ijece-140502292014050229202732CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece168237458202022202MahsaMemariA.Harifia.Khalili820202214515410.66224/ijece.28949.20.2.145http://ijece.org/fa/Article/28949http://ijece.org/fa/Article/Download/28949http://ijece.org/fa/Article/Download/28949http://ijece.org/fa/Article/Download/28949http://ijece.org/fa/Article/Download/28949http://ijece.org/fa/Article/Download/28949http://ijece.org/fa/Article/Download/28949http://ijece.org/fa/Article/Download/28949[1] O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, "State-of-the-art in artificial neural network applications: a survey," Heliyon, vol. 4, no. 11, Article ID: e00938, Nov. 2018. [2] N. L. da Costa, M. D. de Lima, and R. Barbosa, "Evaluation of feature selection methods based on artificial neural network weights," Expert Syst. Appl., vol. 168, no. 7, Article ID: 114312, Apr. 2021. [3] J. Zhou, H. G. Amir, F. Chen, and A. Holzinger, "Evaluating the quality of machine learning explanations: a survey on methods and metrics," Electronics, vol. 10, no. 5, Article ID: 593, 19 pp., Mar. 2021. [4] M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley, 2020. [5] J. Beyerer, M. Richter, and M. Nagel, Pattern Recognition: Introduction, Features, Classifiers and Principles. Walter de Gruyter GmbH & Co KG, 2017. [6] T. A. Al-Asadi, O. J. Ahmed, R. Hidayat, and A. A. Ramli, "A survey on web mining techniques and applications," Int. J. on Adv. Sci. Eng. and Inf. Tech., vol. 7, no. 4, pp. 1178-1184, 2017. [7] Y. Cao, T. A. Geddes, J. Y. H. Yang, and P. Yang, "Ensemble deep learning in bioinformatics," Nat. Mach. Intell., vol. 2, no. 9, pp. 500-508, Aug. 2020. [8] I. Sadgali, N. Sael, and F. Benabbou, "Performance of machine learning techniques in the detection of financial frauds," Procedia Comput Sci, vol. 148, no. 6, pp. 45-54, 2019. [9] J. Henriquez and W. Kristjanpoller, "A combined independent component analysis-neural network model for forecasting exchange rate variation," Appl Soft Comput, vol. 83, no. C, Article ID: 105654, Oct. 2019. [10] I. N. Da Silva, D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. dos Reis Alves, "Artificial neural network architectures and training processes," Artif. Neural Netw., Springer, Cham, pp. 21-28, 2017. [11] C. Singh, W. J. Murdoch, and B. Yu, Hierarchical Interpretations for Neural Network Predictions, arXiv preprint arXiv: 1806.05337, 2018. [12] F. Ros, M. Pintore, and J. R. Chrétien, "Automatic design of growing radial basis function neural networks based on neighboorhood concepts," Chemometrics and Intelligent Laboratory Systems, vol. 87, no. 2, pp. 231-240, Jun. 2007. [13] A. G. Ivakhnenko, "Polynomial theory of complex systems," IEEE Trans. on Systems, Man, and Cybernetics, vol. 1, no. 4, pp. 364-378, Oct. 1971. [14] C. T. Lin, M. Prasad, and A. Saxena, "An improved polynomial neural network classifier using real-coded genetic algorithm," IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 45, no. 11, pp. 1389-1401, Nov. 2015. [15] L. L. Huang, A. Shimizu, Y. Hagihara, and H. Kobatake, "Face detection from cluttered images using a polynomial neural network," Neurocomputing, vol. 51, no. 12, pp. 197-211, Apr. 2003. [16] S. B. Roh, T. C. Ahn, and W. Pedrycz, "Fuzzy linear regression based on Polynomial Neural Networks," Expert Systems with Applications, vol. 39, no. 10, pp. 8909-8928, Aug. 2012. [17] C. C. Huang, W. C. Chen, C. Y. Shen, Y. J. Chen, C. Y. Chang, and R. C. Hwang, "Signal processing by polynomial NN and equivalent polynomial function," in Proc. 1st Int. Conf. on, Pervasive Computing Signal Processing and Applications, PCSPA’10, pp. 460-463, Harbin, China, 17-19 Sept. 2010. [18] L. Zjavka and W. Pedrycz, "Constructing general partial differential equations using polynomial and neural networks," Neural Networks, vol. 73, no. 1, pp. 58-69, Jan. 2016. [19] M. Mehrabi, M. Sharifpur, and J. P. Meyer, "Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina_water nanofluids," International Communications in Heat and Mass Transfer, vol. 39, no. 7, pp. 971-977, Aug. 2012. [20] M. F. Zarandi, I. B. Türksen, J. Sobhani, and A. A. Ramezanianpour, "Fuzzy polynomial neural networks for approximation of the compressive strength of concrete," Applied Soft Computing, vol. 8, no. 1, pp. 488-498, Jan. 2008. [21] S. Dehuri, B. B. Misra, A. Ghosh, and S. B. Cho, "A condensed polynomial neural network for classification using swarm intelligence," Applied Soft Computing, vol. 11, no. 3, pp. 3106-3113, Apr. 2011. [22] M. Li, J. Tian, and F. Chen, "Improving multiclass pattern recognition with a co-evolutionary RBFNN," Pattern Recognition Letters, vol. 29, no. 4, pp. 392-406, Mar. 2008. [23] B. B. Misra, S. Dehuri, P. K. Dash, and G. Panda, "A reduced and comprehensible polynomial neural network for classification," Pattern Recognition Letters, vol. 29, no. 12, pp. 1705-1712, Sept. 2008. [24] Z. Qi, Y. Tian, and Y. Shi, "Laplacian twin support vector machine for semi-supervised classification," Neural Networks, vol. 35, no. 4, pp. 46-53, Nov. 2012. [25] S. Dehuri and S. B. Cho, "Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: a review and state-of-the-art," Computer Science Review, vol. 3, no. 1, pp. 19-40, Feb. 2009. [26] Z. Qi, Y. Tian, and Y. Shi, "Structural twin support vector machine for classification," Knowledge-Based Systems, vol. 43, no. 7, pp. 74-81, May 2013. [27] Q. Hou, L. Liu, L. Zhen, and L. Jing, "A novel projection nonparallel support vector machine for pattern classification," Eng. Appl. Artif. Intell., vol. 75, no. 7, pp. 64-75, Oct. 2018. [28] W. J. Chen, Y. H. Shao, C. N. Li, Y. Q. Wang, M. Z. Liu, and Z. Wang, "NPrSVM: nonparallel sparse projection support vector machine with efficient algorithm," Appl. Soft Comput., vol. 90, no. 3, Article ID: 106142, May 2020. [29] H. Pant, M. Sharma, and S. Soman, "Twin neural networks for the classification of large unbalanced datasets," Neurocomputing, vol. 343, no. 4, pp. 34-49, May 2019. [30] R. Rastogi, P. Saigal, and S. Chandra, "Angle-based twin parametric-margin support vector machine for pattern classification," Knowl. Based. Syst., vol. 139, no. 6, pp. 64-77, Jan. 2018. [31] S. K. Oh, W. Pedrycz, and B. J. Park, "Polynomial neural networks architecture: analysis and design," Computers & Electrical Engineering, vol. 29, no. 6, pp. 703-725, May 2003. [32] S. Mirjalili and A. Lewis, "The whale optimization algorithm," Advances in Engineering Software, vol. 95, no. 6 pp. 51-67, May 2016. [33] W. A. Watkins and W. E. Schevill, "Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus," J. of Mammalogy, vol. 60, no. 1, pp. 155-163, Feb. 1979.mohammadrezamollakhalili meybodimasoumehzojaji820202210111810.66224/ijece.29033.20.2.101http://ijece.org/fa/Article/29033http://ijece.org/fa/Article/Download/29033http://ijece.org/fa/Article/Download/29033http://ijece.org/fa/Article/Download/29033http://ijece.org/fa/Article/Download/29033http://ijece.org/fa/Article/Download/29033http://ijece.org/fa/Article/Download/29033http://ijece.org/fa/Article/Download/29033[1] D. P. Williamson and D. B. Shmoys, The Design of Approximation Algorithms, Cambridge University Press, 2011. [2] V. V Vazirani, Approximation Algorithms, Berlin: springer, 2001. [3] E. G. Talbi, Metaheuristics: from Design to Implementation, vol. 74, John Wiley & Sons, 2009. [4] C. C. Sims, "Graphs and finite permutation groups," Math. Zeitschrift, vol. 95, no. 1, pp. 76-86, Feb. 1967. [5] G. Cooperman and E. Robinson, "Memory-based and disk-based algorithms for very high degree permutation groups," in Proc. of the Int. Symp. on Symbolic and Algebraic Computation, pp. 66-73, Philadelphia, PA, USA, 3-6 Aug. 2003. [6] J. Grabowski and J. Pempera, "The permutation flow shop problem with blocking. A tabu search approach," Omega, vol. 35, no. 3, pp. 302-311, Jun. 2007. [7] J. Kaabi and Y. Harrath, "Permutation rules and genetic algorithm to solve the traveling salesman problem," Arab J. Basic Appl. Sci., vol. 26, no. 1, pp. 283-291, Jan. 2019. [8] H. Ishii, S. Shiode, T. Nishida, and Y. Namasuya, "Stochastic spanning tree problem," Discret. Appl. Math., vol. 3, no. 1, pp. 263-273, Nov. 1981. [9] S. A. Dehkordi, et al., "A survey on data aggregation techniques in IoT sensor networks," Wirel. Networks, vol. 26, no. 2, pp. 1243-1263, Feb. 2020. [10] C. Buchheim and M. Jünger, "Linear optimization over permutation groups," Discret. Optim., vol. 2, no. 4, pp. 308-319, Dec. 2005. [11] M. A. L. Thathachar and P. S. Sastry, Networks of Learning Automata: Techniques for Online Stochastic Optimization, Springer Science & Business Media, 2003. [12] M. A. L. Thathachar and P. S. Sastry, "Varieties of learning automata: an overview," IEEE Trans. Syst. Man, Cybern. Part B, vol. 32, no. 6, pp. 711-722, Dec. 2002. [13] M. L. Tsetlin, Automaton Ttheory and Modeling of Biological Systems, vol. 102, pp.160-196, Academic Press New York, 1973. [14] Q. Sang, Z. Lin, and S. T. Acton, "Learning automata for image segmentation," Pattern Recognit. Lett., vol. 74, pp. 46-52, Apr. 2016. [15] J. A. Torkestani and M. R. Meybodi, "Mobility-based multicast routing algorithm for wireless mobile ad-hoc networks: a learning automata approach," Comput. Commun., vol. 33, no. 6, pp. 721-735, Apr. 2010. [16] M. Shojafar, S. Abolfazli, H. Mostafaei, and M. Singhal, "Improving channel assignment in multi-radio wireless mesh networks with learning automata," Wirel. Pers. Commun., vol. 82, no. 1, pp. 61-80, May 2015. [17] M. Fahimi and A. Ghasemi, "A distributed learning automata scheme for spectrum management in self-organized cognitive radio network," IEEE Trans. Mob. Comput., vol. 16, no. 6, pp. 1490-1501, Aug. 2016. [18] H. Mostafaei, "Stochastic barrier coverage in wireless sensor networks based on distributed learning automata," Comput. Commun., vol. 55, pp. 51-61, Jan. 2015. [19] H. Mostafaei and M. R. Meybodi, "Maximizing lifetime of target coverage in wireless sensor networks using learning automata," Wirel. Pers. Commun., vol. 71, no. 2, pp. 1461-1477, Jul. 2013. [20] H. Mostafaei, A. Montieri, V. Persico, and A. Pescapé, "A sleep scheduling approach based on learning automata for WSN partialcoverage," J. Netw. Comput. Appl., vol. 80, pp. 67-78, Feb. 2017. [21] S. Misra, P. V. Krishna, K. Kalaiselvan, V. Saritha, and M. S. Obaidat, "Learning automata-based QoS framework for cloud IaaS," IEEE Trans. Netw. Serv. Manag., vol. 11, no. 1, pp. 15-24, Feb. 2014. [22] P. V. Krishna, S. Misra, D. Nagaraju, V. Saritha, and M. S. Obaidat, "Learning automata based decision making algorithm for task offloading in mobile cloud," in Proc. IEEE Int. Conf. Comput. Inf. Telecommun. Syst., 6 pp., Kunming, China, 6-8 Jul. 2016. [23] A. A. Rahmanian, M. Ghobaei-Arani, and S. Tofighy, "A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment," Futur. Gener. Comput. Syst., vol. 79, pp. 54-71, Feb. 2018. [24] A. Rezvanian and M. R. Meybodi, "A new learning automata-based sampling algorithm for social networks," Int. J. Commun. Syst., vol. 30, no. 5, pp. 1-21, Mar. 2017. [25] J. A. Torkestani and M. R. Meybodi, "Solving the minimum spanning tree problem in stochastic graphs using learning automata," in Proc. Int. Conf. on Information Management and Engineering, pp. 643-647, Kuala Lumpur, Malaysia, 3-5 Apr. 2009. [26] M. R. Meybodi and H. Beigy, "Solving stochastic shortest path problem using Monte Carlo sampling method: a distributed learning automata approach," Neural Networks and Soft Computing, Springer, pp. 626-631, 2003. [27] A. Rezvanian and M. R. Meybodi, "Finding minimum vertex covering in stochastic graphs: a learning automata approach," Cybern. Syst., vol. 46, no. 8, pp. 698-727, Nov. 2015. [28] M. R. Mollakhalili Meybodi and M. R. Meybodi, "Extended distributed learning automata," Appl. Intell., vol. 41, no. 3, pp. 923-940, Oct. 2014. [29] J. A. Torkestani and M. R. Meybodi, "A new vertex coloring algorithm based on variable action-set learning automata," Comput. Informatics, vol. 29, no. 3, pp. 447-466, 2010. [30] M. R. Mirsaleh and M. R. Meybodi, "A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem," Memetic Comput., vol. 8, no. 3, pp. 211-222, Sept. 2016. [31] J. A. Torkestani and M. R. Meybodi, "A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs," J. Supercomput., vol. 59, no. 2, pp. 1035-1054, Feb. 2012. [32] M. Alipour, "A learning automata based algorithm for solving traveling salesman problem improved by frequency-based pruning," Environment, vol. 46, no. 17, pp. 7-13, Jan. 2012. [33] M. Hasanzadeh-Mofrad and A. Rezvanian, "Learning automata clustering," J. Comput. Sci., vol. 24, pp. 379-388, Jan. 2018. [34] A. Rezvanian, A. M. Saghiri, S. M. Vahidipour, M. Esnaashari, and M. R. Meybodi, Recent Advances in Learning Automata, vol. 754, Springer Nature, 2018. [35] M. A. L. Harita and B. Thathachar, "Learning automata with changing number of actions," IEEE Trans. Syst. Man, Cybern.-Part A Syst. Humans, vol. 17, no. 6, pp. 1095-1100, Nov./Dec. 1987. [36] H. Beigy and M. Meybodi, Intelligent Channel Assignment in Cellular Networks: A Learning Automata Approach, Amirkabir University of Technology, 2004. [37] M. Thathachar and K. Ramakrishnan, "A hierarchical system of learning automata," IEEE Trans. Syst. Man. Cybern., vol. 11, no. 3, pp. 236-241, Mar. 1981. [38] H. Beigy and M. R. Meybodi, "Utilizing distributed learning automata to solve stochastic shortest path problems," Int. J. Uncertainty, Fuzziness Knowledge-Based Syst., vol. 14, no. 5, pp. 591-615, Oct. 2006. [39] F. Norman, "On the linear model with two absorbing," J. Math. Psychol., vol. 5, no. 2, pp. 225-241, Jun. 1968. [40] S. Lakshmivarahan and M. Thathachar, "Bounds on the convergence probabilities of learning automata," IEEE Trans. Syst. Man, Cybern.-Part A Syst. Humans, vol. 6, no. 11, pp. 756-763, Jun. 1976. [41] J. Nešetřil, E. Milková, and H. Nešetřilová, "Otakar Borůvka on minimum spanning tree problem translation of both the 1926 papers, comments, history," Discrete Math., vol. 233, no. 1-3, pp. 3-36, Apr. 2001. [42] R. C. Prim, "Shortest connection networks and some generalizations," Bell Syst. Tech. J., vol. 36, no. 6, pp. 1389-1401, Nov. 1957. [43] J. B. Kruskal, "On the shortest spanning sub tree of a graph and the traveling salesman problem," American Mathematical Society, vol. 17, no. 1, pp. 48-50, Feb. 1956. [44] R. L. Graham and P. Hell, "On the history of the minimum spanning tree problem," IEEE Ann. Hist. Comput., vol. 7, no. 1, pp. 43-57, Jan. 1985. [45] M. Mares, "Two linear time algorithms for MST on minor closed graph classes," Arch. Math., vol. 40, pp. 315-320, 2004. [46] M. Khan, V. S. A. Kumar, G. Pandurangan, and G. Pei, "A fast distributed approximation algorithm for minimum spanning trees in the SINR model," in Proc. of the 26th Int. Conf. on Distributed Computing, DISC'12, pp. 409-410, Salvador, Brazil, 16-18 Oct. 2012. [47] D. R. Karger, P. N. Klein, and R. E. Tarjan, "A randomized linear-time algorithm to find minimum spanning trees," J. ACM, vol. 42, no. 2, pp. 321-328, Mar. 1995. [48] K. W. Chong, Y. Han, and T. W. Lam, "Concurrent threads and optimal parallel minimum spanning trees algorithm," J. ACM, vol. 48, no. 2, pp. 297-323, Mar. 2001. [49] J. Garay, S. Kutten, and D. Peleg, "A sublinear time distributed algorithm for minimum-weight-spanning tree," SIAM J. Comput., vol. 27, no. 1, pp. 302-326, Feb. 1998. [50] M. Khan and G. Pandurangan, "A fast distributed approximation algorithm for minimum spanning trees," Distributed Computing, vol. 20, no.6, pp. 391-402, Apr. 2008. [51] H. Ohlsson, O. Gustafsson, and L. Wanhammar, "Implementation of low complexity FIR filters using a minimum spanning tree," in Proc. of the 12th IEEE Mediterranean Electrotechnical Conf., pp. 261-264, Dubrovnik, Croatia, 12- 15 May 2004. [52] Y. Xu, V. Olman, and D. Xu, "Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees," Bioinformatics, vol. 18, no. 4, pp. 536-545, Apr. 2002. [53] N. Päivinen, "Clustering with a minimum spanning tree of scale-free-like structure," Pattern Recognit. Lett., vol. 26, no. 7, pp. 921-930, May 2005. [54] T. Asano, B. Bhattacharya, M. Keil, and F. Yao, "Clustering algorithms based on minimum and maximum spanning trees," in Proc. of the 4th Annual Symp. on Computational Geometry, SCG'88, pp. 252-257, Urbana-Champaign, IL, USA, 6-8 Jun. 1988. [55] B. Ma, A. Hero, J. Gorman, and O. Michel, "Image registration with minimum spanning tree algorithm," in Proc. Int. Conf. on Image, vol. 1, pp. 481-484, Vancouver, Canada, 10-13 Sept. 2000. [56] N. Christofides, "Worst-case analysis of a new heuristic for the travelling salesman problem," Operations Research Forum, vol. 3, no. 1, 4 pp., Mar. 2022. [57] P. H. A. Sneath, "The application of computers to taxonomy," J. Gen. Microbiol., vol. 17, no. 1, pp. 201-226, Aug. 1957. [58] K. J. Supowit, D. A. Plaisted, and E. M. Reingold, "Heuristics for weighted perfect matching," in Proc. of the 12th Annual ACM Symp. on Theory of Computing, STOC'80, pp. 398-419, Los Angeles, CA, USA, 28- 30 Apr. 1980. [59] H. Lshii and T. Nishida, "Stochastic bottleneck spanning tree problem," Networks, vol. 13, no. 3, pp. 443-449, Sept. 1983. [60] I. B. Mohd, "Interval elimination method for stochastic spanning tree problem," Appl. Math. Comput., vol. 66, no. 2-3, pp. 325-341, Dec. 1994. [61] م. قربعلی¬پور درو و م. ر. میبدی، "یافتن درخت پوشای مینیمم در گرافهای تصادفی با استفاده از اتوماتاهای یادگیر،" مجموعه مقالات چهاردهمین کنفرانس سالانه انجمن کامپیوتر ایران، 7 صص.، تهران، 21-20 اسفند 1387. [62] J. Akbari Torkestani and M. R. Meybodi, "Learning automata-based algorithms for solving stochastic minimum spanning tree problem," Appl. Soft Comput. J., vol. 11, no. 6, pp. 4064-4077, Sept. 2011. [63] K. Liu and Q. Qing Zhao, Stochastic Online Learning for Network Optimization under Random Unknown Weights, pp. 1-18, 2012. [64] M. R. M. Meybodi and M. R. Meybodi, Extended Distributed Learning Automata: A New Method for Solving Stochastic Graph Optimization Problems, arXiv:1308.2772, p. 37, Aug. 2013. [65] K. R. Hutson and D. R. Shier, Online Supplement to 'Minimum Spanning Trees in Networks with Varying Edge Weights', pp. 1-8, retived on 29 Oct. 2020 from http://www.math.clemson.edu/~shierd/Shier/appendix2.pdf.MaryamNajimi820202217318010.66224/ijece.29104.20.2.173http://ijece.org/fa/Article/29104http://ijece.org/fa/Article/Download/29104http://ijece.org/fa/Article/Download/29104http://ijece.org/fa/Article/Download/29104http://ijece.org/fa/Article/Download/29104http://ijece.org/fa/Article/Download/29104http://ijece.org/fa/Article/Download/29104http://ijece.org/fa/Article/Download/29104[1] L. Dai, et al., "Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends," IEEE Commun. Mag., vol. 53, no. 9, pp. 74-81, Sept. 2015. [2] S. M. R. Islam, N. Avazov, O. A. Dobre, and K. S. Kwak, "Power domain non-orthogonal multiple access (NOMA) in 5G systems: potentials and challenges," IEEE Commun. Surveys Tuts., vol. 19, no. 2, pp. 721-742, 2nd Quart., 2017. [3] Y. Liu, Z. Qin, M. Elkashlan, Y. Gao, and L. Hanzo, "Enhancing the physical layer security of non-orthogonal multiple access in large-scale networks," IEEE Trans. Wireless Commun., vol. 16, no. 3, pp. 1656-1672, Mar. 2017. [4] C. Zhong and Z. Zhang, "Non-orthogonal multiple access with cooperative full-duplex relaying," IEEE Commun. Lett., vol. 20, no. 12, pp. 2478-2481, Dec. 2016. [5] B. He, A. Liu, N. Yang, and V. K. N. Lau, "On the design of secure non-orthogonal multiple access systems," IEEE J. Sel. Areas Commun., vol. 35, no. 10, pp. 2196-2206, Oct. 2017. [6] F. Zhou, Z. Chu, H. Sun, R. Q. Hu, and L. Hanzo, "Artificial noise aided secure cognitive beamforming for cooperative MISO-NOMA using SWIPT," IEEE J. Sel. Areas Commun., vol. 36, no. 4, pp. 930-931, Apr. 2018. [7] Y. Feng, S. Yan, Z. Yang, N. Yang, and J. Yuan, "Beamforming design and power allocation for secure transmission with NOMA," IEEE Trans. Wireless Commun., vol. 18, no. 5, pp. 2639-2651, May 2019. [8] F. Jameel, S. Wyne, G. Kaddoum, and T. Duong, "A comprehensive survey on cooperative relaying and jamming strategies for physical layer security," IEEE Commun. Surv. Tuts., vol. 21, no. 3, pp. 2734-2771, Jul. 2019. [9] M. Liu and Y. Liu, "Power allocation for secure SWIPT systems with wireless-powered cooperative jamming," IEEE Commun. Lett., vol. 21, no. 6, pp. 1353-1356, Jun. 2017. [10] Y. Bi and A. Jamalipour, "Accumulate then transmit: towards secure wireless powered communication networks," IEEE Trans. Vehicular Technology, vol. 67, no. 7, pp. 6301-6310, Jul. 2018. [11] K. Cao, et al., "Improving physical layer security of uplink NOMA via energy harvesting jammer," IEEE Trans. on Information Forensics and Security, vol. 16, pp. 786-799, 2020. [12] B. Rashid, A. Ahmad, S. Saleem, and A. Khan, "Joint energy efficient power and sub-channel allocation for uplink MC-NOMA networks," International J. of Communication Systems, vol. 33, no. 17, Article ID: e4606, 25 Nov. 2020. [13] K. Cao, et al., "On the security enhancement of uplink NOMA systems with jammer selection," IEEE Trans. on Communications, vol. 68, no. 9, pp. 5747 - 5763, Sept. 2020. [14] Y. Zou, "Physical-layer security for spectrum sharing systems," IEEE Trans. Wireless Commun., vol. 16, no. 2, pp. 1319-1329, Feb. 2017. [15] K. Cao, B. Wang, H. Ding, T. Li, and F. Gong, "Optimal relay selection for secure NOMA systems under untrusted users," IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 1942-1955, Feb. 2020. [16] W. Yu and R. Lui, "Dual methods for nonconvex spectrum optimization of multicarrier systems," IEEE Trans. on Commun, vol. 54, no. 7, pp. 1310-1322, Jul. 2006. [17] D. P. Bertsekas, A. Nedic, and A. E. Ozdaglar, Convex Analysis and Optimization. Belmont, MA: Athena Scientific, 2003.MehdiHosseinzadeh AghdamمرتضیآنالوییJafarTanha820202216417210.66224/ijece.29155.20.2.164http://ijece.org/fa/Article/29155http://ijece.org/fa/Article/Download/29155http://ijece.org/fa/Article/Download/29155http://ijece.org/fa/Article/Download/29155http://ijece.org/fa/Article/Download/29155http://ijece.org/fa/Article/Download/29155http://ijece.org/fa/Article/Download/29155http://ijece.org/fa/Article/Download/29155[1] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience, Hoboken, NJ, 2nd Edition, 2000. [2] D. D. Lee and H. S. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, Jan. 1999. [3] D. Kuang, J. Choo, and H. Park, "Nonnegative matrix factorization for interactive topic modeling and document clustering," In M. Emre Celebi (Ed.), Partitional Clustering Algorithms, pp. 215-243, Springer Cham, 2015. [4] S. Z. Li, X. Hou, H. Zhang, and Q. Cheng, "Learning spatially localized, parts-based representation," in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR'01, pp. 207-212, Kauai, HI, USA, 8-14. Dec. 2001. [5] W. Xu, X. Liu, and Y. Gong, "Document clustering based on nonnegative matrix factorization," in Proc. of the 26th Annual Int ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 267-273, Toronto, Canada, 28 Jul.-1 Aug. 2003. [6] D. D. Lee and H. S. Seung, "Algorithms for non-negative matrix factorization," in Proc. of the 2001 Conf. Advances in Neural Information Processing Systems, pp. 556-562, Vancouver, Canada, 3-8 Dec. 2001. [7] R. Sandler and M. Lindenbaum, "Nonnegative matrix factorization with earth mover's distance metric for image analysis," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1590-1602, Aug. 2011. [8] V. P. Pauca, J. Piper, and R. J. Plemmons, "Nonnegative matrix factorization for spectral data analysis," Linear Algebra and Its Applications, vol. 416, no. 1, pp. 29-47, Jul. 2006. [9] M. H. Aghdam and M. D. Zanjani, "A novel regularized asymmetric non-negative matrix factorization for text clustering," Information Processing & Management, vol. 58, no. 6, Article ID: 102694, 6 pp., Nov. 2021. [10] A. J. Mohammed, Y. Yusof, and H. Husni, "Document clustering based on firefly algorithm," J. of Computer Science, vol. 11, no. 3, pp. 453-465, Mar. 2015. [11] A. J. Mohammed, Y. Yusof, and H. Husni, "Determining number of clusters using firefly algorithm with cluster merging for text clustering," in Proc. Int. Visual Informatics Conf., pp. 14-24, Bangi, Malaysia, 17- 19 Nov. 2015. [12] A. Abraham, S. Das, and A. Konar, "Document clustering using differential evolution," in Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 1784-1791, Vancouver, Canada, 16-21 Jul. 2006. [13] A. Kumar and H. Daum´e, "A co-training approach for multi-view spectral clustering," in Proc. 28th Int. Conf. on Machine Learning, ICML’11, pp. 393-400, Bellevue, WA, USA, 28 Jun.-2 Jul. 2011. [14] A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: analysis and an algorithm," in Proc. of the 2002 Conf. Advances in Neural Information Processing Systems, pp. 849-856, Vancouver, Canada, 9-14 Dec. 2002. [15] D. Yogatama and K. Tanaka-Ishii, "Multilingual spectral clustering using document similarity propagation," in Proc. of the Conf. on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, vol. 2, pp. 871-879, Singapore, 6-7 Aug. 2009. [16] D. Toli´c, N. Antulov-Fantulin, and I. Kopriva, "A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering," Pattern Recognition, vol. 82, pp. 40-55, Oct. 2018. [17] C. C. Aggarwal and C. Zhai, Mining Text Data, Springer Science & Business Media, 2012. [18] F. Shahnaz, M. W. Berry, V. P. Pauca, and R. J. Plemmons, "Document clustering using nonnegative matrix factorization," Information Processing & Management, vol. 42, no. 2, pp. 373-386, Mar. 2006. [19] E. F. Gonzalez and Y. Zhang, Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization, Tech. Rep., 2005. [20] N. Gillis and S. A. Vavasis, "Fast and robust recursive algorithms for separable nonnegative matrix factorization," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 36, no. 4, pp. 698-714, Nov. 2013. [21] K. Huang, N. D. Sidiropoulos, and A. Swami, "Non-negative matrix factorization revisited: uniqueness and algorithm for symmetric decomposition," IEEE Trans. on Signal Processing, vol. 62, no. 1, pp. 211-224, Oct. 2013. [22] K. Kimura, M. Kudo, and Y. Tanaka, "A column-wise update algorithm for nonnegative matrix factorization in bregman divergence with an orthogonal constraint," Machine Learning, vol. 103, no. 2, pp. 285-306, May 2016. [23] F. Shang, L. Jiao, and F. Wang, "Graph dual regularization non-negative matrix factorization for co-clustering," Pattern Recognition, vol. 45, no. 6, pp. 2237-2250, Jun. 2012. [24] X. Ma, P. Sun, and G. Qin, "Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability," Pattern Recognition, vol. 71, pp. 361-374, Nov. 2017. [25] A. David and S. Vassilvitskii, "K-means++: The advantages of careful seeding," in Proc. of the 18th annual ACM-SIAM Symp. on Discrete Algorithms, SODA’07, pp. 1027-1035, New Orleans, LA, USA, 7-9 Jan. 2007. [26] J. N. Myhre, K. Ø. Mikalsen, S. Løkse, and R. Jenssen, "Robust clustering using a knn mode seeking ensemble," Pattern Recognition, vol. 76, pp. 491-505, Apr. 2018. [27] W. Jiang, W. Liu, and F. L. Chung, "Knowledge transfer for spectral clustering," Pattern Recognition, vol. 81, pp. 484-496, Sept. 2018. [28] U. Von Luxburg, "A tutorial on spectral clustering," Statistics and Computing, vol. 17, no. 4, pp. 395-416, Aug. 2007. [29] J. V. Munoz, M. A. Goncalves, Z. Dias, and R. da S. Torres, "Hierarchical clustering-based graphs for large scale approximate nearest neighbor search," Pattern Recognition, vol. 96, Article ID: 106970, Dec. 2019. [30] L. Rokach and O. Maimon, Clustering Methods, in Data Mining and Knowledge Discovery Handbook. Springer, pp. 321-352, 2005. [31] H. Xiong and D. Kong, "Elastic nonnegative matrix factorization," Pattern Recognition, vol. 90, pp. 464-475, Jun. 2019. [32] N. X. Vinh, J. Epps, and J. Bailey, "Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance," J. of Machine Learning Research, vol. 11, pp. 2837-2854, Dec. 2010. [33] Z. Yang, R. Algesheimer, and C. J. Tessone, "A comparative analysis of community detection algorithms on artificial networks," Scientific Reports, vol. 6, no. 1, pp. 1-18, Aug. 2016. [34] T. M. Mitchell, "Machine learning and data mining," Communications of the ACM, vol. 42, no. 11, pp. 30-36, Nov. 1999.rezasarabi miyanajisamjabbehdarinassermodiri820202211913310.66224/ijece.29183.20.2.119http://ijece.org/fa/Article/29183http://ijece.org/fa/Article/Download/29183http://ijece.org/fa/Article/Download/29183http://ijece.org/fa/Article/Download/29183http://ijece.org/fa/Article/Download/29183http://ijece.org/fa/Article/Download/29183http://ijece.org/fa/Article/Download/29183http://ijece.org/fa/Article/Download/29183[1] D. G. O. Rourke, Internet of Things (IoT) Cybersecurity Colloquium Internet of Things Cybersecurity Colloquium, 2017. [2] M. Ammar, G. Russello, and B. Crispo, "Internet of Things: a survey on the security of IoT frameworks," J. Inf. Secur. Appl., vol. 38, no. 1, pp. 8-27, Feb. 2018. [3] J. Li, Y. Qu, F. Chao, H. P. H. Shum, E. S. L. Ho, and L. Yang, "Machine learning algorithms for network intrusion detection," In L. F. Sikos (Ed.), AI in Cybersecurity, pp. 151-179, Vol. 151, Springer, 1989. [4] R. Mahmoud, T. Yousuf, F. Aloul, and I. Zualkernan, "Internet of Things (IoT) security : current status, challenges and prospective Measures," in Proc. 10th In. Conf. for Internet Technology and Secured Transactions, ICITST’15, pp. 336-341, London, UK, 14-16 2015. [5] M. F. Aziz, A. N. Khan, J. Shuja, I. A. Khan, F. G. Khan, and A. ur R. Khan, "A lightweight and compromise-resilient authentication scheme for IoTs," Trans. on Emerging Telecommunications Technologies, vol. 33, no. 3, pp. 1-17, Nov. 2019. [6] M. Abomhara and G. M. Køien, "Security and privacy in the Internet of Things: current status and open issues," in Proc. Int. Conf. Priv. Secur. Mob. Syst., 8 pp., Aalborg, Denmark, 11-14 May 2014. [7] I. Alqassem and D. Svetinovic, "A taxonomy of security and privacy requirements for the Internet of Things (IoT)," in Proc. IEEE Int. Conf. Ind. Eng. Eng. Manag., pp. 1244-1248, Bandar Sunway, Malaysia, 9-12 Dec. 2014. [8] Y. H. Chuang, N. W. Lo, C. Y. Yang, and S. W. Tang, "A lightweight continuous authentication protocol for the Internet of Things," Sensors, vol. 18, no. 4, pp. 1-26, Apr. 2018. [9] I. Traore, et al., "Dynamic sample size detection in learning command line sequence for continuous authentication," IEEE Trans. Syst. Man, Cybern. Part Bvol. 42, no. 5, pp. 1343-1356, Oct. 2012. [10] S. Mondal and P. Bours, "Continuous authentication in a real world settings," in Proc. 8th Int. Conf. on Advances in Pattern Recognition, ICAPR’15, 6 pp., Kolkata, India, 4-7 Jan. 2015. [11] A. B. Buduru and S. S. Yau, "An effective approach to continuous user authentication for touch screen smart devices," in Proc. IEEE Int. Conf. on Software Quality, Reliability and Security, QRS’15, pp. 219-226, Vancouver, Canada, 3-5Aug. 2015. [12] S. Mondal and P. Bours, "Continuous authentication and identification for mobile devices: combining security and forensics," in Proc. IEEE Int.Workshop on Information Forensics and Security, WIFS’15, 6 pp., Rome, Italy, 16-19 Nov. 2015. [13] M. L. Brocardo, I. Traore, and I. Woungang, "Toward a framework for continuous authentication using stylometry," in Proc. IEEE 28th Int. Conf. on Advanced Information Networking and Applications, pp. 106-115, Victoria, Canada, 13-16 May 2014. [14] C. Shen, Z. Cai, and X. Guan, "Continuous authentication for mouse dynamics: a pattern-growth approach," in Proc. IEEE/IFIP Int. Conf. on Dependable Systems and Networks, DSN’12, 12 pp., Boston, MA, USA, 25-28 Jun. 2012. [15] O. O. Bamasag and S. Arabia, "Towards continuous authentication in internet of things based on secret sharing scheme, " in Proc. of the Workshop on Embedded Systems Security, WESS’15, 8 pp., Amsterdam, The Netherlands, 4-9 Oct 2015. [16] H. Sethi, M. Arkko, J. Keranen, and A. Back, Practical Considerations and Implementation Experiences in Securing Smart Object Networks. Draft-Ietf-Lwig-Crypto-Sensors-06, 2018. [17] C. Bormann, M. Ersue, and A. Kernen, Terminology for Constrained-Node Networks, no. 7228. RFC Editor, May 2014. [18] T. Kothmayr, C. Schmitt, W. Hu, M. Brünig, and G. Carle, "DTLS based security and two-way authentication for the Internet of Things," Ad Hoc Networks, vol. 11, no. 8, pp. 2710-2723, Nov. 2013. [19] E. Rescorla and N. Modadugu, Datagram Transport Layer Security Version 1.2. RFC 6347, Internet Engineering Task Force (IETF). 2012. [20] P. Gope and T. Hwang, "Untraceable sensor movement in distributed IoT infrastructure," IEEE Sens. J., vol. 15, no. 9, pp. 5340-5348, Sept. 2015. [21] Y. Kawamoto, H. Nishiyama, N. Kato, Y. Shimizu, A. Takahara, and T. Jiang, "Effectively collecting data for the location-based authentication in Internet of Things," IEEE Syst. J., vol. 11, no. 3, pp. 1403-1411, Sept. 2017. [22] M. Durairaj and K. Muthuramalingam, "A new authentication scheme with elliptical curve cryptography for Internet of Things (IoT) environments," Int. J. Eng. Technol., vol. 7, no. 2, pp. 119-124, 2018. [23] M. T. Hammi, B. Hammi, P. Bellot, and A. Serhrouchni, "Bubbles of trust: a decentralized blockchain-based authentication system for IoT," Comput. Secur., vol. 78, no. 1, pp. 126-142, Jul. 2018. [24] S. Banerjee, V. Odelu, A. K. Das, S. Chattopadhyay, and Y. Park, "An efficient, anonymous and robust authentication scheme for smart home environments," Sensors, vol. 20, no. 4, pp. 1-19, Feb. 2020. [25] M. Shuai, N. Yu, H. Wang, and L. Xiong, "Anonymous authentication scheme for smart home environment with provable security," Comput. Secur., vol. 86, no. 3, pp. 132-146, Sept. 2019. [26] T. Shimshon, R. Moskovitch, L. Rokach, and Y. Elovici, "Continuous verification using keystroke dynamics," in Proc. Int. Conf. on Computational Intelligence and Securitypp. 411-415, Nanning, China, 11-14 Dec.2010. [27] P. Porambage, C. Schmitt, P. Kumar, A. Gurtov, and M. Ylianttila, "Two-phase authentication protocol for wireless sensor networks in distributed IoT applications," in Proc. IEEE Wireless Communications and Networking Conf., WCNC’14, pp. 2728-2733, Istanbul, Turkey, 6-9 Apr. 2014. [28] K. O. Bailey, J. S. Okolica, and G. L. Peterson, "User identification and authentication using multi-modal behavioral biometrics," Comput. Secur., vol. 43pp. 77-89, Mar. 2014. [29] G. Peng, G. Zhou, D. T. Nguyen, X. Qi, Q. Yang, and S. Wang, "Continuous authentication with touch behavioral biometrics and voice on wearable glasses," IEEE Trans. Human-Machine Syst., vol. 47, no. 3, pp. 404-416, Jun. 2017. [30] K. Niinuma, U. Park, and A. K. Jain, "Soft biometric traits for continuous user authentication," IEEE Trans. Inf. Forensics Secur., vol. 5, no. 4, pp. 771-780, Dec. 2010. [31] K. Mock, J. Weaver, and M. Milton, "Real-time continuous iris recognition for authentication using an eye tracker," in Proc. of the 2012 ACM Conf. on Computer and Communications Security, CCS’12, pp. 1007-1009, Raleigh, NC, USA, 16-18 Oct. 2012. [32] L. Zhou, C. Su, W. Chiu, and K. Yeh, "You think, therefore you are: transparent authentication system with brainwave-oriented bio-features for IoT networks," IEEE Trans. Emerg. Top. Comput., vol. 8, no. 2, pp. 303-312, Apr. 2020. [33] P. N. Mahalle, N. R. Prasad, and R. Prasad, "Threshold cryptography-based group authentication (TCGA) scheme for the Internet of Things (IoT)," in Proc. 4th Inte. Conf. on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems, VITAE’14, 5 pp., Aalborg, Denmark, 11-14 May 2014. [34] S. Seitz, L. Gerdes, S. Selander, G. Mani, and M. Kumar, Use Cases for Authentication and Authorization in Constrained Environments, RFC 7744, Internet Engineering Task Force (IETF). 2016. [35] H. Khemissa and D. Tandjaoui, "A lightweight authentication scheme for E-health applications in the context of Internet of Things," in Proc. 9th Int. Conf. Next Gener. Mob. Appl. Serv. Technol., pp. 90-95, Cambridge, UK, 9-11 Sept. 2015. [36] H. Khemissa and D. Tandjaoui, "A novel lightweight authentication scheme for heterogeneous wireless sensor networks in the context of Internet of Things," in Proc. Wirel. Telecommun. Symp., 6 pp., London, UK, 18-20 Apr. 2016. [37] M. Hamada, S. Kumari, and A. Kumar, "Secure anonymous mutual authentication for star two-tier wireless body area networks," Comput. Methods Programs Biomed., vol. 135, pp. 37-50, Jul. 2016. [38] C. Chen, B. Xiang, T. Wu, and K. Wang, "An anonymous mutual authenticated key agreement scheme for wearable sensors in wireless body area networks," Appl. Sci. (Basel), vol. 8, no. 7, pp. 1-15, Jul. 2018. [39] Z. Xu, C. Xu, W. Liang, J. Xu, and H. Chen, "A lightweight mutual authentication and key agreement scheme for medical Internet of Things," IEEE Access, vol. 7, pp. 53922-53931, 2019. [40] S. Swain, Priority Based Rate Control in Wireless Sensor Networks, 2013. [41] A. Armando, D. Basin, Y. Boichut, Y. Chevalier, and L. Compagna, "The AVISPA Tool for the Automated Validation," in Proc. Int. Conf. on Computer Aided Verification, CAV’05, pp. 281-285, Edinburgh, Scotland, UK, 6-10 Jul. 2005. [42] D. Dolev and A. Yao, "On the security of public key protocols," IEEE Trans. Inf. Theory, vol. 29, no. 2, pp. 198-208, Mar. 1983. [43] R. Amin and G. P. Biswas, "A secure light weight scheme for user authentication and key agreement in multi-gateway based wireless sensor networks," Ad Hoc Networks, vol. 36no. 1, pp. 58-80, Jun. 2016.سیناغضنفری پورM.KhademiAbbasEbrahimi moghadam820202215516310.66224/ijece.29195.20.2.155http://ijece.org/fa/Article/29195http://ijece.org/fa/Article/Download/29195http://ijece.org/fa/Article/Download/29195http://ijece.org/fa/Article/Download/29195http://ijece.org/fa/Article/Download/29195http://ijece.org/fa/Article/Download/29195http://ijece.org/fa/Article/Download/29195http://ijece.org/fa/Article/Download/29195[1] R. Mayer, R. Neumayer, and A. Rauber, "Combination of audio and lyrics features for genre classification in digital audio collections," in Proc. of the 16th ACM Int. Conf. on Multimedia, pp. 159-168, Vancouver, Canada, 26-31 Oct. 2008. [2] R. Rajan and H. A. Murthy, "Music genre classification by fusion of modified group delay and melodic features," in 23rd National Conf. on Communications, NCC’17, 6 pp. Chennai, India, 2-4 Mar 2017. [3] Y. Wang, "Research on music recognition algorithm based on RBF neural network," Revista de la Facultad de Ingenieria, vol. 32, no. 8, pp. 707-712, Jan. 2017. [4] G. K. Birajdar and M. D. Patil, "Speech/music classification using visual and spectral chromagram features," J. of Ambient Intelligence and Humanized Computing, vol. 11, no. 1, pp. 329-347, Jan. 2020. [5] J. H. Foleiss and T. F. Tavares, "Texture selection for automatic music genre classification," Applied Soft Computing, vol. 89, no. C, Article ID: 106127, Apr. 2020. [6] W. H. Chang, J. L. Li, Y. S. Lin, and C. C. Lee, "A genre-affect relationship network with task-specific uncertainty weighting for recognizing induced emotion in music," in Proc. IEEE Int. Conf. on Multimedia and Expo, ICME’18, 6 pp., San Diego, CA, USA, 23-27 Jul. 2018. [7] A. Elbir, H. O. İlhan, G. Serbes, and N. Aydın, "Short time fourier transform based music genre classification," in Proc. Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT’18, 4 pp., Istanbul, Turkey, 18-19 Apr.. 2018. [8] E. Simas Filho, E. Borges Jr., and A. Fernandes Jr., "Genre classification for brazilian music using independent and discriminant features," Journal of Communication and Information Systems, vol. 33, no. 1, pp. 104-112, May 2018. [9] Y. M. G. Costa, L. S. Oliveira, A. L. Koerich, F. Gouyon, and J. G. Martins, "Music genre classification using LBP textural features," Signal Processing, vol. 92, no. 11, pp. 2723-2737, Nov. 2012. [10] A. K. Singh, R. Singh, and A. Dwivedi, "Mel frequency cepstral coefficients based text independent Automatic Speaker Recognition using matlab," in Proc. Int. Conf. on Reliability Optimization and Information Technology, ICROIT’14, pp. 524-527, Faridabad, India, 6-8 Feb. 2014. [11] C. Silla, C. A. A. Kaestner, and A. L. Koerich, "Automatic music genre classification using ensemble of classifiers," in Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 1687-1692, Montreal, Canada, 7-10 Oct. 2007. [12] G. Tzanetakis and P. Cook, "Musical genre classification of audio signals," IEEE Trans. on Speech and Audio Processing, vol. 10, no. 5, pp. 293-302, Jul. 2002. [13] Y. Bengio, A. Courville, and P. Vincent, "Representation learning: a review and new perspectives," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, Aug. 2013. [14] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, May 2015. [15] W. Shi and X. Fan, "Speech classification based on cuckoo algorithm and support vector machines," in Proc. 2nd IEEE Int. Conf. on Computational Intelligence and Applications, ICCIA’17, pp. 98-102, Beijing, China, 8-11 Sept. 2017. [16] S. Sharma, P. Fulzele, and I. Sreedevi, "Novel hybrid model for music genre classification based on support vector machine," IEEE Symp. on Computer Applications & Industrial Electronics, ISCAIE’18, pp. 395-400, , Penang, Malaysia, 28-29 Apr. 2018. [17] D. Chaudhary, N. P. Singh, and S. Singh, "Genre based classification of hindi music," in Proc. Int. Conf. on Innovations in Bio-Inspired Computing and Applications, pp. 73-82, Kochi, India, 23-24 Nov. 2019. [18] J. Li, J. Ding, and X. Yang, "The regional style classification of chinese folk songs based on GMM-CRF model," in Proc. of the 9th Int. Conf. on Computer and Automation Engineering, ICCAE'17, pp. 66-72, Sydney, Australia, 18-21 Feb. 2017. [19] C. Kaur and R. Kumar, "Study and analysis of feature based automatic music genre classification using Gaussian mixture model," in Proc. Int. Conf. on Inventive Computing and Informatics, ICICI’17, pp. 465-468, , Coimbatore, India, 23-24 Nov. 2017. [20] D. G. Bhalke, B. Rajesh, and D. S. Bormane, "Automatic genre classification using fractional fourier transform based mel frequency cepstral coefficient and timbral features," Archives of Acoustics, vol. 42, no. 2, pp. 213-222, Jan. 2017. [21] A. Sridharan, Music Similarity Estimation, Master's Projects, 607, 2018, DOI: https://doi.org/10.31979/etd.8nz2-b9yavol [22] A. Acharya, Detecting the Trend in Musical Taste Over the Decade: A Novel Feature Extraction Algorithm to Classify Musical Content with Simple features, arXiv preprint arXiv:1901.02053, 2018. [23] Y. LeCun, et al., "Backpropagation applied to handwritten zip code recognition," Neural Computation, vol. 1, pp. 541-551, 1989. [24] E. J. Humphrey and J. P. Bello, "Rethinking automatic chord recognition with convolutional neural networks," in Proc. 11th Int. Conf. on Machine Learning and Applications, vol. 2, pp. 357-362, Boca Raton, FL, USA, 12-15 Dec. 2012. [25] E. J. Humphrey, J. P. Bello, and Y. LeCun, "Moving beyond feature design: deep architectures and automatic feature learning in music informatics," in Proc. 13th Int. Society for Music Information Retrieval Conf. ,ISMIR’12, pp. 403-408, Porto, Portugal, 8-12 Oct. 2012. [26] J. Schlüter and S. Böck, "Improved musical onset detection with convolutional neural networks," in Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP’14, pp. 6979-6983, Florence, Italy, 4-9 May 2014. [27] T. Nakashika, C. Garcia, and T. Takiguchi, "Local-feature-map integration using convolutional neural networks for music genre classification," in Proc. 13th Annual Conf. of the Int. Speech Communication Association, INTERSPEECH’12, pp. 1752-1755, Portland, ON, USA, Sept. 2012. [28] R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. on Systems, Man, and Cybernetics, vol. 6, no. 3, pp. 610-621, Jan. 1973. [29] G. Gwardys and D. M. Grzywczak, "Deep image features in music information retrieval," International J. of Electronics and Telecommunications, vol. 60, no. 4, pp. 321-326, Dec. 2014. [30] S. Sigtia and S. Dixon, "Improved music feature learning with deep neural networks," in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP’14, pp. 6959-6963, Florence, Italy, 4-9 May 2014. [31] Y. M. G. Costa, L. S. Oliveira, and C. Silla, "An evaluation of convolutional neural networks for music classification using spectrograms," Applied Soft Computing, vol. 52, no. C, pp. 28-38, Mar. 2017. [32] L. Nanni, et al., "Combining visual and acoustic features for music genre classification," Expert Systems with Applications, vol. 45, no. C, pp. 108-117, Mar. 2016. [33] L. Nanni, Y. M. G. Costa, D. R. Lucio, C. N. Silla, and S. Brahnam, "Combining visual and acoustic features for bird species classification," in Proc. IEEE 28th Int. Conf. on Tools with Artificial Intelligence, ICTAI’16, pp. 396-401, San Jose, CA, USA, 6-8 Nov. 2016. [34] F. Medhat, D. Chesmore, and J. Robinson, "Masked conditional neural networks for audio classification," in Proc. Int.Conf. on Artificial Neural Networks. pp. 349-358, Alghero, Italy, 11-14 Sept. 2017. [35] F. Medhat, D. Chesmore, and J. Robinson, "Automatic classification of music genre using masked conditional neural networks," in Proc. IEEE Int. Conf. on Data Mining, ICDM’17, pp. 979-984, New Orleans, LA, USA, 18-21 Nov. 2017. [36] L. R. Aguiar, M. G. Y. Costa, and C. Silla, "Exploring data augmentation to improve music genre classification with convnets," in Proc. Int. Joint Conf. on Neural Networks, IJCNN’18, 8 pp., Rio de Janeiro, Brazil, 8-13 Jul 2018. [37] L. Feng, S. Liu, and J. Yao, Music Genre Classification with Paralleling Recurrent Convolutional Neural Network, arXiv preprint arXiv:1712.08370, Dec. 2017. [38] S. Panwar, A. Das, M. Roopaei, and P. Rad, "A deep learning approach for mapping music genres," in 12th System of Systems Engineering Conf., SoSE’17, 5 pp., Waikoloa, HI, USA, 18-21 Jun. 2017. [39] J. Schlüter and S. Böck, "Musical onset detection with convolutional neural networks," in Proc. 6th Int. Workshop on Machine Learning and Music, MML’13, 4 pp. Prague, Czech Republic, 23-23 Sept. 2013. [40] S. Oramas, et al., "Multimodal deep learning for music genre classification," Trans. of the International Society for Music Information Retrieval, vol. 1, no. 1, pp. 4-21, Sept. 2018. [41] J. Jakubik, "Evaluation of gated recurrent neural networks in music classification tasks," in Proc. of 38th Int. Conf. on Information Systems Architecture and Technology, ISAT’17, pp. 27-37, Szklarska Poręba, Poland, 17-19 Sept. 2018. [42] N. Chen and S. Wang, "High-level music descriptor extraction algorithm based on combination of multi-channel CNNs and LSTM," in Proc. 18th Int. Society for Music Information Retrieval Conf., ISMIR’17, pp. 509-514, Suzhou, China, 23-27 Oct. 2017. [43] D. Ghosal and M. H. Kolekar, "Musical genre and style recognition using deep neural networks and transfer learning," in Proc. APSIPA Annual Summit and Conf., pp. 978-988, Hawaii, HI, USA, 12-15 Nov. 2018. [44] P. Fulzele, R. Singh, N. Kaushik, and K. Pandey, "A hybrid model for music genre classification using LSTM and SVM," in Proc. 11th Int. Conf. on Contemporary Computing, IC3’18, 3 pp., Noida, India, 2-4 Aug. 2018. [45] R. J. M. Quinto, R. O. Atienza, and N. M. C. Tiglao, "Jazz music sub-genre classification using deep learning," in Proc. IEEE Region 10 Conf., TENCON’17, pp. 3111-3116, Penang, Malaysia, 5-8 Nov. 2017. [46] L. Soboh, I. Elkabani, and Z. Osman, "Arabic cultural style based music classification," in Proc. Int. Conf. on New Trends in Computing Sciences ICTCS’17, pp. 6-11, Amman, Jordan, 11-13 Oct. 2017. [47] S. Kanchana, K. Meenakshi, and V. Ganapathy, "Comparison of genre based tamil songs classification using term frequency and inverse document frequency," Research J. Pharm. and Tech, vol. 10, no. 5, pp. 1449-1454, Jul. 2017. [48] A. Sridharan, M. Moh, and T. Moh, "Similarity estimation for classical indian music," in Proc. 17th IEEE Int. Conf. on Machine Learning and Applications, ICMLA’18, pp. 814-819, Orlando, FL, USA, 17-20 Dec. 2018. [49] S. Chowdhuri, "PhonoNet: multi-stage deep neural networks for raga identification in hindustani classical music," in Proc. of the 2019 on Int. Conf. on Multimedia Retrieval, pp. 197-201, Ottawa, Canada, 10-13 Jun. 2019. [50] M. Bhatt and T. Patalia, "Neural network based Indian folk dance song classification using MFCC and LPC," Int. J. Intell. Eng. Syst., vol. 10, no. 3, pp. 173-183, Jun. 2017. [51] F. Mahardhika, H. L. H. S. Warnars, Y. Heryadi, and Lukas, "Indonesian's dangdut music classification based on audio features," in Proc. Indonesian Association for Pattern Recognition Int. Conf., INAPR’18, pp. 99-103 Jakarta, Indonesia, 7-8 Sept.2018. [52] س. محمودان و ا. بنوشی، "دسته‌بندی خودکار گام ماهور موسیقی ایرانی توسط یک شبکه عصبی مصنوعی،" دومین کنفرانس بین‌المللی آکوستیک و ارتعاشات دانشگاه صنعتی شریف، صص. 9-1 ، تهران، دی 1391. [53] H. Hajimolahoseini, R. Amirfattahi, and M. Zekri, "Real-time classification of Persian musical dastgahs using artificial neural network," in Proc. 16th CSI Int. Symp. on Artificial Intelligence and Signal Processing, AISP’12, pp. 157-160, Shiraz, Iran, 2-3 May 2012. [54] ب. باباعلی، آ. گرگان محمدی و ا. فرجی دیزجی، "نوا: دادگان موسیقي سنتي ایراني براي تشخیص دستگاه و سازهاي اصیل ایراني،" پردازش سیگنال پیشرفته، جلد 8، شماره 2، صص. 134-125، پاییز و زمستان 1398. [55] Md. Kamrul Hasan, S. Hussain, M. T. Hossain Setu, and Md. N. Ibne Nazrul, "Signal reshaping using dominant harmonic for pitch estimation of noisy speech," Signal Process. vol. 86, no. 5, pp. 1010-1018, May 2006. [56] Q. Wang, X. Zhao, and J. Xu, "Pitch detection algorithm based on normalized correlation function and central bias function," in Proc. 10th Int. Conf. on Communications and Networking in China, ChinaCom’15, pp. 617-620, Shanghai, China, 15-17 Aug. 2015. [57] B. S. Atal, "Automatic speaker recognition based on pitch contours," the J. of the Acoustical Society of America, vol. 52, no. 6B, pp. 1687-1697, Dec. 1972. [58] S. Gonzalez and M. Brookes, "A pitch estimation filter robust to high levels of noise (PEFAC)," in Proc. 19th European Signal Processing Conf., pp. 451-455, Barcelona, Spain, 29 Aug.-3 Sept. 2011. [59] A. M. Noll, "Cepstrum pitch determination," The J. of the Acoustical Society of America, vol. 41, no. 2, pp. 293-309, Feb. 1967. [60] T. Drugman and A. Alwan, Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics, arXiv preprint arXiv:2001.00459, Dec. 2019. [61] http://colah.github.io/posts/2015-08-Understanding-LSTMs [62] A. Graves, N. Jaitly, and A. Mohamed, "Hybrid speech recognition with deep bidirectional LSTM," in Proc. IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 273-278, Olomouc, Czech Republic, 8-12 Dec. 2013. [63] E. Charniak, Introduction to Deep Learning, the MIT Press, 2019. [64] س. غضنفری‌پور، ح. نظام‌آبادی‌پور و ع. راشدی، "تركیب ویژگي‌ها به كمك الگوریتم جستجوي گرانشي در بازیابي موسیقي ایراني مبتني بر محتوا در دستگاه ماهور،" اولین كنفرانس محاسبات تكاملي و هوش جمعي، صص. 70-65، کرمان، 21-19 اسفند 1394.مهدی خزائی820202213414410.66224/ijece.29240.20.2.134http://ijece.org/fa/Article/29240http://ijece.org/fa/Article/Download/29240http://ijece.org/fa/Article/Download/29240http://ijece.org/fa/Article/Download/29240http://ijece.org/fa/Article/Download/29240http://ijece.org/fa/Article/Download/29240http://ijece.org/fa/Article/Download/29240http://ijece.org/fa/Article/Download/29240[1] P. Agrawal, Y. Jui-Hung, C. Jyh-Cheng, and Z. Tao, "IP multimedia subsystems in 3GPP and 3GPP2: overview and scalability issues," Communications Magazine, IEEE, vol. 46, no. 1, pp. 138-145, Jan. 2008. [2] C. Shen, H. Schulzrinne, and E. Nahum, "Session Initiation Protocol (SIP) Server Overload Control: Design and Evaluation," in Principles, Systems and Applications of IP Telecommunications. Services and Security for Next Generation Networks, S. Henning, S. Radu, and N. Saverio, Eds.: Springer-Verlag, pp. 149-173, 2008. [3] V. Hilt and I. Widjaja, "Controlling overload in networks of SIP servers," in Proc. IEEE Int. Conf. on Network Protocols, pp. 83-93, Orlando, FL, USA, 19-22 Oct. 2008. [4] J. Davin, P. Riley, and M. Veloso, "CommLang: Communication for Coachable Agents," Robot Soccer World Cup VIII, vol. 3276, D. Nardi, M. Riedmiller, C. Sammut, and J. Santos-Victor, Eds. (Lecture Notes in Computer Science: Springer Berlin Heidelberg, pp. 46-59, 2005. [5] M. Wooldridge, An Introduction to MultiAgent Systems, Wiley, 2009. [6] م. عبدوس، ارائه یک مدل یادگیري تقویتی با نظارت چندسطحی در سیستم‌هاي چندعامله هولونی، پايان‌نامه دكتري در مهندسي كامپيوتر (هوش مصنوعي و رباتيك)، مهندسی کامپیوتر، دانشگاه علم و صنعت ایران، 1392. [7] H. C. Hsieh and J. L. Chen, "Distributed multi-agent scheme support for service continuity in IMS-4G-Cloud networks," Computers & Electrical Engineering, vol. 42, pp. 49-59, Feb. 2015. [8] M. Abdoos, N. Mozayani, and A. C. Bazzan, "Hierarchical control of traffic signals using Q-learning with tile coding," Applied Intelligence, vol. 40, no. 2, pp. 201-213, Mar. 2014. [9] M. Abdoos, N. Mozayani, and A. L. C. Bazzan, "Holonic multi-agent system for traffic signals control," Engineering Applications of Artificial Intelligence, vol. 26, no. 5-6, pp. 1575-1587, May/Jun. 2013. [10] Y. Yao, V. Hilaire, A. Koukam, and W. Cai, "A holonic model in wireless sensor networks," in Proc. Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing, pp. 491-495, Harbin, China, 15-17 Aug. 2008. [11] S. M. Hosseini and N. Mozayeni, "An intelligent method for resource management in wireless networks," in Proc. 5th Conf. on Information and Knowledge Technology, IKT’13, pp. 371-376, Shiraz, Iran, 28-30 May 2013. [12] M. Cossentino, N. Gaud, V. Hilaire, S. Galland, and A. Koukam, "ASPECS: an agent-oriented software process for engineering complex systems," Autonomous Agents and Multi-Agent Systems, vol. 20, no. 2, pp. 260-304, 2010. [13] M. Poikselkä, The IMS: IP Multimedia Concepts and Services, J. Wiley & Sons, 2006. [14] V. K. Gurbani and R. Jain, "Transport protocol considerations for session initiation protocol networks," Bell Labs Technical J., vol. 9, no. 1, pp. 83-97, 2004. [15] M. Ohta, "Overload control in a SIP signaling network," International J. of Electrical and Electronics Engineering, vol. 3, no. 2, pp. 87-92, 2009. [16] E. N. V. Hilt, C. Shen, and A. Abdelal, "Design considerations for session initiation protocol (SIP) overload control," Internet Engineering Task Force (IETF), Request for Comments (RFC) 6357, 2011. [17] J. Liao, J. Wang, T. Li, J. Wang, J. Wang, and X. Zhu, "A distributed end-to-end overload control mechanism for networks of SIP servers," Comput. Netw., vol. 56, no. 12, pp. 2847-2868, 12 Aug. 2012. [18] A. Akbar, S. M. Basha, and S. A. Sattar, "A cooperative overload control method for SIP servers," in Proc. Int. Conf. on Communications and Signal Processing, ICCSP’15, pp. 1296-1300, Melmaruvathur, India, 2-4 Apr. 2015. [19] H. Dong-Yeop, P. Ji Hong, Y. Seung-Wha, and K. Ki-Hyung, "A window-based overload control considering the number of confirmation massages for SIP server," in Proc.4th Int. Conf. on Ubiquitous and Future Networks, ICUFN’12, pp. 180-185, Phuket, Thailand, 4-6 Jul. 2012. [20] S. Jing, T. Ruixiong, H. Jinfeng, and Y. Bo, "Rate-based SIP flow management for SLA satisfaction," in Proc. IFIP/IEEE Int. Symp. on Integrated Network Management, pp. 125-128, Long Island, NY, USA, 1-5 Jun. 2009. [21] R. G. Garroppo, S. Giordano, S. Niccolini, and S. Spagna, "A prediction-based overload control algorithm for SIP servers," IEEE Trans. on Network and Service Management, vol. 8, no. 1, pp. 39-51, Mar. 2011. [22] S. V. Azhari, M. Homayouni, H. Nemati, J. Enayatizadeh, and A. Akbari, "Overload control in SIP networks using no explicit feedback: a window based approach," Computer Communications, vol. 35, no. 12, pp. 1472-1483, 1 Jul. 2012. [23] A. Abdelal and W. Matragi, "Signal-based overload control for SIP servers," in Proc. 7th IEEE Consumer Communications and Networking Conf., 7 pp., Las Vegas, NV, USA, 9-12 Jan. 2010. [24] Y. Hong, C. Huang, and J. Yan, "Applying control theoretic approach to mitigate SIP overload," Telecommunication Systems, vol. 54, no. 4, pp. 387-404, Dec. 2013. [25] A. Montazerolghaem, M. H. Yaghmaee, A. Leon-Garcia, M. Naghibzadeh, and F. Tashtarian, "A load-balanced call admission controller for IMS cloud computing," IEEE Trans. on Network and Service Management, vol. 13, no. 4, pp. 806-822, Dec. 2016. [26] A. Montazerolghaem, M. H. Y. Moghaddam, and A. Leon-Garcia, "OpenSIP: toward software-defined SIP networking," IEEE Trans. on Network and Service Management, vol. 15, no. 1, pp. 184-199, 2018. [27] M. Khazaei and N. Mozayani, "A dynamic distributed overload control mechanism in SIP networks with holonic multi-agent systems," Telecommunication Systems, vol. 63, no. 3, pp. 437-455, 2016. [28] M. Khazaei and N. Mozayani, "Overload management with regard to fairness in session initiation protocol networks by holonic multiagent systems," International J. of Network Management, vol. 27, no. 3, Article ID: e1969, May/Jun. 2017. [29] ا. اسماعيلي، ارائه روشی به منظور کنترل ترافیک در تقاطع شهري با استفاده از سیستم‌هاي چندعاملی هولونی، پايان‌نامه كارشناسي ارشد مهندسي كامپيوتر (هوش مصنوعي و رباتيك)، مهندسي كامپيوتر، دانشگاه علم و صنعت ایران، 1389. [30] J. Liao, J. Wang, T. Li, J. Wang, J. Wang, and X. Zhu, "A distributed end-to-end overload control mechanism for networks of SIP servers," Computer Networks, vol. 56, no. 12, pp. 2847-2868, Aug. 2012. [31] J. Wang, J. Liao, T. Li, J. Wang, J. Wang, and Q. Qi, "Probe-based end-to-end overload control for networks of SIP servers," J. of Network and Computer Applications, vol. 41, pp. 114-125, May 2014.BoshraPishgooAhmadakbari azirani82020228310010.66224/ijece.32159.20.2.83http://ijece.org/fa/Article/32159http://ijece.org/fa/Article/Download/32159http://ijece.org/fa/Article/Download/32159http://ijece.org/fa/Article/Download/32159http://ijece.org/fa/Article/Download/32159http://ijece.org/fa/Article/Download/32159http://ijece.org/fa/Article/Download/32159http://ijece.org/fa/Article/Download/32159[1] M. Antonakakis, et al., "Understanding the mirai botnet," in Proc. 26th USENIX Security Symp., pp. 1093-1110, Vancouver, Canada, 16-18 Aug. 2017. [2] A. Marzano, et al., "The evolution of bashlite and mirai IoT botnets," in Proc. IEEE Symp. on Computers and Communications, ISCC'18, pp. 813-818, Natal, Brazil,25-28 Jun. 2018. [3] S. García, A. Zunino, and M. Campo, "Survey on network‐based botnet detection methods," Security and Communication Networks, vol. 7, no. 5, pp. 878-903, May. 2014. [4] R. Alhajri, R. Zagrouba, and F. Al-Haidari, "Survey for anomaly detection of IoT botnets using machine learning auto-encoders," Int. J. Appl. Eng. Res, vol. 14, no. 10, pp. 2417-2421, Jul. 2019. [5] R. Azmi and B. Pishgoo, "STLR: a novel danger theory based structural TLR algorithm," The ISC International J. of Information Security, vol. 5, no. 2, pp. 209-225, Mar. 2014. [6] R. Azmi and B. Pishgoo, "SHADuDT: secure hypervisor-based anomaly detection using danger theory," Computers & Security, vol. 39, no. 1, pp. 268-288, Nov. 2013. [7] L. Yin, L. Qin, Z. Jiang, and X. Xu, "A fast parallel attribute reduction algorithm using Apache Spark," Knowledge-Based Systems, vol. 212, Article ID: 106582, Jan. 2021. [8] Y. Wu and J. Tang, "Research progress of attribute reduction based on rough set in context of big data," Computer Engineering and Applications, vol. 55, no. 6, pp. 31-38, May 2019. [9] I. A. Gheyas and L. S. Smith, "Feature subset selection in large dimensionality domains," Pattern Recognition, vol. 43, no. 1, pp. 5-13, Jan. 2010. [10] D. B. Skillicorn and S. M. McConnell, "Distributed prediction from vertically partitioned data," J. of Parallel and Distributed Computing, vol. 68, no. 1, pp. 16-36, Jan. 2008. [11] M. Riahi-Madvar, A. A. Azirani, B. Nasersharif, and B. Raahemi, "A new density-based subspace selection method using mutual information for high dimensional outlier detection," Knowledge-Based Systems, vol. 216, Article ID: 106733, Mar. 2021. [12] M. Banerjee and S. Chakravarty, "Privacy preserving feature selection for distributed data using virtual dimension," in Proc. of the 20th ACM Int. Conf. on Information and Knowledge Management, pp. 2281-2284, Glasgow, Scotland, UK, 24-28 Oct. 2011. [13] M. Bramer, Principles of Data Mining, vol. 180, pp. 231-238, London: Springer, 2007. [14] J. Qian, P. Lv, X. Yue, C. Liu, and Z. Jing, "Hierarchical attribute reduction algorithms for big data using MapReduce," Knowledge-Based Systems, vol. 73, no. 1, pp. 18-31, Jan. 2015. [15] H. Chen, T. Li, Y. Cai, C. Luo, and H. Fujita, "Parallel attribute reduction in dominance-based neighborhood rough set," Information Sciences, vol. 373, pp. 351-368, Dec. 2016. [16] W. Ding, J. Wang, and J. Wang, "Multigranulation consensus fuzzy-rough based attribute reduction," Knowledge-Based Systems, vol. 198, Article IDL. 105945, Jun. 2020. [17] H. Kalkan and B. Çetisli, "Online feature selection and classification," in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP’11, pp. 2124-2127, Prague, Czech Republic, 22-27 May 2011. [18] D. Levi and S. Ullman, "Learning to classify by ongoing feature selection," Image Vision Comput, vol. 28, no. 4, pp. 715-723, Jun. 2010. [19] N. AlNuaimi, M. M. Masud, M. A. Serhani, and N. Zaki, "Streaming feature selection algorithms for big data: a survey," Applied Computing and Informatics, vol. 18, no. 1-2, pp. 113-135, Jul. 2020. [20] N. Parveen and M. Ananthi, "Data processing for large database using feature selection," in Proc. 2nd Int. Conf. on Computing and Communications Technologies, ICCCT'17, pp. 321-326, Chennai, India, 23-24 Feb. 2017. [21] L. Brezočnik, I. Fister, and V. Podgorelec, "Swarm intelligence algorithms for feature selection: a review," Applied Sciences, vol. 8, no. 9, Article ID:. 1521, Sept 2018. [22] N. Abd-Alsabour, "A review on evolutionary feature selection," in Proc. European Modelling Symp., pp. 20-26, Pisa, Italy, 21-23 Oct. 2014. [23] N. Heidari, R. Azmi, and B. Pishgoo, "Fabric textile defect detection, by selecting a suitable subset of wavelet coefficients, through genetic algorithm," International J. of Image Processing, vol. 5, no. 1, pp. 25-35, Jan. 2011. [24] R. Azmi, B. Pishgoo, N. Norozi, M. Koohzadi, and F. Baesi, "A hybrid GA and SA algorithms for feature selection in recognition of hand-printed Farsi characters," in Proc. IEEE Int. Conf. on Intelligent Computing and Intelligent Systems, vol. 3, pp. 384-387, Xiamen, China ,29-31Oct. 2010. [25] Y. Xing, H. Shu, H. Zhao, D. Li, and L. Guo, "Survey on botnet detection techniques: classification, methods, and evaluation," Mathematical Problems in Engineering, vol. 2021, no. 1, pp. 1-24, Jan. 2021. [26] S. Almutairi, S. Mahfoudh, S. Almutairi, and J. S. Alowibdi, "Hybrid botnet detection based on host and network analysis," J. of Computer Networks and Communications, vol. 2020, no. 1, pp. 1-17, Jan. 2020. [27] A. Karim, R. B. Salleh, M. Shiraz, et al., "Botnet detection techniques: review, future trends, and issues," J. of Zhejiang University-Science C, vol. 15, no. 11, pp. 943-983, Nov. 2014. [28] K. Sinha, V. Arun, and B. Julian, "Tracking temporal evolution of network activity for botnet detection," https:// arxiv.org/abs/1908.03443, 2013. [29] W. T. Strayer, R. Walsh, C. Livadas, and D. Lapsley, "Detecting botnets with tight command and control," in Proc. 31st IEEE Conf. on Local Computer Networks, pp. 195-202, Tampa, FL, USA, 14-16 Nov. 2006. [30] E. Passerini, R. Paleari, L. Martignoni, and D. Bruschi, "Fluxor: detecting and monitoring fast-flux service networks," in Proc. Int. Conf. on Detection of Intrusions and Malware and Vulnerability Assessment, pp. 186-206, Paris, France, 10-11 Jul. 2008. [31] T. F. Yen and M. K. Reiter, "Traffic aggregation for malware detection," in Proc. Int. Conf. on Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 207-227, Paris, France, 10-11 Jul. 2008.. [32] S. Kondo and N. Sato, "Botnet traffic detection techniques by C&C session classification using SVM," in Proc. Int. Workshop on Security, pp. 91-104, Nara, Japan, 29-31 Oct. 2007. [33] J. François, S. Wang, and T. Engel, "BotTrack: tracking botnets using NetFlow and PageRank," in Proc. Int. Conf. on Research in Networking, pp. 1-14, Valencia, Spain, 9-13 May 2011. [34] G. Gu, R. Perdisci, J. Zhang, and W. Lee, "Botminer: clustering analysis of network traffic for protocol-and structure-independent botnet detection," in Proc. USENIX Security Symp., pp. 139-154, San Jose, CA, USA, 28 Jul.-1 Aug. 2008. [35] W. Jung, H. Zhao, M. Sun, and G. Zhou, "IoT botnet detection via power consumption modeling," Smart Health, vol. 15, Article ID: 100103, Mar. 2020. [36] Y. Zhang and Z. O. U. Fu-Tai, "Detection method of malicious domain name based on knowledge map," Communications Technology, vol. 53, no. 1, pp. 168-173, Jan. 2020. [37] C. Yin, Research on Network Anomaly Detection Technology Based on Deep Learning, University of Information Engineering, Strategic Support Forces, Zhengzhou, China, 2018. [38] R. Vinayakumar, et al., "A visualized botnet detection system based deep learning for the internet of things networks of smart cities," IEEE Trans. on Industry Applications, vol. 56, no. 4, pp. 4436-4456, Feb. 2020. [39] S. I. Popoola, et al., "Federated deep learning for zero-day botnet attack detection in IoT edge devices," IEEE Internet of Things J., vol. 9, no. 9, pp. 3930-3944, Jul. 2021. [40] A. Almomani, "Fast-flux hunter: a system for filtering online fast-flux botnet," Neural Computing and Applications, vol. 29, no. 7, pp. 483-493, Aug. 2018. [41] M. Alauthman, N. Aslam, M. Alkasassbeh, S. Khan, A. AL-qerem, and K. K. Raymond Choo, "An efficient reinforcement learning-based botnet detection approach," J. of Network and Computer Applications, vol. 52, Article ID: 102479, Jan. 2019. [42] H. T. Nguyen, Q. D. Ngo, D. H. Nguyen, et al., "PSI-rooted subgraph: a novel feature for iot botnet detection using classifier algorithms," ICT Express, vol. 6, no. 2, pp. 128-138, Jun. 2020. [43] D. Zhuang and J. M. Chang, "PeerHunter: detecting peerto-peer botnets through community behavior analysis," in Proc. of the IEEE Conf. on Dependable and Secure Computing, pp. 493-500, Taipei, Taiwan, 7-10 Aug.. 2017. [44] M. Habib, I. Aljarah, H. Faris, and S. Mirjalili, "Multiobjective particle swarm optimization for botnet detection in internet of things," Evolutionary Machine Learning Techniques, pp. 203-209, Berlin: Germany, Springer, 2020. [45] A. Al Shorman, H. Faris, and I. Aljarah, "Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection," J. of Ambient Intelligence and Humanized Computing, vol. 11, no. 7, pp. 2809-2825, Jul. 2020. [46] S. Y. Huang, C. H. Mao, and H. M. Lee, "Fast-flux service network detection based on spatial snapshot mechanism for delay-free detection," in Proc. of the 5th ACM Symposium on Information, Computer and Communications Security, pp. 101-111, ، Beijing China , 13-16 Apr. 2010. [47] S. Garg, M. Guizani, S. Guo, and C. Verikoukis, "Guest editorial special section on AI-driven developments in 5G-envisioned industrial automation: big data perspective," IEEE Trans. on Industrial Informatics, vol. 16, no. 2, pp. 1291-1295, Nov. 2020. [48] X. Wang, Q. Yang, and X. Jin, "Periodic communication detection algorithm of botnet based on quantum computing," J. of Quantum Electronics, vol. 33, no. 2, pp. 182-187, Mar. 2016. [49] M. Albanese, S. Jajodia, and S. Venkatesan, "Defending from stealthy botnets using moving target defenses," IEEE Security & Privacy, vol. 16, no. 1, pp. 92-97, Feb. 2018. [50] Z. Zha, A. Wang, Y. Guo, D. Montgomery, and S. Chen, "BotSifter: an SDN-based online bot detection framework in data centers," in Proc. of the IEEE Conf. on Communications and Network Security, CNS’19, pp. 142-150, Washington, D.C., USA, 10-12 Jun. 2019. [51] G. Spathoulas, N. Giachoudis, G. P. Damiris, and G. Theodoridis, "Collaborative blockchain-based detection of distributed denial of service attacks based on internet of things botnets," Future Internet, vol. 11, Article ID: 226, Oct. 2019. [52] J. Warren and N. Marz, Big Data: Principles and Best Practices of Scalable Realtime Data Systems, Simon and Schuster, 2015. [53] B. Twardowski and D. Ryzko, "Multi-agent architecture for real-time big data processing," in Proc. IEEE/WIC/ACM Int.Joint Conf. on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 333-337, Warsaw, Poland, 11-13 Aug. 2014. [54] M. Di Capua, E. Di Nardo, and A. Petrosino, "An architecture for sentiment analysis in twitter," in Proc. of Int. Conf. on E-learning, Germany, 10 pp., Berlin, Germany, 11-12 Sept. 2015. [55] V. Nair, "Aligning Machine Learning for the Lambda Architecture," 2015. [56] -, Madrid. Lambdoop. Retrieved from www.lambdoop.com, 2014. [57] -, MemSQL. The Lambda Architecture Simplified, 2016. [58] V. Astakhov and M. Chayel, Lambda Architecture for Batch and Real-Time Processing on AWS with Spark Streaming and Spark SQL, Amazon Web Services, p. 12, 2015. [59] S. P. T. Krishnan and J. L. U. Gonzalez, Building Your Next Big Thing with Google Cloud Platform: A Guide for Developers and Enterprise Architects, Apress, 2015. [60] W. Fan and A. Bifet, "Mining big data: current status, and forecast to the future," ACM SIGKDD Explorations Newsletter, vol. 14, no. 2, pp. 1-5, Apr. 2013. [61] S. Landset, T. M. Khoshgoftaar, A. N. Richter, and T. Hasanin, "A survey of open source tools for machine learning with big data in the Hadoop ecosystem," J. of Big Data, vol. 2, no. 1, pp. 1-36, Dec. 2015. [62] A. Bifet, "Mining big data in real time," Informatica, vol. 37, no. 1, pp. 15-20, Jan. 2013. [63] A. Mahesh and P. Manimegalai, "An efficient data processing architecture for smart environments using large scale machine learning," IIOAB J., Special Issue, Emerging Technologies in Networking and Security, vol. 7, no. 9, pp. 795-803, Aug. 2016. [64] C. H. Kumar and A. S. Sangari, "An efficient distributed data processing method for smart environment," Indian J. Sci. Technol., vol. 9, no. 31, pp. 380-384, Aug. 2016. [65] X. Liu and P. S. Nielsen, "Scalable prediction-based online anomaly detection for smart meter data," Information Systems, vol. 77, no. 3, pp. 34-47, Sept. 2018. [66] G. Iuhasz, D. Pop, and I. Dragan, "Architecture of a scalable platform for monitoring multiple big data frameworks," Scalable Computing: Practice and Experience, vol. 17, no. 4, pp. 313-321, Oct. 2016. [67] M. Kiran, et al., "Lambda architecture for cost-effective batch and speed big data processing," in Proc. IEEE Int Conf. on Big Data (Big Data), pp. 2785-2792, Santa Clara, CA, USA, 29 Oct.-1 Nov. 2015. [68] -, Oryx 1, Retrieved from https://github.com/certxg/oryx-1, 2013. [69] -,Oryx2, Retrieved from http://oryx.io/, 2014. [70] R. C. Fernandez, et al., "Liquid: unifying nearline and offline big data integration," in Proc. 7th Biennial Conf. on Innovative Data Systems Research, CIDR’15, 8 pp., Asilomar, CA, USA, 4-7 Jan. 2105. [71] D. Namiot, "On big data stream processing," International J. of Open Information Technologies, vol. 3, no. 8, pp. 48-51, aUG. 2015. [72] L. Magnoni, et al., "Monitoring WLCG with lambda-architecture: a new scalable data store and analytics platform for monitoring at petabyte scale," J. of Physics: Conf. Series, vol. 664, no. 5, Article ID:. 052023, Dec. 2015. [73] F. Yang, et al., The RADStack: Open Source Lambda Architecture for Interactive Analytics, 2017. [74] B. Pishgoo, A. A. Azirani, and B. Raahemi, "A hybrid distributed batch-stream processing approach for anomaly detection," Information Sciences, vol. 543, pp. 309-327, Jan. 2021. [75] J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: a new perspective," Neurocomputing, vol. 300, no. 7, pp. 70-79, Jul. 2018. [76] N. Y. Almusallam, Z. Tari, P. Bertok, and A. Y. Zomaya, "Dimensionality reduction for intrusion detection systems in multi-data streams-a review and proposal of unsupervised feature selection scheme," Emergent Computation, vol. 2017, pp. 467-487, Jan. 2017. [77] J. Li and H. Liu, "Challenges of feature selection for big data analytics," IEEE Intelligent Systems, vol. 32, no. 2, pp. 9-15, Mar. 2017. [78] R. Xu, et al., "Dynamic feature selection algorithm based on Q-learning mechanism," Applied Intelligence, vol. 51, no. 10, pp. 7233-7244, Oct. 2021. [79] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos, "Recent advances and emerging challenges of feature selection in the context of big data," Knowledge-Based Systems, vol. 86, no. 9, pp. 33-45, Sept. 2015. [80] J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu, "Feature selection: a data perspective," ACM Computing Surveys (CSUR), vol. 50, no. 6, pp. 1-45, Dec. 2017. [81] C. Fahy and S. Yang, "Dynamic feature selection for clustering high dimensional data streams," IEEE Access, vol. 7, pp. 127128-127140, Jul. 2019. [82] J. Jesus, A. Canuto, and D. Araújo, "Dynamic feature selection based on pareto front optimization," in International Joint Conf. on Neural Networks, IJCNN’18, 8 pp., Rio de Janeiro, Brazi, 8-13 Jul. 2018. [83] R. D. O. Nunes, C. A. Dantas, A. M. Canuto, and J. C. Xavier-Júnior, "An unsupervised-based dynamic feature selection for classification tasks," in Proc. Int. Joint Conf. on Neural Networks, IJCNN’16, pp. 4213-4220, Vancouver, Canada, 24-29 Jul. 2016. [84] J. P. Barddal, H. M. Gomes, F. Enembreck, and B. Pfahringer, "A survey on feature drift adaptation: definition, benchmark, challenges and future directions," J. of Systems and Software, vol. 127, no. 5, pp. 278-294, May 2017. [85] G. Wei, J. Zhao, Y. Feng, A. He, and J. Yu, "A novel hybrid feature selection method based on dynamic feature importance," Applied Soft Computing, vol. 93, no. 8, Article ID:. 106337, Aug. 2020. [86] S. Perkins, K. Lacker, and J. Theiler, "Grafting: fast, incremental feature selection by gradient descent in function space," The J. of Machine Learning Research, vol. 3, no. 3, pp. 1333-1356, Mar. 2003. [87] I. Katakis, G. Tsoumakas, and I. Vlahavas, "Dynamic feature space and incremental feature selection for the classification of textual data streams," in Proc. Int. Workshop on Knowledge Discovery from Data Streams, ECML/PKDD’06, . pp. 107-116, Berlin, Germany, 18-22 Sept. 2006. [88] J. Zhou, D. Foster, R. Stine, and L. Ungar, "Streaming feature selection using alpha-investing," in Proc. of the 11th ACM SIGKDD International Conf. on Knowledge Discovery in Data Mining, pp. 384-393, Chicago, IL, USA, 21-24 Aug. 2005. [89] X. Wu, K. Yu, H. Wang, and W. Ding, "Online streaming feature selection," in Proc. 27th Int. Conf. on Machine Learning, ICML’10, . pp. 1159-1166, 21-24, Jun. 2010. [90] X. Wu, K. Yu, W. Ding, H. Wang, and X. Zhu, "Online feature selection with streaming features," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1178-1192, Sept. 2012. [91] C. Zhang, J. Ruan, and Y. Tan, "An incremental feature subset selection algorithm based on boolean matrix in decision system," Convergence Information Technology, vol. 6, no. 12, pp. 16-23, Dec. 2011. [92] M. Masud, J. Gao, L. Khan, J. Han, and B. M. Thuraisingham, "Classification and novel class detection in concept-drifting data streams under time constraints," IEEE Trans. on Knowledge and Data Engineering, vol. 23, no. 6, pp. 859-874, Jun. 2010. [93] H. L. Nguyen, Y. K. Woon, W. K. Ng, and L. Wan, "Heterogeneous ensemble for feature drifts in data streams," in Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining, 12 pp., Kuala Lumpur, Malaysia, 29 May-1 Jun. 2012. [94] K. Yu, X. Wu, W. Ding, and J. Pei, "Towards scalable and accurate online feature selection for big data," in Proc. IEEE Int. Conf. on Data Mining, pp. 660-669, Shenzhen, China, 14-17 Dec. 2014. [95] F. Wang, J. Liang, and Y. Qian, "Attribute reduction: a dimension incremental strategy," Knowledge-Based Systems, vol. 39, no. 2, pp. 95-108, Feb. 2013. [96] S. Eskandari and M. M. Javidi, "Online streaming feature selection using rough sets," International J. of Approximate Reasoning, vol. 69, no. 2, pp. 35-57, Feb. 2016. [97] M. M. Javidi and S. Eskandari, "Streamwise feature selection: a rough set method," International J. of Machine Learning and Cybernetics, vol. 9, no. 4, pp. 667-676, Apr. 2018. [98] J. P. Barddal, H. M. Gomes, F. Enembreck, B. Pfahringer, and A. Bifet, "On dynamic feature weighting for feature drifting data streams," in Proc. Joint European Conf. on Machine Learning and Knowledge Discovery in Databases, pp. 129-144, Riva del Garda, Italy, 19-23 Sept. 2016. [99] J. P. Barddal, F. Enembreck, H. M. Gomes, A. Bifet, and B. Pfahringer, "Merit-guided dynamic feature selection filter for data streams," Expert Systems with Applications, vol. 116, no. 2, pp. 227-242, Feb. 2019. [100] J. C. Chamby-Diaz, M. Recamonde-Mendoza, and A. L. Bazzan, "Dynamic correlation-based feature selection for feature drifts in data streams," in Proc. 8th Brazilian Conf. on Intelligent Systems, BRACIS’19, pp. 198-203, Salvador, Brazil, 15-18 Oct. 2019. [101] J. P. Barddal, F. Enembreck, H. M. Gomes, A. Bifet, and B. Pfahringer, "Boosting decision stumps for dynamic feature selection on data streams," Information Systems, vol. 83, no. 7, pp. 13-29, Jul. 2019. [102] S. Sahmoud and H. R. Topcuoglu, "A general framework based on dynamic multi-objective evolutionary algorithms for handling feature drifts on data streams," Future Generation Computer Systems, vol. 102, no. 1, pp. 42-52, Jan. 2020. [103] Y. Meidan, et al., "N-baiot-network-based detection of iot botnet attacks using deep autoencoders," IEEE Pervasive Computing, vol. 17, no. 3, pp. 12-22, Jul.-Sept. 2018. [104] -, N-BaIoT Dataset, Retrieved from https://archive.ics.uci.edu/ml/datasets/detection_of_IoT_botnet_attacks_N_BaIoT#, 2018. [105] C. Kolias, G. Kambourakis, A. Stavrou, and J. Voas, "DDoS in the IoT: mirai and other botnets," Computer, vol. 50, no. 7, pp. 80-84, Jul. 2017. [106] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Proc. the 14th int. joint Conf. on Artificial intelligence, IJCAI'95, vol. 2, pp. 1137-1145, Montreal, Canada, 20-25 Aug. 1995. [107] T. G. Dietterich, "Approximate statistical tests for comparing supervised classification learning algorithms," Neural Computation, vol. 10, no. 7, pp. 1895-1923, Oct. 1998.