pp.1-14, 2017.##[51] Y. Chen, X. Liu, X. Li, X. Liu, Y. Yao, G. Hu, F. Pei. "A dynamic time warping (DTW) distance based k -medoids method". Landscape and Urban Planning, vol.160, pp.48-60, 2017.##[52] Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y." An efficient k-means clustering algorithm: analysis and implementation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp.881-892.2002.##[53] Celebi, M. E., H. A. Kingravi, and P. A. Vela "A comparative study of efficient initialization methods for the k-means clustering algorithm". Expert Syst, Appl. 40(1), 200-210.2003.##[54] R. Rajabioun, "Cuckoo Optimization Algorithm", Applied Soft Computing, Vol.11, No.8, pp. 5508- 5518, 2011.##[55] F. Petitjean, A. Ketterlin, P. Gancarski, "A global averaging method for dynamic time warping, with applications to clustering", Pattern Recognition, vol. 44, no.3, pp. 678-693,2011.##[56] M.Lichman, "UCI machine learning repository". http://archive.ics.uci.edu/ml .2013.##[57] W.L. Junger, A.P. de Leon, "Imputation of missing data in time series for air pollutants", Atmospheric Environment, vol.102, pp. 96-104,2015.##[58] S.A Rahman, Y. Huang, J. Claassen, "Combining Fourier and Lagged k-Nearest Neighbor Imputation for Biomedical Time Series Data", Nathaniel Heintzman, and Samantha Kleinberg", J Biomed Inform, vol.58, pp.198-207, 2016.##[59] M. G. Rahman, M. Z. Islam, "Missing value imputation using a fuzzy clustering-based EM approach", Knowledge and Information Systems, vol.46 (2), pp. 389-422, 2016.##[60] R. Deb, A. Liew. "Missing value imputation for the analysis of incomplete traffic accident data, " Information Sciences, vol.339, pp274-289 .2016.##[61] M.E. Quinteros, S. Lu, C. BlazquezCárdenas-R, J.P., X. Ossa, J.-M. DelgadoSaborit, R.M. Harrison, P. Ruiz-Rudolph, "Use of data imputation tools to reconstruct incomplete air quality datasets: A case-study in Temuco, Chile", Atmospheric Environment, 2018.##[62] B. Golden, B. Grand, F. Rossi. "Mean Absolute Percentage Error for regression models", Neurocomputing, vol.192, pp.38-48. 2016.##[63] M. Misuraca, M. Spano, S. Balbi, "BMS: An improved Dunn index for Document Clustering validation", Communications in Statistics, pp. 0361-0926, 2018.##[1] R. H. Shumway and D. S. Stoffer, "Time series analysis and its applications: with R examples", Springer Science & Business Media, Fourth edition, 2017.##[2] Ratanamahatana C., "Multimedia retrieval using time series representation and relevance feedback", in: Proceedings of 8th International Conference on Asian Digital Libraries, 2005, pp. 400-405.##[3] C.Ratanamahatana, V. Niennattrakul, "Clustering multimedia data using time series", in: Proceedings of the International Conference on Hybrid InformationTechnology, ICHIT '06, 2016, pp.372-379.##[4] M.S. Mahmoud, M.F. Emzir, "State estimation with asynchronous multi-rate multi-smart sensors", Information Sciences, vol.196, pp.15-27, 2012.##[5] S. Mohamed, T. Marwala, "Neural network based techniques for estimating missing data in databases", pp. 27-32, 2005.##[6] W. Qiao, Z. GAO, R.G. Harley, "Continuous on-line identification of nonlinear plants in power systems with missing sensor measurements", IEEE, pp. 1729-1734, 2005.##[7] J. Honaker, G. King, "What to do about missing values in time-series cross-section data", American Journal of Political Science, vol.54 (2), pp.561-581, 2010.##[8] J. Lin, E. Keogh, S. Lonardi, J. Lankford, D. Nystrom, "Visually mining and monitoring massive time series", in: Proceedings of 2004ACM SIGKDD International Conference on Knowledge Discovery and data Mining - KDD '04, 2004, 460-475.##[9] R.J.A. Little, D.B. Rubin, "Statistical analysis with missing data", 3rd Edition, 2014.##[10] M. Amiri, R. Jensen, "Missing data imputation using fuzzy-rough methods", Neurocomputing, vol.196, pp.15-27, 2016.##[11] C.K. Enders, "Applied Missing Data Analysis", Guilford Press. ISBN 978-1-60623-639-0 .2010.##[12] D. M. Kreindler, C. J. Lumsden, "The effects of the irregular sample and missing data in time series analysis", Nonlinear Dynamics Psychology and Life Sciences, vol.10(2), pp.187-214, 2012.##[13] C.De Boor, E. Mathématicien, "A practical guide to splines", Mathematical Sciences, vol. 27, 2005.##[14] D. Mondal, D. B. Percival, "Wavelet variance analysis for gappy time series", Annals Inst. Stat. Math, vol.62, pp. 943-966, 2010.##[15] K. Rehfeld, N. Marwan, J. Heitzig, "Comparison of correlation analysis techniques for irregularly sampled time series", Nonlinear Process. Geophys, vol.18, 2011.##[16] P.J. Garca-Laencina, J.-L Sancho-Gómez. "Pattern classification with missing data: a review", Neural Comput, vol.19, 2010.##[17] R. Mazumder, T. Hastie, R. Tibshirani, "Spectral regularization algorithms for learning large incomplete matrices", Machine learning research, vol.11, pp. 2287-2322, 2010.##[18] Y. Koren, R. Bell, C. Volinsky, "Matrix factorization techniques for recommender systems", Comput, vol.42, 2009.##[19] I. R. White, P. Royston, A. M. Wood, "Multiple imputation using chained equations: issues and guidance for practice", Stat. medicine, vol.30, pp. 377-399, 2011.##[20] B. J. Wells, K. M. Chagin, A. S. Nowacki, M. W. Kattan, "Strategies for handling missing data in electronic health record derived data," EGEMS 1, 2013.##[21] C. Lipton, C. Kale," Modeling Missing Data in Clinical Time Series with RNNs", Machine Learning for Healthcare, pp.56, 2016.##[22] Li. Li, J. Zhang, Y. Wang, "Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method", IEEE Transactions on Intelligent Transportation Systems, vol.20, pp. 2933 - 2943, 2019.##[23] A. McLinden, V. Fioletov, W. Shephard, N. Krotkov, "Space-based detection of missing sulfur dioxide sources of global air pollution", Nature Geoscience, vol.9, pp. 496-500, 2016.##[24] R. Mahmoudvand, P. Canas, "Missing value imputation in time series using Singular Spectrum Analysis", International Journal of Energy and Statistics, vol. 04,165005, 2016.##[25] N. Bokde, W. Beck, "A novel imputation methodology for time series based on pattern sequence forecasting", Pattern Recognition Letters, vol.116, pp.88-96, 2018.##[26] T.T. Hong Phan, E. Poisson Caillault, A. Lefebvre, A. Bigand, "Dynamic Time Warping-based imputation for univariate time series data", Pattern Recognition Letters, 2017.##[27] Z. Che, S. Purushotham, K. Cho, D. Sontag, Y. Liu."Recurrent Neural Networks for Multivariate Time Series with Missing Values", Scientific reports, vol.6085, pp.85-99, 2018.##[28] W.S. David, Wong. "Interpolation: Inverse‐Distance Weighting", The International Encyclopedia of Geography, pp.156-173, 2017.##[29] G. Mei, N. Xu & L. Xu, "Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search", pp.1389, 2016.##[30] J. Pratama, H. Pramoedyo, R. Fitriani, "comparison of inverse distance weighted and natural neighbor interpolation method at air temperature data in malang region", Cauchy, vol.5, pp.48-54, 2018.##[31] S. Aghabozorgi, A. SeyedShirkhorshidi, T. YingWah,"Time-seriesclustering-A decadereview", Information Systems, vol.53, pp.16-38, 2015.##[32] P. Roelofsen, "Time series clustering", Master thesis Business Analytic, Vrije Universiteit Amsterdam, 2018.##[33] Z. Bankó, J. Abonyi, "Correlation based dynamic time warping of multivariate time series", Expert Systems with Applications, vol.39, no.17, pp.12814-12823, 2012.##[34] Guanyu Wang, "A Comparative Study of Cuckoo Algorithm and Ant Colony Algorithm in Optimal Path Problems", MATEC Web of Conferences 232, 03003, 2018.##[35] M. Jalal, M. Goharzay, "Cuckoo search algorithm for applied structural and design optimization: float system for experimental setups", Computational Design and Engineering, vol.6, no.159-172, 2018.##[36] J. Tang, G. Zhang, Y. Wang, H. Wang, F. Liu, "A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation", Transportation Research Part C: Emerging Technologies, vol.51, no. 29-40, 2015.##[37] W.L. Junger, A.P. de Leon, "Imputation of missing data in time series for air pollutants", Atmospheric Environment, vol. 102, pp. 96-104.2015.##[38] L. Folguera, J. Zupan, D. Cicerone, J.F. Magallanes, "Self-organizing maps for imputation of missing data in incomplete data matrices", Chemometrics and Intelligent Laboratory Systems, vol. 143, pp.146-151,2015.##[39] T.T. Hong Phan, E. Poisson Caillault, A. Lefebvre, A. Bigand, "Dynamic Time Warping-based imputation for univariate time series data", Pattern Recognition Letters S0167-8655(17)30275-1, 2017.##[40] N. Bokde, M. W. Beck, F. Marttinez," A novel imputation methodology for time series based on pattern sequence forecasting", Pattern Recognition Letters, pp. 88-96, 2018.##[41] T.T. Hong Phan, A. Bigand, É. P. Caillault," A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series", Computational Intelligence and Soft Computing, pp. 1-15, 2018.##[42] J. Tang, G. Zhang, Y. Wang, H. Wang," A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation", Transportation Research, vol. 51, pp. 29-40, 2015.##[43] S. Sridevi, S. Rajaram, C. Parthiban, S. SibiArasan, C. Swadhikar, "Imputation for the Analysis of Missing Values and Prediction of Time Series Data", International Conference on Recent Trends in Information Technology,2011.##[44] C. O. Resende, A. Santana, F. Lobato, "Time series imputation using genetic programming and Lagrange interpolation", 5th Brazilian Conference on Intelligent Systems, 2016.##[45] Y. Jane Nancya, H.Nehemiah Khannaa, K.Arputharaj, "Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework", vol.112, pp. 63-79, 2017.##[46] S. Aghabozorgi, A. SeyedShirkhorshidi, T. YingWah,"Time-seriesclustering-A decadereview", Information Systems, vol.53, pp.16-38, 2015.##[47] P. Roelofsen, "Time series clustering", Master thesis Business Analytic, Vrije Universiteit Amsterdam, 2018.##[48] C. Cassisi, P. Montalto, M. Aliotta, A. Cannata, A. Pulvirenti, "Similarity measures and dimensionality reduction techniques for time series data mining", In A. Karahoca (Ed.), Advances in Data Mining Knowledge Discovery and Applications, Chapter 03, pp.71 - 96, 2012.##[49] Aach, J. and G. M. Church. "Aligning gene expression time series with time warping algorithms", Bioinformatics, vol.17, no.6, pp. 495-508, 2001.##[50] T. Gorecki, "Classification of time series using combination of dtw and lcss dissimilarity measures", Communications in Statistics, Simulation and Computation, pp.1-14, 2017.##[51] Y. Chen, X. Liu, X. Li, X. Liu, Y. Yao, G. Hu, F. Pei. "A dynamic time warping (DTW) distance based k -medoids method". Landscape and Urban Planning, vol.160, pp.48-60, 2017.##[52] Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y." An efficient k-means clustering algorithm: analysis and implementation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp.881-892.2002.##[53] Celebi, M. E., H. A. Kingravi, and P. A. Vela "A comparative study of efficient initialization methods for the k-means clustering algorithm". Expert Syst, Appl. 40(1), 200-210.2003.##[54] R. Rajabioun, "Cuckoo Optimization Algorithm", Applied Soft Computing, Vol.11, No.8, pp. 5508- 5518, 2011.##[55] F. Petitjean, A. Ketterlin, P. Gancarski, "A global averaging method for dynamic time warping, with applications to clustering", Pattern Recognition, vol. 44, no.3, pp. 678-693,2011.##[56] M.Lichman, "UCI machine learning repository". http://archive.ics.uci.edu/ml .2013.##[57] W.L. Junger, A.P. de Leon, "Imputation of missing data in time series for air pollutants", Atmospheric Environment, vol.102, pp. 96-104,2015.##[58] S.A Rahman, Y. Huang, J. Claassen, "Combining Fourier and Lagged k-Nearest Neighbor Imputation for Biomedical Time Series Data", Nathaniel Heintzman, and Samantha Kleinberg", J Biomed Inform, vol.58, pp.198-207, 2016.##[59] M. G. Rahman, M. Z. Islam, "Missing value imputation using a fuzzy clustering-based EM approach", Knowledge and Information Systems, vol.46 (2), pp. 389-422, 2016.##[60] R. Deb, A. Liew. "Missing value imputation for the analysis of incomplete traffic accident data, " Information Sciences, vol.339, pp274-289 .2016.##[61] M.E. Quinteros, S. Lu, C. BlazquezCárdenas-R, J.P., X. Ossa, J.-M. DelgadoSaborit, R.M. Harrison, P. Ruiz-Rudolph, "Use of data imputation tools to reconstruct incomplete air quality datasets: A case-study in Temuco, Chile", Atmospheric Environment, 2018.##[62] B. Golden, B. Grand, F. Rossi. "Mean Absolute Percentage Error for regression models", Neurocomputing, vol.192, pp.38-48. 2016.##[63] M. Misuraca, M. Spano, S. Balbi, "BMS: An improved Dunn index for Document Clustering validation", Communications in Statistics, pp. 0361-0926, 2018.## ##

ارائه یک الگوریتم مسیریابی جدید مبتنی بر الگوریتم‌های ژنتیک و تبرید شبیه‌سازی‌شده برای شبکه‌های موردی بین‌خودرویی A New Hybrid Routing Algorithm based on Genetic Algorithm and Simulated Annealing for Vehicular Ad hoc Networks 1 1 در سال‌های اخیر شبکه‌های موردی بین‌خودرویی به‌عنوان یک فناوری نوظهور سعی کرده است با کنترل هوشمند ترافیک، تعداد تصادفات خودروها را کاهش دهد. در این شبکه‌ها حرکت سریع خودروها، پویایی توپولوژی و محدودیت‌های منابع شبکه، مسیریابی را به یک چالش­ اساسی تبدیل کرده، لذا ارائه یک الگوریتم مسیریابی پایدار و مطمئن یک راه‌کار مناسب برای حفظ پارامترهای کیفیت سرویس خدمات این شبکه‌ها است. در این مقاله یک الگوریتم مسیریابی جدید مبتنی بر خوشه‌­بندی به نام GCAR بر اساس الگوریتم ژنتیک ارائه می‌شود که ابتدا خودروها در یک زیرساخت مبتنی بر خوشه‌­های پویا قرار می‌­گیرند و سپس با انتخاب دو خودرو در هر خوشه به‌عنوان خودروهای دروازه بین خوشه‌­های همسایه، یک زنجیره خودرویی تشکیل می‌شود. همچنین برای خوشه­‌بندی از ترکیب الگوریتم ژنتیک و الگوریتم تبرید شبیه­‌سازی‌شده استفاده می‌شود که سعی می‌کنند سرخوشه‌های مناسب را شناسایی کنند. نتایج شبیه­‌سازی‌­های انجام‌شده در نرم‌افزار متلب نشان می­‌دهد که به‌طور متوسط نرخ کشف مسیر الگوریتم پیشنهادی 4/18%، تعداد خوشه ایجاد‌شده 55/2%، توان عملیاتی 45/3% و نرخ دریافت صحیح بسته‌ها 18/14% نسبت به رویکرد PassCAR عملکرد بهتری دارد. همچنین ارزیابی هم‌گرایی، انحراف معیار و خطای استاندارد الگوریتم پیشنهادی اثبات‌کننده سرعت هم‌گرایی و پایداری بالای آن است. 2 In recent years, Vehicular Ad-hoc Networks (VANET) as an emerging technology have tried to reduce road damage and car accidents through intelligent traffic controlling. In these networks, the rapid movement of vehicles, topology dynamics, and the limitations of network resources engender critical challenges in the routing process. Therefore, providing a stable and reliable routing algorithm is a necessary requirement to maintain the Quality of Service (QoS) parameters of VANETs. In this paper, a new routing algorithm based on the clustering technique is proposed, which is called GCAR. In the proposed algorithm, the appropriate cluster heads are selected based on the genetic algorithm then two vehicles are selected between the neighboring clusters as the gateways and a vehicle chain is formed by these vehicles. Moreover, a combination of genetic algorithm and simulated annealing is applied to identify the suitable clusters. The conducted simulations in MATLAB tool indicate that, respectively, path discovery ratio, the number of clusters, throughput, and packet delivery ratio of the proposed algorithm have been improved by 18.4%, 2.55%, 3.45%, and 14.18% in comparison to the PassCAR approach. Furthermore, evaluation of the convergence, standard deviation, and standard error of the proposed algorithm prove its high convergence speed and stability. 61 72 2019/12/192019/10/252019/12/22019/12/302020/04/12 1399/1/24 2021/12/62020/02/82020/08/182020/10/132021/05/24 1400/3/3 نیره سعیدی nayyerh saeedi گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تبریز ایران nayyersaeedi@gmail.com شهرام بابائی Shahram Babaie گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تبریز ایران hw.tab.au@gmail.com Vehicular Ad-hoc Networks (VANET) Routing Clustering Genetic Algorithm Simulated annealing شبکه‌های موردی بین‌خودرویی مسیریابی خوشه‌بندی الگوریتم ژنتیک الگوریتم تبرید شبیه‌سازی‌شده [1] A. Ebrahimi and S.O. Pourdanesh, "The Use of Cognitive Radio Networks Routing to Reduce Energy Consumption in Wireless Sensor Networks Dayjkstra Algorithm," JSTE, vol. 1, pp. 31-38, 2017.##[2] A. Shadnia and M. Romoozi, "Proposing Enhanced Strategy for Transmitting and Routing In Content-Based Vehicular Ad Hoc Networks Based On Geographical Map," Road Quarterly Journal, vol. 25, pp. 31-38, 2018.##[3] R. Aranbezhad and S. Babaie, "A routing algorithm based on movement direction and position of vehicles for vehicular Ad hoc networks," Journal of Soft Computing and Information Technology, vol. 9, pp. 206-213, 2020.##[4] S. Ebrahimi Mood, M. M. Javidi, M. R. Khosravi, "Proposing a Constrained-GSA for the Vehicle Routing Problem," Journal of Signal and Data Processing, vol. 18 (4), pp. 23-36, 2022.##[5] H. Hartenstein and K. P. Laberteaux, "A tutorial survey on vehicular ad hoc networks," IEEE Commun. Mag., vol. 46, pp. 164-171, Jun. 2008.##[6] M. Afrashteh and S. Babaie, "A Route Segmented Broadcast Protocol based on RFID for Emergency Message Dissemination in Vehicular Ad-hoc Networks," IEEE Transactions on Vehicular Technol.ogy, vol. 69, pp. 16017-16026, 2020.##[7] A. Rasheed, S. Gillani, S. Ajmal, and A. Qayyum, "Vehicular Ad Hoc Network (VANET): A Survey, Challenges, and Applications," in Vehicular Ad-Hoc Networks for Smart Cities, A. Laouiti, A. Qayyum, and M. Mohamad Saad, Eds. Springer, Singapore, 2017, pp. 39-51.##[8] S. Boussoufa-lahlah, F. Semchedine, and L. Bouallouche-medjkoune, "Geographic routing protocols for Vehicular Ad hoc NETworks ( VANETs ): A survey," Veh. Commun., vol. 11, pp. 20-31, 2018.##[9] A. Ullah, S. Yaqoob, M. Imran, and H. Ning, "Emergency Message Dissemination Schemes Based on Congestion Avoidance in VANET and Vehicular FoG Computing," IEEE Access, vol. 7, pp. 1570-1585, 2019.##[10] C. Cooper, D. Franklin, F. Safaei, and M. Abolhasan, "A Comparative Survey of VANET Clustering Techniques," IEEE Commun. Surv. Tutorials, vol. 19, pp. 657-681, 2019.##[11] X. Zeng, M. Yu, and D. Wang, "A New Probabilistic Multi-Hop Broadcast Protocol for Vehicular Networks," IEEE Trans. Veh. Technol., vol. 67, pp. 12165-12176, 2018.##[12] F. Cunha et al., "Data communication in VANETs: Protocols, applications and challenges," Ad Hoc Networks, vol. 44, pp. 90-103, 2016.##[13] F. Li, X. Song, H. Chen, X. Li, and Y. Wang, "Hierarchical routing for vehicular Ad Hoc networks via reinforcement learning," IEEE Trans. Veh. Technol., vol. 68, pp. 1852-1865, 2019.##[14] S. S. Wang and Y. S. Lin, "PassCAR: A passive clustering aided routing protocol for vehicular ad hoc networks," Comput. Commun., vol. 36, pp. 170-179, 2013.##[15] K. C. Lin, J. C. Hung, and J. ting Wei, "Feature selection with modified lion's algorithms and support vector machine for high-dimensional data," Applied Soft Computing Journal, vol. 68. pp. 669-676, 2018.##[16] M. Fahad et al., "Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks," Comput. Electr. Eng, vol. 70, pp. 853-870, 2018.##[17] H. Bagherlou and A. Ghaffari, "A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks," J. Supercomput., vol. 74, pp. 2528-2552, Jun. 2018.##[18] S. Chatterjee and S. Das, "Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network," Inf. Sci. (Ny)., vol. 295, pp. 67-90, 2015.##[19] D. Sedighizadeh and H. Mazaheripour, "Optimization of multi objective vehicle routing problem using a new hybrid algorithm based on particle swarm optimization and artificial bee colony algorithm considering Precedence constraints," Alexandria Eng. J., vol. 57, pp. 2225-2239, 2018.##[1] ابراهیمی، افسانه و پور دانش، سیدعبید "استفاده از مسیریابی شبکه‌های رادیو شناختی جهت کاهش مصرف انرژی در شبکه‌های حسگر بی‌سیم به کمک الگوریتم دایجکسترا"، مجله دو فصلنامه مباحث برگزیده در انرژی، جلد1، شماره 1، صفحات 38-31، 1396.##[1] A. Ebrahimi and S.O. Pourdanesh, "The Use of Cognitive Radio Networks Routing to Reduce Energy Consumption in Wireless Sensor Networks Dayjkstra Algorithm," JSTE, vol. 1, pp. 31-38, 2017.##[2] شادنیا، آسیه و رموزی، مرتضی "ارایه بهبود استراتژی ارسال و مسیریابی در الگوی محتوا محور شبکه‌های خودرویی مبتنی بر نقشه جغرافیایی"، مجله فصلنامه جاده، جلد49، شماره 93، صفحات 47-31، 1396.##[2] A. Shadnia and M. Romoozi, "Proposing Enhanced Strategy for Transmitting and Routing In Content-Based Vehicular Ad Hoc Networks Based On Geographical Map," Road Quarterly Journal, vol. 25, pp. 31-38, 2018.##[3] عرب‌نژاد، رعنا و بابائی، شهرام " ارائه یک الگوریتم مسیر¬یابی مبتنی بر جهت حرکت و موقعیت خودروها برای شبکه¬های موردی بین¬خود¬رویی" مجله علمی پژوهشی رایانش نرم و فناوری اطلاعات، جلد 9، شماره 3، صفحات 213-206، 1399.##[3] R. Aranbezhad and S. Babaie, "A routing algorithm based on movement direction and position of vehicles for vehicular Ad hoc networks," Journal of Soft Computing and Information Technology, vol. 9, pp. 206-213, 2020.##[4] ابراهیمی مود، سپهر، جاویدی، محمد مسعود و خسروی، محمدرضا " ارائه الگوریتم جست و جوی گرانشی مقید و حل مسأله مسیریابی وسایل نقلیه" مجله فصل نامه پردازش علائم و داده‌ها، جلد 18، شماره 4، صفحات 36-23، 1400.##[4] S. Ebrahimi Mood, M. M. Javidi, M. R. Khosravi, "Proposing a Constrained-GSA for the Vehicle Routing Problem," Journal of Signal and Data Processing, vol. 18 (4), pp. 23-36, 2022.##[5] H. Hartenstein and K. P. Laberteaux, "A tutorial survey on vehicular ad hoc networks," IEEE Commun. Mag., vol. 46, pp. 164-171, Jun. 2008.##[6] M. Afrashteh and S. Babaie, "A Route Segmented Broadcast Protocol based on RFID for Emergency Message Dissemination in Vehicular Ad-hoc Networks," IEEE Transactions on Vehicular Technol.ogy, vol. 69, pp. 16017-16026, 2020.##[7] A. Rasheed, S. Gillani, S. Ajmal, and A. Qayyum, "Vehicular Ad Hoc Network (VANET): A Survey, Challenges, and Applications," in Vehicular Ad-Hoc Networks for Smart Cities, A. Laouiti, A. Qayyum, and M. Mohamad Saad, Eds. Springer, Singapore, 2017, pp. 39-51.##[8] S. Boussoufa-lahlah, F. Semchedine, and L. Bouallouche-medjkoune, "Geographic routing protocols for Vehicular Ad hoc NETworks ( VANETs ): A survey," Veh. Commun., vol. 11, pp. 20-31, 2018.##[9] A. Ullah, S. Yaqoob, M. Imran, and H. Ning, "Emergency Message Dissemination Schemes Based on Congestion Avoidance in VANET and Vehicular FoG Computing," IEEE Access, vol. 7, pp. 1570-1585, 2019.##[10] C. Cooper, D. Franklin, F. Safaei, and M. Abolhasan, "A Comparative Survey of VANET Clustering Techniques," IEEE Commun. Surv. Tutorials, vol. 19, pp. 657-681, 2019.##[11] X. Zeng, M. Yu, and D. Wang, "A New Probabilistic Multi-Hop Broadcast Protocol for Vehicular Networks," IEEE Trans. Veh. Technol., vol. 67, pp. 12165-12176, 2018.##[12] F. Cunha et al., "Data communication in VANETs: Protocols, applications and challenges," Ad Hoc Networks, vol. 44, pp. 90-103, 2016.##[13] F. Li, X. Song, H. Chen, X. Li, and Y. Wang, "Hierarchical routing for vehicular Ad Hoc networks via reinforcement learning," IEEE Trans. Veh. Technol., vol. 68, pp. 1852-1865, 2019.##[14] S. S. Wang and Y. S. Lin, "PassCAR: A passive clustering aided routing protocol for vehicular ad hoc networks," Comput. Commun., vol. 36, pp. 170-179, 2013.##[15] K. C. Lin, J. C. Hung, and J. ting Wei, "Feature selection with modified lion's algorithms and support vector machine for high-dimensional data," Applied Soft Computing Journal, vol. 68. pp. 669-676, 2018.##[16] M. Fahad et al., "Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks," Comput. Electr. Eng, vol. 70, pp. 853-870, 2018.##[17] H. Bagherlou and A. Ghaffari, "A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks," J. Supercomput., vol. 74, pp. 2528-2552, Jun. 2018.##[18] S. Chatterjee and S. Das, "Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network," Inf. Sci. (Ny)., vol. 295, pp. 67-90, 2015.##[19] D. Sedighizadeh and H. Mazaheripour, "Optimization of multi objective vehicle routing problem using a new hybrid algorithm based on particle swarm optimization and artificial bee colony algorithm considering Precedence constraints," Alexandria Eng. J., vol. 57, pp. 2225-2239, 2018.## ##

روشی جدید برای احراز هویت دوطرفه در اینترنت اشیا A Mutual Authentication Method for Internet of Things 1 1 امروزه شاهد گسترش خدمات مختلف اینترنت اشیا در حوزه‌های مختلف از قبیل نظارت و سلامت هستیم. این خدمات از طریق دستگاه‌های هوشمند در هر مکان و زمانی می‌تواند در دسترس کاربران قرار گیرند. این در حالی است که این دسترسی‌ها می‌تواند مسأله امنیت و حریم خصوصی را به امری حساس و حیاتی تبدیل کند. گزارش‌های دریافتی نشان می‌دهد که تعداد دستگاه‌های اینترنت اشیا تا سال 2030 به عدد 44/25 بیلیون خواهد رسید و این در حالی است که 26% حملات اینترنت اشیا در سال 2019 مربوط به عدم احراز هویت بوده است. به همین دلیل احراز هویت کاربران در اینترنت اشیا به یکی از حساس‌ترین مفاهیم امنیتی تبدیل شده است. در این مقاله یک پروتکل احراز هویت دوطرفه دستگاه‌به‌دستگاه برای شبکه‌های خانگی هوشمند، ارائه شده است. این پروتکل بر اساس رمزنگاری نامتقارن برای احراز هویت دستگاه‌های موجود در شبکه طراحی شده و در آن تمامی دستگاه‌ها یک کلید جلسه خصوصی مشترک دارند. برای حصول اطمینان از امنیت ارتباط‌ها در هر جلسه، کلیدهای جلسه پس از هر جلسه ارتباطی، تغییر می‌کنند. برنامه‌نویسی طرح پیشنهادی به‌وسیله HLPSL، شبیه‌سازی و ارزیابی بهینگی با ابزار SPAN و AVISPA انجام شده است. تحلیل‌های امنیتی نشان می‌دهد که پروتکل پیشنهادی در مقابل حملات امنیتی، پایداری خود را حفظ می‌کند. 2 Today, we are witnessing the expansion of various Internet of Things (IoT) applications and services such as monitoring and health. These services are delivered to users via smart devices anywhere and anytime. Forecasts show that the IoT, which is controlled online in the user environment, will reach 25 billion devices worldwide by 2020. Data security is one of the main concerns in the IoT. The IoT is supposed to deal with a population of about billions of objects, so the number of malicious attacks can be very high and alarming given the global connection (anyone access) and the wide availability (access to any place at any time). However, these accesses can make security and privacy critical. Reports show that 26% of IoT attacks in 2019 were related to non-authentication, which is why IoT authentication has become one of the most sensitive security concepts. IoT devices are usually left unattended and this makes it easy for an attacker to target such equipment. For example, security breaches and unwanted changes in patient's health parameters in smart health care systems can cause wrong treatments or even lead to his death. The fact that each device in the IoT knows who it is communicating with and at what level of access is one of the important aspects of security, especially in cases where various devices with different capabilities have to perform common tasks and cooperate with each other. IoT authentication is a trust model to protect control access and data when information travels between devices. So far, different methods have been proposed for authentication in the IoT network. These methods are usually based on the public key, private key, random key distribution, and hash function. A point that should be taken into account in IoT authentication is that IoT networks and devices have limited bandwidth, low memory, low processing power, and energy limitations. Therefore, the proposed method should pay special attention to such limitations. In addition, IoT authentication needs to ensure enhanced security features such as confidentiality, data integrity, reliability, maintainability, scalability, and privacy to their consumers. This paper proposes a two-way or mutual authentication protocol in which both devices authenticate each other without human intervention in a smart home network. The proposed protocol is based on asymmetric encryption for authentication of devices, which have a shared private session key, along with hashing operations in the network. Also, to ensure the security of communications at each session, each device has a one-time private session key. The session keys are changed regularly to ensure the security of sessions between devices. The proposed protocol is programmed by HLPSL and simulated and verified by the SPAN and AVISPA tools. The security analysis results show the proposed protocol is extremely practical, and secure against potential attacks. 73 86 2019/12/192019/10/252019/12/22019/12/302020/04/122020/04/15 1399/1/27 2021/12/62020/02/82020/08/182020/10/132021/05/242022/01/19 1400/10/29 پیام محمودی نصر Payam Mahmoudi-Nasr دانشکده مهندسی و فناوری، دانشگاه مازندران ایران p.mahmoudi@umz.ac.ir حسین کیمیا Hossein Kimia مؤسسه آموزش عالی صنعتی مازندران ایران hosseinkimia@gmail.com Internet of Things Authentication protocol Cryptography Security AVISPA اینترنت اشیا پروتکل احراز هویت رمزنگاری امنیت AVISPA [1] M. El-hajj, A. Fadlallah, M. Chamoun, and A. Serhrouchni, "A survey of internet of things (IoT) Authentication schemes," Sensors, vol. 19, pp. 1141, 2019.##[2] M. Wazid, A. K. Das, R. Hussain, G. Succi, and J. J. Rodrigues, "Authentication in cloud-driven IoT-based big data environment: Survey and outlook," Journal of Systems Architecture, vol. 97, pp. 185-196, 2019.##[3] M. Saadeh, A. Sleit, M. Qatawneh, and W. Almobaideen, "Authentication techniques for the internet of things: A survey," in 2016 cybersecurity and cyberforensics conference (CCC), 2016, pp. 28-34.##[4] S. Kalra and S. K. Sood, "Secure authentication scheme for IoT and cloud servers," Pervasive and Mobile Computing, vol. 24, pp. 210-223, 2015.##[5] M. A. Crossman and H. Liu, "Study of authentication with IoT testbed," in 2015 IEEE International Symposium on Technologies for Homeland Security (HST), 2015, pp. 1-7.##[6] H. Ren, Y. Song, S. Yang, and F. Situ, "Secure smart home: A voiceprint and internet based authentication system for remote accessing," in 2016 11th International Conference on Computer Science & Education (ICCSE), 2016, pp. 247-251.##[7] A. Ukil, S. Bandyopadhyay, A. Bhattacharyya, and A. Pal, "Lightweight security scheme for vehicle tracking system using CoAP," in Proceedings of the International Workshop on Adaptive Security, 2013, pp. 1-8.##[8] L. Barreto, A. Celesti, M. Villari, M. Fazio, and A. Puliafito, "An authentication model for IoT clouds," in 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015, pp. 1032-1035.##[9] S. Janbabaei, H. Gharaee, and N. Mohammadzadeh, "The Lightweight Authentication Scheme with Capabilities of Anonymity and Trust in Internet of Things (IoT)," Signal and Data Processing, vol. 15, no. 4, pp. 111-122, 2019.##[10] M. Dammak, O. R. M. Boudia, M. A. Messous, S. M. Senouci, and C. Gransart, "Token-based lightweight authentication to secure IoT networks," in 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2019: IEEE, pp. 1-4.##[11] K. Park et al., "LAKS-NVT: Provably Secure and Lightweight Authentication and Key Agreement Scheme without Verification Table in Medical Internet of Things," IEEE Access, vol. 8, pp. 119387-119404, 2020.##[12] V. Plantevin, A. Bouzouane, B. Bouchard, and S. Gaboury, "Towards a more reliable and scalable architecture for smart home environments," Journal of Ambient Intelligence and Humanized Computing, vol. 10, pp. 2645-2656, 2019.##[13] I. Lee and K. Lee, "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, vol. 58, pp. 431-440, 2015.##[14] V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, "A survey on IoT security: application areas, security threats, and solution architectures," IEEE Access, vol. 7, pp. 82721-82743, 2019.##[15] N. Moustafa, "A Systemic IoT-Fog-Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing," arXiv preprint arXiv:1906.01055, 2019.##[16] Y. Atwady and M. Hammoudeh, "A survey on authentication techniques for the internet of things," in Proceedings of the International Conference on Future Networks and Distributed Systems, 2017.##[17] V. Shivraj, M. Rajan, M. Singh, and P. Balamuralidhar, "One time password authentication scheme based on elliptic curves for Internet of Things (IoT)," in 2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW), 2015, pp. 1-6.##[18] W.-T. Su, W.-M. Wong, and W.-C. Chen, "A survey of performance improvement by group-based authentication in IoT," in 2016 International Conference on Applied System Innovation (ICASI), 2016, pp. 1-4.##[19] F. Chu, R. Zhang, R. Ni, and W. Dai, "An improved identity authentication scheme for internet of things in heterogeneous networking environments," in 2013 16th International Conference on Network-Based Information Systems, 2013, pp. 589-593.##[20] X. Yao, X. Han, X. Du, and X. Zhou, "A lightweight multicast authentication mechanism for small scale IoT applications," IEEE Sensors Journal, vol. 13, pp. 3693-3701, 2013.##[21] N. Shone, C. Dobbins, W. Hurst, and Q. Shi, "Digital memories based mobile user authentication for IoT," in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015, pp. 1796-1802.##[22] H. Tschofenig, "Fixing user authentication for the internet of things (iot)," Datenschutz und Datensicherheit-DuD, vol. 40, pp. 222-224, 2016.##[23] L. Takkinen, "Analysing security protocols with AVISPA," in TKK T-110.7290 research seminar on network security, 2006.##[24] S. Emerson, Y.-K. Choi, D.-Y. Hwang, K.-S. Kim, and K.-H. Kim, "An OAuth based authentication mechanism for IoT networks," in 2015 International Conference on Information and Communication Technology Convergence (ICTC), 2015, pp. 1072-1074.##[25] http://www.avispa-project.org/, online, 2020.##[1] M. El-hajj, A. Fadlallah, M. Chamoun, and A. Serhrouchni, "A survey of internet of things (IoT) Authentication schemes," Sensors, vol. 19, pp. 1141, 2019.##[2] M. Wazid, A. K. Das, R. Hussain, G. Succi, and J. J. Rodrigues, "Authentication in cloud-driven IoT-based big data environment: Survey and outlook," Journal of Systems Architecture, vol. 97, pp. 185-196, 2019.##[3] M. Saadeh, A. Sleit, M. Qatawneh, and W. Almobaideen, "Authentication techniques for the internet of things: A survey," in 2016 cybersecurity and cyberforensics conference (CCC), 2016, pp. 28-34.##[4] S. Kalra and S. K. Sood, "Secure authentication scheme for IoT and cloud servers," Pervasive and Mobile Computing, vol. 24, pp. 210-223, 2015.##[5] M. A. Crossman and H. Liu, "Study of authentication with IoT testbed," in 2015 IEEE International Symposium on Technologies for Homeland Security (HST), 2015, pp. 1-7.##[6] H. Ren, Y. Song, S. Yang, and F. Situ, "Secure smart home: A voiceprint and internet based authentication system for remote accessing," in 2016 11th International Conference on Computer Science & Education (ICCSE), 2016, pp. 247-251.##[7] A. Ukil, S. Bandyopadhyay, A. Bhattacharyya, and A. Pal, "Lightweight security scheme for vehicle tracking system using CoAP," in Proceedings of the International Workshop on Adaptive Security, 2013, pp. 1-8.##[8] L. Barreto, A. Celesti, M. Villari, M. Fazio, and A. Puliafito, "An authentication model for IoT clouds," in 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015, pp. 1032-1035.##[9] S. Janbabaei, H. Gharaee, and N. Mohammadzadeh, "The Lightweight Authentication Scheme with Capabilities of Anonymity and Trust in Internet of Things (IoT)," Signal and Data Processing, vol. 15, no. 4, pp. 111-122, 2019.##[10] M. Dammak, O. R. M. Boudia, M. A. Messous, S. M. Senouci, and C. Gransart, "Token-based lightweight authentication to secure IoT networks," in 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2019: IEEE, pp. 1-4.##[11] K. Park et al., "LAKS-NVT: Provably Secure and Lightweight Authentication and Key Agreement Scheme without Verification Table in Medical Internet of Things," IEEE Access, vol. 8, pp. 119387-119404, 2020.##[12] V. Plantevin, A. Bouzouane, B. Bouchard, and S. Gaboury, "Towards a more reliable and scalable architecture for smart home environments," Journal of Ambient Intelligence and Humanized Computing, vol. 10, pp. 2645-2656, 2019.##[13] I. Lee and K. Lee, "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, vol. 58, pp. 431-440, 2015.##[14] V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, "A survey on IoT security: application areas, security threats, and solution architectures," IEEE Access, vol. 7, pp. 82721-82743, 2019.##[15] N. Moustafa, "A Systemic IoT-Fog-Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing," arXiv preprint arXiv:1906.01055, 2019.##[16] Y. Atwady and M. Hammoudeh, "A survey on authentication techniques for the internet of things," in Proceedings of the International Conference on Future Networks and Distributed Systems, 2017.##[17] V. Shivraj, M. Rajan, M. Singh, and P. Balamuralidhar, "One time password authentication scheme based on elliptic curves for Internet of Things (IoT)," in 2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW), 2015, pp. 1-6.##[18] W.-T. Su, W.-M. Wong, and W.-C. Chen, "A survey of performance improvement by group-based authentication in IoT," in 2016 International Conference on Applied System Innovation (ICASI), 2016, pp. 1-4.##[19] F. Chu, R. Zhang, R. Ni, and W. Dai, "An improved identity authentication scheme for internet of things in heterogeneous networking environments," in 2013 16th International Conference on Network-Based Information Systems, 2013, pp. 589-593.##[20] X. Yao, X. Han, X. Du, and X. Zhou, "A lightweight multicast authentication mechanism for small scale IoT applications," IEEE Sensors Journal, vol. 13, pp. 3693-3701, 2013.##[21] N. Shone, C. Dobbins, W. Hurst, and Q. Shi, "Digital memories based mobile user authentication for IoT," in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015, pp. 1796-1802.##[22] H. Tschofenig, "Fixing user authentication for the internet of things (iot)," Datenschutz und Datensicherheit-DuD, vol. 40, pp. 222-224, 2016.##[23] L. Takkinen, "Analysing security protocols with AVISPA," in TKK T-110.7290 research seminar on network security, 2006.##[24] S. Emerson, Y.-K. Choi, D.-Y. Hwang, K.-S. Kim, and K.-H. Kim, "An OAuth based authentication mechanism for IoT networks," in 2015 International Conference on Information and Communication Technology Convergence (ICTC), 2015, pp. 1072-1074.##[25] http://www.avispa-project.org/, online, 2020.## ##

ارائه یک الگوریتم برای حل مسایل بهینه‌سازی عمومی مبتنی بر تئوری دومینو Providing an algorithm for solving general optimization problems based on Domino theory 1 1 بهینه‌سازی یک فعالیت مهم و تعیین‌کننده در طراحی ساختاری است. طراحان زمانی قادر خواهند بود طرح‌های بهتری تولید کنند که بتوانند با روش‌های بهینه‌سازی در صرف زمان و هزینه طراحی صرفه‌جویی نمایند. بسیاری از مسائل بهینه‌سازی در مهندسی، طبیعتاً پیچیده‌تر و مشکل‌تر از آن هستند که با روش‌های مرسوم بهینه‌سازی نظیر روش برنامه‌ریزی ریاضی و نظایر آن قابل حل باشند. جهان اطراف ما می­تواند پایه بسیاری از رفتارهای هدفمند باشد که دقت در اشیاء پیرامون، ما را در شناخت این رفتارها و  نظم رو به سمت هدف یاری می‌­رساند. در این مقاله یک الگوریتم بهینه­‌سازی جدید بر پایه الگوی بازی دومینو ارائه گردیده است. بازی دومینو متشکل از مجموعه­‌ای از تکه­‌هایی است که با یک وحدت گروهی، یک نظم روبه­ هدف را شکل داده‌­اند. تلاش برای ایجاد یک الگوریتم بهینه‌سازی جدید بر پایه تئوری این بازی، ما را به انجام این تحقیق رهنمون ساخت. الگوی حرکت دومینویی در یک محیط شبیه‌­ساز پیاده‌سازی گردید و نتایج نشان داد که الگوریتم حاصل، الگوی مناسبی برای یافتن پاسخ‌­های بهینه جهت مسائل پیچیده می‌­باشد. 2 Optimization is a very important process in engineering. Engineers can create better production only if they make use of optimization tools in reduction of its costs including consumption time. Many of the engineering real-word problems are of course non-solvable mathematically (by mathematical programming solvers). Therefore, meta-heuristic optimization algorithms are needed to solve these problems. Based on this assumption, many new meta-heuristic optimization algorithms have been proposed inspired by natural phenomena, such as IWO [58], BBO [59], WWO [61], and so on. Inspired by domino toppling theory, we proposed an optimization algorithm. Using domino pieces, we can create countless complex structures. To simulate the domino movement in the search space of a problem, we consider the particles in the search space as the domino pieces and, by creating an optimal path, we will try to direct the dominoes to the optimal path. The optimal paths will be updated in each iteration. After initializing the dominoes randomly at the beginning of each evaluation, the picking piece or the first moving piece will be identified and then the particles will be selected by the optimal path. Applying a motion equation to each domino will move the dominoes forward in that direction. At first, a predefined dominoes will be randomly distributed in the problem space. Choosing the optimal path will accelerate the convergence of the domino particles towards the target. After choosing the path in current iteration, we now have to do the domino movement. The particles will move to a new location by applying the new location equation. By applying this equation, each domino piece will sit on the track ahead of itself. The front piece will also move to a new location by applying an equation separate from the rest. After moving the dominoes to the new location, the worst iteration of the previous iteration will be removed from the problem space. In the new iteration, the optimal domino path, the new locations of domino pieces and the global optimum will be updated. At the end of the algorithm, the global optimum will be determined as the optimal solution. This method is implemented in a simulator environment. To evaluate the performance of the Domino Optimization algorithm, we use a complete benchmark including 30 objective functions called CEC 2014 [67] that are single-objective numerical functions. In all cases, we set the population size to 50, the dimension size to 30, and the number of fitness function evaluation to 150,000. We compare the proposed Domino Optimization algorithm (DO) with the algorithms LOA [57], ICS [62], NPSO [63], MOHS [64], BCSO [65] and FFFA [66]. The results obtained from the 3 unimodal functions show that the proposed method is able to achieve a better solution than any of the state of the art algorithms at the equal resources. Results in the multimodal functions show that the proposed method has the best performance in finding the optimal solution in all of the available 13 functions in this section. In all of 6 functions in the hybrid section, the quality of the proposed method is better than all of the state of the art algorithms at the equal resources. The standard deviation values ​​of the proposed method, which are often small numbers, indicate algorithm convergence around the optimal solution. Also among the available methods, two algorithms, named NPSO and LOA, have good results after the proposed method. In the convergence analysis of dominoes, the diversity of objective functions in 100 distinct iterations shows a big value at the beginning of the algorithm, and a low value at the end of the algorithm. 87 106 2019/12/192019/10/252019/12/22019/12/302020/04/122020/04/152019/12/4 1398/9/13 2021/12/62020/02/82020/08/182020/10/132021/05/242022/01/192020/08/18 1399/5/28 محمدنبی امیدوار MohammadNabi Omidvar گروه کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی ایران me.omidvar@gmail.com صمد نجاتیان Samad Nejatian گروه برق، واحد یاسوج، دانشگاه آزاد اسلامی ایران nejatian@iauyasooj.ac.ir حمید پروین Hamid Parvin گروه کامپیوتر، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی ایران parvinhamid@gmail.com کرم الله باقری فرد Karamolla Bagherifard گروه کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی ایران karam.bagherifard@gmail.com وحیده رضایی Vahideh Rezaie گروه ریاضی، واحد یاسوج، دانشگاه آزاد اسلامی ایران v.rezaie@iauyasooj.ac.ir Algorithm Optimization Dominoes Cost Function Behavior الگوریتم بهینه‌سازی دومینو تابع هزینه رفتار [1] Haupt R. L.; Haupt S. E, "Practical Genetic Algorithms," 2nd Edition, John Wiley & Sons Inc, 2004.##[2] S. B. L. Vandenberghe, "Convex Optimization," Cambridge University Press, 2004.##[3] W. Sun, Y. Yuan, "Optimization Theory and Methods: Nonlinear Programming," Springer Science + Business Media, LLC Press, 2006.##[4] J. Nocedal, S. J. Wright, "Numerical Optimization," 2nd Edition, Springer Science + Business Media, LLC Press,2006.##[5] J. Holland," Genetic algorithms and the optimal allocation of trials", SIAM J. Comput, Vol.2 , pp. 88-105, 1979.##[6] J. Kennedy, R. Eberhart, "Particle Swarm Optimization" , Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.##[7] D. Karaboga, B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", Journal of Global Optimization, vol. 39 , pp. 459-471, 2007.##[8] M. Dorigo, V. Maniezzo, A. Colorni, "Ant System: Optimization by a colony of cooperating agents", IEEE Transactions on Systems, Man, and Cybernetics - Part B, vol.26, pp. 29-41, 1996.##[9] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, "Optimization by simulated annealing", Science vol.220, pp. 671-680 ,1983.##[10] F.W. Glover, "Tabu search: A tutorial", Interfaces, vol. 20, pp.74-94, 1990.##[11] D. T. Pham, S. Otri, A. Afify, M. Mahmuddin, , H. Al-Jabbouli, "Data clustering using the bees algorithm", 40th CIRP International Seminar on Manufacturing Systems, p. p. s.p., 2007.##[12] X. Miao, J. Chu, , L. Zhang, J. Qiao, "An Evolutionary Neural Network Approach to Simple Prediction of Dam Deformation", Journal of Information & Computational Science, vol.10 , pp.315-1324, 2013.##[13] Z.W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001.##[14] R. P. Feynman, "Simulating physics with computers," International Journal of Theoretical Physics, vol. 21, no. 6-7, pp. 467-488, 1982.##[15] R. P. Feynman, "Quantummechanical computers," Foundations of Physics, vol. 16, no. 6, pp. 507-531, 1986.##[16] A. Narayanan and M.Moore, "Quantum-inspired genetic algorithms," in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '96), May 1996, pp. 61-66.##[17] J. Sun,W.Xu, and B. Feng, "Aglobal search strategy of quantumbehaved particle swarm optimization," in Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, December 2004, pp. 111-116.##[18] Y. Wang, X. Feng, Y. Huang et al., "A novel quantum swarm evolutionary algorithm and its applications," Neurocomputing, vol. 70, no. 4-6, pp. 633-640, 2007.##[19] S. I. Birbil and S. Fang, "An electromagnetism-like mechanism for global optimization," Journal of Global Optimization, vol. 25, no. 3, pp. 263-282, 2003.##[20] O. K. Erol and I. Eksin, "A new optimizationmethod: Big Bang-Big Crunch," Advances in Engineering Software, vol. 37, no. 2, pp.106-111, 2006.##[21] R. A. Formato, "Central force optimization: a new metaheuristic with applications in applied electromagnetics," Progress in Electromagnetics Research, vol. 77, pp. 425-491, 2007.##[22] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.##[23] L. Xie, J. Zeng, and Z. Cui, "General framework of artificial physics optimization algorithm," in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC '09), IEEE, December 2009, pp. 1321-1326.##[24] J. Flores, R. L'opez, and J. Barrera, "Gravitational interactions optimization," in Learning and Intelligent Optimization, pp. 226-237, Springer, Berlin, Germany, 2011.##[25] K. F. P'al, "Hysteretic optimization for the Sherrington-Kirkpatrick spin glass," Physica A, vol. 367, pp. 261-268, 2006.##[26] A. Kaveh and S. Talatahari, "A novel heuristic optimization method: charged system search," Acta Mechanica, vol. 213, no.3, pp. 267-289, 2010.##[27] H. Shah-Hosseini, "Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation," International Journal of Computational Science and Engineering, vol. 6, no. 1-2, pp. 132-140, 2011.##[28] L. Jiao, Y. Li, M. Gong, and X. Zhang, "Quantum-inspired immune clonal algorithm for global optimization," IEEE Transactions on Systems, Man, and Cybernetics B, vol. 38, no. 5, pp. 1234-1253, 2008.##[29] W. Li, Q. Yin, and X. Zhang, "Continuous quantum ant colony optimization and its application to optimization and analysis of induction motor structure," in Proceedings of the IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA '10), September 2010, pp. 313-317.##[30] Y. Zhang, L.Wu, Y. Zhang, and J.Wang, "Immune gravitation inspired optimization algorithm," in Advanced Intelligent Computing, pp. 178-185, Springer, Berlin,Germany, 2012.##[31] A. Layeb, S. Meshoul, and M. Batouche, "quantum genetic algorithm for multiple RNA structural alignment," in Proceedings of the 2nd Asia International Conference on Modelling and Simulation (AIMS '08), May 2008, pp. 873-878.##[32] D. Chang and Y. Zhao, "A dynamic niching quantum genetic algorithm for automatic evolution of clusters," in Proceedings of the 14th International Conference on Computer Analysis of Images and Patterns, vol. 22011, pp. 308-315.##[33] J. Xiao, Y. Yan, Y. Lin, L. Yuan, and J. Zhang, "A quantuminspired genetic algorithm for data clustering," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '08), June 2008, pp. 1513-1519.##[34] H. Talbi, A. Draa, and M. Batouche, "A new quantum-inspired genetic algorithm for solving the travelling salesman problem," in Proceedings of the IEEE International Conference on Industrial Technology (ICIT '04), vol. 3, December 2004 , pp. 1192-1197.##[35] K.-H. Han, K.-H. Park, C.-H. Lee, and J.-H. Kim, "Parallel quantum-inspired genetic algorithm for combinatorial optimization problem," in Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, IEEE, May 2001, pp. 1422-1429.##[36] L. Yan, H. Chen, W. Ji, Y. Lu, and J. Li, "Optimal VSM model and multi-object quantum-inspired genetic algorithm for web information retrieval," in Proceedings of the 1st International Symposium on Computer Network and Multimedia Technology (CNMT '09), IEEE, December 2009, pp. 1-4.##[37] Z.Mo, G. Wu, Y. He, and H. Liu, "quantum genetic algorithm for scheduling jobs on computational grids," in Proceedings of the International Conference on Measuring Technology and Mechatronics Automation (ICMTMA '10), March 2010, pp. 964-967.##[38] Y. Zhang, J. Liu, Y. Cui, X. Hei, and M. Zhang, "An improved quantumgenetic algorithmfor test suite reduction," in Proceedings of the IEEE International Conference on Computer Science and Automation Engineering (CSAE '11), June 2011, pp. 149-153.##[39] J. Lee,W. Lin, G. Liao, and T. Tsao, "quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system," International Journal of Electrical Power and Energy Systems, vol. 33, no. 2, pp. 189-197, 2011.##[40] J. Dai and H. Zhang, "A novel quantum genetic algorithm for area optimization of FPRM circuits," in Proceedings of the 3rd International Symposium on Intelligent Information Technology Application (IITA 09), November 2009, pp. 408-411.##[41] L. Chuang, Y. Chiang, and C. Yang, "A quantum genetic algorithm for operon prediction," in Proceedings of the IEEE 26th International Conference on Advanced Information Networking and Applications (AINA '12 ), March 2012, pp. 269-275.##[42] H. Xing, X. Liu, X. Jin, L. Bai, and Y. Ji, "A multi-granularity evolution based quantum genetic algorithm for QoS multicast routing problem in WDM networks," Computer Communications, vol. 32, no. 2, pp. 386-393, 2009.##[43] W. Luo, "A quantum genetic algorithm based QoS routing protocol for wireless sensor networks," in Proceedings of the IEEE International Conference on Software Engineering and Service Sciences (ICSESS '10), IEEE, July 2010, pp. 37-40.##[44] J. Wang and R. Zhou, "A novel quantum genetic algorithm for PID controller," in Proceedings of the 6th International Conference on Advanced Intelligent Computing Theories and Applications: Intelligent Computing, 2010, pp. 72-77.##[45] B.Han, J. Jiang, Y.Gao, and J.Ma, "Aquantumgenetic algorithm to solve the problem of multivariate," Communications in Computer and Information Science, vol. 243, no. 1, pp. 308-314, 2011.##[46] Y. Zheng, J. Liu, W. Geng, and J. Yang, "Quantum-inspired genetic evolutionary algorithm for course timetabling," in Proceedings of the 3rd International Conference on Genetic and Evolutionary Computing (WGEC '09), October 2009, pp. 750-753.##[47] X. J. Zhang, S. Li, Y. Shen, and S. M. Song, "Evaluation of several quantum genetic algorithms in medical image registration applications," in Proceedings of the IEEE International Conference on Computer Science and Automation Engineering (CSAE '12), vol. 2, IEEE, 2012, pp. 710-713.##[48] H. Talbi, A. Draa, and M. Batouche, "A new quantum-inspired genetic algorithm for solving the travelling salesman problem," in Proceedings of the IEEE International Conference on Industrial Technology (ICIT '04), December 2004, pp. 1192-1197.##[49] S. Bhattacharyya and S. Dey, "An efficient quantum inspired genetic algorithm with chaotic map model based interference and fuzzy objective function for gray level image thresholding," in Proceedings of the International Conference on Computational Intelligence and Communication Systems (CICN '11), IEEE, October 2011, pp. 121-125.##[50] K. Benatchba, M. Koudil, Y. Boukir, and N. Benkhelat, "Image segmentation using quantum genetic algorithms," in Proceedings of the 32nd Annual Conference on IEEE Industrial Electronics (IECON '06), IEEE, November 2006, pp. 3556-3562.##[51] M. Liu, C. Yuan, and T. Huang, "A novel real-coded quantum genetic algorithm in radiation pattern synthesis for smart antenna," in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '07), IEEE, December 2007, pp. 2023-2026.##[52] R. Popa, V. Nicolau, and S. Epure, "A new quantum inspired genetic algorithm for evolvable hardware," in Proceedings of the 3rd International Symposium on Electrical and Electronics Engineering (ISEEE '10), September 2010, pp. 64-69.##[53] H. Yu and J. Fan, "Parameter optimization based on quantum genetic algorithm for generalized fuzzy entropy thresholding segmentation method," in Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '08), vol. 1, IEEE, October 2008. pp. 530-534.##[54] P. C. Shill,M. F. Amin, M. A. H. Akhand, and K.Murase, "Optimization of interval type-2 fuzzy logic controller using quantum genetic algorithms," in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE '12), June 2012, pp. 1-8.##[55] M. Cao and F. Shang, "Training of process neural networks based on improved quantum genetic algorithm," in Proceedings of the WRI World Congress on Software Engineering (WCSE'09), vol. 2, May 2009, pp. 160-165.##[56] Y. Sun and M. Ding, "quantum genetic algorithm for mobile robot path planning," in Proceedings of the 4th International Conference on Genetic and Evolutionary Computing (ICGEC'10), December 2010, pp. 206-209.##[57] M. Yazdani, F. Jolaei, "Lion Optimization Algorithm (LOA) : Anature-inspired meta heuristic algorithm," Journal of Computational Designand Engineering, pp.24-36, 2016.##[58] A.R. Mehrabian, C. Lucas, "A novel numerical optimization algorithm inspired from weed colonization", Ecol. Inform, vol.1(4), pp.355-66, 2006.##[59] D. Simon "Biogeography-based optimization," Evolut, Comput.IEEE, Trans, vol.12(6), pp.702-13, 2008.##[60] Yang X-S. "A new meta heuristicbat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization", NICSO 2010, Springer; pp. 65-74, 2010.##[61] Y-J. Zheng,"Water wave optimization :a new nature-inspired meta heuristic," Comput. Oper.Res, pp. 55:1-11, 2014.##[62] J. Wang, B. Zhou, Sh. Zhou, "An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation," Hindawi Publishing Corporation Computational Intelligence and Neuroscience, Vol. 2016, pp. 8, 2016.##[63] Ch-F. Wang, K. Liu, "A Novel Particle Swarm Optimization Algorithm for Global Optimization," Hindawi Publishing Corporation Computational Intelligence and Neuroscience, V. 2016, pp. 9, 2016.##[64] C. Cubukcuoglu, I. Chatzikonstantinou, M. Fatih Tasgetiren, S. Sariyildiz, Q-K. Pan, "A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements," Algorithms 2016, 9, 51; doi:10.3390/a9030051, 2016.##[65] I. Obagbuwa, A. Philips Abidoye, "Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem," Algorithms 2016, 9, 59; doi:10.3390/a9030059, 2016.##[66] R M. Rizk Allah, "Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems," International Journal of Swarm Intelligence and Evolutionary Computation, http://dx.doi.org/10.4172/2090-4908.1000134, 2016.##[67] J., Liang, B. Qu, and P. Suganthan Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Computational Intelligence Laboratory,2013.##[68] M., Yasrebi, A., Eskandar-Baghban, H., Parvin, M., Mohammadpour, "Optimization inspiring from behavior of raining in nature: droplet optimization algorithm," International Journal of Bio-Inspired Computation, Vol.12, no.3, pp. 152-163, 2018.##[69] K., Pytel, "Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems," Applied Artificial Intelligence, Published online: 24 Feb 2020, Pages, 550-563, DOI: 10.1080/08839514.2020.1730631##[70] H., Parvin, S., Nejatian M., Mohammadpour, "Explicit memory based ABC with a clustering strategy for updating and retrieval of memory in dynamic environments," Applied Intelligence, Volume 48 , V, 11., pages, 4317-4337, doi: ##https://doi.org/10.1007/s10489-018-1197-z##[71] X., Qian, X., Wang, Y., Su and L., He, 2018. An effective hybrid evolutionary algorithm for solving the numerical optimization problems. Journal of Physics: Conference Series 1004 (1): article id. 012020. doi:10.1088/1742-6596/1004/1/012020.##[72] X., Donga, Yongle., C, "A novel genetic algorithm for large scale colored balanced traveling salesman problem,"##[73] C., HyukGeun, K., Jinhyun, Y,. Yourim., M., Byung-Ro, "Investigation of incremental hybrid genetic algorithm with subgraph isomorphism problem, " Swarm and Evolutionary Computation, vol.49, pp.75-86, 2019.##[74]##[75] M. moradi, S. nejatian , H. parvin , K. A. Bagherifard and V.rezaie , "Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization", JSDP, vol. 18, no. 3, pp. 127-146,2021.##[1] Haupt R. L.; Haupt S. E, "Practical Genetic Algorithms," 2nd Edition, John Wiley & Sons Inc, 2004.##[2] S. B. L. Vandenberghe, "Convex Optimization," Cambridge University Press, 2004.##[3] W. Sun, Y. Yuan, "Optimization Theory and Methods: Nonlinear Programming," Springer Science + Business Media, LLC Press, 2006.##[4] J. Nocedal, S. J. Wright, "Numerical Optimization," 2nd Edition, Springer Science + Business Media, LLC Press,2006.##[5] J. Holland," Genetic algorithms and the optimal allocation of trials", SIAM J. Comput, Vol.2 , pp. 88-105, 1979.##[6] J. Kennedy, R. Eberhart, "Particle Swarm Optimization" , Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.##[7] D. Karaboga, B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", Journal of Global Optimization, vol. 39 , pp. 459-471, 2007.##[8] M. Dorigo, V. Maniezzo, A. Colorni, "Ant System: Optimization by a colony of cooperating agents", IEEE Transactions on Systems, Man, and Cybernetics - Part B, vol.26, pp. 29-41, 1996.##[9] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, "Optimization by simulated annealing", Science vol.220, pp. 671-680 ,1983.##[10] F.W. Glover, "Tabu search: A tutorial", Interfaces, vol. 20, pp.74-94, 1990.##[11] D. T. Pham, S. Otri, A. Afify, M. Mahmuddin, , H. Al-Jabbouli, "Data clustering using the bees algorithm", 40th CIRP International Seminar on Manufacturing Systems, p. p. s.p., 2007.##[12] X. Miao, J. Chu, , L. Zhang, J. Qiao, "An Evolutionary Neural Network Approach to Simple Prediction of Dam Deformation", Journal of Information & Computational Science, vol.10 , pp.315-1324, 2013.##[13] Z.W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001.##[14] R. P. Feynman, "Simulating physics with computers," International Journal of Theoretical Physics, vol. 21, no. 6-7, pp. 467-488, 1982.##[15] R. P. Feynman, "Quantummechanical computers," Foundations of Physics, vol. 16, no. 6, pp. 507-531, 1986.##[16] A. Narayanan and M.Moore, "Quantum-inspired genetic algorithms," in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '96), May 1996, pp. 61-66.##[17] J. Sun,W.Xu, and B. Feng, "Aglobal search strategy of quantumbehaved particle swarm optimization," in Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, December 2004, pp. 111-116.##[18] Y. Wang, X. Feng, Y. Huang et al., "A novel quantum swarm evolutionary algorithm and its applications," Neurocomputing, vol. 70, no. 4-6, pp. 633-640, 2007.##[19] S. I. Birbil and S. Fang, "An electromagnetism-like mechanism for global optimization," Journal of Global Optimization, vol. 25, no. 3, pp. 263-282, 2003.##[20] O. K. Erol and I. Eksin, "A new optimizationmethod: Big Bang-Big Crunch," Advances in Engineering Software, vol. 37, no. 2, pp.106-111, 2006.##[21] R. A. Formato, "Central force optimization: a new metaheuristic with applications in applied electromagnetics," Progress in Electromagnetics Research, vol. 77, pp. 425-491, 2007.##[22] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.##[23] L. Xie, J. Zeng, and Z. Cui, "General framework of artificial physics optimization algorithm," in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC '09), IEEE, December 2009, pp. 1321-1326.##[24] J. Flores, R. L'opez, and J. Barrera, "Gravitational interactions optimization," in Learning and Intelligent Optimization, pp. 226-237, Springer, Berlin, Germany, 2011.##[25] K. F. P'al, "Hysteretic optimization for the Sherrington-Kirkpatrick spin glass," Physica A, vol. 367, pp. 261-268, 2006.##[26] A. Kaveh and S. Talatahari, "A novel heuristic optimization method: charged system search," Acta Mechanica, vol. 213, no.3, pp. 267-289, 2010.##[27] H. Shah-Hosseini, "Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation," International Journal of Computational Science and Engineering, vol. 6, no. 1-2, pp. 132-140, 2011.##[28] L. Jiao, Y. Li, M. Gong, and X. Zhang, "Quantum-inspired immune clonal algorithm for global optimization," IEEE Transactions on Systems, Man, and Cybernetics B, vol. 38, no. 5, pp. 1234-1253, 2008.##[29] W. Li, Q. Yin, and X. Zhang, "Continuous quantum ant colony optimization and its application to optimization and analysis of induction motor structure," in Proceedings of the IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA '10), September 2010, pp. 313-317.##[30] Y. Zhang, L.Wu, Y. Zhang, and J.Wang, "Immune gravitation inspired optimization algorithm," in Advanced Intelligent Computing, pp. 178-185, Springer, Berlin,Germany, 2012.##[31] A. Layeb, S. Meshoul, and M. Batouche, "quantum genetic algorithm for multiple RNA structural alignment," in Proceedings of the 2nd Asia International Conference on Modelling and Simulation (AIMS '08), May 2008, pp. 873-878.##[32] D. Chang and Y. Zhao, "A dynamic niching quantum genetic algorithm for automatic evolution of clusters," in Proceedings of the 14th International Conference on Computer Analysis of Images and Patterns, vol. 22011, pp. 308-315.##[33] J. Xiao, Y. Yan, Y. Lin, L. Yuan, and J. Zhang, "A quantuminspired genetic algorithm for data clustering," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '08), June 2008, pp. 1513-1519.##[34] H. Talbi, A. Draa, and M. Batouche, "A new quantum-inspired genetic algorithm for solving the travelling salesman problem," in Proceedings of the IEEE International Conference on Industrial Technology (ICIT '04), vol. 3, December 2004 , pp. 1192-1197.##[35] K.-H. Han, K.-H. Park, C.-H. Lee, and J.-H. Kim, "Parallel quantum-inspired genetic algorithm for combinatorial optimization problem," in Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, IEEE, May 2001, pp. 1422-1429.##[36] L. Yan, H. Chen, W. Ji, Y. Lu, and J. Li, "Optimal VSM model and multi-object quantum-inspired genetic algorithm for web information retrieval," in Proceedings of the 1st International Symposium on Computer Network and Multimedia Technology (CNMT '09), IEEE, December 2009, pp. 1-4.##[37] Z.Mo, G. Wu, Y. He, and H. Liu, "quantum genetic algorithm for scheduling jobs on computational grids," in Proceedings of the International Conference on Measuring Technology and Mechatronics Automation (ICMTMA '10), March 2010, pp. 964-967.##[38] Y. Zhang, J. Liu, Y. Cui, X. Hei, and M. Zhang, "An improved quantumgenetic algorithmfor test suite reduction," in Proceedings of the IEEE International Conference on Computer Science and Automation Engineering (CSAE '11), June 2011, pp. 149-153.##[39] J. Lee,W. Lin, G. Liao, and T. Tsao, "quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system," International Journal of Electrical Power and Energy Systems, vol. 33, no. 2, pp. 189-197, 2011.##[40] J. Dai and H. Zhang, "A novel quantum genetic algorithm for area optimization of FPRM circuits," in Proceedings of the 3rd International Symposium on Intelligent Information Technology Application (IITA 09), November 2009, pp. 408-411.##[41] L. Chuang, Y. Chiang, and C. Yang, "A quantum genetic algorithm for operon prediction," in Proceedings of the IEEE 26th International Conference on Advanced Information Networking and Applications (AINA '12 ), March 2012, pp. 269-275.##[42] H. Xing, X. Liu, X. Jin, L. Bai, and Y. Ji, "A multi-granularity evolution based quantum genetic algorithm for QoS multicast routing problem in WDM networks," Computer Communications, vol. 32, no. 2, pp. 386-393, 2009.##[43] W. Luo, "A quantum genetic algorithm based QoS routing protocol for wireless sensor networks," in Proceedings of the IEEE International Conference on Software Engineering and Service Sciences (ICSESS '10), IEEE, July 2010, pp. 37-40.##[44] J. Wang and R. Zhou, "A novel quantum genetic algorithm for PID controller," in Proceedings of the 6th International Conference on Advanced Intelligent Computing Theories and Applications: Intelligent Computing, 2010, pp. 72-77.##[45] B.Han, J. Jiang, Y.Gao, and J.Ma, "Aquantumgenetic algorithm to solve the problem of multivariate," Communications in Computer and Information Science, vol. 243, no. 1, pp. 308-314, 2011.##[46] Y. Zheng, J. Liu, W. Geng, and J. Yang, "Quantum-inspired genetic evolutionary algorithm for course timetabling," in Proceedings of the 3rd International Conference on Genetic and Evolutionary Computing (WGEC '09), October 2009, pp. 750-753.##[47] X. J. Zhang, S. Li, Y. Shen, and S. M. Song, "Evaluation of several quantum genetic algorithms in medical image registration applications," in Proceedings of the IEEE International Conference on Computer Science and Automation Engineering (CSAE '12), vol. 2, IEEE, 2012, pp. 710-713.##[48] H. Talbi, A. Draa, and M. Batouche, "A new quantum-inspired genetic algorithm for solving the travelling salesman problem," in Proceedings of the IEEE International Conference on Industrial Technology (ICIT '04), December 2004, pp. 1192-1197.##[49] S. Bhattacharyya and S. Dey, "An efficient quantum inspired genetic algorithm with chaotic map model based interference and fuzzy objective function for gray level image thresholding," in Proceedings of the International Conference on Computational Intelligence and Communication Systems (CICN '11), IEEE, October 2011, pp. 121-125.##[50] K. Benatchba, M. Koudil, Y. Boukir, and N. Benkhelat, "Image segmentation using quantum genetic algorithms," in Proceedings of the 32nd Annual Conference on IEEE Industrial Electronics (IECON '06), IEEE, November 2006, pp. 3556-3562.##[51] M. Liu, C. Yuan, and T. Huang, "A novel real-coded quantum genetic algorithm in radiation pattern synthesis for smart antenna," in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '07), IEEE, December 2007, pp. 2023-2026.##[52] R. Popa, V. Nicolau, and S. Epure, "A new quantum inspired genetic algorithm for evolvable hardware," in Proceedings of the 3rd International Symposium on Electrical and Electronics Engineering (ISEEE '10), September 2010, pp. 64-69.##[53] H. Yu and J. Fan, "Parameter optimization based on quantum genetic algorithm for generalized fuzzy entropy thresholding segmentation method," in Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '08), vol. 1, IEEE, October 2008. pp. 530-534.##[54] P. C. Shill,M. F. Amin, M. A. H. Akhand, and K.Murase, "Optimization of interval type-2 fuzzy logic controller using quantum genetic algorithms," in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE '12), June 2012, pp. 1-8.##[55] M. Cao and F. Shang, "Training of process neural networks based on improved quantum genetic algorithm," in Proceedings of the WRI World Congress on Software Engineering (WCSE'09), vol. 2, May 2009, pp. 160-165.##[56] Y. Sun and M. Ding, "quantum genetic algorithm for mobile robot path planning," in Proceedings of the 4th International Conference on Genetic and Evolutionary Computing (ICGEC'10), December 2010, pp. 206-209.##[57] M. Yazdani, F. Jolaei, "Lion Optimization Algorithm (LOA) : Anature-inspired meta heuristic algorithm," Journal of Computational Designand Engineering, pp.24-36, 2016.##[58] A.R. Mehrabian, C. Lucas, "A novel numerical optimization algorithm inspired from weed colonization", Ecol. Inform, vol.1(4), pp.355-66, 2006.##[59] D. Simon "Biogeography-based optimization," Evolut, Comput.IEEE, Trans, vol.12(6), pp.702-13, 2008.##[60] Yang X-S. "A new meta heuristicbat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization", NICSO 2010, Springer; pp. 65-74, 2010.##[61] Y-J. Zheng,"Water wave optimization :a new nature-inspired meta heuristic," Comput. Oper.Res, pp. 55:1-11, 2014.##[62] J. Wang, B. Zhou, Sh. Zhou, "An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation," Hindawi Publishing Corporation Computational Intelligence and Neuroscience, Vol. 2016, pp. 8, 2016.##[63] Ch-F. Wang, K. Liu, "A Novel Particle Swarm Optimization Algorithm for Global Optimization," Hindawi Publishing Corporation Computational Intelligence and Neuroscience, V. 2016, pp. 9, 2016.##[64] C. Cubukcuoglu, I. Chatzikonstantinou, M. Fatih Tasgetiren, S. Sariyildiz, Q-K. Pan, "A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements," Algorithms 2016, 9, 51; doi:10.3390/a9030051, 2016.##[65] I. Obagbuwa, A. Philips Abidoye, "Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem," Algorithms 2016, 9, 59; doi:10.3390/a9030059, 2016.##[66] R M. Rizk Allah, "Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems," International Journal of Swarm Intelligence and Evolutionary Computation, http://dx.doi.org/10.4172/2090-4908.1000134, 2016.##[67] J., Liang, B. Qu, and P. Suganthan Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Computational Intelligence Laboratory,2013.##[68] M., Yasrebi, A., Eskandar-Baghban, H., Parvin, M., Mohammadpour, "Optimization inspiring from behavior of raining in nature: droplet optimization algorithm," International Journal of Bio-Inspired Computation, Vol.12, no.3, pp. 152-163, 2018.##[69] K., Pytel, "Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems," Applied Artificial Intelligence, Published online: 24 Feb 2020, Pages, 550-563, DOI: 10.1080/08839514.2020.1730631##[70] H., Parvin, S., Nejatian M., Mohammadpour, "Explicit memory based ABC with a clustering strategy for updating and retrieval of memory in dynamic environments," Applied Intelligence, Volume 48 , V, 11., pages, 4317-4337, doi: ##https://doi.org/10.1007/s10489-018-1197-z##[71] X., Qian, X., Wang, Y., Su and L., He, 2018. An effective hybrid evolutionary algorithm for solving the numerical optimization problems. Journal of Physics: Conference Series 1004 (1): article id. 012020. doi:10.1088/1742-6596/1004/1/012020.##[72] X., Donga, Yongle., C, "A novel genetic algorithm for large scale colored balanced traveling salesman problem,"##[73] C., HyukGeun, K., Jinhyun, Y,. Yourim., M., Byung-Ro, "Investigation of incremental hybrid genetic algorithm with subgraph isomorphism problem, " Swarm and Evolutionary Computation, vol.49, pp.75-86, 2019.##[74]##[75] M. moradi, S. nejatian , H. parvin , K. A. Bagherifard and V.rezaie , "Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization", JSDP, vol. 18, no. 3, pp. 127-146,2021.## ##

مروری بر روش‌های تحلیل احساس در متون فارسی Sentiment analysis methods in Persian text: A survey 1 1 با رشد چشم‌گیر رسانه‌های اجتماعی مانند توییتر و افزایش  نظرات کاربران در تارنماهای تجارت الکترونیکی و تارنماهای خبری، افراد و سازمان‌ها به‌طور فزاینده‌ای از نظرات در این رسانه‌ها برای تصمیم‌گیری خود استفاده می‌کنند. تحلیل احساس یکی از روش‌های تحلیل نظرات کاربران است که در سال‌های اخیر مورد توجه قرار گرفته است. تحلیل احساس روی هر زبانی نیازمندی‌های مختص به خود را دارد و به‌کارگیری روش‌ها، ابزارها و منابع زبان انگلیسی به‌طور مستقیم در زبان فارسی با محدودیت‌هایی روبه‌رو است. متون نوشته‌شده به زبان فارسی ویژگی‌های خاصی دارند که نیازمند روش‌های خاص تحلیل احساس هستند که متفاوت از زبان انگلیسی است. در این مقاله، پژوهش‌های تحلیل احساس که روی متون به زبان فارسی انجام شده است، مورد بررسی و مقایسه قرار می‌گیرد. ابتدا رویکردهای تحلیل احساس، وظایف و سطوح تحلیل احساس تشریح می‌شود. در ادامه تلاش می‌شود که مروری روی روش‌های به‌کارگرفته‌شده برای وظایف تحلیل احساس متون فارسی انجام شود و جایگاه کارهای انجام‌شده در زبان فارسی روشن شود. همچنین منابع داده­ای ایجاد و منتشر‌شده برای تحلیل احساس متون فارسی معرفی شده است. در نهایت با توجه به مطالعات انجام گرفته روی آخرین پیشرفت‌های تحلیل احساس، مسائل و چالش‌هایی که در زبان فارسی به آن پرداخته نشده را برشمرده و نقشه راهی برای پژوهش‌های آینده پردازش متون فارسی ارائه می‌شود.   2 With the explosive growth of social media such as Twitter and Instagram, reviews on e-commerce websites, and comments on news websites, individuals and organizations are increasingly using analyzing opinions in these media for their decision-making and designing strategies. Sentiment analysis is one of the techniques used to analyze users' opinions in recent years. The Persian language has specific features and thereby requires unique methods and models to be adopted for sentiment analysis, which are different from those in English and other languages. This paper identifies the characteristics and limitations of the Persian language. Sentiment analysis in each language has specified prerequisites; hence, the direct use of methods, tools, and resources developed for the English language in Persian has its limitations. The present study aims to investigate and compare previous sentiment analysis studies on Persian texts and describe views presented in articles published in the last decade. First, the sentiment analysis levels, approaches, and tasks are described. Then, a detailed survey of the applied sentiment analysis methods used for Persian texts is presented, and previous works in this field are discussed. The advantages and disadvantages of each proposed method are demonstrated. Moreover, the publicly available sentiment analysis resources of Persian texts are studied, and the characteristics and differences of each are highlighted. As a result, according to the recent development of the sentiment analysis field, some issues and challenges not being addressed in Persian texts are listed, and some guidelines are provided for future research on Persian texts. Future requirements of Persian text for improving the sentiment analysis system are detailed. 107 132 2019/12/192019/10/252019/12/22019/12/302020/04/122020/04/152019/12/42019/12/15 1398/9/24 2021/12/62020/02/82020/08/182020/10/132021/05/242022/01/192020/08/182021/05/23 1400/3/2 زینب رجبی Zeinab Rajabi پژوهشگاه ارتباطات و فناوری اطلاعات ایران rajabi.ze@gmail.com محمدرضا ولوی Mohamadreza valavi مجتمع برق و کامپیوتر، دانشگاه صنعتی مالک اشتر ایران valavi@mut.ac.ir مریم حورعلی Maryam Hourali مجتمع برق و کامپیوتر، دانشگاه صنعتی مالک اشتر ایران mhourali@mut.ac.ir Sentiment Analysis Opinion Mining Sentiment Classification Sentiment Data Resource Persian Language تحلیل احساس نظرکاوی طبقه‌بندی قطبیت مجموعه داده‌های تحلیل احساس زبان فارسی [1] E. Cambria, S. Poria, A. Hussain, and B. Liu, "Computational Intelligence for Affective Computing and Sentiment Analysis [Guest Editorial]", IEEE Computational Intelligence Magazine, vol. 14(2), pp. 16-17, 2019.##[2] B. Liu, "Sentiment analysis and opinion mining", Synthesis lectures on human language technologies, vol. 5(1), pp. 1-167, 2012.##[3] B. Liu, "Sentiment Analysis and Subjectivity", Handbook of natural language processing, vol. 2, pp. 627-666, 2010.##[4] A. Keramatfar, and H. Amirkhani, "Bibliometrics of sentiment analysis literature", Journal of Information Science, vol. 45(1), pp. 3-15, 2019.##[5] R. Piryani, D. Madhavi, and V.K. Singh, "Analytical mapping of opinion mining and sentiment analysis research during 2000-2015", Information Processing & Management, vol.53(1), pp. 122-150, 2017.##[6] W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Engineering Journal, vol.5(4), pp. 1093-1113, 2014.##[7] K. Ravi, and V. Ravi, "A survey on opinion mining and sentiment analysis: Tasks, approaches and applications", Knowledge-Based Systems, vol.89, pp. 14-46, 2015.##[8] A. Montoyo, P. MartíNez-Barco, and A. Balahur, "Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments", Decision Support Systems, vol. 53(4), pp. 675-679, 2012.##[9] A.N. Jebaseeli, and E. Kirubakaran, "A survey on sentiment analysis of (product) reviews", International Journal of Computer Applications, vol. 47(11), 2012.##[10] D. Hussein, "A survey on sentiment analysis challenges", Journal of King Saud University-Engineering Sciences, vol. 30(4), pp. 330-338, 2018.##[11] N. Boudad, R. Faizi, R.O.H. Thami, and R. Chiheb, "Sentiment analysis in Arabic: A review of the literature", Ain Shams Engineering Journal, 2017.##[12] S.L. Lo, E. Cambria, R. Chiong, and D. Cornforth, "Multilingual sentiment analysis: from formal to informal and scarce resource languages", Artificial Intelligence Review,. Vol.48(4), pp. 499-527, 2017.##[13] K. Dashtipour, S. Poria, A. Hussain, E. Cambria, A.Y. Hawalah, A. Gelbukh, and Q. Zhou, "Multilingual sentiment analysis: state of the art and independent comparison of techniques", Cognitive computation, vol.8(4), pp. 757-771, 2016.##[14] E.F. Can, A. Ezen-Can, and F. Can, "Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data", arXiv preprint arXiv:1806.04511, 2018.##[15] A. Balahur, J.M. Hermida, and A. Montoyo, "Detecting implicit expressions of sentiment in text based on commonsense knowledge", in Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, 2011, Association for Computational Linguistics.##[16] M.E. Basiri, A.R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A framework for sentiment analysis in persian" Open Transactions on Information Processing, Vol.1(3), pp. 1-14, 2014.##[17] E. Asgarian, M. Kahani, and S. Sharifi, "The impact of sentiment features on the sentiment polarity classification in Persian reviews", Cognitive Computation, vol. 10(1), pp. 117-135, 2018.##[18] R. Dehkharghani, "SentiFars: A Persian Polarity Lexicon for Sentiment Analysis", ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), vol. 19(2), pp. 21, 2019.##[19] E. Cambria, "An introduction to concept-level sentiment analysis," in Mexican International Conference on Artificial Intelligence, 2013. Springer.##[20] N. Cristianini, and J. Shawe-Taylor, "An introduction to support vector machines and other kernel-based learning methods", 2000: Cambridge university press.##[21] T. Joachims, "Making large-scale svm learning practical", I n A dvan ces in Ker n e l Meth od s. 1998, MIT Press.##[22] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques", in Proceedings of the ACL-02 conference on Empirical methods in natural language processing,Vol. 10, 2002,##[23] P.D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews", in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, Association for Computational Linguistics.##[24] S. Dasgupta, and V. Ng, "Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification", in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009.##[25] S. Poria, E. Cambria, G. Winterstein, and G.-B. Huang, "Sentic patterns: Dependency-based rules for concept-level sentiment analysis", Knowledge-Based Systems, Vol. 69, pp. 45-63, 2014.##[26] E. Cambria, and A. Hussain, "Sentic computing: a common-sense-based framework for concept-level sentiment analysis", Springer, Vol. 1. 2015.##[27] E. Cambria, S. Poria, F. Bisio, R. Bajpai, and I. Chaturvedi, "The CLSA model: a novel framework for concept-level sentiment analysis", in International Conference on Intelligent Text Processing and Computational Linguistics, Springer, 2015.##[28] Z. Rajabi, M.R. Valavi, and M. Hourali, "A Context-Based Disambiguation Model for Sentiment Concepts Using a Bag-of-Concepts Approach", Cognitive Computation, pp. 1-14, 2020.##[29] K. Dashtipour, M. Gogate, J. Li, F. Jiang, B. Kong, and A. Hussain, "A hybrid Persian sentiment analysis framework: Integrating dependency grammar-based rules and deep neural networks", Neurocomputing, 2020. 380: pp. 1-10.##[30] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, Feature extraction in opinion mining for Persian text, in 2nd National Conference on Computer Science, 2013.##[31] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, "Feature extraction in opinion mining through Persian reviews", Journal of AI and Data Mining, vol. 3(2), pp. 169-179, 2015.##[32] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, "Feature extraction in opinion mining through Persian reviews", Journal of AI and Data Mining, vol. 3(2), pp. 169-179, 2015.##[33] F. Mirojagh, M. Shahraki, "Fake opinion detection using a supervised approach", International Conference on Electrical Engineering and Computer Science, 2014.##[34] S. Zirpe, and B. Joglekar, "Polarity shift detection approaches in sentiment analysis: A survey", in 2017 International Conference on Inventive Systems and Control (ICISC), IEEE, 2017.##[35] M. Shams, A. Shakery, and H. Faili, "A non-parametric LDA-based induction method for sentiment analysis," in The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), IEEE, 2012.##[36] A. Bagheri, and M. Saraee, "Persian sentiment analyzer: A framework based on a novel feature selection method", International Journal of Artificial Intelligence, vol. 12(2), pp. 115-129, 2014.##[37] A. Bagheri, M. Saraee, and F. de Jong. "Sentiment classification in Persian: Introducing a mutual information-based method for feature selection", in 2013 21st Iranian Conference on Electrical Engineering (ICEE), IEEE, 2013.##[38] E. Vaziripour, C. Giraud-Carrier, and D. Zappala, "Analyzing the political sentiment of Tweets in Farsi", in Tenth International AAAI Conference on Web and Social Media, 2016.##[39] F. Amiri, S. Scerri, and M. Khodashahi. "Lexicon-based sentiment analysis for Persian Text", in Proceedings of the International Conference Recent Advances in Natural Language Processing, 2015.##[40] S. Alimardani, and A. Aghaie, Opinion mining in Persian language using supervised algorithms, Journal of Information Systems and Telecommunication (JIST), 2015.##[41] S. Alimardani, and A. AGHAIE, "Opinion mining in Persian language using supervised algorithms and sentiment lexicon", Journal of Information Technology Management, vol.7, pp. 345-362, 2015.##[42] S. Sadidpour, , H. Shirazi, N.M. Sharef, B. Minaei-Bidgoli, and M.E. Sanjaghi, "Context-Sensitive Opinion Mining using Polarity Patterns", International Journal of Advanced Computer Science and Applications (IJACSA), pp.7, 2016.##[43] M. Saraee, and A. Bagheri, "Feature selection methods in Persian sentiment analysis", in International Conference on Application of Natural Language to Information Systems. Springer, 2013.##[44] Li, Y, H. Guo, Q. Zhang, M. Gu, and J. Yang, "Imbalanced text sentiment classification using universal and domain-specific knowledge," Knowledge-Based Systems, vol.160, pp. 1-15, 2018.##[45] S. Noferesti, and M. Shamsfard, "Using Linked Data for polarity classification of patients' experiences", Journal of biomedical informatics,vol. 57, pp. 6-19, 2015.##[46] M.Najafzadeh, S. Rahati Quchan, and R. Ghaemi, "A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis", Signal and Data Processing,vol. 15(2), pp. 89-102, 2018.##[47] I. Dehdarbehbahani, A. Shakery, and H. Faili, "Semi-supervised word polarity identification in resource-lean languages", Neural Networks, vol. 58, pp. 50-59, 2014.##[48] E.Asgarian, , M. Kahani, and S. Sharifi, "HesNegar: Persian Sentiment WordNet", Signal and Data Processing,vol, vol.15(1), pp. 71-86, 2018.##[49] B. Sabeti, , P. Hosseini, G. Ghassem-Sani, and S.A. Mirroshandel, LexiPers: An ontology based sentiment lexicon for Persian. in GCAI. 2016.##[50] S. Deng, A.P. Sinha, and H. Zhao, "Adapting sentiment lexicons to domain-specific social media texts", Decision Support Systems, vol. 94, pp. 65-76, 2017.##[51] P. Hosseini, H. Maleki, A.A. Ramaki, , M. Anvari, and S.A. Mirroshandel, "A sentiment analysis corpus for Persian(SentiPers)," in 3rd National Conference on Computational Linguistics, 2013.##[52] P. Hosseini, A.A. Ramaki, H. Maleki, M. Anvari, and S.A. Mirroshandel, SentiPers: a sentiment analysis corpus for Persian. arXiv preprint arXiv:1801.07737, 2018.##[53] M. Moradi, P. Khosravizade, and V. Bahram, "Constructing tagged corpora with a web approach as a corpus", in 2th symposium on computational Linguistics, 2012.##[54] K. Dashtipour, A. Hussain, Q. Zhou, A. Gelbukh, A.Y. Hawalah, and E. Cambria. "PerSent: a freely available Persian sentiment lexicon", in International Conference on Brain Inspired Cognitive Systems, 2016, Springer.##[55] A. Esuli, and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of LREC. 2006. Citeseer.##[56] P.J. Stone, D.C. Dunphy, and M.S. Smith, The general inquirer: A computer approach to content analysis, 1966.##[57] T.Wilson, J. Wiebe, and P. Hoffmann, "Recognizing contextual polarity in phrase-level sentiment analysis", in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 2005.##[58] C.H.E. Gilbert, Vader: A parsimonious rule-based model for sentiment analysis of social media text. in Eighth International Conference on Weblogs and Social Media (ICWSM-14). Available at (20/04/16) http://comp. social. gatech. edu/papers/icwsm14. vader. hutto. pdf. 2014.##[59] S. Huang, Z. Niu, and C. Shi, "Automatic construction of domain-specific sentiment lexicon based on constrained label propagation," Knowledge-Based Systems, vol. 56, pp. 191-200, 2014.##[60] S.Tan, and Q. Wu, "A random walk algorithm for automatic construction of domain-oriented sentiment lexicon," Expert Systems with Applications, vol. 38(10), pp. 12094-12100, 2011.##[61] K. Dashtipour, A. Raza, A. Gelbukh, R. Zhang, E. Cambria, and A. Hussain, "PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words", in International Conference on Brain Inspired Cognitive Systems, Springer, 2019.##[62] A. Balahur, and G. Jacquet, Sentiment analysis meets social media-Challenges and solutions of the field in view of the current information sharing context. Information Processing & Management, 2015. 51(4): p. 428-432.##[63] D. Osimo, and F. Mureddu, "Research challenge on opinion mining and sentiment analysis", Universite de Paris-Sud, Laboratoire LIMSI-CNRS, Bâtiment, 2012. 508.##[64] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, "New avenues in opinion mining and sentiment analysis", IEEE Intelligent Systems, vol. 28(2), pp. 15-21, 2013.##[65] Z. Rajabi, M. Valavi, and M. Hourali, "A model for enriching sentiment lexicon based on semantic knowledge base", 10st Iranian C4I Conference, 2017.##[66] Z. Rajabi, M. Valavi, and M. Hourali, "A context-based model for disambiguating the sentiment concepts using the common-sense knowledge", C4I Journal, vol.2(2), pp. 32-47, 2018.##[67] J. Liao, S. Wang, and D. Li, "Identification of fact-implied implicit sentiment based on multi-level semantic fused representation" Knowledge-Based Systems, vol.165, pp. 197-207, 2019.##[68] C. Hung, "Word of mouth quality classification based on contextual sentiment lexicons", Information Processing & Management, vol. 53(4), pp. 751-763, 2017.##[69] H. Saif, Y. He, M. Fernandez, and H. Alani, "Contextual semantics for sentiment analysis of Twitter", Information Processing & Management, vol. 52(1), pp. 5-19, 2016.##[70] S. Noferesti, and M. Shamsfard, "Resource construction and evaluation for indirect opinion mining of drug reviews", PloS one, vol. 10(5), pp. e0124993, 2015.##[71] L. Zhang, S. Wang, and B. Liu, "Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews", Data Mining and Knowledge Discovery, 2018. Vol.8(4), p p,1253.##[72] T. Young, D. Hazarika, S. Poria, and E. Cambria, "Recent trends in deep learning based natural language processing", ieee Computational intelligenCe magazine, vol.13(3), pp. 55-75, 2018.##[73] K. Dashtipour, M. Gogate, A. Adeel, C. Ieracitano, H. Larijani, and A. Hussain, "Exploiting deep learning for persian sentiment analysis", in International Conference on Brain Inspired Cognitive Systems, Springer, 2018.##[1] E. Cambria, S. Poria, A. Hussain, and B. Liu, "Computational Intelligence for Affective Computing and Sentiment Analysis [Guest Editorial]", IEEE Computational Intelligence Magazine, vol. 14(2), pp. 16-17, 2019.##[2] B. Liu, "Sentiment analysis and opinion mining", Synthesis lectures on human language technologies, vol. 5(1), pp. 1-167, 2012.##[3] B. Liu, "Sentiment Analysis and Subjectivity", Handbook of natural language processing, vol. 2, pp. 627-666, 2010.##[4] A. Keramatfar, and H. Amirkhani, "Bibliometrics of sentiment analysis literature", Journal of Information Science, vol. 45(1), pp. 3-15, 2019.##[5] R. Piryani, D. Madhavi, and V.K. Singh, "Analytical mapping of opinion mining and sentiment analysis research during 2000-2015", Information Processing & Management, vol.53(1), pp. 122-150, 2017.##[6] W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Engineering Journal, vol.5(4), pp. 1093-1113, 2014.##[7] K. Ravi, and V. Ravi, "A survey on opinion mining and sentiment analysis: Tasks, approaches and applications", Knowledge-Based Systems, vol.89, pp. 14-46, 2015.##[8] A. Montoyo, P. MartíNez-Barco, and A. Balahur, "Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments", Decision Support Systems, vol. 53(4), pp. 675-679, 2012.##[9] A.N. Jebaseeli, and E. Kirubakaran, "A survey on sentiment analysis of (product) reviews", International Journal of Computer Applications, vol. 47(11), 2012.##[10] D. Hussein, "A survey on sentiment analysis challenges", Journal of King Saud University-Engineering Sciences, vol. 30(4), pp. 330-338, 2018.##[11] N. Boudad, R. Faizi, R.O.H. Thami, and R. Chiheb, "Sentiment analysis in Arabic: A review of the literature", Ain Shams Engineering Journal, 2017.##[12] S.L. Lo, E. Cambria, R. Chiong, and D. Cornforth, "Multilingual sentiment analysis: from formal to informal and scarce resource languages", Artificial Intelligence Review,. Vol.48(4), pp. 499-527, 2017.##[13] K. Dashtipour, S. Poria, A. Hussain, E. Cambria, A.Y. Hawalah, A. Gelbukh, and Q. Zhou, "Multilingual sentiment analysis: state of the art and independent comparison of techniques", Cognitive computation, vol.8(4), pp. 757-771, 2016.##[14] E.F. Can, A. Ezen-Can, and F. Can, "Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data", arXiv preprint arXiv:1806.04511, 2018.##[15] A. Balahur, J.M. Hermida, and A. Montoyo, "Detecting implicit expressions of sentiment in text based on commonsense knowledge", in Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, 2011, Association for Computational Linguistics.##[16] M.E. Basiri, A.R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A framework for sentiment analysis in persian" Open Transactions on Information Processing, Vol.1(3), pp. 1-14, 2014.##[17] E. Asgarian, M. Kahani, and S. Sharifi, "The impact of sentiment features on the sentiment polarity classification in Persian reviews", Cognitive Computation, vol. 10(1), pp. 117-135, 2018.##[18] R. Dehkharghani, "SentiFars: A Persian Polarity Lexicon for Sentiment Analysis", ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), vol. 19(2), pp. 21, 2019.##[19] E. Cambria, "An introduction to concept-level sentiment analysis," in Mexican International Conference on Artificial Intelligence, 2013. Springer.##[20] N. Cristianini, and J. Shawe-Taylor, "An introduction to support vector machines and other kernel-based learning methods", 2000: Cambridge university press.##[21] T. Joachims, "Making large-scale svm learning practical", I n A dvan ces in Ker n e l Meth od s. 1998, MIT Press.##[22] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques", in Proceedings of the ACL-02 conference on Empirical methods in natural language processing,Vol. 10, 2002,##[23] P.D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews", in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, Association for Computational Linguistics.##[24] S. Dasgupta, and V. Ng, "Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification", in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009.##[25] S. Poria, E. Cambria, G. Winterstein, and G.-B. Huang, "Sentic patterns: Dependency-based rules for concept-level sentiment analysis", Knowledge-Based Systems, Vol. 69, pp. 45-63, 2014.##[26] E. Cambria, and A. Hussain, "Sentic computing: a common-sense-based framework for concept-level sentiment analysis", Springer, Vol. 1. 2015.##[27] E. Cambria, S. Poria, F. Bisio, R. Bajpai, and I. Chaturvedi, "The CLSA model: a novel framework for concept-level sentiment analysis", in International Conference on Intelligent Text Processing and Computational Linguistics, Springer, 2015.##[28] Z. Rajabi, M.R. Valavi, and M. Hourali, "A Context-Based Disambiguation Model for Sentiment Concepts Using a Bag-of-Concepts Approach", Cognitive Computation, pp. 1-14, 2020.##[29] K. Dashtipour, M. Gogate, J. Li, F. Jiang, B. Kong, and A. Hussain, "A hybrid Persian sentiment analysis framework: Integrating dependency grammar-based rules and deep neural networks", Neurocomputing, 2020. 380: pp. 1-10.##[30] گلپر رابوکی، ع.، ج. رضایی نور، و س.ا. ضرغامی فر، استخراج ویژگیها در اندیشه کاوی مورد استفاده در متون فارسی, در دومین همایش ملی کامپیوتر. 1392.##[30] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, Feature extraction in opinion mining for Persian text, in 2nd National Conference on Computer Science, 2013.##[31] گلپر رابوکی، ع.، ج. رضایی نور، و س.ا. ضرغامی فر، استخراج ویژگیها و بسط لغتنامه در اندیشه کاوی مورد استفاده در متون فارسی، در دومین همایش ملی پژوهش های کاربردی در علوم کامپیوتر و فناوری اطلاعات. 1393.##[31] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, "Feature extraction in opinion mining through Persian reviews", Journal of AI and Data Mining, vol. 3(2), pp. 169-179, 2015.##[32] E. Golpar-Rabooki, S. Zarghamifar, and J. Rezaeenour, "Feature extraction in opinion mining through Persian reviews", Journal of AI and Data Mining, vol. 3(2), pp. 169-179, 2015.##[33] میر اجاق، ف.ا. ، م.ح. ندیمی شهرکی، ارائه یک رویکرد نظارتی برای تشخیص هرز نظرات جعلی, در کنفرانس بین‌المللی مهندسی برق و علوم کامپیوتر. 1394.##[33] F. Mirojagh, M. Shahraki, "Fake opinion detection using a supervised approach", International Conference on Electrical Engineering and Computer Science, 2014.##[34] S. Zirpe, and B. Joglekar, "Polarity shift detection approaches in sentiment analysis: A survey", in 2017 International Conference on Inventive Systems and Control (ICISC), IEEE, 2017.##[35] M. Shams, A. Shakery, and H. Faili, "A non-parametric LDA-based induction method for sentiment analysis," in The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), IEEE, 2012.##[36] A. Bagheri, and M. Saraee, "Persian sentiment analyzer: A framework based on a novel feature selection method", International Journal of Artificial Intelligence, vol. 12(2), pp. 115-129, 2014.##[37] A. Bagheri, M. Saraee, and F. de Jong. "Sentiment classification in Persian: Introducing a mutual information-based method for feature selection", in 2013 21st Iranian Conference on Electrical Engineering (ICEE), IEEE, 2013.##[38] E. Vaziripour, C. Giraud-Carrier, and D. Zappala, "Analyzing the political sentiment of Tweets in Farsi", in Tenth International AAAI Conference on Web and Social Media, 2016.##[39] F. Amiri, S. Scerri, and M. Khodashahi. "Lexicon-based sentiment analysis for Persian Text", in Proceedings of the International Conference Recent Advances in Natural Language Processing, 2015.##[40] S. Alimardani, and A. Aghaie, Opinion mining in Persian language using supervised algorithms, Journal of Information Systems and Telecommunication (JIST), 2015.##[41] علی مردانی, س. and ع. آقایی, ارائه روش نظارتی برای نظرکاوی در زبان فارسی با استفاده از لغت نامه و الگوریتم SVM. مدیریت فناوری اطلاعات دانشکده مدیریت دانشگاه تهران, 1394. 7(2): p. 345-362.##[41] S. Alimardani, and A. AGHAIE, "Opinion mining in Persian language using supervised algorithms and sentiment lexicon", Journal of Information Technology Management, vol.7, pp. 345-362, 2015.##[42] S. Sadidpour, , H. Shirazi, N.M. Sharef, B. Minaei-Bidgoli, and M.E. Sanjaghi, "Context-Sensitive Opinion Mining using Polarity Patterns", International Journal of Advanced Computer Science and Applications (IJACSA), pp.7, 2016.##[43] M. Saraee, and A. Bagheri, "Feature selection methods in Persian sentiment analysis", in International Conference on Application of Natural Language to Information Systems. Springer, 2013.##[44] Li, Y, H. Guo, Q. Zhang, M. Gu, and J. Yang, "Imbalanced text sentiment classification using universal and domain-specific knowledge," Knowledge-Based Systems, vol.160, pp. 1-15, 2018.##[45] S. Noferesti, and M. Shamsfard, "Using Linked Data for polarity classification of patients' experiences", Journal of biomedical informatics,vol. 57, pp. 6-19, 2015.##[46] نجف زاده ، م.، س. راحتی قوچانی ، و س. قائمی یک چارچوب نیمه نظارتی مبتنی بر لغت نامه وفقی خودساخت جهت تحلیل نظرات فارسی. پردازش علائم و داده ها، 1397. 36(2): p. 89-101.##[46] M.Najafzadeh, S. Rahati Quchan, and R. Ghaemi, "A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis", Signal and Data Processing,vol. 15(2), pp. 89-102, 2018.##[47] I. Dehdarbehbahani, A. Shakery, and H. Faili, "Semi-supervised word polarity identification in resource-lean languages", Neural Networks, vol. 58, pp. 50-59, 2014.##[48] عسگریان، ا. م. کاهانی، و ش. شریفی، حس نگار: شبکه واژگان حسی فارسی. فصلنامه پردازش علائم و داده ها، 1397. 35(1): p. 71-86.##[48] E.Asgarian, , M. Kahani, and S. Sharifi, "HesNegar: Persian Sentiment WordNet", Signal and Data Processing,vol, vol.15(1), pp. 71-86, 2018.##[49] B. Sabeti, , P. Hosseini, G. Ghassem-Sani, and S.A. Mirroshandel, LexiPers: An ontology based sentiment lexicon for Persian. in GCAI. 2016.##[50] S. Deng, A.P. Sinha, and H. Zhao, "Adapting sentiment lexicons to domain-specific social media texts", Decision Support Systems, vol. 94, pp. 65-76, 2017.##[51] حسینی، پ.، ح. ملکی گلندوز، ع. احمدیان رمکی، م. انواری رستمکلایی، و س. میرروشندل, پیکره فارسی تحلیل احساس سنتی پرس: توسعه یک پیکره ی تحلیل احساس متنی برای زبان فارسی، در سومین همایش ملی زبان شناسی رایانشی. 1392.##[51] P. Hosseini, H. Maleki, A.A. Ramaki, , M. Anvari, and S.A. Mirroshandel, "A sentiment analysis corpus for Persian(SentiPers)," in 3rd National Conference on Computational Linguistics, 2013.##[52] P. Hosseini, A.A. Ramaki, H. Maleki, M. Anvari, and S.A. Mirroshandel, SentiPers: a sentiment analysis corpus for Persian. arXiv preprint arXiv:1801.07737, 2018.##[53] مرادی ، م.، پ. خسروی‌زاده، و و. وزیرنژاد ساخت پیکره‌های نشانه‌گذاری‌شده با رویکرد وب به عنوان پیکره، در دومین هم‌اندیشی زبان‌شناسی رایانشی. 1391: تهران.##[53] M. Moradi, P. Khosravizade, and V. Bahram, "Constructing tagged corpora with a web approach as a corpus", in 2th symposium on computational Linguistics, 2012.##[54] K. Dashtipour, A. Hussain, Q. Zhou, A. Gelbukh, A.Y. Hawalah, and E. Cambria. "PerSent: a freely available Persian sentiment lexicon", in International Conference on Brain Inspired Cognitive Systems, 2016, Springer.##[55] A. Esuli, and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of LREC. 2006. Citeseer.##[56] P.J. Stone, D.C. Dunphy, and M.S. Smith, The general inquirer: A computer approach to content analysis, 1966.##[57] T.Wilson, J. Wiebe, and P. Hoffmann, "Recognizing contextual polarity in phrase-level sentiment analysis", in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 2005.##[58] C.H.E. Gilbert, Vader: A parsimonious rule-based model for sentiment analysis of social media text. in Eighth International Conference on Weblogs and Social Media (ICWSM-14). Available at (20/04/16) http://comp. social. gatech. edu/papers/icwsm14. vader. hutto. pdf. 2014.##[59] S. Huang, Z. Niu, and C. Shi, "Automatic construction of domain-specific sentiment lexicon based on constrained label propagation," Knowledge-Based Systems, vol. 56, pp. 191-200, 2014.##[60] S.Tan, and Q. Wu, "A random walk algorithm for automatic construction of domain-oriented sentiment lexicon," Expert Systems with Applications, vol. 38(10), pp. 12094-12100, 2011.##[61] K. Dashtipour, A. Raza, A. Gelbukh, R. Zhang, E. Cambria, and A. Hussain, "PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words", in International Conference on Brain Inspired Cognitive Systems, Springer, 2019.##[62] A. Balahur, and G. Jacquet, Sentiment analysis meets social media-Challenges and solutions of the field in view of the current information sharing context. Information Processing & Management, 2015. 51(4): p. 428-432.##[63] D. Osimo, and F. Mureddu, "Research challenge on opinion mining and sentiment analysis", Universite de Paris-Sud, Laboratoire LIMSI-CNRS, Bâtiment, 2012. 508.##[64] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, "New avenues in opinion mining and sentiment analysis", IEEE Intelligent Systems, vol. 28(2), pp. 15-21, 2013.##[65] رجبی، ز.، م. ولوی، و م. حورعلی، ارائه مدلی برای غنی سازی واژگان سنجمانی بر مبنای پایگاه دانش معنایی، در کنفرانس فرماندهی و کنترل. 1396.##[65] Z. Rajabi, M. Valavi, and M. Hourali, "A model for enriching sentiment lexicon based on semantic knowledge base", 10st Iranian C4I Conference, 2017.##[66] رجبی، ز.، م. ولوی، و م. حورعلی، ارائه یک مدل مبتنی بر زمینه برای رفع ابهام از مفاهیم حسی با کمک دانش عرفی. فصلنامه علمی-پژوهشی فرماندهی و کنترل، 1397. 2(2): p. 32-47.##[66] Z. Rajabi, M. Valavi, and M. Hourali, "A context-based model for disambiguating the sentiment concepts using the common-sense knowledge", C4I Journal, vol.2(2), pp. 32-47, 2018.##[67] J. Liao, S. Wang, and D. Li, "Identification of fact-implied implicit sentiment based on multi-level semantic fused representation" Knowledge-Based Systems, vol.165, pp. 197-207, 2019.##[68] C. Hung, "Word of mouth quality classification based on contextual sentiment lexicons", Information Processing & Management, vol. 53(4), pp. 751-763, 2017.##[69] H. Saif, Y. He, M. Fernandez, and H. Alani, "Contextual semantics for sentiment analysis of Twitter", Information Processing & Management, vol. 52(1), pp. 5-19, 2016.##[70] S. Noferesti, and M. Shamsfard, "Resource construction and evaluation for indirect opinion mining of drug reviews", PloS one, vol. 10(5), pp. e0124993, 2015.##[71] L. Zhang, S. Wang, and B. Liu, "Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews", Data Mining and Knowledge Discovery, 2018. Vol.8(4), p p,1253.##[72] T. Young, D. Hazarika, S. Poria, and E. Cambria, "Recent trends in deep learning based natural language processing", ieee Computational intelligenCe magazine, vol.13(3), pp. 55-75, 2018.##[73] K. Dashtipour, M. Gogate, A. Adeel, C. Ieracitano, H. Larijani, and A. Hussain, "Exploiting deep learning for persian sentiment analysis", in International Conference on Brain Inspired Cognitive Systems, Springer, 2018.## ##

ارائه روشی برای استخراج اطلاعات ساختاریافته محدود به دامنه از صفحات وب فارسی Presenting a method for extracting structured domain-dependent information from Farsi Web pages 1 1 استخراج اطلاعات ساختاریافته از متون وب یکی از وظایف اصلی در حوزه وب‌کاوی، پردازش زبان طبیعی و استخراج اطلاعات است. در سال‌های اخیر، روش‌های مختلفی برای استخراج اطلاعات ساختاریافته از متون انگلیسی وب ارائه شده است. اغلب روش‌های موجود برای استخراج اطلاعات در مورد انواع موجودیت‌ها، به یک آنتولوژی از پیش تعریف‌شده نیاز دارند که شامل دانش کامل در مورد موجودیت‌ها و خصلت‌های آن‌ها است. مشکل اصلی این روش‌ها عدم توانایی آن‌ها در استخراج اطلاعات موجودیت‌هایی است که مشخصات آن‌ها از قبل در آنتولوژی تعریف نشده‌اند. در این پژوهش، روش جدیدی برای استخراج خودکار اطلاعات ساختاریافته محدود به دامنه از متون فارسی صفحات وب ارائه شده است که نیازی به دانش پیش‌زمینه در مورد موجودیت‌ها و خصلت‌های آن‌ها ندارد. روش پیشنهادی شامل سه مؤلفه پیش‌پردازش، تحلیل معنایی و نگاشت قاب است. تمرکز اصلی روش پیشنهادی به افزودن اطلاعات معنایی به گزاره‌های مسند‌آرگومان و استخراج اطلاعات معنادار و محدود به دامنه از گزاره‌ها معطوف شده است. اطلاعات استخراج‌شده در این روش، هم ساختاریافته بوده و هم به مدخل‌های آنتولوژی عمومی DBPedia نگاشت شده‌اند، به‌نحوی‌که پردازش آن‌ها به‌وسیله ماشین به سهولت انجام می‌شود. برای ارزیابی روش پیشنهادی، یک مجموعه‌داده کوچک در زبان فارسی ایجاد شده است و روش پیشنهادی و سایر روش‌ها بر روی این مجموعه‌داده مورد ارزیابی قرار گرفته‌اند. نتایج آزمایش‌ها برتری روش پیشنهادی را در مقایسه با سایر روش‌ها برحسب برخی از معیارهای کارایی نشان می‌دهد. 2 Extracting structured information about entities from web texts is an important task in web mining, natural language processing, and information extraction. Information extraction is useful in many applications including search engines, question-answering systems, recommender systems, machine translation, and etc. An information extraction system aims to identify the entities from the text and extract their related information to form a profile of the target entity. In recent years, several methods have been proposed for extracting structured information from web text. The majority of existing methods for extracting entity-centric information require a predefined ontology. The ontology includes the complete knowledge of the entities and their attributes. The main challenge of these methods is their inability to extract information about entities that are not already defined in the ontology. Besides, the existing methods have ignored semantic information extraction and have not linked the extracted information to the general ontology entries. This highlights that introducing new methods for semantic information extraction is an open problem and there is room for more efforts in this field. As an element of research, we proposed a new method for the automatic extraction of semantically structured information from Farsi web text. The proposed method does not require background knowledge about the entities and their properties. The proposed method consists of three main phases including pre-processing, semantic analysis and frame extraction. To fulfill these phases, we use a combination of language resources, text processing tools, and distant ontologies. The main focuses of the proposed method are to enrich the predicate-argument frames with the semantic information extracted from distant ontologies, extract the entity-related information from predicate-argument frames, and link the extracted information with their corresponding sense in DBPedia ontology. The issue facilitates the processing of Farsi texts by computers. To evaluate the proposed method, we created a small Farsi dataset containing 100 complete sentences. Then, the proposed method is compared with three information extraction methods on this dataset. The results of experiments show the superiority of the proposed method compared to counterpart methods in terms of precision and F1 measures. 133 146 2019/12/192019/10/252019/12/22019/12/302020/04/122020/04/152019/12/42019/12/152019/12/26 1398/10/5 2021/12/62020/02/82020/08/182020/10/132021/05/242022/01/192020/08/182021/05/232021/06/20 1400/3/30 حجت امامی Hojjat Emami گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه بناب ایران emami@ubonab.ac.ir Web mining information extraction natural language processing ontology structured-semantic information وب‌کاوی استخراج اطلاعات پردازش زبان طبیعی آنتولوژی اطلاعات ساختاریافته محدود به دامنه [1] A. A. Barforoush, H. Shirazi, and H. Emami, "A new classification framework to evaluate the entity profiling on the Web: past, present and future," ACM Comput. Surv., vol. 50, no. 3, pp. 1-39, 2017.##[2] H. Emami, H. Shirazi, and A. A. Barforoush, "A Semantic approach to person profile extraction from Farsi documents," J. Inf. Syst. Telecommun., vol. 4, no. 4, pp. 232-243, 2016.##[3] W. Li, R. Srihari, C. Niu, and X. Li, "Entity profile extraction from large corpora," in Pacific Association for Computational Linguistics Conference (PACLING-2003), Harifax, Canada, 2003.##[4] Y. Chen, S. Y. Mei Lee, and C. R. Huang, "A robust web personal name information extraction system," Expert Syst. Appl., vol. 39, no. 3, pp. 2690-2699, 2012.##[5] U. Distant and S. Machine, "Domain-Specific Relation Extraction Using Distant Supervision Machine Learning," in Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015), 2015, pp. 978-989.##[6] N. T. Nakashole, "Automatic extraction of facts, relations, and entities for web-scale knowledge base population," University of Saarland, 2013.##[7] S. Soderland, B. Roof, B. Qin, and S. Xu, "Adapting Open Information Extraction to Domain-Specific Relations," AI Mag., vol. 31, no. 3, pp. 93-102, 2010.##[8] S. Soderland, J. Gilmer, R. Bart, O. Etzioni, and D. Weld, "Open Information Extraction to KBP Relations in 3 Hours," in Proceedings of TAC-KBP 2013, Maryland, USA, 2013.##[9] M. Shamsfard, "Challenges and open problems in Persian text processing," in Proceedings of 5th Language & Technology Conference (LTC), Poznań, Poland, 2011, pp. 65-69.##[10] Y. Chen, S. Lee, and C. Huang, "Polyuhk: A robust information extraction system for web personal names," in 2nd Web People Search Evaluation Workshop (WePS 2009), 18th WWW Conference, Madrid, Spain, 2009.##[11] S. Lyons and D. Smith, "Domain-specific information extraction structures," Proc. - Int. Work. Database Expert Syst. Appl. DEXA, vol. 2002-Janua, pp. 80-84, 2002.##[12] Y. Shinyama and S. Sekine, "Preemptive information extraction using unrestricted relation discovery," in Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, New York, USA, 2006, pp. 304-311.##[13] N. Chambers and D. Jurafsky, "Unsupervised learning of narrative event chains," in Proceedings of the Association of Computational Linguistics (ACL), Columbus, Ohio, 2008, pp. 789-797.##[14] N. Kasch and T. Oates, "Mining script-like structures from the web," in Proceedings of the NAACL HLT, Los Angeles, California, 2010, pp. 34-42.##[15] N. Chambers and D. Jurafsky, "Template-Based Information Extraction without the Templates," in HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA, USA, 2011.##[16] M. Banko, M. Cafarella, and S. Soderland, "Open information extraction from the web," in International Joint Conferences on Artificial Intelligence, Hyderabad, India, 2007, pp. 2670-2676.##[17] R. Johansson and P. Nugues, "LTH : Semantic Structure Extraction using Nonprojective Dependency Trees," in Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), Prague, 2007, pp. 227-230.##[18] M. Scaiano and D. Inkpen, "Automatic frame extraction from sentences," in Canadian Conference on Artificial Intelligence, Kelowna, British Columbia, 2009, pp. 110-120.##[19] C. Bejan, C.A., Hathaway, "UTD-SRL: A Pipeline Architecture for Extracting Frame Semantic Structures," in Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), Prague, Czech Republic, 2007.##[20] H. Fadaei and M. Shamsfard, "Extracting conceptual relations from Persian resources," in ITNG2010 - 7th International Conference on Information Technology: New Generations, Las Vegas, Nevada, USA, 2010, pp. 244-248.##[21] M. Moradi, B. Vazirnezhad, and M. Bahrani, "Commonsense Knowledge Extraction for Persian Language: A Combinatory Approach," Iran. J. Inf. Process. Manag., vol. 31, no. 1, pp. 109-124, 2015.##[22] M. Shamsfard, "Lexico-syntactic and Semantic Patterns for Extracting Knowledge from Persian Texts," Int. J. Comput. Sci. Eng., vol. 2, no. 6, pp. 2190-2196, 2010.##[23] H. Emami, H. Shirazi, A. A. Barforoush, and M. Hourali, "A Pattern-Matching Method for Extracting Personal Information in Farsi Content," U.P.B. Sci. Bull., Ser. C, vol. 78, no. 1, pp. 125-138, 2016.##[24] R. Al-Rfou, V. Kulkarni, B. Perozzi, and S. Skiena, "Polyglot-NER: Massive Multilingual Named Entity Recognition," in Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, British Columbia, Canada, 2015, pp. 586-594.##[25] F. Fallahi and M. Shamsfard, "Recognizing Anaphora Reference in Persian Sentences," Int. J. Comput. Sci. Issues, vol. 8, no. 2, pp. 324-329, 2011.##[26] A. Moro, A. Raganato, and R. Navigli, "Entity linking meets word sense disambiguation: a unified approach," Trans. Assoc. Comput. Linguist., vol. 2, pp. 231-244, 2014.##[27] Z. M. Arani and A. Abdollahzadeh Barforoush, "Semantic Role Labeling using Syntactic Dependency Analysis and Noun Semantic Catergory," in 20th Annual Conference of Computer Society of Iran, Mashhad, Iran (In Farsi), 2015, pp. 619-624.##[28] K. Kipper, A. Korhonen, N. Ryant, and M. Palmer, "A large-scale classification of English verbs," Lang. Resour. Eval., vol. 42, no. 1, pp. 21-40, 2008.##[29] E. Loper, S. Yi, and M. Palmer, "Combining lexical resources: mapping between propbank and verbnet," in Proceedings of the 7th International Workshop on Computational Linguistics, Tilburg, Netherlands, 2007.##[30] D. M. W. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation," J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2011.##[31] C. Baker and M. Ellsworth, "SemEval'07 Task 19: Frame Semantic Structure Extraction," in Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), Prague, Czech Republic, 2007, pp. 99-104.##[1] A. A. Barforoush, H. Shirazi, and H. Emami, "A new classification framework to evaluate the entity profiling on the Web: past, present and future," ACM Comput. Surv., vol. 50, no. 3, pp. 1-39, 2017.##[2] H. Emami, H. Shirazi, and A. A. Barforoush, "A Semantic approach to person profile extraction from Farsi documents," J. Inf. Syst. Telecommun., vol. 4, no. 4, pp. 232-243, 2016.##[3] W. Li, R. Srihari, C. Niu, and X. Li, "Entity profile extraction from large corpora," in Pacific Association for Computational Linguistics Conference (PACLING-2003), Harifax, Canada, 2003.##[4] Y. Chen, S. Y. Mei Lee, and C. R. Huang, "A robust web personal name information extraction system," Expert Syst. Appl., vol. 39, no. 3, pp. 2690-2699, 2012.##[5] U. Distant and S. Machine, "Domain-Specific Relation Extraction Using Distant Supervision Machine Learning," in Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015), 2015, pp. 978-989.##[6] N. T. Nakashole, "Automatic extraction of facts, relations, and entities for web-scale knowledge base population," University of Saarland, 2013.##[7] S. Soderland, B. Roof, B. Qin, and S. Xu, "Adapting Open Information Extraction to Domain-Specific Relations," AI Mag., vol. 31, no. 3, pp. 93-102, 2010.##[8] S. Soderland, J. Gilmer, R. Bart, O. Etzioni, and D. Weld, "Open Information Extraction to KBP Relations in 3 Hours," in Proceedings of TAC-KBP 2013, Maryland, USA, 2013.##[9] M. Shamsfard, "Challenges and open problems in Persian text processing," in Proceedings of 5th Language & Technology Conference (LTC), Poznań, Poland, 2011, pp. 65-69.##[10] Y. Chen, S. Lee, and C. Huang, "Polyuhk: A robust information extraction system for web personal names," in 2nd Web People Search Evaluation Workshop (WePS 2009), 18th WWW Conference, Madrid, Spain, 2009.##[11] S. Lyons and D. Smith, "Domain-specific information extraction structures," Proc. - Int. Work. Database Expert Syst. Appl. DEXA, vol. 2002-Janua, pp. 80-84, 2002.##[12] Y. Shinyama and S. Sekine, "Preemptive information extraction using unrestricted relation discovery," in Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, New York, USA, 2006, pp. 304-311.##[13] N. Chambers and D. Jurafsky, "Unsupervised learning of narrative event chains," in Proceedings of the Association of Computational Linguistics (ACL), Columbus, Ohio, 2008, pp. 789-797.##[14] N. Kasch and T. Oates, "Mining script-like structures from the web," in Proceedings of the NAACL HLT, Los Angeles, California, 2010, pp. 34-42.##[15] N. Chambers and D. Jurafsky, "Template-Based Information Extraction without the Templates," in HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA, USA, 2011.##[16] M. Banko, M. Cafarella, and S. Soderland, "Open information extraction from the web," in International Joint Conferences on Artificial Intelligence, Hyderabad, India, 2007, pp. 2670-2676.##[17] R. Johansson and P. Nugues, "LTH : Semantic Structure Extraction using Nonprojective Dependency Trees," in Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), Prague, 2007, pp. 227-230.##[18] M. Scaiano and D. Inkpen, "Automatic frame extraction from sentences," in Canadian Conference on Artificial Intelligence, Kelowna, British Columbia, 2009, pp. 110-120.##[19] C. Bejan, C.A., Hathaway, "UTD-SRL: A Pipeline Architecture for Extracting Frame Semantic Structures," in Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), Prague, Czech Republic, 2007.##[20] H. Fadaei and M. Shamsfard, "Extracting conceptual relations from Persian resources," in ITNG2010 - 7th International Conference on Information Technology: New Generations, Las Vegas, Nevada, USA, 2010, pp. 244-248.##[21] M. Moradi, B. Vazirnezhad, and M. Bahrani, "Commonsense Knowledge Extraction for Persian Language: A Combinatory Approach," Iran. J. Inf. Process. Manag., vol. 31, no. 1, pp. 109-124, 2015.##[22] M. Shamsfard, "Lexico-syntactic and Semantic Patterns for Extracting Knowledge from Persian Texts," Int. J. Comput. Sci. Eng., vol. 2, no. 6, pp. 2190-2196, 2010.##[23] H. Emami, H. Shirazi, A. A. Barforoush, and M. Hourali, "A Pattern-Matching Method for Extracting Personal Information in Farsi Content," U.P.B. Sci. Bull., Ser. C, vol. 78, no. 1, pp. 125-138, 2016.##[24] R. Al-Rfou, V. Kulkarni, B. Perozzi, and S. Skiena, "Polyglot-NER: Massive Multilingual Named Entity Recognition," in Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, British Columbia, Canada, 2015, pp. 586-594.##[25] F. Fallahi and M. Shamsfard, "Recognizing Anaphora Reference in Persian Sentences," Int. J. Comput. Sci. Issues, vol. 8, no. 2, pp. 324-329, 2011.##[26] A. Moro, A. Raganato, and R. Navigli, "Entity linking meets word sense disambiguation: a unified approach," Trans. Assoc. Comput. Linguist., vol. 2, pp. 231-244, 2014.##[27] Z. M. Arani and A. Abdollahzadeh Barforoush, "Semantic Role Labeling using Syntactic Dependency Analysis and Noun Semantic Catergory," in 20th Annual Conference of Computer Society of Iran, Mashhad, Iran (In Farsi), 2015, pp. 619-624.##[28] K. Kipper, A. Korhonen, N. Ryant, and M. Palmer, "A large-scale classification of English verbs," Lang. Resour. Eval., vol. 42, no. 1, pp. 21-40, 2008.##[29] E. Loper, S. Yi, and M. Palmer, "Combining lexical resources: mapping between propbank and verbnet," in Proceedings of the 7th International Workshop on Computational Linguistics, Tilburg, Netherlands, 2007.##[30] D. M. W. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation," J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2011.##[31] C. Baker and M. Ellsworth, "SemEval'07 Task 19: Frame Semantic Structure Extraction," in Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), Prague, Czech Republic, 2007, pp. 99-104.## ##

بازسازی سریع تصویر رادار روزنه مصنوعی دارای خطای فاز با استفاده از ارائه تُنُک Fast Reconstruction of SAR Images with Phase Error Using Sparse Representation 1 1 عدم دقت در مدل تصویربرداری رادار روزنه مصنوعی باعث ایجاد خطای فاز در سامانه و مات‌شدن تصاویر بازسازی‌شده می­شود. روش‌های بازسازی تصویر این رادار در حضور خطای فاز و مبتنی بر تنک‌بودن مشخصه­ی بازتاب، به‌طورمعمول نیاز به محاسبات بالا و حافظه زیادی دارند. در این مقاله، جهت کاهش محاسبات بازسازی تصویر براساس ارائه تنک سیگنال در حضور خطای فاز، یک روش سریع مبتنی بر جایگزینی مشاهدات دقیق حاصل از روش­های تنک با مشاهدات تقریبی حاصل از روش­های فیلتر منطبق ارائه می‌شود. در این روش، علاوه‌بر حفظ توانایی بازسازی سیگنال­‌های زیرنمونه­برداری‌شده، پیچیدگی محاسباتی و حافظه مورد نیاز نیز به میزان زیادی کاهش می­یابد. شبیه­سازی­های رایانه‌ای این نتیجه را هم برای ابعاد متفاوت تصاویر و هم برای نرخ­های زیرنمونه­برداری مختلف نشان می­دهند. کیفیت تصاویر بازسازی‌شده نیز به‌دلیل کاهش اثرات لوب­های فرعی حاصل از تقریب، قابل مقایسه با تصاویر بدون تقریب است. 2 In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR have gained a significant research interest. This method offers the advantage of reducing the sampling rate but also suffers from speed processing limitation and it needs a huge amount of memory to reconstruct the image. On the other hand, inaccuracy in SAR model induces phase error to the results and makes the reconstructed image blurry. Existing sparse methods in the presence of phase error, have high computational costs and need a lot of processing time. In addition, these methods take up considerable space in the memory for saving the measurement matrix. In this paper, a fast method is proposed to reduce the computational cost of image reconstruction, based on the signal sparsity in the presence of phase error. The proposed method consists of substituting accurate observations of sparsity methods with approximated observations of matched filter methods. In this method, the output of Range-Doppler matched filter is reconstructed with sparse representation, and error phase is estimated simultaneously. This method leads to a nonconvex optimization problem and to solve that, we use the majorization minimization method. The phase error and reconstructed image are estimated in an iterative procedure. The use of approximated observation, eliminates the need for carrying out big matrix multiplications, and Fast Fourier Transformation, as a low computational cost operation, can be employed instead. In addition to computation speed, this method does not need any memory space for saving measurement matrices. In our numerical simulations, we compared the speed of processing and the mean square error (MSE) of reconstructed images for the proposed method with the state-of-the-art sparse method for different sizes of image and under-sampling rates. It is shown in simulations that the reconstructed image from our method has a slightly lower quality and higher MSE, because of the sidelobes effect of the matched filter output. However, in certain conditions, the speed of the proposed method is more than a hundred times faster than the compared method. The achieved processing speed with no need for the memory to store the measurement matrix at the expense of slightly lower image quality would be acceptable for most applications. 147 160 2019/12/192019/10/252019/12/22019/12/302020/04/122020/04/152019/12/42019/12/152019/12/262020/01/9 1398/10/19 2021/12/62020/02/82020/08/182020/10/132021/05/242022/01/192020/08/182021/05/232021/06/202022/05/11 1401/2/21 میلاد جوادزاده milad javadzade دانشکده مهندسی برق، دانشگاه علم و صنعت ایران ایران milad7068@yahoo.com محمدحسین کهایی Mohammad Hosein Kahaei دانشکده مهندسی برق، دانشگاه علم و صنعت ایران ایران kahaei@iust.ac.ir علی اصغر بهشتی شیرازی Aliasghar Beheshti Shirazi دانشکده مهندسی برق، دانشگاه علم و صنعت ایران ایران abeheshti@iust.ac.ir Synthetic aperture radar Compressed sensing Sparsity Range-Doppler algorithm رادار روزنه مصنوعی حس‌گری فشرده ارائه تنک الگوریتم برد‌داپلر [1] X. Mao and D. Zhu, "Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery", Computational Imaging, IEEE Transactions on, vol. 2, no. 4, pp. 524-539, December. 2016.##[2] M. Behzad Fallahpour, H. Dehghani, A. Jabbar Rashidi, A. Sheikhi, "Modelling and Software Implementation of SAR Imaging System Performance in Spotlight Mode", Signal and Data Processing, vol. 13, no. 4, pp. 3-18, 2017.##[3] C. V. Jakowatz, D. E. Wahl, P. H. Eichel, D. C. Ghiglia, and P. A. Thompson, "Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach", Springer Science & Business Media, 2012.##[4] W. Carrara, R. Goodman, and R. Majewski, "Spotlight-Mode Synthetic Aperture Radar", Signal Processing Algorithms, Artech House, 1995.##[5] D. Wahl, P. Eichel, D. Ghiglia, and C. Jakowatz, "Phase gradient autofocus-a robust tool for high resolution SAR phase correction", Aerospace and Electronic Systems, IEEE Transactions on, vol.30, no.4, pp 827-835, July 1994.##[6] R. Morrison, M. N. Do, and D. Munson, "MCA: A multichannel approach to SAR autofocus", Image Processing, IEEE Transaction on, vol.18, no.4, pp.840-853, April 2009.##[7] S. Kelly, M. Yaghoobi, and M. Davies, "Sparsity-based autofocus for undersampled synthetic aperture radar", Aerospace and Electronic Systems, IEEE Transactions on, vol.50, no.2, pp.972-986, 2014.##[8] G. Ian, and H. Frank Wong, "Digital processing of synthetic aperture radar data", Artech house 2005.##[9] N. Ö. Önhon, and M. Çetin, "A sparsity-driven approach for joint SAR imaging and phase error correction", Image Processing, IEEE Transactions on, vol.21, no.4, pp.2075-2088, 2012.##[10] J. Fang, Z. Xu, B. Zhang, W. Hong, and Y. Wu, "Fast compressed sensing SAR imaging based on approximated observation", Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, no.1, pp.352-363, 2014.##[11] S. Uḡur, O. Arıkan, and A. Cafer Gürbüz, "SAR image reconstruction by expectation maximization based matching pursuit", Digital Signal Processing, vol 37, pp.75-84, 2015.##[12] D. W. Warner, D. Ghiglia, A. FitzGerrell, and J. Beaver, "Two-dimensional phase gradient autofocus," International Symposium on Optical Science and Technology. International Society for Optics and Photonics, vol.4123, pp.162-173, 2000.##[13] M. Yaghoobi, T. Blumensath, and M. Davies, "Dictionary learning for sparse approximations with the majorization method", Signal Processing, IEEE Transaction on, vol.57, no.6, pp.2178-2191, June 2009.##[14] A. S. Khwaja, L. Ferro-Famil, and E. Pottier, "SAR raw data simulation using high precision focusing methods," in Proc. Eur. Radar Conf. (EuRAD), pp. 33-36, 2005.##[15] G. Franceschetti, R. Guida, A. Iodice, D. Riccio, and G. Ruello, "Efficient simulation of hybrid stripmap/spotlight SAR raw signals from extended scenes," Geosci. Remote Sens, IEEE Transaction on, vol.42, no.11, pp.2385-2396, 2004.##[16] X. Dong, and Y. Zhang, "A novel compressive sensing algorithm for SAR imaging", Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, no.2, pp.708-720, 2014.##[17] M. Schlutz, "synthetic Aperture Radar Imaging Simulated in Matlab", M.S Thesis of California Polytechnic State University, San Luis Obispo, 2009.##[1] X. Mao and D. Zhu, "Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery", Computational Imaging, IEEE Transactions on, vol. 2, no. 4, pp. 524-539, December. 2016.##[2] M. Behzad Fallahpour, H. Dehghani, A. Jabbar Rashidi, A. Sheikhi, "Modelling and Software Implementation of SAR Imaging System Performance in Spotlight Mode", Signal and Data Processing, vol. 13, no. 4, pp. 3-18, 2017.##[2] م. بهزاد فلاح پور، ح. دهقانی، ع. جبار رشیدی، ع. شیخی، "مدل‌سازی و پیاده‌سازی نرم‌افزاری عملکرد سامانه‌های تصویربرداری SAR در حالت نورافکن"، پردازش علائم و داده‌ها، جلد 13، شماره 4، ۱۳۹۵.##[3] C. V. Jakowatz, D. E. Wahl, P. H. Eichel, D. C. Ghiglia, and P. A. Thompson, "Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach", Springer Science & Business Media, 2012.##[4] W. Carrara, R. Goodman, and R. Majewski, "Spotlight-Mode Synthetic Aperture Radar", Signal Processing Algorithms, Artech House, 1995.##[5] D. Wahl, P. Eichel, D. Ghiglia, and C. Jakowatz, "Phase gradient autofocus-a robust tool for high resolution SAR phase correction", Aerospace and Electronic Systems, IEEE Transactions on, vol.30, no.4, pp 827-835, July 1994.##[6] R. Morrison, M. N. Do, and D. Munson, "MCA: A multichannel approach to SAR autofocus", Image Processing, IEEE Transaction on, vol.18, no.4, pp.840-853, April 2009.##[7] S. Kelly, M. Yaghoobi, and M. Davies, "Sparsity-based autofocus for undersampled synthetic aperture radar", Aerospace and Electronic Systems, IEEE Transactions on, vol.50, no.2, pp.972-986, 2014.##[8] G. Ian, and H. Frank Wong, "Digital processing of synthetic aperture radar data", Artech house 2005.##[9] N. Ö. Önhon, and M. Çetin, "A sparsity-driven approach for joint SAR imaging and phase error correction", Image Processing, IEEE Transactions on, vol.21, no.4, pp.2075-2088, 2012.##[10] J. Fang, Z. Xu, B. Zhang, W. Hong, and Y. Wu, "Fast compressed sensing SAR imaging based on approximated observation", Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, no.1, pp.352-363, 2014.##[11] S. Uḡur, O. Arıkan, and A. Cafer Gürbüz, "SAR image reconstruction by expectation maximization based matching pursuit", Digital Signal Processing, vol 37, pp.75-84, 2015.##[12] D. W. Warner, D. Ghiglia, A. FitzGerrell, and J. Beaver, "Two-dimensional phase gradient autofocus," International Symposium on Optical Science and Technology. International Society for Optics and Photonics, vol.4123, pp.162-173, 2000.##[13] M. Yaghoobi, T. Blumensath, and M. Davies, "Dictionary learning for sparse approximations with the majorization method", Signal Processing, IEEE Transaction on, vol.57, no.6, pp.2178-2191, June 2009.##[14] A. S. Khwaja, L. Ferro-Famil, and E. Pottier, "SAR raw data simulation using high precision focusing methods," in Proc. Eur. Radar Conf. (EuRAD), pp. 33-36, 2005.##[15] G. Franceschetti, R. Guida, A. Iodice, D. Riccio, and G. Ruello, "Efficient simulation of hybrid stripmap/spotlight SAR raw signals from extended scenes," Geosci. Remote Sens, IEEE Transaction on, vol.42, no.11, pp.2385-2396, 2004.##[16] X. Dong, and Y. Zhang, "A novel compressive sensing algorithm for SAR imaging", Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.7, no.2, pp.708-720, 2014.##[17] M. Schlutz, "synthetic Aperture Radar Imaging Simulated in Matlab", M.S Thesis of California Polytechnic State University, San Luis Obispo, 2009.## ##

بهینه‌سازی سامانه‌های پرسش و پاسخ با استفاده از بهینه‌سازی ازدحام ذرات تسریع‌شده Optimizing question answering systems by Accelerated Particle Swarm Optimization (APSO) 1 1 سامانه‌های پرسش و پاسخ، موتورهای جستجویی هستند که توانایی‌ ارائه پاسخی کوتاه و دقیق را به یک پرسش دارند. به عبارت دیگر، پرسشی که یک موتور جستجو، با مجموعه‌ای از اسناد پاسخ می‌دهد، یک ساماه پرسش و پاسخ، با یک پاراگراف، جمله یا کلمه پاسخ می‌دهد. در این مقاله، یک راه‌کار برای بهینه‌سازی عملکرد سامانه‌های پرسش و پاسخ تک‌زبانه به زبان انگلیسی و مبتنی بر وب، ارائه شده است. با توجه به اینکه الگوریتم‌های تکاملی برای مسائل با فضای جستجوی بزرگ مناسب هستند، در این مقاله برای بهینه‌سازی عملکرد این سامانه‌ها، APSO مورد استفاده قرار گرفته است. در این پژوهش هدف، ارائه روشی است که دقت و سرعت بالاتری نسبت به سامانه‌های موجود در انتخاب پاسخ از میان اسناد بازیابی شده داشته باشد. روش پیشنهادی بر روی مجموعه‌داده استاندارد به میزان دقتی (Top1 Accuracy) برابر با 527/0 دست یافته است و همچنین شاخص MRR نتایج حاصل از آن برابر 711/0 محاسبه شده است، که این نتایج نسبت به بیش‌تر پژوهش‌های مرتبط  پیشرفت داشته‌اند. در عین حال، سرعت آن نسبت به همه کارهای مشابه بهبود یافته است. 2 One of the most important research areas in natural language processing is Question Answering Systems (QASs). Existing search engines, with Google at the top, have many remarkable capabilities. However, there is a basic limitation; search engines do not have deduction capability which a QAS is expected to have. In this perspective, a search engine may be viewed as a semi-mechanized QAS. Upgrading a search engine such to a QAS is a task whose complexity is hard to exaggerate. To achieve success, new concepts and ideas are needed to address difficult problems which arise when knowledge has to be dealt with in an environment of imprecision, uncertainty and partial truth.  QASs are search engines that have the ability to provide a brief and accurate answer to each question in natural language for instance, the question that a search engine answers with a set of documents, a QAS answers with a paragraph, sentence or etc. In this paper, a solution is proposed to optimize the performance and speed of web-based QASs for answering English questions.  As evolutionary algorithms are suitable for issues with large search space, in this approach we have used an evolutionary algorithm to optimize QASs. In this regard, we have chosen APSO which is a simplified version of PSO. The proposed method consists of five main stages: question analysis, pre-process, retrieval, extraction and ranking. We have tried to provide a method that would be more accurate in choosing the most probable answer from the documents that have been retrieved by the standard search engine and at the same time, be faster than similar methods. In ranking process, various attributes can be extracted from the text that are used in APSO. For this purpose, in addition to selecting a sentence from the text and examining its attributes, different cut parts of the sentence are selected each time by changing the beginning and end points of the cut part. The attributes which have been used in this study are: 1. Number of unigrams similar to the question words, 2. Number of bigrams similar to the question words, 3. Number of unigrams similar to the question words in the cut part, 4. Number of bigrams similar to the question words in the cut part, 5. Number of synonyms with the question words and 6. Number of synonyms with the question words in the cut part. The fitness function is the weighted sum of these attributes. Top-1 accuracy and MRR are the most valid metrics for measuring the performance of QASs. The proposed method has achieved the accuracy (top-1 accuracy) of 0.527 with respect to the standard dataset and the MRR of it, is 0.711. Both of these results are improved compared to most similar systems. In addition, the time taken to answer the input question in the proposed method, has been significantly reduced compared to similar methods. In general, the accuracy and MRR in this paper have progressed and the system needs less time to find the answer, in comparison with existing QASs. 161 174 2019/12/192019/10/252019/12/22019/12/302020/04/122020/04/152019/12/42019/12/152019/12/262020/01/92019/12/14 1398/9/23 2021/12/62020/02/82020/08/182020/10/132021/05/242022/01/192020/08/182021/05/232021/06/202022/05/112021/01/24 1399/11/5 نسیم توحیدی Nasim Tohidi گروه مهندسی کامپیوتر، دانشگاه الزهرا (س) ایران n.tohidi@student.alzahra.ac.ir سیدمحمدحسین هاشمی نژاد Seyed Mohammad Hossein Hasheminejad گروه مهندسی کامپیوتر، دانشگاه الزهرا (س) ایران smh.hasheminejad@Alzahra.ac.ir question answering system natural language processing accelerated particle swarm optimization (APSO) سامانه پرسش و پاسخ پردازش زبان طبیعی الگوریتم بهینه‌سازی ازدحام ذرات تسریع‌شده [1] F. Sebastiani, "Machine Learning in in Automated Text Categorization," ACM Computing Surveys (CSUR), vol. 34, pp. 1-47, 2002.##[2] H. Sundblad, "Question Classification in Question Answering Systems," 2007.##[3] M. Bazrafshan, S. Heidari, E. Dorodi, A. Rahnama, Z. Sarabi, E. Sherkat, M.Fahordi, M. Lali, M. Homan, SMH. Hashemienjad, "Question Answering for Quran," ITRC, 2012.##[4] Tohidi, Nasim; Rustamov, Rustam B., "Short Overview of Advanced Metaheuristic Methods," International Journal on Technical and Physical Problems of Engineering (IJTPE), vol. 14, pp. 84-97, 2022.##[5] D. Graff, "The AQUAINT Corpus of English News Text LDC2002T31," Web Download. Philadelphia: Linguistic Data Consortium, 2002.##[6] I.Khodadi, M. Saniee Abadeh, "A Memetic-Based Approach for Web-Based Question Answering," Information Technology and Computer Science, vol. 9, pp. 39-45, 2014.##[7] J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in in Proceedings of the IEEE International Conference on Neural Networks, 1995.##[8] A. L. Ballardini, "A tutorial on Particle Swarm Optimization Clustering," ArXiv, vol. abs/1809.01942, 2018.##[9] Y. XS, Nature-Inspired Metaheuristic Algorithms., Luniver Press, 2008.##[10] N. Tohidi, Ch. Dadkhah, "Improving the performance of video Collaborative Filtering Recommender Systems using Optimization Algorithm," International Journal of Nonlinear Analysis and Applications, vol. 11, no. 1, pp. 283-295, 2020.##[11] A. Figueroa, G. Neumann, "Genetic Algorithms for Data-DrivenWeb Question Answering, Massachusetts Institute of Technology," Evolutionary Computation, 2008.##[12] A. Mishra and S. Kumar Jain, "A survey on question answering systems with classification," Elsevier, Computer and Information Sciences, vol. 28, pp. 345-361, 2015.##[13] N. Tohidi, Ch. Dadkhah, B. Rustamov, "Optimizing the performance of Persian multi-objective question answering system," in 16th International Conference on Technical and Physical Problems of Electrical Engineering, Istanbul, 2020.##[14] N. Tohidi, Ch. Dadkhah, B. Rustamov, "Optimizing Persian Multi-objective Question Answering System," International Journal on Technical and Physical Problems of Engineering (IJTPE), vol. 13, pp. 62-69, 2021.##[15] H.Yu, D. Kaufman, "A cognitive evaluation of four online Search Engines for Answering Definitional Questioned posed by Physicians," in Pacific Symposium on Biocomputing, 2007.##[16] N. Tohidi, S.M.H. Hasheminejad, "MOQAS: Multi-objective question answering system," Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3495-3512, 2019.##[17] N. Smith, A. Heilman, M. Hwa, "Question Generation as a Competitive Undergraduate Course Project," in In Proceedings of the NSF Workshop on the Question Generation Shared Task and Evaluation Challenge, Arlington, VA, 2008.##[18] I. Khodadi, M. Saniee Abadeh, "Genetic programming-based feature learning for question answering," Elsevier, Information Processing and Management, vol. 40, 2015.##[19] A.Severyn, A.Moschitti, "Automatic Feature Engineering for Answer Selection and Extraction," in EMNLP Conference, 2013.##[20] A.Severyn, M.Nicosia, A. Moschitti, "Learning adaptable patterns for passage reranking," in CoNLL Conference, 2013.##[21] A.Severyn, A.Moschitti, "Structural relationships for large-scale learning of answer re-ranking," in ACM, In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012.##[22] A. Moschitti, S. Quarteroni, "Linguistic kernels for answer re-ranking in question answering systems", Elsevier, Information Processing and Management, vol. 47, p. 825-842, 2011.##[23] M. H. Heie, E. W. D.Whittaker, S. Furui, "Question answering using statistical language modelling," Computer Speech and Language, vol. 26, no. 3, pp. 193-209, 2012.##[24] P. Moreda, H. Llorens, E. Saquete, M. Palomar, "Combining semantic information in question answering systems," Information Processing & Management, vol. 47, no. 6, pp. 870-885, 2011.##[25] S. Yoon, A. Jatowt, K. Tanaka, "Detecting Intent of Web Queries Using Questions and Answers in CQA Corpus," in 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Lyon, 2011.##[26] S. Kandasamy, A.Cherukuri, "Information retrieval for Question Answering System using Knowledge based Query Reconstruction by adapted LESK and LATENT Semantic analysis," International Journal of Computer Science & Applications, vol. 14, no. 2, 2017.##[27] D. Croce, A. Moschitti, R. Basili, "Structured lexical similarity via convolution kernels on dependency trees," in Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011.##[28] H.Toba, Z. Ming, Y. Adriani, M. Chua, "Discovering high quality answers in community question answering archives using a hierarchy of classifiers," Elzevier, Information Sciences, vol. 261, pp. 101-115, 2014.##[29] Z. Yu, L. Su, L. Zhao, Q. Mao, C. Guo, "Question classification based on co-training style semi-supervised learning," Elzevier, Pattern Recognition Letters, vol. 31, no. 13, pp. 1975-1980, 2010.##[30] A. Ulysse Côté, Kh. Richard; Lamontagne, Luc; Bergeron, Jonathan; Laviolette, François; Bergeron-Guyard, Alexandre;, "Optimizing Question-Answering Systems Using Genetic Algorithms," in Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, 2015.##[31] K. Karpagam, A. Saradha, "A Hybrid Optimization Technique for Effective Document Clustering in Question Answering System." ICTACT Journal on Soft Computing, vol. 7, no. 3, pp. 1447-1451, 2017.##[32] Ojokoh, Bolanle; Adebisi, Emmanue, "A Review of Question Answering Systems," Journal of Web Engineering, vol. 17-8, pp. 717-758, 2019.##[33] S.M.H. Hasheminejad, "An Evolutionary Approach to Identify Logical Components," Journal of Systems and Software, vol. 96, pp. 24-50, 2014.##[1] F. Sebastiani, "Machine Learning in in Automated Text Categorization," ACM Computing Surveys (CSUR), vol. 34, pp. 1-47, 2002.##[2] H. Sundblad, "Question Classification in Question Answering Systems," 2007.##[3] بذرافشان، مرجان؛ حیدری، سمانه؛ درودی، احسان؛ رهنما، علی؛ سارابی، زهرا؛ سعیدی، پریسا؛ شرکت، احسان؛ فرهودی، مژگان؛ لآلی، مجید؛ مهیار، هومن؛ هاشمی نژاد، محمدحسین, "ایجاد و راهاندازی سامانهی پرسش و پاسخ خودکار در حوزهی قرآنی," پژوهشکده فناوری اطلاعات, 1392.##[3] M. Bazrafshan, S. Heidari, E. Dorodi, A. Rahnama, Z. Sarabi, E. Sherkat, M.Fahordi, M. Lali, M. Homan, SMH. Hashemienjad, "Question Answering for Quran," ITRC, 2012.##[4] Tohidi, Nasim; Rustamov, Rustam B., "Short Overview of Advanced Metaheuristic Methods," International Journal on Technical and Physical Problems of Engineering (IJTPE), vol. 14, pp. 84-97, 2022.##[5] D. Graff, "The AQUAINT Corpus of English News Text LDC2002T31," Web Download. Philadelphia: Linguistic Data Consortium, 2002.##[6] I.Khodadi, M. Saniee Abadeh, "A Memetic-Based Approach for Web-Based Question Answering," Information Technology and Computer Science, vol. 9, pp. 39-45, 2014.##[7] J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in in Proceedings of the IEEE International Conference on Neural Networks, 1995.##[8] A. L. Ballardini, "A tutorial on Particle Swarm Optimization Clustering," ArXiv, vol. abs/1809.01942, 2018.##[9] Y. XS, Nature-Inspired Metaheuristic Algorithms., Luniver Press, 2008.##[10] N. Tohidi, Ch. Dadkhah, "Improving the performance of video Collaborative Filtering Recommender Systems using Optimization Algorithm," International Journal of Nonlinear Analysis and Applications, vol. 11, no. 1, pp. 283-295, 2020.##[11] A. Figueroa, G. Neumann, "Genetic Algorithms for Data-DrivenWeb Question Answering, Massachusetts Institute of Technology," Evolutionary Computation, 2008.##[12] A. Mishra and S. Kumar Jain, "A survey on question answering systems with classification," Elsevier, Computer and Information Sciences, vol. 28, pp. 345-361, 2015.##[13] N. Tohidi, Ch. Dadkhah, B. Rustamov, "Optimizing the performance of Persian multi-objective question answering system," in 16th International Conference on Technical and Physical Problems of Electrical Engineering, Istanbul, 2020.##[14] N. Tohidi, Ch. Dadkhah, B. Rustamov, "Optimizing Persian Multi-objective Question Answering System," International Journal on Technical and Physical Problems of Engineering (IJTPE), vol. 13, pp. 62-69, 2021.##[15] H.Yu, D. Kaufman, "A cognitive evaluation of four online Search Engines for Answering Definitional Questioned posed by Physicians," in Pacific Symposium on Biocomputing, 2007.##[16] N. Tohidi, S.M.H. Hasheminejad, "MOQAS: Multi-objective question answering system," Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3495-3512, 2019.##[17] N. Smith, A. Heilman, M. Hwa, "Question Generation as a Competitive Undergraduate Course Project," in In Proceedings of the NSF Workshop on the Question Generation Shared Task and Evaluation Challenge, Arlington, VA, 2008.##[18] I. Khodadi, M. Saniee Abadeh, "Genetic programming-based feature learning for question answering," Elsevier, Information Processing and Management, vol. 40, 2015.##[19] A.Severyn, A.Moschitti, "Automatic Feature Engineering for Answer Selection and Extraction," in EMNLP Conference, 2013.##[20] A.Severyn, M.Nicosia, A. Moschitti, "Learning adaptable patterns for passage reranking," in CoNLL Conference, 2013.##[21] A.Severyn, A.Moschitti, "Structural relationships for large-scale learning of answer re-ranking," in ACM, In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012.##[22] A. Moschitti, S. Quarteroni, "Linguistic kernels for answer re-ranking in question answering systems", Elsevier, Information Processing and Management, vol. 47, p. 825-842, 2011.##[23] M. H. Heie, E. W. D.Whittaker, S. Furui, "Question answering using statistical language modelling," Computer Speech and Language, vol. 26, no. 3, pp. 193-209, 2012.##[24] P. Moreda, H. Llorens, E. Saquete, M. Palomar, "Combining semantic information in question answering systems," Information Processing & Management, vol. 47, no. 6, pp. 870-885, 2011.##[25] S. Yoon, A. Jatowt, K. Tanaka, "Detecting Intent of Web Queries Using Questions and Answers in CQA Corpus," in 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Lyon, 2011.##[26] S. Kandasamy, A.Cherukuri, "Information retrieval for Question Answering System using Knowledge based Query Reconstruction by adapted LESK and LATENT Semantic analysis," International Journal of Computer Science & Applications, vol. 14, no. 2, 2017.##[27] D. Croce, A. Moschitti, R. Basili, "Structured lexical similarity via convolution kernels on dependency trees," in Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011.##[28] H.Toba, Z. Ming, Y. Adriani, M. Chua, "Discovering high quality answers in community question answering archives using a hierarchy of classifiers," Elzevier, Information Sciences, vol. 261, pp. 101-115, 2014.##[29] Z. Yu, L. Su, L. Zhao, Q. Mao, C. Guo, "Question classification based on co-training style semi-supervised learning," Elzevier, Pattern Recognition Letters, vol. 31, no. 13, pp. 1975-1980, 2010.##[30] A. Ulysse Côté, Kh. Richard; Lamontagne, Luc; Bergeron, Jonathan; Laviolette, François; Bergeron-Guyard, Alexandre;, "Optimizing Question-Answering Systems Using Genetic Algorithms," in Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, 2015.##[31] K. Karpagam, A. Saradha, "A Hybrid Optimization Technique for Effective Document Clustering in Question Answering System." ICTACT Journal on Soft Computing, vol. 7, no. 3, pp. 1447-1451, 2017.##[32] Ojokoh, Bolanle; Adebisi, Emmanue, "A Review of Question Answering Systems," Journal of Web Engineering, vol. 17-8, pp. 717-758, 2019.##[33] S.M.H. Hasheminejad, "An Evolutionary Approach to Identify Logical Components," Journal of Systems and Software, vol. 96, pp. 24-50, 2014.## ##

بررسی جامع ترکیب روش‌های محلی ‌و ‌سراسری انتخاب ویژگی برای شناسایی درخواست در تلگرام A General Investigation on the Combination of Local and Global Feature Selection Methods for Request Identification on Telegram 1 1 تلگرام سرویس پیام‌رسان متن‌بازی مبتنی بر رایانش ابری است. تلگرام به دلایلی همچون پشتیبانی از زبان­ها، امکان ایجاد گروه و کانال با تعداد کاربران متعدد، به پیام‌رسانی محبوب و پرکاربرد تبدیل ‌شد. داده‌های متنی زیادی که در گروه‌های تلگرامی وجود دارد حاوی دانش پنهانی هستند. استخراج این دانش‌ها، نظیر درخواست‌های موجود در پیام‌های کاربران می‌تواند سودمند باشد. لذا با شناسایی درخواست‌ها می‌توان به نیازهای کاربران پاسخ داد و به دسترسی سریع آن‌ها به خواسته‌هایشان کمک کرد که این امر موجب توسعه کسب‌وکار کاربران می‌شود. با توجه به ابعاد بالای فضای ویژگی‌ها در داده‌های متنی، کاهش ویژگی‌ها از طریق انتخاب ویژگی ضرورت می­یابد. از روش‌های انتخاب ویژگی، دو روش مبتنی برفیلتر محلی و سراسری انتخاب شد. با بررسی و ترکیب پرکاربردترین آن­ها به زیرمجموعه بهینه­ای از ویژگی‌های بااهمیت دست ‌یافتیم. این روش ترکیبی، با کاهش بهینه ویژگی­ها سبب افزایش دقت در شناسایی درخواست، افزایش کارایی دسته‌بندی متن، کاهش زمان آموزش و محاسبات شد.   2 Nowadays, the use of various messaging services is expanding worldwide with the rapid development of Internet technologies. Telegram is a cloud-based open-source text messaging service. According to the US Securities and Exchange Commission and based on the statistics given for October 2019 to present, 300 million people worldwide used telegram per month. Telegram users are more concentrated in countries such as Iran, Venezuela, Nigeria, Kenya, Russia, and Ukraine. This messenger has become a popular and extensively used messenger because it supports various languages and provides diverse services such as creating groups and channels with a large number of users and members. There is a large amount of contextual data on telegram groups containing hidden knowledge; the extraction of this knowledge can be beneficial. The requests on telegram users' messages are examples of this sort of data with hidden knowledge. Hence, identifying requests can respond to users' needs and help them fulfill their desires immediately; this drives users' business development. The authors identified these requests in a telegram search engine named the Idekav system of Yazd University. Then, the authors created opportunities to earn money by sending these requests to the business owners who were able to respond to them. Given the high dimensions of feature space in contextual data, it is necessary to reduce attributes using feature selection.        In the present study, the appropriate features were selected for Persian text classification and request identification. Among the feature selection methods, two local and global filter-based methods were chosen. By general investigation and combining the most extensively used filter-based FS methods, an optimal subset of important features was obtained. This hybrid feature selection method resulted in increased request identification accuracy, improved Persian text classification efficiency, and reduced training time and computation by optimizing the feature reduction. Of course, it is noteworthy that the classification accuracy is reduced in some methods; however, this value is negligible compared to the feature reduction value. Incorporating the concept of opinion mining into the analysis of emotions and questions can be a method to identify positive or negative demand in social networks. Therefore, the requests in the Persian telegram messages can be identified using opinion mining researches. For experiments in the present article, a dataset called Persian is used, which is extracted from the Idekav system. The selection of suitable features to increase model accuracy in request identification is an important part of this research. The support vector machine was employed to calculate accuracy. Given the acceptable results of the SVM, its various kernels were also calculated. Micro-averaging and macro-averaging criteria were also used for evaluation. Model inputs include many optimal feature subsets. Furthermore, feature selection methods have been proposed to produce suitable features for each model for increasing the accuracy of the model. Afterward, among all the features investigated, appropriate features have been selected for each of the applied feature selection models. For a more precise explanation, the main innovations of the present study are as follows: Use of the most common filters based on local and global feature selection methods to find the optimal feature set. Use of hybrid methods to create suitable features for predictive models of accuracy in Persian text classification and their application in identifying requests in Persian messages on telegram. Selecting suitable features to increase accuracy and reduce computational time for each of the models under consideration. In this regard, in addition to picking an efficient algorithm, it is attempted to provide a method for making more appropriate choices. Evaluation and testing of the proposed models for a large set of Persian data and many different features. 175 196 2019/12/192019/10/252019/12/22019/12/302020/04/122020/04/152019/12/42019/12/152019/12/262020/01/92019/12/142020/01/12 1398/10/22 2021/12/62020/02/82020/08/182020/10/132021/05/242022/01/192020/08/182021/05/232021/06/202022/05/112021/01/242021/03/2 1399/12/12 محمدعلی زارع چاهوکی Mohammad Ali Zare Chahooki گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه یزد ایران chahooki@yazd.ac.ir زهرا خلیفه زاده zahra khalifeh zadeh گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه یزد ایران zahra.kh2005@gmail.com Feature Selection Text mining Classification Accuracy Machine Learning انتخاب ویژگی متن‌کاوی دقت دسته‌بندی یادگیری ماشین [1] Iran Analytical News Agency, "In which countries do telegram messengers favor?", khabaronline.ir, July. 2, 2019. [Online]. Available: khabaronline.ir/news/1275665. [Accessed:4 January 2020].##[2] Economics News, "Latest statistics from the mostpopular social networks in Iran", eghtesadnews.com, April. 9, 2019. [Online]. Available: https://b2n.ir/661242. [Accessed:4 January 2020].##[3] Wikipedia contributors, "Telegram (software)," Wikipedia, The Free Encyclopedia, 27 December 2019, 15:24 UTC. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Telegram_(software);oldid=932678184. [Accessed:4 January 2020].##[4] S. Ranganath, X. Hu, J. Tang, S. Wang, and H. Liu, "Understanding and Identifying Rhetorical Questions in Social Media,", ACM Transactions on Intelligent Systems and Technology, vol. 9, pp. 1-22, 2018.##[5] J. Zhang, A. Spirling, and C. Danescu-Niculescu-Mizil, "Asking too much? The rhetorical role of questions in political discourse,", arXiv preprint arXiv:1708.02254, 2017.##[6] S. Ranganath, S. Wang, X. Hu, J. Tang, and H. Liu, "Facilitating Time Critical Information Seeking in Social Media,", IEEE Transactions on Knowledge and Data Engineering, vol. PP, pp. 1-1, 2017.##[7] A. D. Walker, P. Alexopoulos, A. Starkey, J. Z. Pan, J. M. Gómez-Pérez, and A. Siddharthan, "Answer Type Identification for Question Answering, ", in Joint International Semantic Technology Conference. Springer, Cham, 2015, pp. 235-251.##[8] W. He, S. Zha, and L. Li, "Social media competitive analysis and text mining: A case study in the pizza industry,", International Journal of Information Management, vol. 33, no. 3, pp. 464-472, Jun. 2013.##[9] H. A. Vamerzani and M. Khademi, "Exploring the Uses and Challenges of Big Data in Opinion Analysis," in Proceedings of the 7th Iranian Conference on Electrical and Electronics Engineering, Gonabad, Islamic Azad University of Gonabad, 2016.##[10] M. Kiani nejad, T. hashemi, and M. rashidi, " Text mining social networks for consumer brand feelings and desires," in Proceedings of the 6th International Conference on Economics, Management and Engineering Sciences, Belgium, International Center for Academic Communication, 2016.##[11] D. Ö. Şahin and E. Kılıç, "Two new feature selection metrics for text classification," Automatika, vol. 60, no. 2, pp. 162-171, 2019.##[12] A. K. Uysal, "An improved global feature selection scheme for text classification," Expert systems with Applications, vol. 43, pp. 82-92, 2016.##[13] M. Nekkaa and D. Boughaci, "Hybrid harmony search combined with stochastic local search for feature selection," Neural Processing Letters, vol. 44, no. 1, pp. 199-220, 2016.##[14] X. Deng, Y. Li, J. Weng, and J. Zhang, "Feature selection for text classification: A review,", Multimedia Tools and Applications, vol. 78, no. 3, pp. 3797-3816, 2019.##[15] L. M. Abualigah, A. T. Khader, M. A. Al-Betar, and O. A. Alomari, "Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering," Expert Systems with Applications, vol. 84, pp. 24-36, 2017.##[16] D. Agnihotri, K. Verma, and P. Tripathi, "Variable global feature selection scheme for automatic classification of text documents," Expert Systems with Applications, vol. 81, pp. 268-281, 2017.##[17] G. BİRİCİK, B. Diri, and A. C. SÖNMEZ, "Abstract feature extraction for text classification," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 20, no. Sup. 1, pp. 1137-1159, 2012.##[18] A. Melo and H. Paulheim, "Local and global feature selection for multilabel classification with binary relevance," Artificial intelligence review, vol. 51, no. 1, pp. 33-60, 2019.##[19] H. Ogura, H. Amano, and M. Kondo, "Distinctive characteristics of a metric using deviations from Poisson for feature selection," Expert Systems with Applications, vol. 37, no. 3, pp. 2273-2281, 2010.##[20] R. Saidi, W. Bouaguel, and N. Essoussi, "Hybrid Feature Selection Method Based on the Genetic Algorithm and Pearson Correlation Coefficient," in Machine Learning Paradigms: Theory and Application, Springer, 2019, pp. 3-24.##[21] N. Nicolosi, "Feature selection methods for text classification," Department of Computer Science, Rochester Institute of Technology, Tech. Rep, 2008.##[22] C. Huang, J. Zhu, Y. Liang, M. Yang, G. P. C. Fung, and J. Luo, "An efficient automatic multiple objectives optimization feature selection strategy for internet text classification," International Journal of Machine Learning and Cybernetics, vol. 10, no. 5, pp. 1151-1163, 2019.##[23] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of machine learning research, vol. 3, no. Mar, pp. 1157-1182, 2003.##[24] G. Chandrashekar and F. Sahin, "A survey on feature selection methods," Computers & Electrical Engineering, vol. 40, no. 1, pp. 16-28, 2014.##[25] Z. Zheng and R. Srihari, "Optimally combining positive and negative features for text categorization," In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.##[26] K. Quinn and O. Zaiane, 'Identifying questions & requests in conversation', in Proceedings of the 2014 International C* Conference on Computer Science & Software Engineering, 2014, pp. 1-6.##[27] B. Li, X. Si, M. R. Lyu, I. King, and E. Y. Chang, 'Question identification on twitter', in Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 2477-2480.##[28] B. Ojokoh, T. Igbe, A. Araoye, and F. Ameh, 'Question identification and classification on an academic question answering site', in 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL), 2016, pp. 223-224.##[29] S. Ranganath, X. Hu, J. Tang, S. Wang, and H. Liu, 'Identifying Rhetorical Questions in Social Media.', in ICWSM, 2016, pp. 667-670.##[30] W. Cohen, V. Carvalho, and T. Mitchell, 'Learning to classify email into "speech acts"', in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 2004, pp. 309-316.##[31] A. Ramzy and A. Elazab, 'Question Identification in Arabic Language Using Emotional Based Features', arXiv preprint arXiv:2008.03843, 2020.##[32] B. Z. Abbasi, S. Hussain, S. Bibi, and M. A. Shah, "Impact of Membership and Non-membership Features on Classification Decision: An Empirical Study for Appraisal of Feature Selection Methods,", in 2018 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1-6.##[33] A. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. W. Mahoney, "Feature selection methods for text classification," in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, pp. 230-239.##[34] A. K. Uysal and S. Gunal, "A novel probabilistic feature selection method for text classification,", Knowledge-Based Systems, vol. 36, pp. 226-235, 2012.##[35] B. Tang, S. Kay, and H. He, "Toward optimal feature selection in naive Bayes for text categorization," IEEE transactions on knowledge and data engineering, vol. 28, no. 9, pp. 2508-2521, 2016.##[36] S. L. Lam and D. L. Lee, "Feature reduction for neural network based text categorization," in Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, 1999, pp. 195-202.##[37] N. G. R. Chawla, "Improved Feature Subset Selection using Hybrid Ant Colony and Perceptron Network," International Journal of Scientific Research and Management, vol. 5, no. 8, pp. 6764-6770, 2017.##[38] S. Gu, R. Cheng, and Y. Jin, "Feature selection for high-dimensional classification using a competitive swarm optimizer," Soft Computing, vol. 22, no. 3, pp. 811-822, 2018.##[39] S. Choi, J. H. Shin, J. Lee, P. Sheridan, and W. D. Lu, "Experimental demonstration of feature extraction and dimensionality reduction using memristor networks," Nano letters, vol. 17, no. 5, pp. 3113-3118, 2017.##[40] H. Naji, W. Ashour, and M. Alhanjouri, "A New Model in Arabic Text Classification Using BPSO/REP-Tree," JOURNAL OF ENGINEERING RESEARCH AND TECHNOLOGY, vol. 4, pp. 28-42, 2017.##[41] N. Kumar, S. Mitra, M. Bhattacharjee, and L. Mandal, "Comparison of Different Classification Techniques Using Different Datasets," Singapore, 2019, pp. 261-272.##[42] M. Labani, P. Moradi, F. Ahmadizar, and M. Jalili, "A novel multivariate filter method for feature selection in text classification problems," Engineering Applications of Artificial Intelligence, vol. 70, pp. 25-37, 2018.##[43] M. A. Hall, "Correlation-based feature selection for machine learning," Doctoral dissertation, University of Waikato, Dept. of Computer Science, 1999.##[44] C. Liu, W. Wang, Q. Zhao, X. Shen, and M. Konan, "A new feature selection method based on a validity index of feature subset,", Pattern Recognition Letters, vol. 92, pp. 1-8, 2017.##[45] V. Vapnik and V. Vapnik, "Statistical learning theory Wiley," New York, pp. 156-160, 1998.##[46] D. Sarkar, "Text Classification,", in Text Analytics with Python, Springer, 2019, pp. 275-342.##[47] M. B. Dastgheib and S. Koleini, "Persian Text Classification Enhancement by Latent Semantic Space," International Journal of Information Science and Management (IJISM), vol. 17, no. 1, p. 33, 2019.##[48] C. Qi, Z. Zhou, Y. Sun, H. Song, L. Hu, and Q. Wang, "Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification," Neurocomputing, vol. 220, pp. 181-190, 2017.##[49] A. K. Uysal, "On two-stage feature selection methods for text classification,", IEEE Access, vol. 6, pp. 43233-43251, 2018.##[50] M. Swamynathan, Mastering machine learning with python in six steps: A practical implementation guide to predictive data analytics using python. Apress, 2019.##[51] Z. Khalifeh-Zadeh, M. A. Z. Chahooki, "An Effective Method of Feature Selection in Persian Text for Improving the Accuracy of Detecting Request in Persian Messages on Telegram," Journal of Information Systems and Telecommunication (JIST), vol. 32, pp. 249-262, 2021.##[1] خبرگزاری تحلیلی ایران، "پیام‌رسان تلگرام در کدام کشورها طرفدار دارد؟"، ۱۱ تیر ۱۳۹۸، برگرفته از لینک: khabaronline.ir/news/1275665، به نقل از سایت: digitalinformationworld.com، تاریخ استخراج: 14 دی 1398.##[1] Iran Analytical News Agency, "In which countries do telegram messengers favor?", khabaronline.ir, July. 2, 2019. [Online]. Available: khabaronline.ir/news/1275665. [Accessed:4 January 2020].##[2] اقتصادنیوز، "آخرین آمار از محبوب‌ترین شبکه‌های اجتماعی در ایران "، اقتصادنیوز سایت مرجع اقتصاد ایران، 20 فروردین 1398، برگرفته از لینک: https://b2n.ir/661242، تاریخ استخراج: 14 دی 1398.##[2] Economics News, "Latest statistics from the mostpopular social networks in Iran", eghtesadnews.com, April. 9, 2019. [Online]. Available: https://b2n.ir/661242. [Accessed:4 January 2020].##[3] Wikipedia contributors, "Telegram (software)," Wikipedia, The Free Encyclopedia, 27 December 2019, 15:24 UTC. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Telegram_(software);oldid=932678184. [Accessed:4 January 2020].##[4] S. Ranganath, X. Hu, J. Tang, S. Wang, and H. Liu, "Understanding and Identifying Rhetorical Questions in Social Media,", ACM Transactions on Intelligent Systems and Technology, vol. 9, pp. 1-22, 2018.##[5] J. Zhang, A. Spirling, and C. Danescu-Niculescu-Mizil, "Asking too much? The rhetorical role of questions in political discourse,", arXiv preprint arXiv:1708.02254, 2017.##[6] S. Ranganath, S. Wang, X. Hu, J. Tang, and H. Liu, "Facilitating Time Critical Information Seeking in Social Media,", IEEE Transactions on Knowledge and Data Engineering, vol. PP, pp. 1-1, 2017.##[7] A. D. Walker, P. Alexopoulos, A. Starkey, J. Z. Pan, J. M. Gómez-Pérez, and A. Siddharthan, "Answer Type Identification for Question Answering, ", in Joint International Semantic Technology Conference. Springer, Cham, 2015, pp. 235-251.##[8] W. He, S. Zha, and L. Li, "Social media competitive analysis and text mining: A case study in the pizza industry,", International Journal of Information Management, vol. 33, no. 3, pp. 464-472, Jun. 2013.##[9] عزیزی وامرزانی, حامد و مریم خادمی. بررسی کاربرد و چالش‌های کلان داده در تحلیل عقاید. هفتمین کنفرانس ملی مهندسی برق و الکترونیک ایران، گناباد، دانشگاه آزاد اسلامی گناباد, 1394.##[9] H. A. Vamerzani and M. Khademi, "Exploring the Uses and Challenges of Big Data in Opinion Analysis," in Proceedings of the 7th Iranian Conference on Electrical and Electronics Engineering, Gonabad, Islamic Azad University of Gonabad, 2016.##[10] کیانی نژاد, محمد؛ طاهره هاشمی و محسن رشیدی. متن‌کاوی شبکه‌های اجتماعی برای احساسات و تمایلات مصرف‌کننده برند، ششمین کنفرانس بین‌المللی اقتصاد، مدیریت و علوم مهندسی، بلژیک، مرکز بین‌المللی ارتباطات دانشگاهی، ۱۳۹۴.##[10] M. Kiani nejad, T. hashemi, and M. rashidi, " Text mining social networks for consumer brand feelings and desires," in Proceedings of the 6th International Conference on Economics, Management and Engineering Sciences, Belgium, International Center for Academic Communication, 2016.##[11] D. Ö. Şahin and E. Kılıç, "Two new feature selection metrics for text classification," Automatika, vol. 60, no. 2, pp. 162-171, 2019.##[12] A. K. Uysal, "An improved global feature selection scheme for text classification," Expert systems with Applications, vol. 43, pp. 82-92, 2016.##[13] M. Nekkaa and D. Boughaci, "Hybrid harmony search combined with stochastic local search for feature selection," Neural Processing Letters, vol. 44, no. 1, pp. 199-220, 2016.##[14] X. Deng, Y. Li, J. Weng, and J. Zhang, "Feature selection for text classification: A review,", Multimedia Tools and Applications, vol. 78, no. 3, pp. 3797-3816, 2019.##[15] L. M. Abualigah, A. T. Khader, M. A. Al-Betar, and O. A. Alomari, "Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering," Expert Systems with Applications, vol. 84, pp. 24-36, 2017.##[16] D. Agnihotri, K. Verma, and P. Tripathi, "Variable global feature selection scheme for automatic classification of text documents," Expert Systems with Applications, vol. 81, pp. 268-281, 2017.##[17] G. BİRİCİK, B. Diri, and A. C. SÖNMEZ, "Abstract feature extraction for text classification," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 20, no. Sup. 1, pp. 1137-1159, 2012.##[18] A. Melo and H. Paulheim, "Local and global feature selection for multilabel classification with binary relevance," Artificial intelligence review, vol. 51, no. 1, pp. 33-60, 2019.##[19] H. Ogura, H. Amano, and M. Kondo, "Distinctive characteristics of a metric using deviations from Poisson for feature selection," Expert Systems with Applications, vol. 37, no. 3, pp. 2273-2281, 2010.##[20] R. Saidi, W. Bouaguel, and N. Essoussi, "Hybrid Feature Selection Method Based on the Genetic Algorithm and Pearson Correlation Coefficient," in Machine Learning Paradigms: Theory and Application, Springer, 2019, pp. 3-24.##[21] N. Nicolosi, "Feature selection methods for text classification," Department of Computer Science, Rochester Institute of Technology, Tech. Rep, 2008.##[22] C. Huang, J. Zhu, Y. Liang, M. Yang, G. P. C. Fung, and J. Luo, "An efficient automatic multiple objectives optimization feature selection strategy for internet text classification," International Journal of Machine Learning and Cybernetics, vol. 10, no. 5, pp. 1151-1163, 2019.##[23] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of machine learning research, vol. 3, no. Mar, pp. 1157-1182, 2003.##[24] G. Chandrashekar and F. Sahin, "A survey on feature selection methods," Computers & Electrical Engineering, vol. 40, no. 1, pp. 16-28, 2014.##[25] Z. Zheng and R. Srihari, "Optimally combining positive and negative features for text categorization," In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.##[26] K. Quinn and O. Zaiane, 'Identifying questions & requests in conversation', in Proceedings of the 2014 International C* Conference on Computer Science & Software Engineering, 2014, pp. 1-6.##[27] B. Li, X. Si, M. R. Lyu, I. King, and E. Y. Chang, 'Question identification on twitter', in Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 2477-2480.##[28] B. Ojokoh, T. Igbe, A. Araoye, and F. Ameh, 'Question identification and classification on an academic question answering site', in 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL), 2016, pp. 223-224.##[29] S. Ranganath, X. Hu, J. Tang, S. Wang, and H. Liu, 'Identifying Rhetorical Questions in Social Media.', in ICWSM, 2016, pp. 667-670.##[30] W. Cohen, V. Carvalho, and T. Mitchell, 'Learning to classify email into "speech acts"', in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 2004, pp. 309-316.##[31] A. Ramzy and A. Elazab, 'Question Identification in Arabic Language Using Emotional Based Features', arXiv preprint arXiv:2008.03843, 2020.##[32] B. Z. Abbasi, S. Hussain, S. Bibi, and M. A. Shah, "Impact of Membership and Non-membership Features on Classification Decision: An Empirical Study for Appraisal of Feature Selection Methods,", in 2018 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1-6.##[33] A. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. W. Mahoney, "Feature selection methods for text classification," in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, pp. 230-239.##[34] A. K. Uysal and S. Gunal, "A novel probabilistic feature selection method for text classification,", Knowledge-Based Systems, vol. 36, pp. 226-235, 2012.##[35] B. Tang, S. Kay, and H. He, "Toward optimal feature selection in naive Bayes for text categorization," IEEE transactions on knowledge and data engineering, vol. 28, no. 9, pp. 2508-2521, 2016.##[36] S. L. Lam and D. L. Lee, "Feature reduction for neural network based text categorization," in Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, 1999, pp. 195-202.##[37] N. G. R. Chawla, "Improved Feature Subset Selection using Hybrid Ant Colony and Perceptron Network," International Journal of Scientific Research and Management, vol. 5, no. 8, pp. 6764-6770, 2017.##[38] S. Gu, R. Cheng, and Y. Jin, "Feature selection for high-dimensional classification using a competitive swarm optimizer," Soft Computing, vol. 22, no. 3, pp. 811-822, 2018.##[39] S. Choi, J. H. Shin, J. Lee, P. Sheridan, and W. D. Lu, "Experimental demonstration of feature extraction and dimensionality reduction using memristor networks," Nano letters, vol. 17, no. 5, pp. 3113-3118, 2017.##[40] H. Naji, W. Ashour, and M. Alhanjouri, "A New Model in Arabic Text Classification Using BPSO/REP-Tree," JOURNAL OF ENGINEERING RESEARCH AND TECHNOLOGY, vol. 4, pp. 28-42, 2017.##[41] N. Kumar, S. Mitra, M. Bhattacharjee, and L. Mandal, "Comparison of Different Classification Techniques Using Different Datasets," Singapore, 2019, pp. 261-272.##[42] M. Labani, P. Moradi, F. Ahmadizar, and M. Jalili, "A novel multivariate filter method for feature selection in text classification problems," Engineering Applications of Artificial Intelligence, vol. 70, pp. 25-37, 2018.##[43] M. A. Hall, "Correlation-based feature selection for machine learning," Doctoral dissertation, University of Waikato, Dept. of Computer Science, 1999.##[44] C. Liu, W. Wang, Q. Zhao, X. Shen, and M. Konan, "A new feature selection method based on a validity index of feature subset,", Pattern Recognition Letters, vol. 92, pp. 1-8, 2017.##[45] V. Vapnik and V. Vapnik, "Statistical learning theory Wiley," New York, pp. 156-160, 1998.##[46] D. Sarkar, "Text Classification,", in Text Analytics with Python, Springer, 2019, pp. 275-342.##[47] M. B. Dastgheib and S. Koleini, "Persian Text Classification Enhancement by Latent Semantic Space," International Journal of Information Science and Management (IJISM), vol. 17, no. 1, p. 33, 2019.##[48] C. Qi, Z. Zhou, Y. Sun, H. Song, L. Hu, and Q. Wang, "Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification," Neurocomputing, vol. 220, pp. 181-190, 2017.##[49] A. K. Uysal, "On two-stage feature selection methods for text classification,", IEEE Access, vol. 6, pp. 43233-43251, 2018.##[50] M. Swamynathan, Mastering machine learning with python in six steps: A practical implementation guide to predictive data analytics using python. Apress, 2019.##[51] Z. Khalifeh-Zadeh, M. A. Z. Chahooki, "An Effective Method of Feature Selection in Persian Text for Improving the Accuracy of Detecting Request in Persian Messages on Telegram," Journal of Information Systems and Telecommunication (JIST), vol. 32, pp. 249-262, 2021.## ##