ijece-140502292014050229203057CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece168237454172024214RozbehBeglariHakemBeitollahi417202424225210.66224/ijece.36724.21.4.242http://ijece.org/fa/Article/36724http://ijece.org/fa/Article/Download/36724http://ijece.org/fa/Article/Download/36724http://ijece.org/fa/Article/Download/36724http://ijece.org/fa/Article/Download/36724http://ijece.org/fa/Article/Download/36724http://ijece.org/fa/Article/Download/36724http://ijece.org/fa/Article/Download/36724[1] D. Kreutz, et al., "Software-defined networking: a comprehensive survey," Proceeding of the IEEE, vol. 103, no. 1, pp. 14-76, Jan. 2015. [2] Q. Li, X. Zou, Q. Huang, J. Zheng, and P. P. C. Lee, "Dynamic packet forwarding verification in SDN," IEEE Trans. on Dependable and Secure Computing, vol. 16, no. 6, pp. 915-929, Dec. 2019. [3] M. Dhawan, R. Poddar, K. Mahajan, and V. Mann, "Sphinx: detecting security attacks in software-defined networks," in Proc. of Network and Distributed System Security Symp., NDSS'15, 15 pp., San Diego, CA, USA, 7-7 Feb. 2015. [4] H. Kim and N. Feamster, "Improving network management with software defined networking," IEEE Communications Magazine, vol. 51, no. 2, pp. 114-119, Feb. 2013. [5] M. Al Ahmad, M. Diab, and S. S. Patra, "Analysis and performance evaluation of openflow controller in SDN using N-policy," in Proc. of Int. Conf. on Recent Advances in Science and Engineering Technology, ICRASET'23, 5 pp., B G NAGARA, India, 23-24 Nov. 2023. [6] X. Zhang, A. Jain, and A. Perrig, "Packet-dropping adversary identification for data plane security," in Proc. of the ACM CoNEXT Conf., Article Id.: 24, 12 pp., Madrid, Spain, 9-12 Dec. 2008. [7] H. J. Kim, C. Basescu, L. Jia, S. B. Lee, Y. C. Hu, and A. Perrig, "Lightweight source authentication and path validation," ACM SIGCOMM Computer Communication Review, vol. 44, no. 4, pp. 271-282, Aug. 2014. [8] H. Beitollahi, D. M. Sharif, and M. Fazeli, "Application layer DDoS attack detection using cuckoo search algorithm-trained radial basis function," IEEE Access, vol. 10, pp. 63844-638542022. [9] S. Shin, V. Yegneswaran, P. Porras, and G. Gu, "AVANT-GUARD: scalable and vigilant switch flow management in software-defined networks," in Proc. of the ACM SIGSAC Conf. on Computer & Communications Security, pp. 413-424, Berlin, Germany, 4-8 Nov. 2013. [10] R. Mahajan, M. Rodrig, D. Wetherall, and J. Zahorjan, "Sustaining cooperation in multi-hop wireless networks," in Proc. of the 2nd Conf. on Symp. on Networked Systems Design & Implementation, vol. 2, pp. 231-244, 2-4 May 2005. [11] R. Aryan, A. Yazidi, F. Brattensborg, O. Kure, and P. E. Engelstad, "SDN spotlight: a real-time openflow troubleshooting framework," J. of Future Generation Computer Systems, vol. 133, pp. 364-377, Aug. 2022. [12] H. Yu, K. Li, and H. Qi, "An active controller selection scheme for minimizing packet-in processing latency in SDN," J. of Security and Communication Networks, vol. 2019, Article ID: 1949343, Oct. 2019. [13] H. Wang, L. Xu, and G. Gu, "FloodGuard: A DoS attack prevention extension in software-defined networks," in Proc. of 45th Annual IEEE/IFIP Int. Conf. on Dependable Systems and Networks, pp. 239-250, Rio de Janeiro, Brazil, 22-25 Jun. 2015. [14] T. Sasaki, C. Pappas, T. Lee, T. Hoefler, and A. Perrig, "SDNsec: forwarding accountability for the SDN data plane," in Proc. of 25th Int. Conf. on Computer Communication and Networks, ICCCN'16, 10 pp., Waikoloa, HI, USA, 1-4 Aug. 2016. [15] X. Liu, A. Li, X. Yang, and D. Wetherall, "Passport: secure and adoptable source authentication," in Proc. of the 5th USENIX Sympo. on Networked Systems Design and Implementation, pp. 365-378, San Francisco, CA, USA 16-18 Apr. 2008. [16] Y. Chen, Y. Yang, X. Zou, Q. Li, and Y. Jiang, "Adaptive distributed software defined networking," J. of Computer Communications, vol. 102, pp. 120-129, Apr. 2017. [17] S. Hong, R. Baykov, L. Xu, S. Nadimpalli, and G. Gu, "Towards SDN-defined programmable byod (bring your own device) security," in Proc. of NDSS'16, 15 pp., San Diego, CA, USA, 21-24 Feb. 2016. [18] H. Hu, W. Han, G. J. Ahn, and Z. Zhao, "Flowguard: building robust firewalls for software-defined networks," in Proc. of 3rd Workshop on Hot Topics in Software Defined Networking, pp. 97-102, Chicago, IL, USA, 22-22 Aug. 2014. [19] O. Blial, M. Ben Mamoun, and R. Benaini, "An overview on SDN architectures with multiple controllers," J. of Computer Networks and Communications, vol. 2016, Article ID: 9396525, Apr. 2016. [20] D. Kreutz, F. M. V. Ramos, and P. Verissimo, "Towards secure and dependable software-defined networks," in Proc. of the 2nd ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, pp. 55-60, Hong Kong, China, 16-16 Aug. 2013.MaryamFasihiMohammad Javadshayeganzahrahosienizahrasejdeh417202428429010.66224/ijece.39190.21.4.284http://ijece.org/fa/Article/39190http://ijece.org/fa/Article/Download/39190http://ijece.org/fa/Article/Download/39190http://ijece.org/fa/Article/Download/39190http://ijece.org/fa/Article/Download/39190http://ijece.org/fa/Article/Download/39190http://ijece.org/fa/Article/Download/39190http://ijece.org/fa/Article/Download/39190 [1] S. Madisetty and M. S. Desarkar, “A Neural Network-Based Ensemble Approach for Spam Detection in Twitter,” IEEE Trans. Comput. Soc. Syst., vol. 5, no. 4, pp. 973–984, Dec. 2018. [2] M. McCord and M. Chuah, “Spam detection on twitter using traditional classifiers,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6906 LNCS, pp. 175–186. [3] X. Zhang, S. Zhu, and W. Liang, “Detecting spam and promoting campaigns in the Twitter social network,” in Proceedings - IEEE International Conference on Data Mining, ICDM, 2012, pp. 1194–1199. [4] A. T. Kabakus and R. Kara, “A Survey of Spam Detection Methods on Twitter,” International Journal of Advanced Computer Science and Applications, 8(3), pp.29-38, 2017. [5] X. Zheng, Z. Zeng, Z. Chen, Y. Yu, and C. Rong, “Detecting spammers on social networks,” Neurocomputing, vol. 159, no. 1, pp. 27–34, Jul. 2015. [6] J. Martinez-Romo and L. Araujo, “Detecting malicious tweets in trending topics using a statistical analysis of language,” Expert Syst. Appl., vol. 40, no. 8, pp. 2992–3000, Jun. 2013. [7] A. M. Al-Zoubi, H. Faris, J. Alqatawna, and M. A. Hassonah, “Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts,” Knowledge-Based Syst., vol. 153, pp. 91–104, Aug. 2018. [8] S. B. S. Ahmad, M. Rafie, and S. M. Ghorabie, “Spam detection on Twitter using a support vector machine and users’ features by identifying their interactions,” Multimed. Tools Appl., vol. 80, no. 8, pp. 11583–11605, Mar. 2021. [9] Z. Alom, B. Carminati, and E. Ferrari, “A deep learning model for Twitter spam detection,” Online Soc. Networks Media, vol. 18, p. 100079, Jul. 2020. [10] X. Ban, C. Chen, S. Liu, Y. Wang, and J. Zhang, “Deep-learnt features for Twitter spam detection,” 2018 Int. Symp. Secur. Priv. Soc. Networks Big Data, Soc. 2018, pp. 22–26, Dec. 2018. [11] Y. Liu, L. Wang, T. Shi, and J. Li, “Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM,” Inf. Syst., vol. 103, p. 101865, Jan. 2022. [12] G. Jain, M. Sharma, and B. Agarwal, “Optimizing semantic LSTM for spam detection,” Int. J. Inf. Technol., vol. 11, no. 2, pp. 239–250, Jun. 2019. [13] G. Jain, M. Sharma, B. A.-A. of M. and Artificial, and undefined 2019, “Spam detection in social media using convolutional and long short term memory neural network,” Springer, 2019. [14] T. Wu, S. Liu, J. Zhang, and Y. Xiang, “Twitter spam detection based on deep learning,” ACM Int. Conf. Proceeding Ser., Jan. 2017. [15] G. M. Shahariar, S. Biswas, F. Omar, F. M. Shah, and S. Binte Hassan, “Spam Review Detection Using Deep Learning,” 2019 IEEE 10th Annu. Inf. Technol. Electron. Mob. Commun. Conf. IEMCON 2019, pp. 27–33, Oct. 2019. [16] A.T.Kabakus, and R .Kara, “‘TwitterSpamDetector’: A Spam Detection Framework for Twitter,” International Journal of Knowledge and Systems Science (IJKSS), 10(3), pp.1-14.2019. [17] H. Shen, F. Ma, X. Zhang, L. Zong, X. Liu, and W. Liang, “Discovering social spammers from multiple views,” Neurocomputing, vol. 225, pp. 49–57, Feb. 2017. [18] K. Lee, J. Caverlee, and S. Webb, “Uncovering social spammers: Social honeypots + machine learning,” in SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010, pp. 435–442. [19] C. Grier, K. Thomas, V. Paxson, and M. Zhang, “@Spam: The underground on 140 characters or less,” in Proceedings of the ACM Conference on Computer and Communications Security, 2010, pp. 27–37. [20] S. Saumya and J. P. Singh, “Spam review detection using LSTM autoencoder: an unsupervised approach,” Electron. Commer. Res., vol. 22, no. 1, pp. 113–133, Mar. 2022. [21] J. V Lochter, T. A. Almeida, and T. C. Alberto, “Tubespam: Comment spam filtering on youtube,” ieeexplore.ieee.org. [22] V. B. Semwal, A. Gupta, and P. Lalwani, “An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognition,” J. Supercomput. 2021, pp. 1–24, Apr. 2021. [23] M. Usama et al., “Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges,” IEEE Access, vol. 7, pp. 65579–65615, 2019. [1] S. Madisetty and M. S. Desarkar, "A neural network-based ensemble approach for spam detection in Twitter," IEEE Trans. Comput. Soc. Syst., vol. 5, no. 4, pp. 973-984, Dec. 2018. [2] M. McCord and M. Chuah, "Spam detection on twitter using traditional classifiers," Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. LNCS6906, pp. 175-186, Sept. 2011. [3] X. Zhang, S. Zhu, and W. Liang, "Detecting spam and promoting campaigns in the Twitter social network," in Proc. IEEE International Conf. on Data Mining, ICDM, pp. 1194-1199, Brussels, Belgium , 10-13 Dec. 2012. [4] A. T. Kabakus and R. Kara, "A survey of spam detection methods on Twitter," International J. of Advanced Computer Science and Applications, vol. 8, no. 3, pp. 29-38, 2017. [5] X. Zheng, Z. Zeng, Z. Chen, Y. Yu, and C. Rong, "Detecting spammers on social networks," Neurocomputing, vol. 159, no. 1, pp. 27-34, Jul. 2015. [6] J. Martinez-Romo and L. Araujo, "Detecting malicious tweets in trending topics using a statistical analysis of language," Expert Syst. Appl., vol. 40, no. 8, pp. 2992-3000, Jun. 2013. [7] A. M. Al-Zoubi, H. Faris, J. Alqatawna, and M. A. Hassonah, "Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts," Knowledge-Based Syst., vol. 153, pp. 91-104, Aug. 2018. [8] S. B. S. Ahmad, M. Rafie, and S. M. Ghorabie, "Spam detection on Twitter using a support vector machine and users' features by identifying their interactions," Multimed. Tools Appl., vol. 80, no. 8, pp. 11583-11605, Mar. 2021. [9] Z. Alom, B. Carminati, and E. Ferrari, "A deep learning model for Twitter spam detection," Online Soc. Networks Media, vol. 18, Article ID: 100079, Jul. 2020. [10] X. Ban, C. Chen, S. Liu, Y. Wang, and J. Zhang, "Deep-learnt features for Twitter spam detection," in Proc. Int. Symp. Secur. Priv. Soc. Networks Big Data, pp. 22-26, Santa Clara, CA, USA, 10-11 Dec. 2018. [11] Y. Liu, L. Wang, T. Shi, and J. Li, "Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM," Inf. Syst., vol. 103, Article ID: 101865, Jan. 2022. [12] G. Jain, M. Sharma, and B. Agarwal, "Optimizing semantic LSTM for spam detection," Int. J. Inf. Technol., vol. 11, no. 2, pp. 239-250, Jun. 2019. [13] G. Jain, M. Sharma, and B. Agarwal, "Spam detection in social media using convolutional and long short term memory neural network," Annals of Mathematics and Artificial Intelligence, vol. 85, no. 1, pp. 21-44, 2019. [14] T. Wu, S. Liu, J. Zhang, and Y. Xiang, "Twitter spam detection based on deep learning," in Proc. ACM Int. Conf. Proc. Ser., 8 pp., Geelong, Australia, 30 Jan.-3 Feb 2017. [15] G. M. Shahariar, S. Biswas, F. Omar, F. M. Shah, and S. Binte Hassan, "Spam review detection using deep learning," in Proc. IEEE 10th Annu. Inf. Technol. Electron. Mob. Commun. Conf., IEMCON’19, pp. 27-33, Vancouver, Canada, 17-19 Oct. 2019. [16] A. T. Kabakus and R. Kara, "‘TwitterSpamDetector’: a spam detection framework for twitter," International J. of Knowledge and Systems Science, vol. 10, no. 3, pp. 1-14, Jul. 2019. [17] H. Shen, et al., "Discovering social spammers from multiple views," Neurocomputing, vol. 225, pp. 49-57, Feb. 2017. [18] K. Lee, J. Caverlee, and S. Webb, "Uncovering social spammers: social honeypots + machine learning," in Proc. SIGIR Proc.-33rd Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 435-442, Geneva, Switzerland, 19-23 Jul. 2010. [19] C. Grier, K. Thomas, V. Paxson, and M. Zhang, "@spam: the underground on 140 characters or less," in Proc. of the ACM Conf. on Computer and Communications Security, pp. 27-37, Chicago, IL, USA, 4-8 Oct. 2010. [20] S. Saumya and J. P. Singh, "Spam review detection using LSTM autoencoder: an unsupervised approach," Electron. Commer. Res., vol. 22, no. 1, pp. 113-133, Mar. 2022. [21] J. V. Lochter, T. A. Almeida, and T. C. Alberto, "TubeSpam: comment spam filtering on YouTube," in Proc. IEEE 14th Int, Conf. on Machine Learning and Applications, pp. 138-143, Miami, FL, USA, 9-11 Dec. 2015. [22] M. M. Abdulhasan, H. Alchilibi, M. A. Mohammed, and R. Nair, "Real-time sentiment analysis and spam detection using machine learning and deep learning," in Proc. 3rd Int. Conf. on Data Science and Big Data Analytics, pp. 507-533, Indore, India, 16-17 Jun. 2023. [23] A. Ahraminezhad, M. Mojarad, and H. Arfaeinia, "An intelligent ensemble classification method for spam diagnosis in social networks," International J. of Intelligent Systems and Applications, vol. 14, no. 1, pp. 24-31, Feb. 2022. [24] Z. Alom, B. Carminati, and E. Ferrari, "A deep learning model for Twitter spam detection," Online Social Networks and Media, Article ID: 100079, Jul. 2020. [25] S. Liu, Y. Wang, J. Zhang, C. Chen, and Y. Xiang, "Addressing the class imbalance problem in twitter spam detection using ensemble learning," Computers & Security, vol. 69, pp. 35-49, Aug. 2017. [26] C. Zhao, Y. Xin, X. Li, Y. Yang, and Y. Chen, "A heterogeneous ensemble learning framework for spam detection in social networks with imbalanced data," Applied Sciences, vol. 10, no. 3, Article ID” 936, Jan. 2020. [27] M. Usama, et al., "Unsupervised machine learning for networking: techniques, applications and research challenges," IEEE Access, vol. 7, pp. 65579-65615, 2019. NasimNavaeiVesalHakami417202425326210.66224/ijece.39930.21.4.253http://ijece.org/fa/Article/39930http://ijece.org/fa/Article/Download/39930http://ijece.org/fa/Article/Download/39930http://ijece.org/fa/Article/Download/39930http://ijece.org/fa/Article/Download/39930http://ijece.org/fa/Article/Download/39930http://ijece.org/fa/Article/Download/39930http://ijece.org/fa/Article/Download/39930[1] A. H. Anwar, C. Kamhoua, and N. Leslie, "Honeypot allocation over attack graphs in cyber deception games," in Proc. IEEE Int. Conf. on Computing, Networking and Communications, ICNC’20, pp. 502-506, Big Island, HI, USA, 17-20 Feb. 2020. [2] L. Chen, Z. Wang, F. Li, Y. Guo, and K. Geng, "A stackelberg security game for adversarial outbreak detection in the Internet of Things," Sensors, vol. 20, no. 3, Article ID: 804, Feb. 2020. [3] A. H. Anwar, C. Kamhoua, and N. Leslie, "A game-theoretic framework for dynamic cyber deception in internet of battlefield things," in Proc. of the 16th EAI Int. Conf. on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 522-526, Houston, TX, USA, 12-14 Nov. 2019. [4] A. Rullo, E. Serra, E. Bertino, and J. Lobo, "Optimal placement of security resources for the Internet of Things," The Internet of Things for Smart Urban Ecosystems, pp. 95-124, Jan. 2019. [5] A. Rullo, D. Midi, E. Serra, and E. Bertino, "Pareto optimal security resource allocation for Internet of Things," ACM Trans. on Privacy and Security, vol. 20, no. 4, pp. 1-30, Nov. 2017. [6] M. Zhu, et al., "A survey of defensive deception: approaches using game theory and machine learning," IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2460-2493, Aug. 2021. [7] A. Rullo, D. Midi, E. Serra, and E. Bertino, "A game of things: strategic allocation of security resources for IoT," in Proc. IEEE/ACM 2nd Int. Conf. on Internet-of-Things Design and Implementation, IoTDI’17, pp. 185-190, Pittsburgh, PA, USA, 18-21 Apr. 2017. [8] M. A. R. Al Amin, S. Shetty, L. Njilla, D. K. Tosh, and C. Kamhoua, "Online cyber deception system using partially observable Monte Carlo planning framework," in Proc. Int. Conf. on Security and Privacy in Communication Systems, vol. 2, pp. 205-223, Orlando, FL, USA, 23-25 Oct. 2019. [9] S. Wang, Q. Pei, J. Wang, G. Tang, Y. Zhang, and X. Liu, "An intelligent deployment policy for deception resources based on reinforcement learning," IEEE Access, vol. 8, pp. 35792-35804, 2020. [10] M. Li, D. Yang, J. Lin, and J. Tang, "Specwatch: a framework for adversarial spectrum monitoring with unknown statistics," Computer Networks, vol. 143, pp. 176-190, Oct. 2018. [11] W. Chen, Y. Wang, and Y. Yuan, "Combinatorial multi-armed bandit: general framework and applications," Proceedings of Machine Learning Research, vol. 28, no. 1, pp. 151-159, Feb. 2013. [12] M. R. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L. A. Grieco, G. Boggia, and M. Dohler, "Standardized protocol stack for the internet of (important) things," IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1389-1406, Dec. 2012. [13] F. Algahtani, T. Tryfonas, and G. Oikonomou, "A reference implemenation for RPL attacks using contiki-NG and Cooja," in Proc. 17th Int. Conf. on Distributed Computing in Sensor Systems, DCOSS’21, pp. 280-286, Pafos, Cyprus, 14-16 Jul. 2021.HoseinBadriMasumehSafkhani417202422924110.66224/ijece.40208.21.4.229http://ijece.org/fa/Article/40208http://ijece.org/fa/Article/Download/40208http://ijece.org/fa/Article/Download/40208http://ijece.org/fa/Article/Download/40208http://ijece.org/fa/Article/Download/40208http://ijece.org/fa/Article/Download/40208http://ijece.org/fa/Article/Download/40208http://ijece.org/fa/Article/Download/40208[1] L. Zhu, K. Gai, and M. Li, Blockchain Technology in Internet of Things, Springer, 2019. [2] R. Pinto, What Role will Blockchains Play in Cybersecurity? Forbes Technology Council. April 3. 2019. https://www.forbes.com/sites/forbestechcouncil/2019/04/03/what-role-will-blockchains-play-in-cybersecurity/ (Accessed Apr. 15, 2019). [3] C. Thompson, How Does the Blockchain Work? (Part 1), Medium, 2016. https://medium.com/blockchain-review/how-does-the-blockchain-work-for-dummies-explained-simply-9f94d386e093 (Accessed Jun. 30, 2021). [4] I. Marco and K. R. Lakhani, "The truth about blockchain," Harv. Bus. Rev., vol. 95, no. 1, pp. 118-127, 2017. [5] D. Freuden, "Hybrid Blockchains: The Best of Both Public and Private, Brave New Coin, 2018. https://bravenewcoin.com/insights/hybrid-blockchains-the-best-of-both-public-and-private (Accessed Jun. 30, 2021). [6] The Investopedia Team, Consensus Mechanism (Cryptocurrency) Definition, https://www.investopedia.com/terms/c/consensus-mechanism-cryptocurrency.asp (Accessed Aug. 13, 2021). [7] D. Hellwig, G. Karlic, and A. Huchzermeier, Build Your Own Blockchain: A Practical Guide to Distributed Ledger Technology, Springer, 2019. [8] س. ع. بنوفاطمه، بررسی الگوریتم اجماع اثبات سهام PoS) ) و 3 شبکه محبوب آن، 1398، https://www.bourseiness.com/41344/proof-of-stake (دسترسی شهریور 15، 1401). [9] M. Castro and B. Liskov, "Practical byzantine fault tolerance," in Proc. of the 3rd Symp. on Operating Systems Design and Implementation, pp. 173-186, New Orleans, LA, USA, 22-22 Feb. 1999. [10] K. Seifried, Over 200 Documented Blockchain Attacks, Vulnerabilities and Weaknesses, CSA| CSA, https://cloudsecurityalliance.org/blog/2020/10/26/blockchain-attacks-vulnerabilities-and-weaknesses/ (Accessed Sept. 6, 2022). [11] V. Patel, "A framework for secure and decentralized sharing of medical imaging data via blockchain consensus," Health Informatic Journal, vol. 25, no. 4, pp. 1398-1411, Dec. 2019. [12] R. Pass, and E. Shi, "Fruitchains: a fair blockchain," in Proc. of the ACM Symp. on Principles of Distributed Computing, pp. 315-324, Washington DC, USA, 25-27 Jul. 2017. [13] M. Milutinovic, W. He, H. Wu, … M. K. the 1st W. on S., and undefined 2016, "Proof of luck: an efficient blockchain consensus protocol," in Proc. of the 1st Workshop on System Software for Trusted Execution, Trento, Italy, 12-16 Dec. 2016. [14] S. Kim, "Two-phase cooperative bargaining game approach for shard-based blockchain consensus scheme," IEEE Access, vol. 7, pp. 127772-127780, 2021. [15] D. Vangulick, B. Cornélusse, and D. Ernst, "Blockchain: a novel approach for the consensus algorithm using Condorcet voting procedure," in Proc. of the IEEE Int. Conf. on Decentralized Applications and Infrastructures, 10 pp. Newark, CA, USA, 4-9 Apr. 2019. [16] M. Ahmed-Rengers and K. Kostiainen, Don’t Mine, Wait in Line: Fair and Efficient Blockchain Consensus with Robust Round Robin, Apr. 2018, http://arxiv.org/abs/1804.07391 (Accessed Aug. 3, 2021). [17] S. Azouvi, P. McCorry, and S. Meiklejohn, Betting on Blockchain Consensus with Fantomette, May 2018, http://arxiv.org/abs/1805.06786 (Accessed Aug. 3, 2021). [18] D. Tosh, S. Shetty, P. Foytik, C. Kamhoua, and L. Njilla, "CloudPoS: a proof-of-stake consensus design for blockchain integrated cloud," in Proc. of the IEEE 11th Int. Conf. on Cloud Computing, pp. 302-309, San Francisco, CA, USA, 2-7 Jul. 2018. [19] B. Shala, U. Trick, A. Lehmann, B. Ghita, and S. Shiaeles, "Novel trust consensus protocol and blockchain-based trust evaluation system for M2M application services," Internet of Things, vol. 7, Article ID: 100058, Sept. 2017. [20] S. Leonardos, D. Reijsbergen, and G. Piliouras, "Weighted voting on the blockchain: Improving consensus in proof of stake protocols," Int. J. Netw. Manag., vol. 30, no. 5, Article ID: e 2093, Sept./Oct. 2020. [21] F. Yang, W. Zhou, Q. Wu, R. Long, … N. X.-I., and U. 2019, "Delegated proof of stake with downgrade: A secure and efficient blockchain consensus algorithm with downgrade mechanism," IEEE Access, vol. 7, pp. 118541-11855, 2019. [22] Y. P. Tsang, K. L. Choy, C. H. Wu, G. T. S. Ho, and H. Y. Lam, "Blockchain-driven IoT for food traceability with an integrated consensus mechanism Access, vol. 7, pp. 129000-129017, 2019. [23] Z. Ren, K. Cong, J. Pouwelse, and Z. Erkin, "Implicit Consensus: Blockchain with Unbounded Throughput," May 2017, http://arxiv.org/abs/1705.11046 (Accessed Aug. 3, 2021) [24] J. Liu, W. Li, G. O. Karame, and N. Asokan, "Scalable Byzantine consensus via hardware-assisted secret sharing," IEEE Trans. on Computers, vol. 68, no. 1, pp. 139-151, Jan. 2019. [25] F. Muratov, A. Lebedev, N. Iushkevich, B. Nasrulin, and M. Takemiya, YAC: BFT Consensus Algorithm for Blockchain, Sept. 2018, http://arxiv.org/abs/1809.00554 (Accessed Aug. 3, 2021). [26] E. Buchman, J. Kwon, and Z. Milosevic, The Latest Gossip on BFT Consensus, Jul. 2018, http://arxiv.org/abs/1807.04938 (Accessed Aug. 3, 2021). [27] N. Alzahrani and N. Bulusu, "A new product anti‐counterfeiting blockchain using a truly decentralized dynamic consensus protocol," Concurrency and Computation Practice and Experience, vol. 32, no. 12, Article ID: e5232, Jun. 2019. [28] F. Bravo-Marquez, S. Reeves and M. Ugarte, "Proof-of-learning: a blockchain consensus mechanism based on machine learning competitions," in Proc. of the IEEE Int. Conf. on Decentralized Applications and Infrastructures, pp. 119-124, Newark, CA, USA, 4-9 Apr. 2019. [29] I. Abraham, D. Malkhi, K. Nayak, L. Ren, and A. Spiegelman, "Solida: A Blockchain Protocol Based on Reconfigurable Byzantine Consensus," Mar. 2018, https://arxiv.org/abs/1612.02916v1 (Accessed Aug. 3, 2021). [30] R. Pass, E. Shi, "Hybrid consensus: efficient consensus in the permissionless model," in Proc. 31st Int. Symp. on Distributed Computing, vol. 91, pp. 39:1-39:16, Vienna, Austria, 16-20 Oct. 2017. [31] T. Zhou, X. Li, and H. Zhao, "DLattice: q permission-less blockchain based on DPoS-BA-DAG consensus for data tokenization," IEEE Access, vol. 7, pp. 39273-39287, 2019. [32] Z. –C. Li, J. –H. Huang, D. –Q. Gao, Y. –H. Jiang, and L. Fan, "ISCP: an improved blockchain consensus protocol.," International Journal of Network Security, vol. 21, no. 3, PP.359-367, May 2019. [33] K. Li, H. Li, H. Hou, K. Li and Y. Chen, "Proof of vote: a high-performance consensus protocol based on vote mechanism & consortium blockchain," in Proc. IEEE 19th Int. Conf. on High Performance Computing and Communications; IEEE 15th Int. Conf. on Smart City; IEEE 3rd Int. Conf. on Data Science and Systems, pp. 466-473, Bangkok, Thailand, 18-20 Dec. 2017. [34] K. Finlow-Bates, A Lightweight Blockchain Consensus Protocol, Aug. 2017, https://www.chainfrog.com/wp-content/uploads/2017/08/consensus.pdf (Accessed Aug. 3, 2021). [35] S. Solat, "RDV: An alternative to proof-of-work and a real decentralized consensus for blockchain," in Proc. the 1st Workshop on Blockchain-enabled Networked Sensor Systems, pp. 25-31, Shenzhen, China, 4-4 Nov. 2018. [36] A. K. Talukder, M. Chaitanya, D. Arnold and K. Sakurai, "Proof of disease: a blockchain consensus protocol for accurate medical decisions and reducing the disease burden," in Proc. of the SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, pp. 257-262, Guangzhou, China, 08-12 Oct. 2018. [37] H. Y. Yuen, et al., "Proof-of-play: A novel consensus model for blockchain-based peer-to-peer gaming system," in Proc. of the ACM Int. Symp. on Blockchain and Secure Critical Infrastructure, pp. 19-28, Auckland, New Zealand, 8-8 Jul. 2019. [38] E. K. Wang, Z. Liang, C. -M. Chen, S. Kumari, and M. Khurram Khan, "PoRX: A reputation incentive scheme for blockchain consensus of IIoT," Future Generation Computer Systems, vol. 102, pp. 140-151, Jan. 2020. [39] S. Bouraga, "A taxonomy of blockchain consensus protocols: A survey and classification framework," Expert Syst. Appl., vol. 168, Article ID: 114384, Apr. 2021. [40] -, Logistic Functions, http://wmueller.com/precalculus/families/1_80.html (Accessed Oct. 01, 2022). [41] I. Syed, PHP: Utility Function for Getting Random Values with Weighting, 2015, https://gist.github.com/irazasyed/f41f8688a2b3b8f7b6df (Accessed Oct. 15, 2022). [42] D. M. Chiu and R. Jain, "Analysis of the increase and decrease algorithms for congestion avoidance in computer networks," Comput. Networks ISDN Syst., vol. 17, no. 1, pp. 1-14, Jun. 1989. [43] E. Heilman, A. Kendler, A. Zohar, and S. Goldberg, Eclipse Attacks on Bitcoin’s Peer-to-Peer Network, Mar. 2015, https://hashingit.com/elements/research-resources/2015-03-20-eclipse-attacks-bitcoin.pdf (Accessed Oct. 15, 2022). [44] M. Deer, What Is an Eclipse Attack? Dec. 2021, https://cointelegraph.com/explained/what-is-an-eclipse-attack (Accessed Oct. 25, 2022).MohammadMahdianS.MojtabaMatinkhah417202426327210.66224/ijece.40810.21.4.263http://ijece.org/fa/Article/40810http://ijece.org/fa/Article/Download/40810http://ijece.org/fa/Article/Download/40810http://ijece.org/fa/Article/Download/40810http://ijece.org/fa/Article/Download/40810http://ijece.org/fa/Article/Download/40810http://ijece.org/fa/Article/Download/40810http://ijece.org/fa/Article/Download/40810[1] J. S. Kumar, G. Sivasankar, and S. S. Nidhyananthan, "An artificial intelligence approach for enhancing trust between social IoT devices in a network," In: A. Hassanien, R. Bhatnagar, N. Khalifa, and M. Taha, (eds) Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Springer, vol. 846, pp. 183-196, 2020. [2] G. Fortino, A. Rovella, W. Russo, and C. Savaglio, On the Classification of Cyberphysical Smart Objects in the Internet of Things, UBICITEC, pp. 86-94, 2014. [3] M. Nitti, L. Atzori, and I. P. Cvijikj, "Friendship selection in the social internet of things: challenges and possible strategies," IEEE Int. Things J., vol. 2, no. 3, pp. 240-247, Jun. 2014. [4] S. Pattar, R. Buyya, K. R. Venugopal, S. Iyengar, and L. Patnaik, "Searching for the IoT resources: fundamentals, requirements, comprehensive review, and future directions," IEEE Commun. Surv. Tutor, vol. 20, no. 3, pp. 2101-2132, Third Quarter 2018. [5] D. Zhang, L. T. Yang, and H. Huang, "Searching in internet of things: vision and challenges," in Proc. of the IEEE 9th Int.l Symp. on Parallel and Distributed Processing with Applications, pp. 201-206, Busan, South Korea, 26-28 May 2011. [6] A. Arjunasamy and T. Ramasamy, "A proficient heuristic for selecting friends in social Internet of Things," in Proc. of the IEEE 10th Int. Conf. on Intelligent Systems and Control, 5 pp., Coimbatore, India, 7-8 Jan. 2016. [7] B. Farhadi, A. M. Rahmani, P. Asghari, and M. Hosseinzadeh, "Friendship selection and management in social internet of things: a systematic review," Computer Networks, vol. 201, Article ID: 108568, Dec. 2021. [8] M. J. Culnan, P. J. McHugh, and J. I. Zubillaga, "How large US companies can use Twitter and other social media to gain business value," MIS Quarterly Executive, vol. 9, no. 4, Article ID: 6, 2010. [9] A. Roy, L. Maxwell, and M. Carson, "How is social media being used by small and medium-sized enterprises?" J. Bus. Behav. Sci. vol. 26, no. 2, pp. 127-137, Summer 2014. [10] A. Kumar, S. K. Singh, and P. K. Chaurasia, "A heuristic model for friend selection in social Internet of Things," In: D. Gupta, R. S. Goswami, S. Banerjee, M. Tanveer, R. B. Pachori, (eds) vol 888. Springer, Singapore. Pattern Recognition and Data Analysis with Applications. pp. 167-181, Singapore: Springer, 2022. [11] S. Rho and Y. Chen, "Social Internet of Things: applications, architectures and protocols," Future Generation Computer Systems, vol. 92, pp.: 959-960, Mar. 2019. [12] C. Marche, L. Atzori, and M. Nitti, "A dataset for performance analysis of the social Internet of Things," in Proc. IEEE 29th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun., 5 pp., Bologna, Italy, 9-12 Sept. 2018. [13] M. Nitti, V. Pilloni, and D. D. Giusto, "Searching the social Internet of Things by exploiting object similarity," in Proc. IEEE 3rd World Forum Internet Things, pp. 371-376, Reston, VA, USA, 12-14 Dec 2016. [14] S. Rho and Y. Chen, "Social Internet of Things: applications, architectures and protocols," Future Gener. Comput. Syst., vol. 82, pp. 667-668, May 2019. [15] L. Militano, M. Nitti, L. Atzori, and A. Iera, "Enhancing the navigability in a social network of smart objects: a shapley-value based approach," Comput. Netw., vol. 103, pp. 1-14, Jul. 2016. [16] A. P. Fiske, "The four elementary forms of sociality: framework for a unified theory of social relations," Psychol. Rev., vol. 99, no. 4, pp. 689-723, Oct. 1992. [17] A. M. Ortiz, D. Hussein, S. Park, S. N. Han, and N. Crespi, "The cluster between Internet of Things and social networks: review and research challenges," IEEE Internet Things J., vol. 1, no. 3, pp. 206-215, Jun. 2014. [18] P. Kumaran and R. Sridhar, "Social Internet of Things (SIoT): techniques, applications and challenges," in Proc. 4th Int. Conf. Trends Electron. Informat., pp. 445-450, Tirunelveli, India, 15-17 Jun. 2020. [19] F. Amin, A. Majeed, A. Mateen, R. Abbasi, and S. O. Hwang, "A systematic survey on the recent advancements in the social Internet of Things," IEEE Access, vol. 10, pp. 63867-63884, 2022. [20] L. Atzori, A. Iera, and G. Morabito, "From "smart objects" to "social objects": the next evolutionary step of the Internet of Things," IEE Commun. Mag., vol. 52, no. 1, pp. 97-105, 2014. [21] F. Amin, A. Ahmad, and G. S. Choi, "Community detection and mining using complex networks tools in social Internet of Things," in Proc. of the TENCON 2018-2018 IEEE Region Ten Conf., pp. 2086-2091, Jeju, South Korea, 28-31 Oct. 2018. [22] [22] J. Travers and S. Milgram, "An experimental study of the small world problem," Sociometry, vol. 32, no. 5, pp. 425-443, 1969. [23] M. Nitti, L. Atzori, and I. P. Cvijikj, "Friendship selection in the social Internet of Things: challenges and possible strategies," IEEE Internet of Things J., vol. 2, no. 3, pp. 240-247, Jun. 2015. [24] R. Rajabioun, "Cuckoo optimization algorithm," Applied Soft Computing, vol. 11, no. 8, pp. 5508-5518, Dec. 2011. [25] V. Latora, V. Nicosia, and G. Russo, Complex Networks: Principles, Methods and Applications, Cambridge University Press, 2017. [26] J. Wu, M. Dong, K. Ota, L. Liang, and Z. Zhou, "Securing distributed storage for social Internet of Things using regenerating code and Blom key agreement," Peer-Peer Netw. Appl., vol. 8, no. 6, pp. 1133-1142, Nov. 2015. [27] M. Nitti, L. Atzori, and I. P. Cvijikj, "Network navigability in the social Internet of Things," in Proc. IEEE World Forum Internet Things, pp. 405-410, Seoul, South Korea, 6-8 Mar. 2014. [28] M. E. J. Newman, "Clustering and preferential attachment in growing networks," Phys. Rev. E, vol. 64, no. 2, Article ID: 25102, Aug. 2001. [29] F. Amin and G. S. Choi, "Advanced service search model for higher network navigation using small world networks," IEEE Access, vol. 9, pp. 70584-70595, 2021. [30] F. Amin and S. O. Hwang, "Automated service search model for the social Internet of Things," Comput. Mater. Continua, vol. 72, no. 3, pp. 5871-5888, 2022. [31] T. Ramasamy and A. Arjunasamy, "Advanced heuristics for selecting friends in social Internet of Things," Wireless Pers. Commun., vol. 97, no. 4, pp. 4951-4965, Dec. 2017. [32] S. Rajendran and R. Jebakumar, "Object recommendation based friendship selection (ORFS) for navigating smarter social objects in SIoT," Microprocessors Microsyst., vol. 80, Article ID: 103358, Feb. 2021. [33] J. P. Barbin, S. Yousefi, and B. Masoumi, "Navigation in the social Internet-of-Things (SIoT) for discovering the influential service-providers using distributed learning automata," J. Supercomput., vol. 77, no. 10, pp. 11004-11031, Oct. 2021. [34] P. Jaccard, "Étude comparative de la distribution florale dans une portion des Alpes et des Jura," Bull Soc Vaudoise Sci Nat, vol. 37, pp. 547-579, 1901. [35] A. Papadimitriou, P. Symeonidis, and Y. Manolopoulos, "Fast and accurate link prediction in social networking systems," J. Syst. Software, vol. 85, no. 9, pp. 2119-2132, Sept. 2012. [36] L. A. Adamic and E. Adar, "Friends and neighbors on the web," Soc. Networks, vol. 25, no. 3, pp. 211-230, Jul. 2003. [37] F. Amin, R. Abbasi, A. Rehman, and G. S. Choi, "An advanced algorithm for higher network navigation in social Internet of Things using smallworld networks," Sensors, vol. 19, no. 9, Article ID: 2007, 2019. [38] S. Mahmoudi, Discrete Manufacturing Cuckoo Search algorithm, Case Study: Graph Coloring, A Thesis Submitted for the Degree M. S. in Computer Engineering-Artificial Intelligence, Faculty of Engineering, University of Nabi Akram, 2012. [39] L. Militano, M. Nitti, L. Atzori, and A. Iera, "Using a distributed shapley-value based approach to ensure navigability in a social network of smart objects," in Proc. IEEE Int. Conf. on Communications, pp 692-697, London, UK, 8-12 Jun. 2015. [40] W. Mardini, Y. Khamayseh, M. B. Yassein, and M. H. Khatatbeh, "Mining Internet of Things for intelligent objects using genetic algorithm," Comput. Electr. Eng., vol. 66, pp. 423-434, Feb. 2018. [41] A. D. Sarma, D. Nanongkai, D. Pandurangan, and P. Tetali, "Distributed random walk," J. ACM, vol. 60, no. 1, Article ID: 2, Feb. 2013.ArashKarimkhaniAmirGhal’e417202429129510.66224/ijece.41153.21.4.291http://ijece.org/fa/Article/41153http://ijece.org/fa/Article/Download/41153http://ijece.org/fa/Article/Download/41153http://ijece.org/fa/Article/Download/41153http://ijece.org/fa/Article/Download/41153http://ijece.org/fa/Article/Download/41153http://ijece.org/fa/Article/Download/41153http://ijece.org/fa/Article/Download/41153[1] IBM, IBM Quantum Computing, https://www.ibm.com/quantum/ [2] M. A. Nielsen and, I.L. Chuang, Quantum Computation and Quantum Information. Cambridge, University Press, Cambridge, 2000. [3] R. Rennie, Oxford Dictionary of Physics, 7rd ed., Oxford University Press, Oxford 2015. [4] D. Deutsch, "Quantum theory, the Church-Turing principle and the universal quantum computer," Proc. R. Soc. Lond. A., vol. 400, no. 1818, pp. 97-117, 1985. [5] D. Deutsch and R. Jozsa, "Rapid solution of problems by quantum computation," Proc. R. Soc. Lond. A., vol. 439, no. 1907, pp. 553-558, 1992. [6] E. Bernstein and U. Vazirani, "Quantum complexity theory," in Proc. of the Twenty-Fifth Annual ACM Symp. on Theory of Computing, STOC’93, pp. 11-20, San Diego, CA, USA, 16-18 May 1993. [7] E. Bernstein and U. Vazirani" Quantum complexity theory," SIAM J. Comput., vol. 26, no. 5, pp.1411-1473, 1997. [8] D. R. Simon, " On The Power of Quantum Computation;" in Proc. of the 35th IEEE Annual Symp. on Foundations of Computer Science, pp. 116-123 Symposium, Santa Fe, NM, USA, 20-22 Nov. 1994. [9] K. Nagata and T. Nakamura, "Some theoritically organized algorithm for quantum computer" Int. J. Theor. Phys., vol. 59, no. 2, pp. 611-621, 2020. [10] K. Nagata and T. Nakamura, "Generalization of Deutsch’s algorithm" Int. J. Theor. Phys., vol. 59, no. 8, pp. 2557-2661, 2020. [11] P. W. Shor,"Algorithms for quantum computations: Discreate log and factoring," Proc. of the 35th IEEE Annual Symp. on Foundations of Computer Science, pp. 124-134, Santa Fe, NM, USA, 20-22 Nov. 1994. [12] E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, Quantum Computation by Adiabatic Evolution, arXive: quant-ph/001106. [13] A. M. Child, E. Farhi, and J. Preskill, "Robustness of adiabatic quantum computation," Phys. Rev. A, vol. 65, no. 1, pp. 0123220-01232210, Jan. 2002. [14] S. Das, R. Kobes, and G. Kunstatter, "Adiabatic quantum computation and Deutsch’s algorithm" Phys. Rev. A, vol. 65, no. 6, pp.0623100-0623107, Jun. 2002. [15] T. Albash and D. A. Lidar "Adiabatic quantum computation", Rev. Mod. Phys., vol. 90, no. 1, pp. 0150020-0150035, Jan./Mar. 2018. ParastooMohagheghSamiraNoferestiMehriRajaei417202427328310.66224/ijece.41529.21.4.273http://ijece.org/fa/Article/41529http://ijece.org/fa/Article/Download/41529http://ijece.org/fa/Article/Download/41529http://ijece.org/fa/Article/Download/41529http://ijece.org/fa/Article/Download/41529http://ijece.org/fa/Article/Download/41529http://ijece.org/fa/Article/Download/41529http://ijece.org/fa/Article/Download/41529[1] H. Kim, H. Cho, and D. Ryu, "Corporate bankruptcy prediction using machine learning methodologies with a focus on sequential data," Computational Economics, vol. 59, pp. 1231-1249, 2022. [2] D. Yousif Mikhail, F. Al-Mukhtar, and S. Wahab Kareem, "A comparative evaluation of cancer classification via TP53 gene mutations using machine learning," Asian Pacific J. of Cancer Prevention, vol. 23, no. 7, pp. 2459-2467, Jul. 2022. [3] L. Yang and Y. Jiachen, "Few-shot cotton pest recognition and terminal," Computers and Electronics in Agriculture, vol. 169, Article ID: 105240, 2020. [4] P. Kumar, R. Bhatnagar, K. Gaur, and A. Bhatnagar, "Classification of imbalanced data: review of methods and applications," IOP Conf. Series: Materials Science and Engineering, vol. 1099, no 1, Article ID: 012077, 2021. [5] C. F. Tsai, W. C. Lin, Y. H. Hu, and G. T. Yao, "Under-sampling class imbalanced datasets by combining clustering analysis and instance selection," Information Sciences, vol. 477, pp. 47-54, Mar. 2019. [6] I. Czarnowski and P. Jedrzejowicz, "An approach to imbalanced data classification based on instance selection and over-sampling," in Proc. 11th Int. Conf.on Computational Collective Intelligence, pp. 601-610, Hendaye, France, 4-6 Sept. 2019. [7] D. Gan, J. Shen, B. An, M. Xu, and N. Liu, "Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis," Computers & Industrial Engineering, vol. 140, Article ID: 106266, Feb. 2020. [8] L. Yang and Y. Jiachen, "Meta-learning baselines and database for few-shot classification in agriculture," Computers and Electronics in Agriculture, vol. 182, Article ID: 106055, Mar. 2021. [9] Z. Peng, Z. Li, J. Zhang, Y. Li, G. J. Qi, and J. Tang, "Few-shot image recognition with knowledge transfer," in Proc. of the IEEE/CVF Int. Conf. on Computer Vision, pp. 441-449, Seoul, South Korea, 27 Oct.-2 Nov. 2019. [10] F. Jimenez, G. Sanchez, J. Palma, and G. Sciavicco, "Three-objective constrained evolutionary instance selection for classification: wrapper and filter approaches," Engineering Applications of Artificial Intelligence, vol. 107, Article ID: 104531, Jan. 2022. [11] G. E. Melo-Acosta, F. Duitama-Muñoz, and J. D. Arias-Londoño, An Instance Selection Algorithm for Big Data in High Imbalanced Datasets Based on LSH, arXiv: 2210.04310, Oct. 2022. [12] X. Chao and L. Zhang, "Few-shot imbalanced classification based on data augmentation," Multimedia Systems, vol. 29, no. 5, pp. 2843-2851, 2023. [13] S. Bej, N. Davtyan, M. Wolfien, M. Nassar, and O. Wolkenhauer, "LoRas: an oversampling approach for imbalanced datasets," Machine Learning, vol. 110, pp. 279-301, 2021. [14] J. C. Requelme, J. S. Aguilar-Ruiz, and M. Toro, "Finding representative patterns with ordered projections," Pattern Recognition, vol. 36, no. 4, pp. 1009-1018, Apr. 2003. [15] D. R. Wilson and T. R. Martinez, "Instance pruning techniques," in Proc. of the 14th Int. Conf. on Machine Learning, pp. 400-411, 8-12 Jul. 1997. [16] M. Moran, T. Cohen, Y. Ben-Zion, and G. Gordon, "Curious instance selection," Information Sciences, vol. 608, pp. 794-808, Aug. 2022. [17] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," J. of Artificial Intelligence Research, vol. 16, pp. 321-357, Jan. 2002. [18] ش. سرگلزایی، ف. حسین‌زاده سلجوقی و ﻫ. آقایاری، "ارائه روشی نوین برای رتبه‌بندی اعداد فازی با استفاده از مرکز محیطی دایره و کاربرد آن در ارزیابی عملکرد مدیریت زنجیره تأمین،" نشریه تصمیم‌گیری و تحقیق در عملیات، دوره 3، شماره 3، صص. 236-248، پاییز 1397. [19] S. N. Kumpati and A. T. Mandayam, Learning Automata: An Introduction, Courier Corporation, 2012. [20] J. C. Dominguz, et al., "Teaching chemical engeering using Jupyter notebook: problem generators and lecturing tools," Education for Chemical Engineers, vol. 37, pp. 1-10, Oct. 2021. [21] M. Grandini, E. Bagli, and G. Visani, Multi-Class Classification: An Overview, arXiv:2008.05756, Aug. 2020.