jist-202605192320260519232534CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-1437121620231144AhmedBelhaniHichemSemiraRaniaKheddaraGhadaHassis1216202326928110.61186/jist.33595.11.44.269http://jist.ir/en/Article/33595http://jist.ir/en/Article/Download/33595http://jist.ir/en/Article/Download/33595http://jist.ir/en/Article/Download/33595http://jist.ir/en/Article/Download/33595http://jist.ir/en/Article/Download/33595http://jist.ir/en/Article/Download/33595http://jist.ir/en/Article/Download/33595[1] L. Dai, B. Wang, Y. Yuan, S. Han, I. Chih-lin, et Z. Wang, « Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends », IEEE Commun. Mag., vol. 53, no 9, p. 74‑81, sept. 2015. [2] M. Moltafet, N. Mokari, M. R. Javan, H. Saeedi, et H. Pishro-Nik, « A New Multiple Access Technique for 5G: Power Domain Sparse Code Multiple Access (PSMA) », IEEE Access, vol. 6, p. 747‑759, 2018. [3] X. Wei et al., « Software Defined Radio Implementation of a Non-Orthogonal Multiple Access System Towards 5G », IEEE Access, vol. 4, p. 9604‑9613, 2016. [4] Z. Ding et al., « Application of Non-Orthogonal Multiple Access in LTE and 5G Networks », IEEE Commun. Mag., vol. 55, no 2, p. 185‑191, févr. 2017. [5] M. Vaezi, Z. Ding, et H. V. Poor, Éd., Multiple Access Techniques for 5G Wireless Networks and Beyond. Cham: Springer International Publishing, 2019. [6] A. E. Mostafa, Y. Zhou, et V. W. S. Wong, « Connection Density Maximization of Narrowband IoT Systems With NOMA », IEEE Trans. Wireless Commun., vol. 18, no 10, p. 4708‑4722, oct. 2019. [7] Y. Yuan et al., « NOMA for Next-Generation Massive IoT: Performance Potential and Technology Directions », IEEE Commun. Mag., vol. 59, no 7, p. 115‑121, juill. 2021. [8] M. B. Shahab, R. Abbas, M. Shirvanimoghaddam, et S. J. Johnson, « Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey », IEEE Commun. Surv. Tutorials, vol. 22, no 3, p. 1805‑1838, 2020. [9] F. A. Khales et G. A. Hodtani, « An evaluation of the coverage region for downlink Non-Orthogonal Multiple Access (NOMA) based on Power Allocation Factor », in 2017 Iran Workshop on Communication and Information Theory (IWCIT), Tehran, Iran, mai 2017, p. 1‑5. [10] Q. C. Li, H. Niu, A. T. Papathanassiou, et G. Wu, « 5G Network Capacity: Key Elements and Technologies », IEEE Veh. Technol. Mag., vol. 9, no 1, p. 71‑78, mars 2014. [11] X. Liang, X. Gong, Y. Wu, D. W. K. Ng, et T. Hong, « Analysis of Outage Probabilities for Cooperative NOMA Users with Imperfect CSI », in 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, déc. 2018, p. 1617‑1623. [12] A. Agarwal, R. Chaurasiya, S. Rai, et A. K. Jagannatham, « Outage Probability Analysis for NOMA Downlink and Uplink Communication Systems With Generalized Fading Channels », IEEE Access, vol. 8, p. 220461‑220481, 2020. [13] N. Tutunchi, A. Haghbin, et B. Mahboobi, « Complexity Reduction in Massive-MIMO-NOMA SIC Receiver in Presence of Imperfect CSI », Journal of Information Systems and Telecommunication (JIST), vol. 2, no 30, p. 113, août 2020. [14] F. Kara et H. Kaya, « BER performances of downlink and uplink NOMA in the presence of SIC errors over fading channels », IET Communications, vol. 12, no 15, p. 1834‑1844, sept. 2018. [15] C. A. Ramos-Arregu’n et al., « FPGA Open Architecture Design for a VGA Driver », Procedia Technology, vol. 3, p. 324‑333, 2012. [16] « Zybo Z7 Reference Manual - Digilent Reference ». accessed on july 04, 2021. [Online]. available on: https://digilent.com/reference/programmable-logic/zybo-z7/reference-manual. [17] T. Tami, T. Messaoudene, A. Ferdjouni, et O. Benzineb, « Chaos secure communication’ implementation in FPGA », in 2018 International Conference on Applied Smart Systems (ICASS), Medea, Algeria, nov. 2018, p. 1‑6. [18] W. Tang, S. Yang, et X. Li, « Implementation of Space-time Coding and Decoding Algorithms for MIMO Communication System Based on DSP and FPGA », in 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Dalian, China, sept. 2019, p. 1‑5. [19] H. Sreenath et G. Narayanan, « FPGA Implementation of Pseudo Chaos-signal Generator for Secure Communication Systems », in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, sept. 2018, p. 804‑807. [20] Q. Yingchao et Y. Feng, « Design and Implementation of Differential Frequency Hopping Communication System Based on FPGA », in 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, déc. 2018, p. 1006‑1010. [21] M. A. Ahmed, K. F. Mahmmod, et M. M. Azeez, « On the performance of non-orthogonal multiple access (NOMA) using FPGA », IJECE, vol. 10, no 2, p. 2151, avr. 2020. [22] M. Mekhfioui, A. Satif, O. Mouhib, R. Elgouri, A. Hadjoudja, et L. Hlou, « Hardware Implementation of Blind Source Separation Algorithm Using ZYBO Z7& Xilinx System Generator », in 2020 5th International Conference on Renewable Energies for Developing Countries (REDEC), Marrakech, Morocco, Morocco, juin 2020, p. 1‑5. [23] T. Assaf, A. Al-Dweik, M. S. E. Moursi, H. Zeineldin, et M. Al-Jarrah, « NOMA Receiver Design for Delay-Sensitive Systems », IEEE Systems Journal, vol. 15, no 4, p. 5606‑5617. [24] H. Semira et F. Kara, « Error Performance of Uplink SIMO-NOMA with Joint Maximum-Likelihood and Adaptive M-PSK », in 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Bucharest, Romania, mai 2021, p. 1‑6. [25] H. Semira, F. Kara, H. Kaya, et H. Yanikomeroglu, « Multi-User Joint Maximum-Likelihood Detection in Uplink NOMA-IoT Networks: Removing the Error Floor », IEEE Wireless Commun. Lett., vol. 10, no 11, p. 2459‑2463, nov. 2021. [26] « Digital Modulation in FPGAs Xilinx using system generator (ASK, BPSK, FSK, OOK, QPSK) - File Exchange - MATLAB Central ». accessed on july 04, 2021. [online]. available on: https://www.mathworks.com/matlabcentral/fileexchange/14650-digital-modulation-in-fpgas-xilinx-using-system-generator-ask-bpsk-fsk-ook-qpsk?s_tid=FX_rc3_behav. [27] H. CJ, S. D. Hanwate, et A. S. Mali, « Hardware Implementation of BPSK System on Virtex2-Pro FPGA Using Xilinx System Generator », IRJES, vol2, issue1,p 18-24, jan 2013.MehdiEmadiMaseudRahgozarFarhadOroumchian1216202330731910.61186/jist.33656.11.44.307http://jist.ir/en/Article/33656http://jist.ir/en/Article/Download/33656http://jist.ir/en/Article/Download/33656http://jist.ir/en/Article/Download/33656http://jist.ir/en/Article/Download/33656http://jist.ir/en/Article/Download/33656http://jist.ir/en/Article/Download/33656http://jist.ir/en/Article/Download/33656[1] A. Guille, H. Hacid, C. Favre, and D. A. Zighed, “Information Diffusion in Online Social Networks: A Survey,” ACM SIGMOD Record, vol. 42, no. 2, pp. 17–28, 2013. [2] R. Badie, A. Aleahmad, M. Asadpour, and M. Rahgozar, “An efficient agent-based algorithm for overlapping community detection using nodes’ closeness,” Physica A: Statistical Mechanics and its Applications, vol. 392, no. 20, pp. 5231–5247, Oct. 2013. [3] Z. Arefian and M.R. Khayyam Bashi. “Scalable Community Detection through Content and Link Analysis in Social Networks,” Journal of Information Systems and Telecommunication (JIST), vol. 4, no.12, pp. 1-10, 2015. [4] A.H. Hosseinian and V. Baradaran. “A multi-objective multi-agent optimization algorithm for the community detection problem,” Journal of Information Systems and Telecommunication (JIST), vol. 6, no. 3, pp. 166-176, 2018. [5] E. Sherkat, M. Rahgozar, and M. Asadpour, “Structural link prediction based on ant colony approach in social networks,” Physica A, vol. 419, pp. 80–94, 2015. [6] V. Martínez, F. Berzal, and J.-C. Cubero, “A Survey of Link Prediction in Complex Networks,” ACM Computing Surveys, vol. 49, no. 4, pp. 1–33, Dec. 2016. [7] M. P. Salvati, J. Bagherzadeh Mohasefi, and S. Sulaimany. “Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection,” Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 35, pp. 183-190, 2021. [8] R. Safa, S. A. Mirroshandel, S. Javadi, and M. Azizi. “Publication venue recommendation based on paper title and co-authors network,” Journal of Information Systems and Telecommunication (JIST), vol. 6, no. 21, pp. 33-40, 2018. [9] K. Rahimkhani, A. Aleahmad, M. Rahgozar, and A. Moeini, “A Fast Algorithm for Finding Most Influential People Based on the Linear,” Expert Systems With Applications, vol. 42, no. 3, pp. 1353–1361, Feb. 2014. [10] M. Emadi and M. Rahgozar, “Twitter sentiment analysis using fuzzy integral classifier fusion,” Journal of Information Science, vol. 46, no. 2, 2020. [11] A.A. Kardan and B. Bozorgi. “Analysis of Main Expert-Finding Algorithms in Social Network in Order to Rank the Top Algorithms,” Journal of Information Systems and Telecommunication (JIST), vol. 5, no. 20, pp. 217-224, 2017. [12] A. Guille, H. Hacid, C. Favre, and D. A. Zighed, “Information Diffusion in Online Social Networks: A Survey,” ACM SIGMOD Record, vol. 42, no. 2, pp. 17–28, 2013. [13] I. Brugere, B. Gallagher, and T. Y. Berger-Wolf, “Network Structure Inference, A Survey: Motivations, Methods, and Applications,” Oct. 2016. [14] O. Gomes, “Sentiment cycles in discrete-time homogeneous networks,” Physica A: Statistical Mechanics and its Applications, vol. 428, pp. 224–238, Jun. 2015. [15] L. Zhao, J. Wang, R. Huang, H. Cui, X. Qiu, and X. Wang, “Sentiment contagion in complex networks,” Physica A: Statistical Mechanics and its Applications, vol. 394, pp. 17–23, Jan. 2014. [16] L. Prokhorenkova, A. Tikhonov, and Y. Nelly Litvak, “When Less Is More: Systematic Analysis of Cascade-Based Community Detection,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 16, no. 4, Jan. 2022. [17] I. Brugere, B. Gallagher, and T. Y. Berger-Wolf, “Network Structure Inference, A Survey: Motivations, Methods, and Applications,” Oct. 2016. [18] M. Gomez-Rodriguez, J. Leskovec, and A. Krause, “Inferring Networks of Diffusion and Influence,” ACM Transactions on Knowledge Discovery from Data, vol. 5, no. 4, pp. 1–37, Feb. 2010. [19] M. Gomez Rodriguez, J. Leskovec, and B. Schölkopf, “Structure and dynamics of information pathways in online media,” in Proceedings of the sixth ACM international conference on Web search and data mining - WSDM ’13, 2013, pp. 23–32. [20] M. G. RODRIGUEZ, J. LESKOVEC, D. BALDUZZI, and B. SCHÖLKOPF, “Uncovering the structure and temporal dynamics of information propagation,” Network Science, vol. 2, no. 01, pp. 26–65, Apr. 2014. [21] LiHuacheng, XiaChunhe, WangTianbo, WenSheng, ChenChao, and XiangYang, “Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques,” ACM Computing Surveys (CSUR), vol. 55, no. 1, pp. 1–51, Nov. 2021. [22] C. Ravazzi, F. Dabbene, C. Lagoa, and A. v. Proskurnikov, “Learning Hidden Influences in Large-Scale Dynamical Social Networks: A Data-Driven Sparsity-Based Approach, in Memory of Roberto Tempo,” IEEE Control Systems, vol. 41, no. 5, pp. 61–103, Oct. 2021. [23] H. Yang et al., “Towards embedding information diffusion data for understanding big dynamic networks,” Neurocomputing, vol. 466, pp. 265–284, Nov. 2021. [24] S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, First Ed. Cambridge, United Kingdom: Cambridge University Press, 1994. [25] K. Chen, P. Luo, and H. Wang, “An influence framework on product word-of-mouth (WoM) measurement,” Information and Management, vol. 54, no. 2, pp. 228–240, Mar. 2017. [26] M. Gomez-Rodriguez, J. Leskovec, and A. Krause, “Inferring Networks of Diffusion and Influence,” ACM Transactions on Knowledge Discovery from Data, vol. 5, no. 4, pp. 1–37, Feb. 2010. [27] M. G. RODRIGUEZ, J. LESKOVEC, D. BALDUZZI, and B. SCHÖLKOPF, “Uncovering the structure and temporal dynamics of information propagation,” Network Science, vol. 2, no. 01, pp. 26–65, Apr. 2014. [28] S. Shaghaghian and M. Coates, “Online Bayesian Inference of Diffusion Networks,” IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 3, pp. 500–512, Sep. 2016. [29] M. Gomez Rodriguez, J. Leskovec, and B. Schölkopf, “Structure and dynamics of information pathways in online media,” in Proceedings of the sixth ACM international conference on Web search and data mining - WSDM ’13, 2013, pp. 23–32. [30] S. A. S. Myers and J. Leskovec, “On the Convexity of Latent Social Network Inference,” in NIPS’10 Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 2, 2010, pp. 1741–1749. [31] S. Wang, X. Hu, P. S. Yu, and Z. Li, “MMRate: inferring multi-aspect diffusion networks with multi-pattern cascades,” Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’14, pp. 1246–1255, 2014. [32] N. Du, L. Song, H. Woo, and H. Zha, “Uncover Topic-Sensitive Information Diffusion Networks,” in Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013, vol. 31, pp. 229–237. [33] D. H. Zhou, W. B. Han, Y. J. Wang, and B. Di Yuan, “Information diffusion network inferring and pathway tracking,” Science China Information Sciences, vol. 58, no. 9, pp. 1–15, Sep. 2015. [34] C. E. Cormen, Thomas H., Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 3rd ed. MIT Press and McGraw-Hill, 2009. [35] J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, and Z. Ghahramani, “Kronecker Graphs: An Approach to Modeling Networks,” Journal of Machine Learning Research, vol. 11, no. Feb, pp. 985–1042, 2010. [36] E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11, 2011, pp. 1082-1090. [37] J. Leskovec and R. Sosič, “SNAP: A General-Purpose Network Analysis and Graph-Mining Library,” ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 1, pp. 1–20, Jul. 2016.maryamNajimi1216202332033010.61186/jist.33968.11.44.320http://jist.ir/en/Article/33968http://jist.ir/en/Article/Download/33968http://jist.ir/en/Article/Download/33968http://jist.ir/en/Article/Download/33968http://jist.ir/en/Article/Download/33968http://jist.ir/en/Article/Download/33968http://jist.ir/en/Article/Download/33968http://jist.ir/en/Article/Download/33968[1] J. Mitola and G. Q. Maguire, "Cognitive radio: Making software radios more personal, " IEEE Pers. Commun., Vol. 6, No. 4, pp. 13-18, Aug. 1999. [2] M. Najimi, A. Ebrahimzadeh, S. M. H. Andargoli, and A. Fallahi, "A novel sensing nodes and decision node selection method for energy efficiency of cooperative spectrum sensing in cognitive sensor networks," IEEE Sensors J., Vol. 13, No. 5, pp. 1610-1621, May 2013. [3] A. Ebrahimzadeh, M. Najimi, S. M. H. Andargoli, and A. Fallahi, "Sensor selection and optimal energy detection threshold for ef_cient cooperative spectrum sensing, " IEEE Trans. Veh. Technol., Vol. 64, No. 4, pp. 1565-1577, Apr. 2015. [4] A. Bagheri, A. Ebrahimzadeh, and M. Najimi, "Sensor selection for extending lifetime of multi-channel cooperative sensing in cognitive sensor networks " Phys. Commun., Vol. 26, pp. 96_105, Feb. 2018. [5] S. Kisseleff, X. Chen, I. F. Akyildiz, and W. H. Gerstacker, "Efficient charging of access limited wireless underground sensor networks," IEEE Trans. Commun., Vol. 64, No. 5, pp. 2130-2142, May 2016. [6] A. Mehrabi, K. Kim, "General framework for network throughput maximization in sink-based energy harvesting wireless sensor networks, " IEEE Trans. Mobile Computing, Vol. 16, No. 7, pp. 1881-1896, July,2017. [7] G. Zheng, Z. Ho, E. A. Jorswieck, and B. Ottersten, "Information and energy cooperation in cognitive radio networks," IEEE Trans. Signal Process., Vol. 62, No. 9, pp. 2290-2303, May 2014. [8] J. Yan, Y. Liu, "A dynamic SWIPT approach for cooperative cognitive radio networks," IEEE Trans. Vehicular Technology, Vol. 66, No. 12, pp. 1122-1136, Dec., 2017. [9] J. R. Birge and F. Louveaux, Introduction to Stochastic Programming 2nd ed. New York, NY, USA: Springer, Jun. 2011. [10] R. Caballero, E. Cerda, M. M. Muñoz, and L. Rey, "Analysis and comparisons of some solution concepts for stochastic programming problems, " Top, Vol. 10, No. 1, pp. 101_123, Jun. 2002. [11] H. Kaschel, K. Toledo, J. Torres Gomez and M. Julia Fernandez- Getino Garcia, "Energy-efficient cooperative spectrum sensing base on stochastic programming in dynamic cognitive radio sensor networks, " IEEE Access Journal, Vol.9, pp.720-732, Dec.2020. [12] W. Lu, T. Nan, Y. Gong, M. Qin, X. Lui, Zh. Xu and Zh. Na, " Joint resource allocation for wireless energy harvesting enabled cognitive sensor networks, " IEEE Access Journal, Vol.6, pp.22480-22488,2018. [13] M. Karimi, S.M.S. Sadough and M.Torabi, "Improved joint spectrum sensing and power allocation for cognitive radio networks using probabilistic spectrum access, " IEEE Syst. Journal, Vol.13, No.4, pp. 3716-3723, Jan.2019. [14] A. Pakmehr and A. Ghaffari , "Coverage improving with energy efficient inwireless sensor networks, "Journal of Information Systems and Telecommunication (JIST), Vol.5, No.1, 2017. [15] M.R. Thaghva, R. Hamlbarani Haghi, A. Hanifi and K. Feizi, "Clustering for reduction of energy consumption in wireless sensor networks by AHP method," Journal of Information Systems and Telecommunication (JIST), Vol.6, No.1, 2018. [16] M. Bavaghar, A. Mohajer and Sara Taghavi Motlagh, "Energy efficient clkustring algorithm for wireless sensor networks," Journal of Information Systems and Telecommunication (JIST), Vol.7, No. 4 , 2019. [17] Zh. Liu, M. Zhao, Y. Yuan and X. Guan, "Subchannel and resource allocation in cognitive radio sensor network with wireless energy harvesting , " Computer Networks, Vol.167, Feb. 2020. [18] M.Sharifi and M. Mohassel Feghhi, "Joint energy and throughput optimization in energy harvesting cognitive sensor networks, " 29th Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, May 2021. [19] S. Ebrahimi Mood and M.M. Javadi, "Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm, "Evolving Systems Journal, Vol.11, pp.575-578, 2020. [20] J-C Charr , K. Deschinkel, R. Haj Mansour and M. Hakem, " Lifetime optimization for partial coverage in heterogeneous sensor networks, " Ad hoc Networks, Vol. 107, 2020. [21] X. Deng, P. Guan, C. Hei, F. Li, J. Liu and N. Xiong, "An intelligent resource allocation scheme in energy harvesting cognitive wireless sensor networks, "IEEE Transactions on Network Science and Engineering, Vol.8, No.2, 1900-1912, 2021. [22] X. Yan, Ch. Huang, J. Gan and X. Wu, "Game theory-based energy efficient clustering algorithm for wireless sensor networks, " Sensors Journal, Vol. 22, No.2, 2022. [23] A. Bagheri, A. Ebrahimzadeh and M. Najimi, "Lifetime maximization by dynamic threshold and sensor selection in multi-channel cognitive sensor networks, " Journal of Information Systems and Telecommunication (JIST), Vol.5, No.4, pp.225-235, 2017. [24] M.Najimi, " Cooperative game approach for mobile primary user localization based on compressive sensing in multi-antenna cognitive sensor networks, " Journal of Information Systems and Telecommunication (JIST), Vol.7, No.2, pp.134-143, 2019. [25] M. Monemian and M. Mahdavi, "Analysis of a new energy-based sensor selection method for cooperative spectrum sensing in cognitive radio networks, " IEEE Sensors J., Vol. 14, No. 9, pp. 3021_3032, Sep. 2014. [26] B. Sklar, "Rayleigh fading channels in mobile digital communication systems part1:Characterization, " IEEE Commun. Mag., Jul. 1997. [27] Y. Ma, D. I. Kim, Zh. Wu, "Optimization of ofdm-based cellular cognitive radio networks, " IEEE Trans. on Communications. Vol. 58, No.8, Aug.2010. [28] S. Maleki, A. Pandharipande, and G. Leus, "Energy-efficient distributed spectrum sensing for cognitive sensor networks, " in Proc. 35th Annu. Conf. IEEE Ind. Electron. Soc., Nov. 2009, pp. 2642–2646. [29] S. Maleki, A. Pandharipande, and G. Leus, "Energy efficient distributed spectrum sensing with convex optimization, " in Proc. 3rd Int. Workshop Comput. Advances in Multi-Sensor Adaptive Processing, Nov.2009, pp. 396–399.JeniferSCarmel Mary BelindaM J1216202334735810.61186/jist.37936.11.44.347http://jist.ir/en/Article/37936http://jist.ir/en/Article/Download/37936http://jist.ir/en/Article/Download/37936http://jist.ir/en/Article/Download/37936http://jist.ir/en/Article/Download/37936http://jist.ir/en/Article/Download/37936http://jist.ir/en/Article/Download/37936http://jist.ir/en/Article/Download/37936[1] H. P. Chan, L. M. Hadjiiski, and R. K. Samala, “Computer-aided diagnosis in the era of deep learning,” Medical Physics, vol. 47, no. 5, pp. e218–e227, May 2020. [2] F. Ritter, T. Boskamp, A. Homeyer, H. Laue, M. Schwier, F. Link, and H. O. Peitgen, “Medical Image Analysis,” IEEE Pulse, vol. 2, no. 6, pp. 60–70, Nov. 2011. [3] J. Ker, L. Wang, J. Rao, and T. Lim, “Deep learning applications in medical image analysis,” IEEE Access, vol. 6, pp. 9375–9389, Dec. 2017. [4] T. Kiyatmoko, “Retinal Vessel Extraction using Dynamic Threshold and Enhancement Image Filter from Retina Fundus,” Journal of InformationSystems & Telecommunication, vol. 6, no. 24, pp. 189-196, Jun. 2019. [5] K. A. Kumar, and R. Boda, “A Threshold-based Brain Tumour Segmentation from MR Images using Multi-Objective Particle Swarm Optimization,” Journalof Information Systems and Telecommunication, vol. 9, no. 36, pp. 218-225, Oct. 2021. [6] M. Jena, S. P. Mishra, and D. Mishra, “A survey on applications of machine learning techniques for medical image segmentation,” International Journal of Engineering & Technology, vol. 7, no. 4, pp. 4489–4495, Nov. 2018. [7] S. Niyas, S. J. Pawan, M. Anand Kumar, and J. Rajan, “Medical image segmentation with 3D convolutional neural networks: A survey,” Neurocomputing, vol. 493, pp. 397–413, Jul. 2022. [8] P. Dutta, P. Upadhyay, M. De, and R. G. Khalkar, “Medical image analysis using deep convolutional neural networks: CNN architectures and transfer learning,” in 2020 International Conference on Inventive Computation Technologies (ICICT), Feb. 2020, pp. 175-180. [9] M. Jogin, Mohana, M. S. Madhulika, G. D. Divya, R. K. Meghana, and S. Apoorva, “Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning,” in 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), May 2018, pp. 2319–2323. [10] E. Gholam, and S. R. KamelTabbakh, “Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks,” Journal of Information Systems and Telecommunication, vol. 9, no. 35, pp.191-196, Jul. 2021. [11] I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv [stat.ML], 2014. [12] Z. Hu, J. Tang, Z. Wang, K. Zhang, L. Zhang, and Q. Sun, “Deep learning for image-based cancer detection and diagnosis − A survey,”Pattern Recognition, vol. 83, pp. 134–149, Nov. 2018. [13] X. Liu, L. Song, S. Liu, and Y. Zhang, “A Review of Deep-Learning-Based Medical Image Segmentation Methods,” Sustainability, vol. 13, no. 3, p. 1224, Jan. 2021. [14] H. C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R.M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, May 2016. [15] S. Kazeminia, C. Baur, A. Kuijper, B. Van Ginneken, N. Navab, S. Albarqouni, and A. Mukhopadhyay, “GANs for medical image analysis,” Artificial Intelligence in Medicine, vol. 109, p. 101938, Sep. 2020. [16] Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu, and X. Yang, “A review of deep learning based methods for medical image multi-organ segmentation,” PhysicaMedica, vol. 85, pp. 107–122, May 2021. [17] B. Halalli, and A. Makandar, “Computer Aided Diagnosis - Medical Image Analysis Techniques,” Breast Imaging, Dec. 2017. [18] L. Chandrashekar, and A. Sreedevi, “A two-stage multi-objective enhancement for fused magnetic resonance image and computed tomography brain images, ”Journal of InformationSystems & Telecommunication, vol. 8, no. 30, pp. 93-104, Aug. 2020. [19] S. Zakariapour, H. Jazayeriy, and M. Ezoji, “Mitosis detection in breast cancer histological images based on texture features using adaboost, ”Journal of InformationSystems & Telecommunication, vol. 5, no. 8, pp. 1-10, Jul. 2017. [20] M. Kumar, S. K. Khatri, and M. Mohammadian, “Breast Cancer Classification Approaches-A Comparative Analysis,” Journal of InformationSystems & Telecommunication, vol. 11, no. 41, pp. 1-11, Jan. 2023. [21] M. M. Badža, and M. Č. Barjaktarović, “Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network,” Applied Sciences, vol. 10, no. 6, p. 1999, Mar. 2020. [22] V. Rachapudi, and G. Lavanya Devi, “Improved convolutional neural network based histopathological image classification,” Evolutionary Intelligence, vol. 14, no. 3, pp. 1337-1343, Feb. 2020. [23] E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 4, pp. 640–651, Jan. 2017. [24] J. Sun, Y. Peng, Y. Guo, and D. Li, “Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN,” Neurocomputing, vol. 423, pp. 34- 45, Jan. 2021. [25] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lecture Notes in Computer Science, pp. 234–241, Oct. 2015. [26] N. V. Dharwadkar, and A. K. Savvashe, “Right Ventricle Segmentation of Magnetic Resonance Image Using the Modified Convolutional Neural Network,” Arabian Journal for Science and Engineering, vol. 46, no. 4, pp. 3713–3722, Jan. 2021. [27] C. Li, X. Song, H. Zhao, L. Feng, T. Hu, Y. Zhang, J. Jiang, J. Wang, J. Xiang, and Y. Sun, “An 8-layer residual U-Net with deep supervision for segmentation of the left ventricle in cardiac CT angiography,” Computer Methods and Programs in Biomedicine,vol. 200, p. 105876, Mar. 2021. [28] Z. Zhou, M. M. RahmanSiddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,”inDeep learning in medical image analysis and multimodal learning for clinical decision support, Cham: Springer, Sep. 2018, pp. 3-11. [29] C. Li, Y. Tan, W. Chen, X. Luo, Y. Gao, X. Jia, and Z. Wang, “Attention unet++: A nested attention-aware U-net for liver CT image segmentation,” in 2020 IEEE International Conference on Image Processing (ICIP), Oct. 2020, pp. 345-349. [30] Milletari, N. Navab, and S. A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV),Oct. 2016, pp. 565-571. [31] X. Guan, G. Yang, J. Yang, X. Xu, W. Jiang, and X. Lai, “3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework,” BMC Medical Imaging, vol. 22, no. 1, Jan. 2022. [32] K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” IEEE Transactions on Pattern Analysis andMachine Intelligence, vol. 42, no. 2, pp. 386–397, Mar. 2017. [33] R. O. Dogan, H. Dogan, C. Bayrak, and T. Kayikcioglu, “A Two-Phase Approach using Mask R-CNN and 3D U-Net for High-Accuracy Automatic Segmentation of Pancreas in CT Imaging,” Computer Methods and Programs in Biomedicine, vol. 207, p. 106141, Aug. 2021. [34] R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights into Imaging, vol. 9, no. 4, pp. 611–629, Jun. 2018. [35] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998. [36] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM,vol. 60, no. 6, pp. 84–90, May 2017. [37] K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv [cs.CV], 2014. [38] C. Szegedy, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [39] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. [40] R. A. Hazarika, A. Abraham, D. Kandar, and A. K. Maji, “An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images,” IEEE Access, vol. 9, pp. 161194–161207, Nov. 2021. [41] K. M. Hosny, M. A. Kassem, and M. M. Fouad, “Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet,” Journal of Digital Imaging, vol. 33, no. 5, pp. 1325–1334, Jun. 2020. [42] Eva-H. Dulf, M. Bledea, T. Mocan, and L. Mocan, “Automatic Detection of Colorectal Polyps Using Transfer Learning,” Sensors, vol. 21, no. 17, p. 5704, Aug. 2021. [43] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, pp. 2818-2826. [44] Z. Hameed, S. Zahia, B. Garcia-Zapirain, J. Javier Aguirre, and A. MaríaVanegas, “Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models,” Sensors, vol. 20, no. 16, p. 4373, Aug. 2020. [45] M. Toğaçar, Z. Cömert, and B. Ergen, “Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method,” Expert Systems with Applications, vol. 149, p. 113274, Jul. 2020. [46] M. M. Eid, and Y. H. Elawady, “Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM,” European Journal of Electrical Engineering and Computer Science, vol. 5, no. 1, pp. 1–8, Jan. 2021. [47] Xiao, B. Liu, L. Geng, F. Zhang, and Y. Liu, “Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network,” Symmetry, vol. 12, no. 11, p. 1787, Oct. 2020. [48] M. Goyal, J. Guo, L. Hinojosa, K. Hulsey, and I. Pedrosa, “Automated kidney segmentation by mask R-CNN in T2-weighted magnetic resonance imaging,” in Medical Imaging2022: Computer-Aided Diagnosis, vol. 12033, pp. 89-94, Apr. 2022. [49] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio,“Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, Oct. 2020. [50] M. D. Cirillo, D. Abramian, and A. Eklund, “Vox2Vox: 3D-GAN for Brain Tumour Segmentation,” Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 274–284, Oct. 2021. [51] W. Wang, G. Wang, X. Wu, X. Ding, X. cao, L. Wang, J. Zhang, and P. Wang “Automatic segmentation of prostate magnetic resonance imaging using generative adversarial networks,” Clinical Imaging, vol. 70, pp. 1–9, Feb. 2021. [52] X. Wei, X. Chen, C. Lai, Y. Zhu, H. Yang, and Y. Du, “Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures,” BioMed Research International, vol. 2021, pp. 1–11, Dec. 2021. [53] J. Ma, Y. Deng, Z. Ma, K. Mao, and Y. Chen, “A Liver Segmentation Method Based on the Fusion of VNet and WGAN,” Computational and Mathematical Methods in Medicine, vol. 2021, pp. 1–12, Oct. 2021. [54] M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein Generative Adversarial Networks,” in International Conference on Machine Learning, Jul. 2017, pp. 214-223. [55] J. Zhang, L. Yu, D. Chen, W. Pan, C. Shi, Y. Niu, X. Yao, X. Xu, and Y. Cheng, “Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images,” Biomedical Signal Processing and Control, vol. 69, p. 102901, Aug. 2021. [56] A. Antoniou, A. Storkey, and H. Edwards, “Data augmentation Generative Adversarial Networks,” arXiv [stat.ML], Nov. 2017. [57] B. Beynek, Ş. Bora, V. Evren, and A. Ugur, “Synthetic Skin Cancer Image Data Generation Using Generative Adversarial Neural Network,” International Journal of Multidisciplinary Studies and Innovative Technologies, vol. 5, no. 2, pp. 147–150, Nov. 2021. [58] B. Ahmad, S. Jun, V. Palade, Q. You, L. Mao, and M. Zhongjie, “Improving Skin Cancer Classification Using Heavy-Tailed Student T-Distribution in Generative Adversarial Networks (TED-GAN),” Diagnostics, vol. 11, no. 11, p. 2147, Nov. 2021. [59] V. K. Waghmare, and M. H. Kolekar, “Brain Tumor Classification Using Deep Learning,” in Internet of Things for Healthcare Technologies, Jun. 2020, pp. 155–175. [60] A. Çinar, and M. Yildirim, “Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture,” Medical Hypotheses, vol. 139, p. 109684, Jun. 2020. [61] S. Chen, J. Zhang, X. Wei, and Q. Zhang, “Alzheimer’s Disease Classification Using Structural MRI Based on Convolutional Neural Networks,” in 2020 2ndInternational Conference on Big-data Service and Intelligent Computation, Dec. 2020, pp.7-13. [62] V. Chouha, S.K. Singh, A. Khamparia, D. Gupta, P. Tiwari, C. Moreira, R. Damaševičius, and V.H.C. De Albuquerque, “A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images,” Applied Sciences, vol. 10, no. 2, p. 559, Jan. 2020. [63] C.J. Lin, and Y.C. Li, “Lung Nodule Classification Using Taguchi-Based Convolutional Neural Networks for Computer Tomography Images,” Electronics,vol. 9, no. 7, p. 1066, Jun. 2020. [64] A. S. Abdel Rahman, S. B. Belhaouari, A. Bouzerdoum, H. Baali, T. Alam, and A. M. Eldaraa, “Breast Mass Tumor Classification using Deep Learning,” in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Feb. 2020, pp. 271-276. [65] Q. A. Al-Haija, and A. Adebanjo, “Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network,” in 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS),Sep. 2020, pp. 1-7. [66] A. Saber, M. Sakr, O. M. Abo-Seida, A. Keshk, and H. Chen, “A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique,” IEEE Access, vol. 9, pp. 71194–71209, May 2021. [67] K. Thurnhofer-Hemsi, and E. Domínguez, “A Convolutional Neural Network Framework for Accurate Skin Cancer Detection,” Neural Processing Letters, vol. 53, no. 5, pp. 3073-3093, Oct. 2020. [68] K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks,” Multimedia Tools and Applications, vol. 9, no. 33, pp. 24029-24055, Jun. 2020. [69] A. B. Bakht, S. Javed, R. Dina, H. Almarzouqi, A. Khandoker, and N. Werghi, “Thyroid Nodule Cell Classification in Cytology Images Using Transfer Learning Approach,” in International Conference on Soft Computing and Pattern Recognition, Dec. 2021, pp. 539–549. [70] W. Chen, Z. Gu, Z. Liu, Y. Fu, Z. Ye, X. Zhang, and L. Xiao, “A New Classification Method in Ultrasound Images of Benign and Malignant Thyroid Nodules Based on Transfer Learning and Deep Convolutional Neural Network,” Complexity, vol. 2021, pp. 1–9, Sep. 2021. [71] Eva-H. Dulf, M. Bledea, T. Mocan, and L. Mocan, “Automatic Detection of Colorectal Polyps Using Transfer Learning,” Sensors, vol. 21, no. 17, p. 5704, Aug. 2021. [72] Y. Bhanothu, A. Kamalakannan, and G. Rajamanickam, “Detection and Classification of Brain Tumor in MRI Images using Deep Convolutional Network,” in 2020 6th International Conference on Advanced Computing and Communication Systems, Mar. 2020, pp. 248-252. [73] W. M. Salama, and M. H. Aly, “Deep learning in mammography images segmentation and classification: Automated CNN approach,” Alexandria Engineering Journal, vol. 60, no. 5, pp. 4701–4709, Oct. 2021. [74] A. Khouani, M. El HabibDaho, S. A. Mahmoudi, M. A. Chikh, and B. Benzineb, “Automated recognition of white blood cells using deep learning,” Biomedical Engineering Letters, vol. 10, no. 3, pp. 359–367, Jul. 2020. [75] H. Yu, and X. Zhang, “Synthesis of Prostate MR Images for Classification Using Capsule Network-Based GAN Model,” Sensors, vol. 20, no. 20, p. 5736, Oct. 2020. [76] S. Kaur, H. Aggarwal, and R. Rani, “Diagnosis of Parkinson’s disease using deep CNN with transfer learning and data augmentation,” Multimedia Tools and Applications, vol. 80, no. 7, pp.10113-10139,Nov. 2020. [77] B. Mondal, N. Das, K. C. Santosh, and M. Nasipuri, “Improved Skin Disease Classification Using Generative Adversarial Network,” in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS),Jul. 2020, pp. 520-525. [78] T. Pang, J. H. D. Wong, W. L. Ng, and C. S. Chan, “Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification,” Computer Methods and Programs in Biomedicine, vol. 203, p. 106018, May 2021. [79] B. Ahmad, J. Sun, Q. You, V. Palade, and Z. Mao, “Brain Tumor Classification Using a Combination of VariationalAutoencoders and Generative Adversarial Networks,” Biomedicines, vol. 10, no. 2, p. 223, Jan. 2022. [80] Y. Li, Y. Chen, and Y. Shi, “Brain Tumor Segmentation Using 3D Generative Adversarial Networks,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 35, no. 4, p.2157002,Aug. 2020. [81] A. Negi, A. N. J. Raj, R. Nersisson, Z. Zhuang, andM. Murugappan, “RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks,” Arabian Journal for Science and Engineering, vol. 45, no. 8, pp. 6399–6410, Apr. 2020. [82] C. Decourt, and L. Duong, “Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI,” Computers in Biology and Medicine, vol. 123, p. 103884, Aug. 2020. [83] Z. Lou, W. Huo, K. Le, and X. Tian, “Whole Heart Auto Segmentation of Cardiac CT Images Using U-Net Based GAN,” in 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Oct. 2020, pp. 192-196. [84] X. Wu, L. Bi, M. Fulham, and J. Kim, “Unsupervised Positron Emission Tomography Tumor Segmentation via GAN based Adversarial Auto-Encoder,” in 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), Dec. 2020, pp. 448-453. [85] L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Scientific Reports, vol. 10, no. 1, pp. 1-12, Nov. 2020. [86] E. Luz, P. Silva, R. Silva, L. Silva, J. Guimarães, G. Miozzo, G. Moreira, and D. Menotti, “Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images,” Research on Biomedical Engineering, Apr. 2021, pp. 1-14. [87] N. S. Punn, and S. Agarwal, “Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks,” Applied Intelligence, vol. 51, no. 5, pp. 2689-202, Oct. 2020. [88] A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, “CovidGAN: DataAugmentation using Auxiliary Classifier GAN for Improved Covid-19 Detection,” IEEE Access, vol. 8, pp. 91916-91923, May 2020. [89] Y. Oh, S. Park, and J. C. Ye, “Deep Learning COVID-19 Features on CXR using Limited Training Data Sets,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2688-2700, May 2020. [90] N. Wang, H. Liu, and C. Xu, “Deep learning for the detection of COVID-19 using transfer learning and model integration,” in 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC), Jul. 2020, pp. 281-284. [91] J. Li, D. Zhang, Q. Liu, R. Bu, and Q. Wei, “COVID-GATNet: A deep learning framework for screening of COVID-19 from chest X-ray images,” in 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Dec. 2020, pp. 1897-1902. [92] M. Ahsan, M. Based, J. Haider, and M. Kowalski, “COVID-19 detection from chest X-ray images using feature fusion and deep learning,” Sensors, vol. 21, no. 4, p.1480, Jan. 2021. [93] A. S. Al-Waisy, S. Al-FahdawiS, M. A. Mohammed, K. H. Abdulkareem, S. A. Mostafa, M. S. Maashi, M. Arif, and B. Garcia-Zapirain, “COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images,” Soft Computing,Nov. 2020, pp. 1-16. [94] X. Li, W. Tan, P. Liu, Q. Zhou, and J. Yang, “Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning,” Journal of Healthcare Engineering, vol. 2021, pp. 1–7, Apr. 2021. [95] Y. Pathak, P. K. Shukla, and K. V. Arya, “Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 4, pp. 1234–1241, Jul. 2021. [96] M. J. Horry, S.Chakraborty, M. Paul, A. Ulhaq, B. Pradhan, M. Saha, and N. Shukla, “COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data,” IEEE Access, vol. 8, pp. 149808–149824, Aug. 2020. [97] V. I. Iglovikov, A. Rakhlin, A. A. Kalinin, and A. A. Shvets, “Paediatric bone age assessment using deep convolutional neural networks,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Cham: Springer International Publishing, Sep. 2018, pp. 300-308. [98] X. Pan, Y. Zhao, H. Chen, D. Wei, C. Zhao, and Z. Wei, “Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset,” International Journal of Biomedical Imaging,vol. 2020, pp. 1–12, Mar. 2020. [99] M. A. Zulkifley, S.R. Abdani, and N.H. Zulkifley, “Automated Bone Age Assessment with Image Registration Using Hand X-ray Images,” Applied Sciences, vol. 10, no. 20, p. 7233, Oct. 2020. [100] Y.Gao, T. Zhu, and X. Xu, “Bone age assessment based on deep convolution neural network incorporated with segmentation,” International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 12, pp.1951-1962, Sep. 2020. [101] S. Li, B. Liu, S. Li, X. Zhu, Y. Yan, and D. Zhang, “A deep learning-based computer-aided diagnosis method of X-ray images for bone age assessment,” Complex & Intelligent Systems, pp.1-11, Apr. 2021. [102] I. Salim, and A. B. Hamza, “Ridge regression neural network for pediatric bone age assessment,” Multimedia Tools and Applications, vol. 80, no. 20, pp. 30461–30478, May 2021. [103] S. S. Halabi, L. M. Prevedello, J. Kalpathy-Cramer, A.B. Mamonov, A. Bilbily, M. Cicero, I. Pan, L. A. Pereira, R. T. Sousa, N. Abdala, and F.C. Kitamura, “The RSNA Pediatric Bone Age Machine Learning Challenge,” Radiology, vol. 290, no. 2, pp.498-503, Feb. 2019. [104] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834–848, Apr. 2018. [105] AlexSWong, “AlexSWong/COVID-Net,” GitHub,Feb. 2022, https://github.com/AlexSWong/COVID-Net. [106] “RSNA Bone Age“, www.kaggle.com. https://www.kaggle.com/datasets/kmader/rsna-bone-age.FatemehMozaffariImanRaeesi VananiPayamMahmoudianBabakSohrabi1216202333134610.61186/jist.38419.11.44.331http://jist.ir/en/Article/38419http://jist.ir/en/Article/Download/38419http://jist.ir/en/Article/Download/38419http://jist.ir/en/Article/Download/38419http://jist.ir/en/Article/Download/38419http://jist.ir/en/Article/Download/38419http://jist.ir/en/Article/Download/38419http://jist.ir/en/Article/Download/38419[1] S. Mitrović, B. Baesens, W. Lemahieu, and J. De Weerdt, “On the operational efficiency of different feature types for telco Churn prediction,” Eur. J. Oper. Res., vol. 267, no. 3, pp. 1141–1155, 2018. [2] K. Coussement, S. Lessmann, and G. Verstraeten, “A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry,” Decis. Support Syst., vol. 95, pp. 27–36, 2017. [3] I. Ullah, B. Raza, A. K. Malik, M. Imran, S. U. Islam, and S. W. Kim, “A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector,” IEEE Access, vol. 7, pp. 60134–60149, 2019. [4] J. Dyche, The CRM handbook: A business guide to customer relationship management. Addison-Wesley Professional, 2002. [5] A. Idris and A. Khan, “Customer churn prediction for telecommunication: Employing various various features selection techniques and tree based ensemble classifiers,” in 2012 15th International Multitopic Conference (INMIC), 2012, pp. 23–27. [6] W. Verbeke, D. Martens, C. Mues, and B. Baesens, “Building comprehensible customer churn prediction models with advanced rule induction techniques,” Expert Syst. Appl., vol. 38, no. 3, pp. 2354–2364, 2011. [7] L. Geiler, S. Affeldt, and M. Nadif, “An effective strategy for churn prediction and customer profiling,” Data Knowl. Eng., vol. 142, p. 102100, 2022. [8] Y. Chen, L. Zhang, Y. Zhao, and B. Xu, “Implementation of penalized survival models in churn prediction of vehicle insurance,” J. Bus. Res., vol. 153, pp. 162–171, 2022. [9] M. Makhtar, S. Nafis, M. A. Mohamed, M. K. Awang, M. N. A. Rahman, and M. M. Deris, “Churn classification model for local telecommunication company based on rough set theory,” J. Fundam. Appl. Sci., vol. 9, no. 6S, pp. 854–868, 2017. [10] W. Buckinx and D. Van den Poel, “Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting,” Eur. J. Oper. Res., vol. 164, no. 1, pp. 252–268, 2005. [11] J. Burez and D. Van den Poel, “Handling class imbalance in customer churn prediction,” Expert Syst. Appl., vol. 36, no. 3, pp. 4626–4636, 2009. [12] A. Dingli, V. Marmara, and N. S. Fournier, “Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry,” Int. J. Mach. Learn. Comput., vol. 7, no. 5, 2017. [13] V. L. Miguéis, D. Van den Poel, A. S. Camanho, and J. F. e Cunha, “Modeling partial customer churn: On the value of first product-category purchase sequences,” Expert Syst. Appl., vol. 39, no. 12, pp. 11250–11256, 2012. [14] V. L. Miguéis, A. Camanho, and J. F. e Cunha, “Customer attrition in retailing: an application of multivariate adaptive regression splines,” Expert Syst. Appl., vol. 40, no. 16, pp. 6225–6232, 2013. [15] Y. Chen, Y. R. Gel, V. Lyubchich, and T. Winship, “Deep ensemble classifiers and peer effects analysis for churn forecasting in retail banking,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2018, pp. 373–385. [16] N. Glady, B. Baesens, and C. Croux, “Modeling churn using customer lifetime value,” Eur. J. Oper. Res., vol. 197, no. 1, pp. 402–411, 2009. [17] Y. Xie, X. Li, E. W. T. Ngai, and W. Ying, “Customer churn prediction using improved balanced random forests,” Expert Syst. Appl., vol. 36, no. 3, pp. 5445–5449, 2009. [18] N. Gordini and V. Veglio, “Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry,” Ind. Mark. Manag., vol. 62, pp. 100–107, 2017. [19] A. D. Rachid, A. Abdellah, B. Belaid, and L. Rachid, “Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context,” Int. J. Electr. Comput. Eng., vol. 8, no. 4, p. 2367, 2018. [20] A. Tamaddoni, S. Stakhovych, and M. Ewing, “The impact of personalised incentives on the profitability of customer retention campaigns,” J. Mark. Manag., vol. 33, no. 5–6, pp. 327–347, 2017. [21] I. Adaji and J. Vassileva, “Predicting churn of expert respondents in social networks using data mining techniques: a case study of stack overflow,” in 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015, pp. 182–189. [22] K. Coussement and D. Van den Poel, “Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques,” Expert Syst. Appl., vol. 34, no. 1, pp. 313–327, 2008. [23] D. F. Benoit and D. Van den Poel, “Improving customer retention in financial services using kinship network information,” Expert Syst. Appl., vol. 39, no. 13, pp. 11435–11442, 2012. [24] M. Á. de la Llave, F. A. López, and A. Angulo, “The impact of geographical factors on churn prediction: an application to an insurance company in Madrid’s urban area,” Scand. Actuar. J., vol. 2019, no. 3, pp. 188–203, 2019. [25] J.-H. Ahn, S.-P. Han, and Y.-S. Lee, “Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry,” Telecomm. Policy, vol. 30, no. 10–11, pp. 552–568, 2006. [26] H. Faris, B. Al-Shboul, and N. Ghatasheh, “A genetic programming based framework for churn prediction in telecommunication industry,” in International Conference on Computational Collective Intelligence, 2014, pp. 353–362. [27] A. S. Halibas, A. C. Matthew, I. G. Pillai, J. H. Reazol, E. G. Delvo, and L. B. Reazol, “Determining the Intervening Effects of Exploratory Data Analysis and Feature Engineering in Telecoms Customer Churn Modelling,” in 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), 2019, pp. 1–7. [28] J. Hu et al., “pRNN: A recurrent neural network based approach for customer churn prediction in telecommunication sector,” in 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 4081–4085. [29] M. Karanovic, M. Popovac, S. Sladojevic, M. Arsenovic, and D. Stefanovic, “Telecommunication Services Churn Prediction-Deep Learning Approach,” in 2018 26th Telecommunications Forum (TELFOR), 2018, pp. 420–425. [30] A. Lemmens and C. Croux, “Bagging and boosting classification trees to predict churn,” J. Mark. Res., vol. 43, no. 2, pp. 276–286, 2006. [31] T. Vafeiadis, K. I. Diamantaras, G. Sarigiannidis, and K. C. Chatzisavvas, “A comparison of machine learning techniques for customer churn prediction,” Simul. Model. Pract. Theory, vol. 55, pp. 1–9, 2015. [32] E. Lima, C. Mues, and B. Baesens, “Monitoring and backtesting churn models,” Expert Syst. Appl., vol. 38, no. 1, pp. 975–982, 2011. [33] A. Amin et al., “Customer churn prediction in the telecommunication sector using a rough set approach,” Neurocomputing, vol. 237, pp. 242–254, 2017. [34] A. Hiziroglu and O. F. Seymen, “Modelling Customer Churn Using Segmentation and Data Mining.,” in DB&IS, 2014, pp. 259–271. [35] V. Bhambri, “Data mining as a tool to predict churn behavior of customers,” Int. J. Manag. Res., pp. 59–69, 2013. [36] M. Clemente-Císcar, S. San Matías, and V. Giner-Bosch, “A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings,” Eur. J. Oper. Res., vol. 239, no. 1, pp. 276–285, 2014. [37] T. Mutanen, V. Österlund, and R. Kinnunen, “Monitoring service adaptation and customer churn in the beginning phase of a new service,” in Fourth International Conference on Data Analytics, DATA ANALYTICS 2015, 2015, pp. 69–73. [38] D. Ringbeck, D. Smirnov, and A. Huchzermeier, “Proactive Retention Management in Retail: Field Experiment Evidence for Lasting Effects,” Available SSRN 3378498, 2019. [39] W. Verbeke, K. Dejaeger, D. Martens, J. Hur, and B. Baesens, “New insights into churn prediction in the telecommunication sector: A profit driven data mining approach,” Eur. J. Oper. Res., vol. 218, no. 1, pp. 211–229, 2012. [40] A. K. Ahmad, A. Jafar, and K. Aljoumaa, “Customer churn prediction in telecom using machine learning in big data platform,” J. Big Data, vol. 6, no. 1, p. 28, 2019. [41] B. Bonev, F. Escolano, and M. Cazorla, “Feature selection, mutual information, and the classification of high-dimensional patterns,” Pattern Anal. Appl., vol. 11, no. 3–4, pp. 309–319, 2008. [42] A. De Caigny, K. Coussement, and K. W. De Bock, “A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees,” Eur. J. Oper. Res., vol. 269, no. 2, pp. 760–772, 2018. [43] T.-H. Hsu, C.-C. Chen, M.-F. Chiang, K.-W. Hsu, and W.-C. Peng, “Inferring potential users in mobile social networks,” in 2014 International Conference on Data Science and Advanced Analytics (DSAA), 2014, pp. 347–353. [44] S. Maldonado, Á. Flores, T. Verbraken, B. Baesens, and R. Weber, “Profit-based feature selection using support vector machines–General framework and an application for customer retention,” Appl. Soft Comput., vol. 35, pp. 740–748, 2015. [45] A. K. Meher, J. Wilson, and R. Prashanth, “Towards a large scale practical churn model for prepaid mobile markets,” in Industrial Conference on Data Mining, 2017, pp. 93–106. [46] K. B. Subramanya and A. Somani, “Enhanced feature mining and classifier models to predict customer churn for an E-retailer,” in 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, 2017, pp. 531–536. [47] J. Van Hulse, T. M. Khoshgoftaar, A. Napolitano, and R. Wald, “Feature selection with high-dimensional imbalanced data,” in 2009 IEEE International Conference on Data Mining Workshops, 2009, pp. 507–514. [48] M. B. Kursa and W. R. Rudnicki, “Feature selection with the Boruta package,” J Stat Softw, vol. 36, no. 11, pp. 1–13, 2010. [49] H. Li, C.-J. Li, X.-J. Wu, and J. Sun, “Statistics-based wrapper for feature selection: An implementation on financial distress identification with support vector machine,” Appl. Soft Comput., vol. 19, pp. 57–67, 2014. [50] H. Xu, Z. Zhang, and Y. Zhang, “Churn prediction in telecom using a hybrid two-phase feature selection method,” in 2009 Third International Symposium on Intelligent Information Technology Application, 2009, vol. 3, pp. 576–579. [51] K. Cao and P. Shao, “Customer churn prediction based on svm-rfe,” in 2008 International Seminar on Business and Information Management, 2008, vol. 1, pp. 306–309. [52] I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, Feature extraction: foundations and applications, vol. 207. Springer, 2008. [53] Y. Li and G. Xia, “The explanation of support vector machine in customer churn prediction,” in 2010 International Conference on E-Product E-Service and E-Entertainment, 2010, pp. 1–4. [54] Y. Saeys, T. Abeel, and Y. Van de Peer, “Robust feature selection using ensemble feature selection techniques,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2008, pp. 313–325. [55] H. Hong, Q. Ye, Q. Du, G. A. Wang, and W. Fan, “Crowd characteristics and crowd wisdom: Evidence from an online investment community,” J. Assoc. Inf. Sci. Technol., vol. 71, no. 4, pp. 423–435, 2020. [56] J. Surowiecki, The wisdom of crowds. Anchor, 2005. [57] W. Pan, Y. Altshuler, and A. Pentland, “Decoding social influence and the wisdom of the crowd in financial trading network,” in 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, 2012, pp. 203–209. [58] A. Bari, P. Peidaee, A. Khera, J. Zhu, and H. Chen, “Predicting financial markets using the wisdom of crowds,” in 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019, pp. 334–340. [59] X. Wu, Q. Ye, Y. Jin, and Y. Li, “Wisdom of Experts and Crowds: Different Impacts of Analyst Recommendation and Online Search on the Stock Market.,” in PACIS, 2019, p. 129. [60] I. Ajzen, “From intentions to actions: A theory of planned behavior,” in Action control, Springer, 1985, pp. 11–39. [61] D. T. Larose and C. D. Larose, Discovering knowledge in data: an introduction to data mining, vol. 4. John Wiley & Sons, 2014. [62] C.-F. Tsai and Y.-H. Lu, “Customer churn prediction by hybrid neural networks,” Expert Syst. Appl., vol. 36, no. 10, pp. 12547–12553, 2009. [63] P. C. Pendharkar, “Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services,” Expert Syst. Appl., vol. 36, no. 3, pp. 6714–6720, 2009. [64] B. Q. Huang, T.-M. Kechadi, B. Buckley, G. Kiernan, E. Keogh, and T. Rashid, “A new feature set with new window techniques for customer churn prediction in land-line telecommunications,” Expert Syst. Appl., vol. 37, no. 5, pp. 3657–3665, 2010. [65] C. Orsenigo and C. Vercellis, “Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification,” Pattern Recognit., vol. 43, no. 11, pp. 3787–3794, 2010. [66] N. Kamalraj and A. Malathi, “An Ordered Fuzzy Rule Induction Based Churn Mining For Telecom Industry,” ICIREIE 2015, p. 17, 2015. [67] B. Al-Shboul, H. Faris, and N. Ghatasheh, “Initializing genetic programming using fuzzy clustering and its application in churn prediction in the telecom industry,” Malaysian J. Comput. Sci., vol. 28, no. 3, pp. 213–220, 2015. [68] J. Zaratiegui, A. Montoro, and F. Castanedo, “Performing highly accurate predictions through convolutional networks for actual telecommunication challenges,” arXiv Prepr. arXiv1511.04906, 2015. [69] A. Rodan and H. Faris, “Echo state network with SVM-readout for customer churn prediction,” in 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2015, pp. 1–5. [70] A. Wangperawong, C. Brun, O. Laudy, and R. Pavasuthipaisit, “Churn analysis using deep convolutional neural networks and autoencoders,” arXiv Prepr. arXiv1604.05377, 2016. [71] M. Azeem, M. Usman, and A. C. M. Fong, “A churn prediction model for prepaid customers in telecom using fuzzy classifiers,” Telecommun. Syst., vol. 66, no. 4, pp. 603–614, 2017. [72] F. Khan and S. S. Kozat, “Sequential churn prediction and analysis of cellular network users—A multi-class, multi-label perspective,” in 2017 25th Signal Processing and Communications Applications Conference (SIU), 2017, pp. 1–4. [73] D. Bell and C. Mgbemena, “Data-driven agent-based exploration of customer behavior,” Simulation, vol. 94, no. 3, pp. 195–212, 2018. [74] L. M. Qaisi, A. Rodan, K. Qaddoum, and R. Al-Sayyed, “Customer churn prediction using data mining approach,” in 2018 Fifth HCT Information Technology Trends (ITT), 2018, pp. 348–352. [75] Y. Beeharry and R. Tsokizep Fokone, “Hybrid approach using machine learning algorithms for customers’ churn prediction in the telecommunications industry,” Concurr. Comput. Pract. Exp., p. e6627, 2021. [76] S. Baghla and G. Gupta, “Performance Evaluation of Various Classification Techniques for Customer Churn Prediction in E-commerce,” Microprocess. Microsyst., vol. 94, p. 104680, 2022. [77] M. A. Khan, M. A. I. Khan, M. Aref, and S. F. Khan, “Cluster & rough set theory based approach to find the reason for customer churn,” Int. J. Appl. Bus. Econ. Res, vol. 14, no. 1, pp. 439–455, 2016. [78] F. Devriendt, J. Berrevoets, and W. Verbeke, “Why you should stop predicting customer churn and start using uplift models,” Inf. Sci. (Ny)., vol. 548, pp. 497–515, 2021. [79] N. N. Y. Vo, S. Liu, X. Li, and G. Xu, “Leveraging unstructured call log data for customer churn prediction,” Knowledge-Based Syst., vol. 212, p. 106586, 2021. [80] B. Erkayman, E. Erdem, T. Aydin, and Z. Mahmat, “New Artificial intelligence approaches for brand switching decisions,” Alexandria Eng. J., vol. 63, pp. 625–643, 2023. [81] J. B. Rollins, “Foundational methodology for data science,” Domino Data Lab, Inc., Whitepaper, 2015. [82] P. Chapman et al., “The CRISP-DM user guide,” in 4th CRISP-DM SIG Workshop in Brussels in March, 1999. [83] I. Guyon and A. Elisseeff, “An introduction to feature extraction,” in Feature extraction, Springer, 2006, pp. 1–25. [84] M. Landry and B. Angela, “Machine Learning with R and H2O,” Mt. View, CA, 2018. [85] S. Barua, M. M. Islam, X. Yao, and K. Murase, “MWMOTE--majority weighted minority oversampling technique for imbalanced data set learning,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 2, pp. 405–425, 2012. [86] P. Cao, O. Zaiane, and D. Zhao, “A measure optimized cost-sensitive learning framework for imbalanced data classification,” in Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, IGI Global, 2014, pp. 48–75. [87] V. Effendy and Z. K. A. Baizal, “Handling imbalanced data in customer churn prediction using combined sampling and weighted random forest,” in 2014 2nd International Conference on Information and Communication Technology (ICoICT), 2014, pp. 325–330. [88] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera, “A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 42, no. 4, pp. 463–484, 2011. [89] H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning,” in International conference on intelligent computing, 2005, pp. 878–887. [90] H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), 2008, pp. 1322–1328. [91] T. M. Khoshgoftaar, M. Golawala, and J. Van Hulse, “An empirical study of learning from imbalanced data using random forest,” in 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), 2007, vol. 2, pp. 310–317. [92] X.-Y. Liu, J. Wu, and Z.-H. Zhou, “Exploratory undersampling for class-imbalance learning,” IEEE Trans. Syst. Man, Cybern. Part B, vol. 39, no. 2, pp. 539–550, 2008. [93] N. V Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, “SMOTEBoost: Improving prediction of the minority class in boosting,” in European conference on principles of data mining and knowledge discovery, 2003, pp. 107–119. [94] J. Elith, J. R. Leathwick, and T. Hastie, “A working guide to boosted regression trees,” J. Anim. Ecol., vol. 77, no. 4, pp. 802–813, 2008. [95] M. Malohlava and A. Candel, “Gradient boosting machine with H2O.” H20 Booklet, http://docs. h2o. ai/h2o/latest-stable/h2o-docs/booklets …, 2017. [96] H. Wickham and M. H. Wickham, “Package tidyverse,” Easily Install Load ‘Tidyverse, 2017. [97] M. Kuhn and H. Wickham, “Recipes: preprocessing tools to create design matrices.” 2018. [98] J. Friedman, T. Hastie, R. Tibshirani, and B. Narasimhan, “Package ‘glmnet,’” CRAN R Repositary, 2021. [99] M. B. Kursa, W. R. Rudnicki, and M. M. B. Kursa, “Package ‘Boruta.’” 2020. [100] S. RColorBrewer and M. A. Liaw, “Package ‘randomForest,’” Univ. California, Berkeley Berkeley, CA, USA, 2018. [101] T. L. Pedersen and M. Benesty, “Package ‘lime.’” 2018. [102] N. Hasbullah, A. J. Mahajar, and M. I. Salleh, “The conceptual framework for predicting loyalty intention in the consumer cooperatives using modified theory of planned behavior,” Int. J. Bus. Soc. Sci., vol. 5, no. 11, 2014. [103] M. R. Khan, J. Manoj, A. Singh, and J. Blumenstock, “Behavioral modeling for churn prediction: Early indicators and accurate predictors of custom defection and loyalty,” in 2015 IEEE International Congress on Big Data, 2015, pp. 677–680.HadisAhmadianSeyed Javad Mahdavi ChabokMaryam Kheirabadi1216202328229310.61186/jist.39439.11.44.282http://jist.ir/en/Article/39439http://jist.ir/en/Article/Download/39439http://jist.ir/en/Article/Download/39439http://jist.ir/en/Article/Download/39439http://jist.ir/en/Article/Download/39439http://jist.ir/en/Article/Download/39439http://jist.ir/en/Article/Download/39439http://jist.ir/en/Article/Download/39439[1] Konstan, J. A., |Introduction to recommender systems. In Proceedings of the 2008 ACM SIGMOD international Conference on Management of Data, Vancouver, Canada, (Jun, 2008). [2] Resnick, P. and Varian, H. R. 1997. Recommender systems, Commun. ACM 40, 3 (Mar, 1997). [3] Schafer, J. B., Konstan, J., and Riedi, J. Recommender systems in e-commerce. In Proceedings of the 1st ACM Conference on Electronic Commerce, Denver, Colorado, United States, (Nov, 1999). [4] Gordan Durovic, Martina Holenko Dlab and Natasa Hoic-Bozic, Educational Recommender Systems: An Overview and Guidelines for Further Research and Development, Croatian Journal of Education Vol.20; No.2, 2018, 531-560. [5] Paula Rodríguez et all, An educational recommender system based on argumentation theory, AI Communications 30 (2017), 19–36. [6] learning Technology Standards Committee, IEEE Standard for Learning Object Metadata. Institute of Electrical and Electronics Engineers, New York (2002). [7] Zhang, S.; Yao, L.; Sun, A. Deep learning based recommender system: A survey and new perspectives. arXiv 2017, arXiv:1707.07435. [8] Ludewig,M.; Jannach, D. Evaluation of Session-based Recommendation Algorithms. arXiv 2018, arXiv:1803.09587. [9] Hidasi, B.; Karatzoglou, A.; Baltrunas, L.; Tikk, D. Session-based Recommendations with Recurrent Neural Networks. In Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico, 2-4 May 2016; pp. 1-10. [10] Paula Rodriguez et all, An educational recommender system based on argumentation theory, AI Communications 30 (2017), 19-36. [11] Zhang, H., et al., MOOCRC: A highly accurate resource recommendation model for use in MOOC environments. Mobile Networks and Applications, 2019. 24(1): p. 34-46. [12] Shuai Zhang, et. all, \Deep Learning based Recommender System: A Survey and New Perspectives", ACM Computing Surveys, Vol. 1, No. 1,2018. [13] Zeynep Batmaz, Ali Yurekli, Alper Bilge and Cihan Kaleli, A review on deep learning for recommender systems: challenges and remedies, Springer Nature B.V. 2018. [14] SHUAI ZHANG,LINA YAO, AIXIN SUN and YI TAY, Nanyang Technological University Deep Learning based Recommender System: A Survey and New Perspectives, 2018, ACM Computing Surveys, Vol. 1, No. 1, Article 1. [15] Chen, H., et al., Enhanced learning resource recommendation based on online learning style model. Tsinghua Science and Technology, 2019. 25(3): p. 348-356. [16] Li, R., et al., Online learning style modeling for course recommendation, in Recent Developments in Intelligent Computing, Communication and Devices. 2019, Springer. p. 1035-1042. [17] Hagemann, N., M.P. O'Mahony, and B. Smyth. Visualising module dependencies in academic recommendations. in Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. 2019. [18] Rodríguez, P., et al., An educational recommender system based on argumentation theory. AI Communications, 2017. 30(1): p. 19-36. [19] Dawen Liang, Rahul G Krishnan, Mathew D Ho man, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. arXiv preprint arXiv:1802.05814 (2018). [20] Neto, J. Multi-agent web recommender system for online educational environments. in International Conference on Practical Applications of Agents and Multi-Agent Systems. 2017. Springer. [21] Rodríguez, P., N. Duque, and S. Rodríguez, Integral Multi-agent Model Recommendation of Learning Objects, for Students and Teachers, in Management Intelligent Systems. 2013, Springer. p. 127-134. [22] Ahmadian Yazdi, H., S.J. Seyyed Mahdavi Chabok, and M. Kheirabadi, Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence, 2021: p. 1-24. [23] Shanker, M., M.Y. Hu, and M.S. Hung, Effect of data standardization on neural network training. Omega, 1996. 24(4): p. 385-397. [24] Zhang, H., Huang, T., Zhihan, Lv., Liu, S., and Yang, H. MOOCRC: A Highly Accurate Resource Recommen- dation Model for Use in MOOC Environments. Springer, Mobile Networks and Applications, Springer ,2018. [25]Charnelli, M.E., Sistemas recomendadores aplicados en Educación. 2019, Universidad Nacional de La Plata. [26]Li, R., et al., Online learning style modeling for course recommendation, in Recent Developments in Intelligent Computing, Communication and Devices. 2019, Springer. p. 1035-1042. [27]Yan, L., et al. Learning Resource Recommendation in E-Learning Systems Based on Online Learning Style. in International Conference on Knowledge Science, Engineering and Management. 2021. Springer. [28] P. Resnick and H.R. Varian. Recommender systems. Communications of the ACM, 40(3):56.58, (1997). MohammadAkhondi DarzikolaeiMohammad RezaKarami-MollaeiMaryamNajimi1216202329430610.61186/jist.39531.11.44.294http://jist.ir/en/Article/39531http://jist.ir/en/Article/Download/39531http://jist.ir/en/Article/Download/39531http://jist.ir/en/Article/Download/39531http://jist.ir/en/Article/Download/39531http://jist.ir/en/Article/Download/39531http://jist.ir/en/Article/Download/39531http://jist.ir/en/Article/Download/39531[1] M.A. Darzikolaei, A.Ebrahimzade, and E.Gholami, "Classification of radar clutters with artificial neural network." In 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2015, pp. 577-581. [2] M.A. Darzikolaei, A.Ebrahimzade, and E.Gholami,"The Separation of Radar Clutters using Multi-Layer Perceptron.", Information Systems & Telecommunication, Vol.1, No.17,2017,pp 1-10. [3] E. Fishler, A.Haimovich, R.Blum, D.Chizhik, L.Cimini, and R.Valenzuela,"MIMO radar: An idea whose time has come", In Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No. 04CH37509), , 2004, pp. 71-78. [4] A. Pakdaman, and H.Bakhshi, "Separable transmit beampattern design for MIMO radars with planar colocated antennas", AEU-International Journal of Electronics and Communications, Vol.89, No.1, 2018,pp.153-159. [5] M.J.Jahromi,and H.K.Bizaki, "Target Tracking in MIMO Radar Systems Using Velocity Vector",Journal of Information Systems and Telecommunication (JIST),Vol.3,No.7, 2014,pp. 150-158. [6] S.H. Mostafavi-Amjad, V. Solouk, and H. Kalbkhani, "Energy-efficient user pairing and power allocation for granted uplink-NOMA in UAV communication systems", Journal of Information Systems and Telecommunication (JIST), Vol. 10, No. 40, 2014, pp.312-323. [7] M. G. Adian, and H. Aghaeenia, "Joint relay selection and power allocation in MIMO cooperative cognitive radio networks", Journal of Information Systems and Telecommunication (JIST), Vol. 1, No. 9, 2015, pp.1-10. [8] X.Mingchi, W.Yi, T.Kirubarajan, and L. Kong, "Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks", IEEE Transactions on Signal Processing, Vol.66, No. 3, 2017, pp.729-743. [9] H. Godrich, A.P. Petropulu, and H. V.Poor,"Power allocation strategies for target localization in distributed multiple-radar architectures", IEEE Transactions on Signal Processing, Vol.59, No. 7, 2011,pp. 3226-3240. [10] H.Chen, T.Shiying, and S.Bin,"Cooperative game approach to power allocation for target tracking in distributed MIMO radar sensor networks", IEEE Sensors Journal,Vol.15, No. 10, 2015, pp.5423-5432. [11] M. Botao, H.Chen, S.Bin, and H,Xiao,"A joint scheme of antenna selection and power allocation for localization in MIMO radar sensor networks", IEEE communications letters, Vol.18, No. 12, 2014,pp.2225-2228. [12] L.Yanxi, Z.He, X.Zhang, and S.Liu, "Transmit and receive sensors joint selection for MIMO radar tracking based on PCRLB", In 2016 IEEE 13th International Conference on Signal Processing (ICSP),2016, pp. 1551-1555. [13] S.Xiyu, N.Zheng, and T.Bai,"Resource allocation schemes for multiple targets tracking in distributed MIMO radar systems",International Journal of Antennas and Propagation, Vol.2017 ,No.1,2017, pp.1-12. [14] Y.Wei, Y.Yuan, R.Hoseinnezhad, and L.Kong,"Resource scheduling for distributed multi-target tracking in netted colocated MIMO radar systems", IEEE Transactions on Signal Processing, Vol.68, No.1, 2020, pp.1602-1617. [15] Q. Cheng, J.Xie, and H.Zhang, "Joint Antenna Placement and Power Allocation for Target Detection in a Distributed MIMO Radar Network", Remote Sensing, Vol.14, No. 11,2022, pp.2650-2662. [16] M.A. Darzikolaei, M.R. K.Mollaei, and M.Najimi,"An effective PSO-based power allocation for target tracking in MIMO radar with widely separated antennas", Physical Communication, Vol.51, No.1, 2022,pp.101544-101557. [17] Y.Wei, Y.Yuan, R.Hoseinnezhad, and L.Kong. "Resource scheduling for distributed multi-target tracking in netted colocated MIMO radar systems",IEEE Transactions on Signal Processing, Vol.68, No.1, 2020,pp.1602-1617. [18] L.Zhengjie, J.Xie, H.Zhang, H.Xiang, and Z.Zhang, "Adaptive sensor scheduling and resource allocation in netted collocated MIMO radar system for multi-target tracking", IEEE Access, Vol.8 ,No.1, 2020, pp.109976-109988. [19] H. Qian, R.S. Blum, and A.M. Haimovich,"Noncoherent MIMO radar for location and velocity estimation: More antennas means better performance",IEEE Transactions on Signal Processing, Vol.58, No. 7, 2010,pp.3661-3680. [20] E. Fishler, A.Haimovich, R.Blum, D.Chizhik, L.Cimini, and R.Valenzuela,"MIMO radar: An idea whose time has come", In Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No. 04CH37509), , 2004, pp. 71-78. [21] C., and A.Nehorai,"Scheduling and power allocation in a cognitive radar network for multiple-target tracking",IEEE Transactions on Signal Processing,Vol.60, No. 2, 2012, pp.715-729. [22] H.Godrich, A.M. Haimovich, and R.S. Blum,"Target localization accuracy gain in MIMO radar-based systems",IEEE Transactions on Information Theory, Vol.56, No. 6,2010, pp. 2783-2803. [23] V.Trees, and L.Harry, Detection, estimation, and modulation theory, part I: detection, estimation, and linear modulation theory. John Wiley & Sons, 2004. [24] S.Palin, J.Tang, Q.He, B.Tang, and X.Tang,"Cramer–Rao bound of parameters estimation and coherence performance for next generation radar",IET Radar, Sonar & Navigation, Vol.7, No. 5, 2013, pp.553-567. [25] E.Russell, and J.Kennedy, "A new optimizer using particle swarm theory",In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43. [26] S.Chenguang, Y.Wang, F.Wang, S.Salous, and J.Zhou,"Joint optimization scheme for subcarrier selection and power allocation in multicarrier dual-function radar-communication system", IEEE Systems Journal, Vol.15, No. 1 ,2020, pp. 947-958. [27] S.Boyd, and L.Vandenberghe, Convex optimization, Cambridge university press, 2004. [28] J.Yan, H.Liu, B.Jiu, and Z.Bao,"Power allocation algorithm for target tracking in unmodulated continuous wave radar network", IEEE sensors journal, Vol.15, No. 2 , 2014,pp.1098-1108.NafisehSadeghiHomayounMahdavi-NasabMansoorZeinaliHosseinPourghasem1216202335936710.61186/jist.39680.11.44.359http://jist.ir/en/Article/39680http://jist.ir/en/Article/Download/39680http://jist.ir/en/Article/Download/39680http://jist.ir/en/Article/Download/39680http://jist.ir/en/Article/Download/39680http://jist.ir/en/Article/Download/39680http://jist.ir/en/Article/Download/39680http://jist.ir/en/Article/Download/39680[1] K. Farajzadeh, E. Zarezadeh, J. Mansouri, "Concept detection in images using SVD features and multi-granularity partitioning and classification", Journal of Information Systems & Telecommunication (JIST), 2017, pp. 172. [2] M.J. Hasan, M. Sohaib, J.M. Kim, “An explainable ai-based fault diagnosis model for bearings”, Sensors, 2021, Vol. 21, No. 12, pp. 4070. [3] M. Ahmad, S. F. Qadri, S. Qadri, I. A. Saeed, S. S. Zareen, Z. Iqbal, A. Alabrah, H. M. Alaghbari, M. Rahman, S. A. Md, "A lightweight convolutional neural network model for liver segmentation in medical diagnosis", Computational Intelligence and Neuroscience, 2022. [4] M. S. Al-Rakhami, M. M. Islam, M. Z. Islam, A. Asraf, A. H. Sodhro, and W. Ding, "Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning", MedRxiv, 2020, pp. 20181339. [5] M. Islam, "An efficient human computer interaction through hand gesture using deep convolutional neural network", SN Computer Science, 2020, Vol. 1, No. 4, pp. 1-9. [6] W. Li. R. Zhang, H. Deng, L. Wang, W. Lin, S. Ji, and D. Shen, "Deep convolutional neural networks for multi-modality isointense infant brain image segmentation", NeuroImage, 2015, Vol. 108, pp. 214-224. [7] A. Sandooghdar, F. Yaghmaee, "Deep Learning Approach for Cardiac MRI Images", Journal of Information Systems and Telecommunication (JIST), 2022, Vol. 1, No. 37, pp. 61. [8] E. Gholam, S.R. Kamel Tabbakh, "Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks", Journal of Information Systems and Telecommunication (JIST), 2021, Vol. 3, No. 35, pp. 191. [9] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradientbased learning applied to document recognition", Proceedings of the IEEE, 1998, Vol. 86, No. 11, pp. 2278-2324. [10] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 1998, VOL. 86, No. 11, pp. 2278-2324. [11] R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Region-based convolutional networks for accurate object detection and segmentation", IEEE transactions on pattern analysis and machine intelligence, 2015, Vol. 38, No. 1, pp. 142-158. [12] N. Audebert, B. Le Saux, and S. Lef`evre, "Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks", ISPRS Journal of Photogrammetry and Remote Sensing, 2018, Vol. 140, pp. 20-32. [13] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems, 2012, Vol. 25. [14] K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv:1409.1556, 2014. [15] Y. Mo, Y. Wu, X. Yang, F. Liu, and Y. Liao, "Review the state-of-the-art technologies of semantic segmentation based on deep learning", Neurocomputing, 2022, Vol. 493, pp. 626-646. [16] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning", in Thirty-first AAAI conference on artificial intelligence, 2017. [17] V. Badrinarayanan, A. Handa, and R. Cipolla, "Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling", arXiv preprint arXiv: 1505.07293, 2015. [18] V. Badrinarayanan, A. Kendall, and R. Cipolla, "Segnet: A deep convolutional encoder-decoder architecture for image segmentation", IEEE transactions on pattern analysis and machine intelligence, 2017, Vol. 39, No.12, pp. 2481-2495. [19] K. Sun, Y. Zhao, B. Jiang, T. Cheng, B. Xiao, D. Liu, Y. Mu, X. Wang, W. Liu, and J. Wang, "High-resolution representations for labeling pixels and regions", arXiv preprint arXiv:1904.04514, 2019. [20] K. Sun, B. Xiao, D. Liu, and J. Wang, "Deep high-resolution representation learning for human pose estimation", in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 5693-5703. [21] D. Marmanis, J. D. Wegner, S. Galliani, K. Schindler, M. Datcu, and U. Stilla, "Semantic segmentation of aerial images with an ensemble of CNSS. ISPRS Annals of the Photogrammetry", Remote Sensing and Spatial Information Sciences, 2016, Vol. 3, pp. 473-480. [22] S. Ioffe, and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift", In International conference on machine learning, 2015, pp. 448-456. [23] V. Badrinarayanan, B. Mishra, and R. Cipolla, "Understanding symmetries in deep networks", arXiv preprint arXiv:1511.01029, 2015. [24] H. Zamanian, H. Farsi, S. Mohamadzadeh, "Improvement in accuracy and speed of image semantic segmentation via convolution neural network encoder-decoder", Information Systems & Telecommunication (JIST), 2018, Vol. 6, No. 3, pp. 128-135. [25] F. Wang, S. Piao, and J. Xie, "CSE-HRNet: A context and semantic enhanced high-resolution network for semantic segmentation of aerial imagery", IEEE Access, 2020, Vol. 8, No. 2, pp. 182475-182489. [26] L. Mou, Y. Hua, and X. X. Zhu, "Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high-resolution aerial images", IEEE Transactions on Geoscience and Remote Sensing, 2020, Vol. 58, No. 11, pp. 7557-7569. [27] H. Luo, C. Chen, L. Fang, X. Zhu, and L. Lu, "High-resolution aerial images semantic segmentation using deep fully convolutional network with channel attention mechanism", IEEE journal of selected topics in applied earth observations and remote sensing, 2019, Vol. 12, No. 9, pp. 3492-3507. [28] N. Mboga, S. Georganos, T. Grippa, M. Lennert, S. Vanhuysse, and E. Wolff, "Fully convolutional networks and geographic object-based image analysis for the classification of VHR imagery", Remote Sensing, 2019, Vol. 11, No. 5, pp. 597. [29] G. Zhang, T. Lei, Y. Cui, and P. Jiang, "A dual-path and lightweight convolutional neural network for high-resolution aerial image segmentation", ISPRS International Journal of Geo-Information, 2019, Vol. 8, No. 12, pp. 582. [30] Z. Tu, X. Chen, A. L. Yuille, and S. C. Zhu, "Image parsing: Unifying segmentation, detection, and recognition", International Journal of computer vision, 2005, Vol. 63, No. 2, pp. 113-140. [31] B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman, "Using multiple segmentations to discover objects and their extent in image collections", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 2006, Vol. 2, pp. 1605-1614. [32] E. Borenstein, and S. Ullman, "Combined top-down/bottom-up segmentation", IEEE Transactions on pattern analysis and machine intelligence, 2008, Vol. 30, No. 12, pp. 2109-2125. [33] J. Wu, J. Zhu, and Z. Tu, "Reverse Image Segmentation: A High-Level Solution to a Low-Level Task", In BMVC, 2014. [34] Q. Zhao, and L. D. Griffin, "Better image segmentation by exploiting dense semantic predictions", arXiv preprint arXiv:1606.01481, 2016. [35] R. Socher, C. C. Lin, A. Y. Ng, and C. D. Manning, "Parsing natural scenes and natural language with recursive neural networks", In Proc. IEEE Int. Conf. Mach. Learn. (ICML), 2011, pp. 129-136. [36] J. Yao, S. Fidler, and R. Urtasun, "Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation", In IEEE conference on computer vision and pattern recognition, 2012, pp. 702-709. [37] A. Kae, K. Sohn, H. Lee, and E. Learned-Miller, "Augmenting CRFs with Boltzmann machine shape priors for image labeling", In Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 2019-2026. [38] H. Myeong, and K. M. Lee, "Tensor-based high-order semantic relation transfer for semantic scene segmentation", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 3073-3080. [39] J. J. Corso, "Toward parts-based scene understanding with pixel-support parts-sparse pictorial structures", Pattern Recognition Letters, 2013, Vol. 34, No. 7, pp. 762-769. [40] Q. Li, Y. Shi, and X. Huang, "Building footprint generation by integrating convolution neural network with feature pairwise conditional random field (FPCRF)", IEEE Transactions on Geoscience and Remote Sensing, 2020, Vol. 58, No. 11, pp. 7502-7519. [41] M. Cramer, "The DGPF-test on digital airborne camera evaluation overview and test design", Photogrammetrie-Fernerkundung-Geoinformation, 2010, pp. 73-82. [42] M.J. Hasan, J.M. Kim, "Bearing fault diagnosis under variable rotational speeds using stockwell transform-based vibration imaging and transfer learning", Applied Sciences, Vol. 8, No. 12, pp. 2357. [43] M.J. Hasan, J. Uddin, S.N. Pinku, "A novel modified SFTA approach for feature extraction", In 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2016, pp. 1-5. [44] M. Ghasemi, M. Kelarestaghi, F. Eshghi, A. Sharifi, "D 3 FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging", Applied Intelligence, 2022, pp. 1-17.ElhamZiaeipourAliRajabzadeh GhotriAlirezaTaghizadeh1216202336838210.61186/jist.40088.11.44.368http://jist.ir/en/Article/40088http://jist.ir/en/Article/Download/40088http://jist.ir/en/Article/Download/40088http://jist.ir/en/Article/Download/40088http://jist.ir/en/Article/Download/40088http://jist.ir/en/Article/Download/40088http://jist.ir/en/Article/Download/40088http://jist.ir/en/Article/Download/40088[1] R.Jain, “Trends and Issues in Softwarization of Networks: What’s In, What’s Out,” IEEE Conf. Netw. Softwarization, Washingt. Univ. Saint Louis, 2018. [2] D. Kreutz, F. M. V Ramos, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, “Software-defined networking: A comprehensive survey,” Proc. IEEE, vol. 103, no. 1, pp. 14–76, 2014. [3] R. Gaikwad, V., Rake, “Software defined networking Market Statistics:2027,”2020. https://www.alliedmarketresearch.com/software-defined-networking-market. [4] J. H. Cox et al., “Advancing software-defined networks: A survey,” IEEE Access, vol. 5, pp. 25487–25526, 2017. [5] N. Bhalani, M. Chavan, “A Survey on Software Defined Network with 5G”, International Journal of scientific & Technology Research, 2020. [6] A. khamseh, M. Lialestani, and reza radfar, “Digital Transformation Model, Based on Grounded Theory,” J. Inf. Syst. Telecomm. , no. 1, pp. 275–284, 2021, doi: 10.52547/jist.9.36.275. [7] S. Brinker, “Martec’s Law: the greatest management challenge of the 21st century,” Chiefmartec. com, 2016. [8] M. Abdulrab, “Factors Affecting Acceptance and the Use of Technology in Yemeni Telecom Companies,” Int. Trans. J. Eng. Manag. Appl. Sci. Technol., vol. 11, no. 6, pp. 1–16, 2020. [9] M. K. Chang and W. Cheung, “Determinants of the intention to use Internet/WWW at work: a confirmatory study,” Inf. Manag., vol. 39, no. 1, pp. 1–14, 2001. [10] F. Nouri, R., Hatami, M. and Ebrahimian, “Effective factors on the acceptance of information technology and its impact on human resources,” Hum. Resour. Manag. Res. Imam Hossein Univ., vol. 9, no. 4, pp. 27–152, 2018. [11] P. C. Lai, “The literature review of technology adoption models and theories for the novelty technology,” JISTEM-Journal Inf. Syst. Technol. Manag., vol. 14, pp. 21–38, 2017. [12] F. B. A. Rahman, M. H. M. Hanafiah, M. Salehuddin, M. Zahari, and L. B. Jipiu, “Systematic Literature Review on The Evolution of Technology Acceptance and Usage Model used in Consumer Behavioural Study,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 11, no. 13, pp. 272–298, 2021. [13] A. M. Momani and M. Jamous, “The evolution of technology acceptance theories,” Int. J. Contemp. Comput. Res., vol. 1, no. 1, pp. 51–58, 2017. [14] J. Corbin and A. Strauss, Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage publications, 2014. [15] A. shirmarz and A. Ghaffari, “An Autonomic Software Defined Network (SDN) Architecture With Performance Improvement Considering,” J. Inf. Syst. Telecomm. , no. 1, pp. 121–129, 2020, doi: 10.29252/jist.8.30.121. [16] A. S. Thyagaturu, A. Mercian, M. P. McGarry, M. Reisslein, and W. Kellerer, “Software defined optical networks (SDONs): A comprehensive survey,” IEEE Commun. Surv. Tutorials, vol. 18, no. 4, pp. 2738–2786, 2016. [17] ETSI, “Improved Operator experience through Experiential Networked Intelligence (ENI)”, 2017, 1st Edition, ISBN No. 979-10-92620-16-0. [18] ONF, “NG-SDN™”, Open Networking Foundation, 2022, Available: https://opennetworking.org/reference-designs/ng-sdn/. [19] Data Bridge Market, “Global Software-Defined Networking Market – Industry Trends and Forecast to 2028,”2021. https://www.databridgemarketresearch.com/reports/global-sdn-market. [20] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Q., pp. 425–478, 2003. [21] IGI Global, “What is Technology Adoption Model? https://www.igi-global.com/dictionary/technology-acceptance-model/29484 [22] E. M. Rogers and F. F. Shoemaker, “Communication of Innovations; A Cross-Cultural Approach.,” 1971. [23] F. Masimba and T. Zuva, “Individual Acceptance of Technology: A Critical Review of Technology Adoption Models and Theories,” Indiana J. Humanit. Soc. Sci., vol. 2, no. 9, pp. 37–48, 2021. [24] W. Russ, “The Relationship between Technology Adoption Determinants and the Intention to Use Software-Defined Networking.” Walden University, 2021. [25] C. Sayginer and T. Ercan, “Understanding determinants of cloud computing adoption using an integrated diffusion of innovation (doi)-technological, organizational and environmental (toe) model,” Humanit. Soc. Sci. Rev., vol. 8, no. 1, pp. 91–102, 2020. [26] V. Chergarova, J. Bezerra, J. Ibarra, and H. Morgan, “Factors influencing the adoption of Software Defined Networking by Research and Educational Networks,” 2019. [27] R. Jayaraman, V., Manickam, A., Rajappa, “The Role of SDN in Network Transformation,” 2019. https://www.tataelxsi.com/news-and-events/the-role-of-sdn-in-network-transformation. [28] N. Shah, P. Giaccone, D. B. Rawat, A. Rayes, and N. Zhao, “Solutions for adopting software defined network in practice,” International Journal of Communication Systems, vol. 32, no. 17. Wiley Online Library, p. e3990, 2019. [29] S. Bekele, B., Kriger, “SP NFV/SDN Adoption”. STL Partners Research for Cisco,” 2017. [30] S. S. S. Mokhtar, A. S. B. Mahomed, Y. A. Aziz, and S. A. Rahman, “Industry 4.0: the importance of innovation in adopting cloud computing among SMEs in Malaysia,” Polish J. Manag. Stud., vol. 22, 2020. [31] P. Maroufkhani, M.-L. Tseng, M. Iranmanesh, W. K. W. Ismail, and H. Khalid, “Big data analytics adoption: Determinants and performances among small to medium-sized enterprises,” Int. J. Inf. Manage., vol. 54, p. 102190, 2020. [32] M. Tsourela and D.-M. Nerantzaki, “An internet of things (IoT) acceptance model. Assessing consumer’s behavior toward IoT products and applications,” Futur. Internet, vol. 12, no. 11, p. 191, 2020. [33] A. M. Aranda, “Software-defined networking: Current state, adoption factors and future impact on network engineers,” 2016. [34] S. Mahankali, I. I. T. Cloud Network Engineer, and S. Rungta, “Adopting Software-Defined Networking in the Enterprise,” White Pap. April, 2014. [35] R. Sahay, W. Meng, and C. D. Jensen, “The application of software defined networking on securing computer networks: A survey,” J. Netw. Comput. Appl., vol. 131, pp. 89–108, 2019. [36] S. Seshadrinathan and S. Chandra, “Exploring Factors Influencing Adoption of Blockchain in Accounting Applications using Technology–Organization–Environment Framework,” J. Int. Technol. Inf. Manag., vol. 30, no. 1, pp. 30–68, 2021. [37] D. Katsianis, I. Neokosmidis, A. Pastor, L. Jacquin, and G. Gardikis, “Factors influencing market adoption and evolution of NFV/SDN Cybersecurity Solutions. Evidence from SHIELD Project,” in 2018 European Conference on Networks and Communications (EuCNC), 2018, pp. 1–5. [38] M. S. Alhilal, A. M. Aldammas, and A. Y. Alnasheri, “Investigation of Critical Success Factors for Adopting Software-Defined Networking,” in 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), 2018, pp. 1–6. [39] B. Gupta, S. Dasgupta, and A. Gupta, “Adoption of ICT in a government organization in a developing country: An empirical study,” J. Strateg. Inf. Syst., vol. 17, no. 2, pp. 140–154, 2008. [40] A. Bazargan, Introduction to qualitative research methods and a combination of common approaches in behavioral sciences. Tehran: Didar, 2009. [41] J. W. Creswell, Educational research: Planning, conducting, and evaluating quantitative. Prentice Hall Upper Saddle River, NJ, 2002. [42] E. Ziaeipour, “Explaining the adoption process of software-oriented networks (SDN) using the Grounded Theory and systems approach,” J. Inf. Communication. Technol., vol. 14, no. 52, pp. 172–194, 2022. [43] C.-H. Cheng and Y. Lin, “Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation,” Eur. J. Oper. Res., vol. 142, no. 1, pp. 174–186, 2002. [44] C.-H. Wu and W.-C. Fang, “Combining the Fuzzy Analytic Hierarchy Process and the fuzzy Delphi method for developing critical competences of electronic commerce professional managers,” Qual. Quant., vol. 45, no. 4, pp. 751–768, 2011. [45] J. Henseler, C. M. Ringle, and R. R. Sinkovics, “The use of partial least squares path modeling in international marketing,” in new challenges to international marketing, Emerald Group Publishing Limited, 2009. [46] J. F. Hair, D. J. Ortinau, and D. E. Harrison, Essentials of marketing research, vol. 2. McGraw-Hill/Irwin New York, NY, 2010. [47] C. M. Ringle, M. Sarstedt, R. Schlittgen, and C. R. Taylor, “PLS path modeling and evolutionary segmentation,” J. Bus. Res., vol. 66, no. 9, pp. 1318–1324, 2013. [48] C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50, 1981. [49] M. Gall, W. Borg, and J. Gall, “Quantitative and qualitative research methods in educational sciences and psychology,” Vol. I. Transl. by Ahmad Reza Nasr al. Tehran SAMAT Shahid Beheshti Univ. Publ., 2004. [50] M. Abbaszadeh, “Validity and reliability in qualitative researches,” J. Appl. Sociol., vol. 23, no. 1, pp. 19–34, 2012. [51] K. Charmaz, Constructing grounded theory: A practical guide through qualitative analysis. Sage, 2006. [52] J. Maxwell, “Understanding and validity in qualitative research,” Harv. Educ. Rev., vol. 62, no. 3, pp. 279–301, 1992. [53] A. Kamanghad, G. Hashemzade, M. A. kazemi, and N. Shadnoosh, “Assessing the Company’s E-Readiness for Implementing Mobile-CRM System,” J. Inf. Syst. Telecomm., no. 1, pp. 65–73, 2019, doi: 10.7508/jist.2019.01.006.