jict-140506210014050621004204CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information and Communication Technologyjict2717-041191120261868RezaaliRezaei MalidarrehAli akbar Farhangi9112026597110.66224/jict.50671.18.68.59http://jour.aicti.ir/fa/Article/50671http://jour.aicti.ir/fa/Article/Download/50671http://jour.aicti.ir/fa/Article/Download/50671http://jour.aicti.ir/fa/Article/Download/50671http://jour.aicti.ir/fa/Article/Download/50671http://jour.aicti.ir/fa/Article/Download/50671http://jour.aicti.ir/fa/Article/Download/50671http://jour.aicti.ir/fa/Article/Download/50671[1] ح. راهلی، م. ر. مردانی، ا. ساده، و ز. امینی، خط‌مشی‌گذاری فناوری‌های نوین دیجیتال با رویکرد تحلیل محتوای کیفی، محیط‌شناسی راهبردی، جلد ۸، شماره ۱، صص ۱۴۳-۱۸۲، ۱۴۰۳.[2] Y. Sun, L. Chen, and H. Wang, Regulatory frameworks for emerging communication technologies: A comparative analysis, Journal of Information Policy, vol. 14, no. 2, pp. 89-108, 2024.[3] م. عطاردی و ح. زیبنده، "تبیین تغییر در خط‌مشی‌های فضای مجازی جمهوری اسلامی ایران مبتنی بر چارچوب ائتلاف مدافع، مدیریت دولتی، جلد ۱۴، شماره ۲، صص ۲۳۵-۲۵۶، ۱۴۰۱.[4] M. Tanja, R. Schmidt, and P. Weber, Global expansion of LEO satellite constellations: Current status and future trajectories," Space Policy, vol. 65, no. 1, pp. 55-72, 2023.[5] ح. وحیدی، در جست‌وجوی دسترسی آزاد؛ رویای کاربران ایرانی برای استفاده از اینترنت ماهواره‌ای، بازار و سرمایه، جلد ۲۲، شماره ۱۳۱، صص ۵۴-۵۵، ۱۴۰۱.[6] ا. ع. رکنی، ع. محسنیان، و ا. رجبی، تحلیل وضعیت خدمات اینترنت ماهواره‌ای مدارپایین در ایران (مطالعه‌ی موردی استارلینک)، مرکز پژوهش‌های مجلس شورای اسلامی، صص ۱-۳۲، ۱۴۰۲.[7] ق. ترابی، ابعاد امنیتی سرویس اینترنت ماهواره‌ای، دیده‌بان امنیت ملی، جلد ۳۷، شماره ۱۰۸، صص ۱۷۱-۱۷۶، ۱۴۰۰.[8] ح. عیوض‌زاده، ا. حضور، م. ذوالقدر، و ف. جعفری، بررسی تحلیلی خط‌مشی‌گذاری جمهوری اسلامی ایران در فضای مجازی با هدف ارائه‌ی الگوی مناسب،جامعه‌شناسی سیاسی ایران، جلد ۵، شماره ۱۱، صص ۲۹۸-۳۱۸، ۱۴۰۱.[9] Y. Chen, X. Ma, and C. Wu, The concept, technical architecture, applications and impacts of satellite internet: A systematic literature review, Heliyon, vol. 70, pp. 961-979, 2024.[10] M. M. Mukto, M. M. Al Mahmud, M. A. Ahmed, I.. Haque, A. W. Reza, and M. S. Arefin, A sustainable approach between satellite and traditional broadband transmission technologies based on green IT, Intelligent Computing & Optimization, vol. 19, no. 1, pp. 275-289, 2022.[11] J. Deutschmann, K. S. Hielscher, and R. German, Broadband internet access via satellite: Performance measurements with different operators and applications, Broadband Coverage in Germany, vol. 16, no. 1, pp. 1-7, 2022.[12] E. O. Goldman, M. P. Fischerkeller, and R. J. Harknett, Cyber persistence theory: Redefining national security in cyberspace. Oxford University Press, 2022.[13] D. Rozenvasser and K. Shulakova, Estimation of the Starlink global satellite system capacity, in International Conference on Applied Innovation in Germany, 2023, vol. 26, no. 2, pp. 55-59.[14] Y. Shaengchart and T. Kraiwanit, "The SpaceX Starlink satellite project: Business strategies and perspectives, Shaengchart, Y., & Kraiwanit, vol. 19, no. 4, pp. 30-37, 2024.[15] J.C. McDowell, Starlink statistics, Jonathan's SpacePages. [Online].Available: https://planet4589.org/space/con/star/stats.html. [Accessed: May 15, 2026].[16] M. A. Schramm, Infrastructural ordering: Satellites, foundation models, and the corporate remaking of global governance, Washington University Global Studies Law Review, vol. 25, no. 4, p. 848, 2026.[17] T. K. Alhasan, From coverage to permission: Governing Starlink and satellite broadband mega-constellations, Telecommunications Policy, vol. 50, no. 3, pp. 101-125, 2026a.[18] A. Alhasan, Multi-layered governance in satellite internet regulation, Journal of Space Law & Policy, vol. 41, no. 2, pp. 112-128, 2026b.[19] E. Grossman, Media and policy making in the digital age, Annual Review of Political Science, vol. 25, no. 1, pp. 443-461, 2022.[20] V. Nesterenko, R. Miskiewicz, and R. Abazov, Marketing communications in the era of digital transformation, Virtual Economics, vol. 6, no. 1, pp. 57-70, 2023.[21] S. Ness and T. Khinvasara, "Emerging threats in cyberspace: Implications for national security policy and healthcare sector, Journal of Engineering Research and Reports, vol. 26, no. 2, pp. 107-117, 2024.[22] S. C. Hofmann and P. Pawlak, Governing cyberspace: Policy boundary politics across organizations, Review of International Political Economy, vol. 30, no. 6, pp. 2122-2149, 2023.[23] J. Eriksson and G. Giacomello, Cyberspace in space: Fragmentation, vulnerability, and uncertainty, Cyber Security Politics, vol. 19, no. 1, pp. 95-108, 2022.[24] N. Mohan, A. E. Ferguson, H. Cech, R. Bose, P. R. Renatin, M. K. Marina, and J. Ott, A multifaceted look at Starlink performance, in Proceedings of the ACM on Web Conference, May 2024, pp. 2723-2734.[25] A. D. Silva and N. Afraz, "OrbitShare: The policy imperative for satellite internet mega-constellation sharing, Telecommunications Policy, vol. 50, no. 6, 2026.[26] M. Kulsa and S. Akçalı Gür, Global governance of low Earth orbit satellites. Routledge, 2025.[27] س. وکیلی، پ. اعتضادی فر، و س. طلعتی، تحلیل و بررسی پیرامون پروژه استارلینک، در پنجمین کنفرانس ملی فناوری در مهندسی برق و کامپیوتر، ۱۴۰۰.[28] م. قلی‌پور، ظهور اینترنت ماهواره‌ای و گزینه‌های راهبردی، دیده‌بان امنیت ملی، جلد ۲۶، شماره ۱۰۸، صص ۹۵-۱۰۲، ۱۴۰۰.[29] B. G. Glaser, A. L. Strauss, and E. Strutzel, The discovery of grounded theory: Strategies for qualitative research," Nursing Research, vol. 17, no. 4, p. 364, 2017.[30] غ. خاکی، روش تحقیق (با رویکردی به پایان‌نامه‌نویسی). تهران: نشر فوزان، ۱۴۰۱[31] A. Strauss and J. M. Corbin, Grounded theory in practice. Sage, 1997.[32] S. Rahimi and M. Khatooni, "Saturation in qualitative research: An evolutionary concept analysis, Int. J. Nurs. Stud. Adv., vol. 6, p. 100174, Jan. 2024, doi: 10.1016/j.ijnsa.2024.100174.[33] W. Zeng and W. Tang, "Fuzzy set theory and its applications, Fuzzy Sets and Systems, vol. 58, no. 2, pp. 157-169, 1993.[34] E. Miller, L. Cross, and M. Lopez, Sampling in qualitative research, FBB Research Group, vol. 19, no. 3, pp. 249-261, 2010.Mohammad AsadiZahraMohammadiHosseinNezamabadi-pourمحدثهسليمانپورمقدم9112026445810.66224/jict.50809.18.68.44http://jour.aicti.ir/fa/Article/50809http://jour.aicti.ir/fa/Article/Download/50809http://jour.aicti.ir/fa/Article/Download/50809http://jour.aicti.ir/fa/Article/Download/50809http://jour.aicti.ir/fa/Article/Download/50809http://jour.aicti.ir/fa/Article/Download/50809http://jour.aicti.ir/fa/Article/Download/50809http://jour.aicti.ir/fa/Article/Download/50809[1] Tran, V.P., Perera, A., Garratt, M.A., Kasmarik, K. and Anavatti, S.G., 2023. Coverage path planning with budget constraints for multiple unmanned ground vehicles. IEEE Transactions on Intelligent Transportation Systems, 24(11), pp.12506-12522. [2] Anwit, R., Jana, P.K. and Obaidat, M.S., 2023. Obstacle adaptive smooth path planning for mobile data collector in the internet of things. IEEE Transactions on Sustainable Computing, 8(4), pp.727-738.[3] Mohammadi, Z., Soleimanpour-Moghadam, M., Abbasi moghadam, D., and Talebi, S., 2021. Maximizing the lifetime of underwater acoustic sensor networks by optimal relay node placement, 45(12), p.115-130.[4] Goerzen, C., Z. Kong, and B. Mettler, A survey of motion planning algorithms from the perspective of autonomous UAV guidance. Journal of Intelligent and Robotic Systems, 2010. 57(1): p. 65-100.[5] Viet, H.H., S.H. An, and T.C. Chung, Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments. Advanced Robotics, 2013. 27(3): p. 159-173.[6] Zhang, H.-y., W.-m. Lin, and A.-x. Chen, Path planning for the mobile robot: A review. Symmetry, 2018. 10(10): p. 450.[7] Liang, W., Research on Robot Path Planning for Dynamic Environment and Cooperation. 2010, Zhejiang University Hangzhou, China.[8] Wu, Y., A survey on population-based meta-heuristic algorithms for motion planning of aircraft. Swarm and Evolutionary Computation, 2021. 62: p. 100844.[9] Ab Wahab, M.N., S. Nefti-Meziani, and A. Atyabi, A comparative review on mobile robot path planning: Classical or meta-heuristic methods? Annual Reviews in Control, 2020. 50: p. 233-252.[10] D. Karaboga, “An Idea based on Honey Bee Swarm for Numerical Optimization, ”Technical Report, Erciyes University, Engineering Faculty, Computer Engineering Department, pp. 1-10, 2005.[11] . B. Wu and C. Qian, ‟ Differential Artificial Bee Colony Algorithm for Global Numerical Optimization,” Journal of Computer, vol. 6, no. 5, pp.841-848 May 2011.[12] Abbas, N.H. and F.M. Ali, Path planning of an autonomous mobile robot using directed artificial bee colony algorithm. International Journal of Computer Applications, 2014. 96(11).[13] Ajeil, F.H., et al., Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments. Sensors, 2020. 20(7): p. 1880.[14] Sierakowski, C.A. and Coelho, L.D.S., 2005, July. Study of two swarm intelligence techniques for path planning of mobile robots. In 16th IFAC world congress, Prague.[15] Sierakowski, C.A. and Coelho, L.D.S., 2006. Path planning optimization for mobile robots based on bacteria colony approach. In Applied soft computing technologies: The challenge of complexity (pp. 187-198). Springer Berlin Heidelberg.[16] Abdi, M.I.I., Khan, M.U., Güneş, A. and Mishra, D., 2020. Escaping local minima in path planning using a robust bacterial foraging algorithm. Applied Sciences, 10(21), p.7905.[17] Song, B., Z. Wang, and L. Zou, An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 2021. 100: p. 106960.[18] Ibraheem, I.K. and F.H. Ajeil, Path planning of an autonomous mobile robot using swarm based optimization techniques. Al-Khwarizmi Engineering Journal, 2016. 12(4): p. 12-25.[19] Nezamabadi-pour H, Soleimanpour-moghadam M. Multi-robot Path Planning in a 3D Environment by Modified Particle Swarm Optimization Algorithm. Journal of Iranian Association of Electrical and Electronics Engineers. 2022 Jul 10;19(3):163-74.[20] P. K. Das, H. S. Behera, and B. K. Panigrahi, “A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning,” Swarm and Evolutionary Computation, vol. 28, pp. 14–28, 2016.[21] Yang, X.-S., Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation, 2011. 3(5): p. 267-274.[22] Saraswathi, M., G.B. Murali, and B. Deepak, Optimal path planning of mobile robot using hybrid cuckoo search-bat algorithm. Procedia computer science, 2018. 133: p. 510-517.[23] Ajeil, F.H., Ibraheem, I.K., Humaidi, A.J. and Khan, Z.H., 2021. A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization. The Journal of Engineering, 2021(1), pp.37-48.[24] Ajeil, F.H., et al., Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm. Applied Soft Computing, 2020. 89: p. 106076.[25] S. Agrawal, B. K. Patle, and S. Sanap, “A systematic review on metaheuristic approaches for autonomous path planning of unmanned aerial vehicles,” Drone Systems and Applications, vol. 12, pp. 1–28, 2024.[26] P. K. Das, H. S. Behera, P. K. Jena, and B. K. Panigrahi, “An intelligent multi-robot path planning in a dynamic environment using improved gravitational search algorithm,” International Journal of Automation and Computing, vol. 18, no. 6, pp. 1032–1044, 2021.[27] Sakcak, B. and LaValle, S.M., 2021, May. Complete path planning that simultaneously optimizes length and clearance. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 10100-10106). IEEE.[28] Yepez-Figueroa JJ, Victores JG, Oa ED, Balaguer C, Jardn A. Design and Development of an Omni directional Three-Wheeled Industrial Mobile Robot Platform. Applied Sciences. 2025 May 9;15(10):5277.[29] Bu S, Yan L, Gao X, Wang G, Zhao P, Chen IM. Design and motion control of spherical robot with built-in four-wheel omnidirectional mobile platform. IEEE Transactions on Instrumentation and Measurement. 2023 Apr 17;72:1-0. [30] Liu Z, Wang H, Wei H, Liu M, Liu YH. Prediction, planning, and coordination of thousand-warehousing robot networks with motion and communication uncertainties. IEEE Transactions on Automation Science and Engineering. 2020 Aug 19;18(4):1705-17.MasoudSiavoushyMohammadmahdiShirmohammadi911202610011410.66224/jict.50906.18.68.100http://jour.aicti.ir/fa/Article/50906http://jour.aicti.ir/fa/Article/Download/50906http://jour.aicti.ir/fa/Article/Download/50906http://jour.aicti.ir/fa/Article/Download/50906http://jour.aicti.ir/fa/Article/Download/50906http://jour.aicti.ir/fa/Article/Download/50906http://jour.aicti.ir/fa/Article/Download/50906http://jour.aicti.ir/fa/Article/Download/50906[1] Wu, J., et al., “Deep learning-based intrusion detection: A survey,” IEEE Access, vol. 8, pp. 219544–219567, 2020. Rana, A., and Sharma, S., “Anomaly detection in IoT networks,” Future Internet, vol. 16, no. 1, pp. 20–33, 2024.[2] Ahmed, M., et al., “Survey on network intrusion detection using deep learning,” Computers & Security, vol. 92, 2020. Mohammadi, H., et al., “Hybrid models in cyber-attack detection,” Neurocomputing, vol. 512, 2023.[3] Gao, L., et al., “Adversarial learning in network defense,” Security and Privacy, vol. 5, no. 2, 2022. Yin, C., et al., “Intrusion detection with LSTM networks,” Journal of Network and Computer Applications, vol. 177, 2021.[4] He, X., et al., “Transformer-based detection systems,” ACM Computing Surveys, vol. 56, no. 1, 2024. Singh, S., and Bedi, P., “Comparative analysis of machine learning techniques,” Expert Systems, 2025. Zhang, T., et al., “Attention mechanisms in cybersecurity,” Computers & Electrical Engineering, 2023.[5] Kumar, R., and Puthal, B., “Recurrent models in anomaly detection,” Procedia Computer Science, vol. 207, 2023. Lin, F., et al., “Evaluation of hybrid IDS,” Information Systems, 2025.[6] Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al., "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews," BMJ, vol. 372, p. n71, 2021.[7] Abeshu, A. Y., & Chilamkurti, N., “Deep learning: The frontier for distributed attack detection in fog-to-things computing,” IEEE Communications Magazine, vol. 56, no. 2, pp. 94-99, 2020. Faris, H., & Aljarah, I., “Improved intrusion detection systems using modern ensemble models,” Journal of Information Security, 2024.[8] Yin, C., et al., “Intrusion detection using CNNs in large-scale networks,” Computer Networks, vol. 183, 2021.[9] Gao, L., et al., “Advanced LSTM techniques for cyberattack prediction,” IEEE Transactions on Neural Networks, vol. 33, 2022.[10] Roy, S., & Dey, N., “RNN approaches in IoT anomaly detection,” Procedia Computer Science, vol. 187, 2022.[11] Li, Y., et al., “GRU-based IDS frameworks for edge computing,” Future Generation Computer Systems, vol. 122, 2023.[12] Sharma, V., & Yadav, P., “Combining GRU and CNN for cyber threat analysis,” Sensors, vol. 21, no. 7, 2021.[13] Tan, Z., & Wang, Y., “Autoencoders for feature extraction in intrusion detection,” Computers & Security, vol. 105, 2021.[14] He, X., et al., “Transformer-based IDS with attention mechanism,” ACM Transactions on Cyber-Physical Systems, vol. 6, no. 1, 2024. Lin, F., et al., “Benchmarking transformer architectures in IDS,” IEEE Access, 2025.[15] Singh, S., & Bedi, P., “A survey on ML models for IDS,” Expert Systems with Applications, vol. 213, 2025. Elhoseny, M., & Hassanien, A. E., “Hybrid deep models for intrusion detection,” Information Fusion, vol. 65, 2023.[16] Kumar, R., et al., “Optimizing deep neural networks for cyber defense,” Journal of Cybersecurity, vol. 10, 2024.[17] Singh, S., & Bedi, P., “A survey on ML models for IDS,” Expert Systems with Applications, vol. 213, 2025.[18] Zhang, Q., et al., “Comprehensive evaluation of DL-based IDS,” Computers & Security, vol. 125, 2023.[19] Lin, F., et al., “Benchmarking transformer architectures in IDS,” IEEE Access, vol. 13, 2025.[20] He, X., et al., “Transformer-based IDS with attention mechanism,” ACM Transactions on Cyber-Physical Systems, vol. 6, no. 1, 2024.[21] Li, Y., et al., “GRU-based IDS frameworks for edge computing,” Future Generation Computer Systems, vol. 122, 2023.[22] Elhoseny, M., & Hassanien, A. E., “Hybrid deep models for intrusion detection,” Information Fusion, vol. 65, 2023.[23] Islam, M. R., et al., “Enhancing cyber attack detection using ensemble models,” IEEE Access, vol. 11, 2023.[24] Singh, S., & Bedi, P., “A survey on ML models for IDS,” Expert Systems with Applications, vol. 213, 2025.[25] Kumar, A., et al., “Transformer-based deep learning framework for detecting APT attacks,” Journal of Cybersecurity, vol. 19, no. 3, pp. 88–101, 2024.[26] Wu, J., et al., “Real-time IDS in critical infrastructure,” IEEE Access, vol. 8, pp. 12345–12360, 2020.[27] Zhang, Y., et al., “Next-gen datasets for cyber threats,” Computers & Security, vol. 119, pp. 101–110, 2024.[28] Gao, H., et al., “Zero-day attack resilience,” Future Generation Computer Systems, vol. 115, pp. 240–250, 2022.[29] Rashid, A. and Nair, R., “Efficient models for constrained environments,” ACM Transactions on Cybersecurity, vol. 6, no. 2, pp. 77–89, 2024.[30] Kumar, S. and Puthal, D., “Deep learning on edge devices,” IEEE Internet of Things Journal, vol. 10, no. 1, pp. 12–21, 2023.[31] Mohammadi, A., et al., “Explainability in security,” Journal of Machine Learning Research, vol. 24, no. 56, pp. 1–20, 2023.[32] Ahmed, T. and Li, X., “Attention for model transparency,” IEEE Transactions on Neural Networks, vol. 36, no. 4, pp. 433–445, 2025.[33] Elhoseny, M. and Hassanien, A., “Continual learning for IDS,” Applied Intelligence, vol. 59, pp. 850–861, 2023.[34] Singh, K. and Bedi, H., “Adversarial robustness in DL,” Computers & Security, vol. 120, pp. 201–212, 2025.[35] Lee, D., et al., “Adversarial training with transformer,” IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 2, pp. 129–139, 2025.[36] Y. Zhao, M. Li, and H. Wang, “Federated IDS Learning for Privacy-Aware Intrusion Detection,” in Proc. IEEE Int. Conf. on Cybersecurity, 2025.[37] M. Zaib and S. B. Ali, “Digital finance, fintech, and income inequality: Opportunities and risks for financial inclusion in Pakistan,” Social Sciences Spectrum, 2026.[38] A. Asim, K. Zafar, and M. Raees, “Gendered Digital Financing Adoption and Women’s Financial Inclusion in Pakistan,” arXiv, 2026.[39] F. Ahmed, S. Mendis, S. Fatima, and R. Usman, “The Role of E‑Commerce in Promoting Digital Financial Inclusion: Empirical Evidence from Pakistan,” ACADEMIA International Journal for Social Sciences, 2026. Hamid RezaGhasemiAli AkbarNeghabiBabakLotfi911202625027110.66224/jict.50911.18.68.250http://jour.aicti.ir/fa/Article/50911http://jour.aicti.ir/fa/Article/Download/50911http://jour.aicti.ir/fa/Article/Download/50911http://jour.aicti.ir/fa/Article/Download/50911http://jour.aicti.ir/fa/Article/Download/50911http://jour.aicti.ir/fa/Article/Download/50911http://jour.aicti.ir/fa/Article/Download/50911http://jour.aicti.ir/fa/Article/Download/50911[1] Zheng, Y. J. (2015). Water wave optimization: a new nature-inspired metaheuristic. Computers & Operations Research, 55, 1-11.[2] Mahfoud, S. W. (1995). A comparison of parallel and sequential niching methods. In Conference on genetic algorithms (Vol. 136, p. 143).‏[3] Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.‏[4] Wang, W. C., Tian, W. C., Xu, D. M., & Zang, H. F. (2024). Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization. Advances in Engineering Software, 195, 103694.‏[5] Fakhouri, H. N., Alkhalaileh, M. S., Hamad, F., Sirhan, N. N., & Fakhouri, S. N. (2024). Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems. Algorithms, 17(12), 589.‏[6] Sun, L., & Wang, B. (2024). Arctic Puffin Optimization Algorithm Based on Multi-Strategy Blending. Journal of Computer and Communications, 12(12), 151-170.‏[7] Xiao, J., Xu, W., & Chen, J. (2024, October). Social media emotional state classification prediction based on Arctic Puffin Algorithm (APO) optimization of Transformer mode. In 2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE) (pp. 1-6). IEEE.‏[8] Lv, Y., Zhao, X., & Mou, Z. Parameter Identification and Verification of Doubly Fed Induction Generator Controllers Based on Arctic Puffin Optimization. Available at SSRN 5104987.‏[9] Tang, W., Dai, J., Liu, B., Hu, W., Gong, K., & Fan, Y. (2024). Aircraft Range Fuel Consumption Prediction Using CNN-LSTM Enhanced by CEEMDAN and Improved Arctic Puffin Optimization Algorithm.‏[10] Firmansyah, R., Widayaka, P. D., Fahrezy, M. R., Saputra, P. P. S., & Septian, D. (2024, October). Voltage Control of DC Microgrid using Arctic Puffin Optimization Algorithm for Tuning of Sliding Mode Control Gain. In 2024 Seventh International Conference on Vocational Education and Electrical Engineering (ICVEE) (pp. 279-282). IEEE.[11] Chen, L., & Aihara, K. (1995). Chaotic simulated annealing by a neural network model with transient chaos. Neural networks, 8(6), 915-930.‏ [12] Yuan, X., Yuan, Y., & Zhang, Y. (2002). A hybrid chaotic genetic algorithm for short-term hydro system scheduling. Mathematics and computers in simulation, 59(4), 319-327.‏[13] Li-Jiang, Y., & Tian-Lun, C. (2003). Application of chaos in genetic algorithms. Communications in Theoretical Physics, 38(2), 168.‏[14] Mingjun, J., & Huanwen, T. (2004). Application of chaos in simulated annealing. Chaos, Solitons & Fractals, 21(4), 933-941.‏[15] Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271.‏[16] Chuanwen, J., & Bompard, E. (2006). A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Mathematics and computers in Simulation, 68(1), 57-65.‏[17] Zuo, X. Q., & Fan, Y. S. (2006). A chaos search immune algorithm with its application to neuro-fuzzy controller design. Chaos, Solitons & Fractals, 30(1), 94-109.‏[18] Xiang, T., Liao, X., & Wong, K. W. (2007). An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Applied Mathematics and Computation, 190(2), 1637-1647.‏[19] Gong, W., & Wang, S. (2008, December). Chaos ant colony optimization and application. In 2009 fourth international conference on internet computing for science and engineering (pp. 301-303). IEEE.‏[20] Alatas, B., Akin, E., & Ozer, A. B. (2009). Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals, 40(4), 1715-1734.‏[21] Alatas, B. (2010). Chaotic harmony search algorithms. Applied mathematics and computation, 216(9), 2687-2699.‏[22] Alatas, B. (2010). Chaotic bee colony algorithms for global numerical optimization. Expert systems with applications, 37(8), 5682-5687.‏[23] Alatas, B. (2011). Uniform big bang–chaotic big crunch optimization. Communications in Nonlinear Science and Numerical Simulation, 16(9), 3696-3703.‏[24] Talatahari, S., Azar, B. F., Sheikholeslami, R., & Gandomi, A. H. (2012). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation, 17(3), 1312-1319.‏[25] Gandomi, A. H., Yang, X. S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89-98.‏[26] Gandomi, A. H., & Yang, X. S. (2014). Chaotic bat algorithm. Journal of computational science, 5(2), 224-232.‏[27] Askarzadeh, A., & dos Santos Coelho, L. (2014). A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model. International journal of hydrogen energy, 39(21), 11165-11174.‏[28] Zhu, W., & Duan, H. (2014). Chaotic predator–prey biogeography-based optimization approach for UCAV path planning. Aerospace science and technology, 32(1), 153-161.‏[29] Koupaei, J. A., & Hosseini, S. M. M. (2015). A new hybrid algorithm based on chaotic maps for solving systems of nonlinear equations. Chaos, Solitons & Fractals, 81, 233-245.‏[30] Bingol, H., & Alatas, B. (2016). Chaotic league championship algorithms. Arabian journal for science and engineering, 41, 5123-5147.‏[31] Heidari, A. A., Ali Abbaspour, R., & Rezaee Jordehi, A. (2017). An efficient chaotic water cycle algorithm for optimization tasks. Neural Computing and Applications, 28, 57-85.‏[32] Kaur, G., & Arora, S. (2018). Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 5(3), 275-284.‏[33] Zhenxing, Z., Rennong, Y. A. N. G., Huanyu, L. I., Yuhuan, F. A. N. G., Zhenyu, H., & Ying, Z. (2019). Antlion optimizer algorithm based on chaos search and its application. Journal of Systems Engineering and Electronics, 30(2), 352-365.[34] Bingol, H., & Alatas, B. (2020). Chaos based optics inspired optimization algorithms as global solution search approach. Chaos, Solitons & Fractals, 141, 110434.‏[35] Aydilek, I. B., Karaçizmeli, I. H., Tenekeci, M. E., Kaya, S., & Gümüşçü, A. (2021). Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems. Sādhanā, 46(2), 65.‏[36] Zhang, X. Y., Zhou, K. Q., Li, P. C., Xiang, Y. H., Zain, A. M., & Sarkheyli-Hägele, A. (2022). An improved chaos sparrow search optimization algorithm using adaptive weight modification and hybrid strategies. Ieee Access, 10, 96159-96179.‏[37] Aydemir, S. B. (2023). A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evolutionary Intelligence, 16(3), 981-996.‏[38] Luo, T., Xie, J., Zhang, B., Zhang, Y., Li, C., & Zhou, J. (2024). An improved levy chaotic particle swarm optimization algorithm for energy-efficient cluster routing scheme in industrial wireless sensor networks. Expert Systems with Applications, 241, 122780.‏[39] Abdelrazek, M., Abd Elaziz, M., & El-Baz, A. H. (2024). CDMO: Chaotic Dwarf Mongoose optimization algorithm for feature selection. Scientific reports, 14(1), 701.‏[40] Abdel-Salam, M., Askr, H., & Hassanien, A. E. (2024). Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems. Expert Systems with Applications, 256, 124882.‏[41] Chauhan, U., Chhabra, H., Jain, P., Dev, A., Chauhan, N., & Kumar, B. (2024). Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models. IFAC Journal of Systems and Control, 27, 100239.‏[42] Farzin, S. (2025). A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation. Water Resources Management, 1-22.‏[43] Qasim, M., Sajid, M., Rajak, R., & Shahid, M. (2025). Task Scheduling Strategy Using Chaotic Whale Optimization Algorithm in Cloud Computing. In Nature-Inspired Optimization Algorithms for Cyber-Physical Systems (pp. 31-52). IGI Global Scientific Publishing.‏[44] B. Lotfi, Neghabi, Ali Akbar, "Evaluation, Comparison, and Performance Analysis of the Coati Optimization Algorithm (COA) with Other Metaheuristic Algorithms," Journal of Southern Communication Engineering, vol. 16, no. 61, pp. 159-240, 2026, doi: 10.71656/jce.2026.1198826.[45] F. Amirghafouri, A. A. Neghabi, H. Shakeri, and Y. E. Sola, "Nature‐inspired meta‐heuristic algorithms for resource allocation in the internet of things," International Journal of Communication Systems, vol. 38, no. 5, p. e6141, 2025.[46] F. Delbari, A. A. Neghabi, and J. Iziy, "Evaluating and Comparing the Performance of the Alibaba and Forty Thieves Algorithm Compared to Selected Meta-heuristic Algorithms," 2025.[47] S. Alimohamadi, Fathi, Mohammad, "Providing an Intrusion Detection System in the Industrial Internet of Things Using the Gray Wolf Algorithm," Journal of Information and Communication Technology, vol. 16, no. 61, pp. 218-228, 2024. [Online]. Available: https://rimag.ir/fa/Article/46422.[48] S. Asadinia, Yaghoubi, Elham, sadeghi, mostafa, "7-PORTNon-blocking Optical Router Design and Efficient Routing Algorithm in 3D Mesh Optical Network on Chip," Journal of Information and Communication Technology, vol. 17, no. 66, pp. -, 2026. [Online]. Available: https://rimag.ir/fa/Article/51065.MandanaRostami Pouya Derakhshan BarjoueiElahe Moradi911202613014710.66224/jict.51423.18.68.130http://jour.aicti.ir/fa/Article/51423http://jour.aicti.ir/fa/Article/Download/51423http://jour.aicti.ir/fa/Article/Download/51423http://jour.aicti.ir/fa/Article/Download/51423http://jour.aicti.ir/fa/Article/Download/51423http://jour.aicti.ir/fa/Article/Download/51423http://jour.aicti.ir/fa/Article/Download/51423http://jour.aicti.ir/fa/Article/Download/51423[1] Alieyan, K., Kadhum, M. M., Anbar, M., Rehman, S. U., & Alajmi, N. K. (2016). An overview of DDoS attacks based on DNS. In Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC) (pp. 276–280). IEEE. https://doi.org/10.1109/ICTC.2016.7763313[2] Tyagi, H., & Kumar, R. (2021). Attack and anomaly detection in IoT networks using supervised machine learning approaches. Revue d’Intelligence Artificielle, 35(1), 11–21.[3] Li, J., Xue, Z., Li, C., & Liu, M. (2021). RTED SD: A real time edge detection scheme for Sybil DDoS on the Internet of Vehicles. IEEE Access, 9, 11296–11305.[4] Gupta, R. K., Mishra, A., & Gupta, B. (2021). Enhanced identity-based encryption for secure key generation in large-scale cloud computing. IEEE Access, 9, 165259–165272. https://doi.org/10.1109/ACCESS.2021.3138503[5] Inuwa, M. M., & Das, R. (2024). A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks. Internet of Things, 26, 101162. https://doi.org/10.1016/j.iot.2023.101162[6] Cherdantseva, Y., & Hilton, J. (2013). A reference model of information assurance security. In Proceedings of the International Conference on Availability, Reliability and Security (ARES) (pp. 546–555). IEEE. https://doi.org/10.1109/ARES.2013.72[7] Kwon, D., Kim, H., Kim, J., Suh, S., Kim, I., & Kim, J. (2019). A survey of deep learning based network anomaly detection. Cluster Computing, 22(5), 949–961.[8] Anderson, J., Carbonell, J., Mitchell, T., Michalski, R., Amarel, S., Tecuci, T., & Kodratoff, Y. (1983). Machine learning: An artificial intelligence approach. Morgan Kaufmann.[9] Kilincer, I., Ertam, F., & Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188, 107840.[10] Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State of the art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4), 2432–2455.[11] Tsimenidis, S., Lagkas, T., & Rantos, K. (2022). Deep learning in IoT intrusion detection. Journal of Network and Systems Management, 30(3), 58. https://doi.org/10.1007/s10922-022-09666-x[12] Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761–768. https://doi.org/10.1016/j.future.2017.10.045 [13] Roopak, M., Tian, G. Y., & Chambers, J. (2019). Deep learning models for cyber security in IoT networks. In Proceedings of the IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 452–457). IEEE. https://doi.org/10.1109/CCWC.2019.8666590[14] Shalaka, M., Pawar, P. M., & Muthalagu, R. (2023). Efficient intelligent intrusion detection system for heterogeneous Internet of Things (HetIoT). Journal of Network and Systems Management, 31(4), 87. https://doi.org/10.1007/s10922-023-09748-7 [15] Mahjabin, T., Xiao, Y., Sun, G., & Jiang, W. (2017). A survey of distributed denial-of-service attack, prevention, and mitigation techniques. International Journal of Distributed Sensor Networks, 13(12), 1–20.[16] University of California, Irvine. (1999). KDD Cup 1999 data [Data set]. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/kdd+cup+1999+data[17] Canadian Institute for Cybersecurity. (n.d.). NSL-KDD dataset [Data set]. University of New Brunswick. https://www.unb.ca/cic/datasets/nsl.html[18] Ramchoun, H., Idrissi, M. J., Ghanou, Y., & Ettaouil, M. (2016). Multilayer perceptron: Architecture optimization and training. International Journal of Interactive Multimedia and Artificial Intelligence, 4(1), 26–30.[19] Hochreiter, S., & Schmidhuber, J. (1997). Long short term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735[20] Alom, M. Z., Bontupalli, V., & Taha, T. M. (2015). Intrusion detection using deep belief networks. In Proceedings of the IEEE National Aerospace and Electronics Conference (NAECON) (pp. 339–344). IEEE. https://doi.org/10.1109/NAECON.2015.7443094[21] Kim, J., Kim, J., Thi Thu, H. L., & Kim, H. (2016). Long short-term memory recurrent neural network classifier for intrusion detection. In Proceedings of the International Conference on Platform Technology and Service (PlatCon) (pp. 1–5). IEEE.[22] Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50.[23] Rahman, M. M., Al Faraj, A., & Alshamrani, A. (2023). Transformer CNN hybrid architecture for real time DDoS attack detection in IoT networks. Computer Networks, 228, 109280. https://doi.org/10.1016/j.comnet.2023.109280[24] Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. https://doi.org/10.1016/j.jisa.2019.102419[25] Yadav, S., & Subramanian, S. (2016). Detection of application layer DDoS attack by feature learning using stacked autoencoder. In Proceedings of the International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT) (pp. 361–366). IEEE.[26] Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017). Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT.Sensors (Basel, Switzerland), 17(9), 1967. https://doi.org/10.3390/s17091967[27] Luo, T., & Nagarajan, S. G. (2018). Distributed anomaly detection using autoencoder neural networks in WSN for IoT. In Proceedings of the IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.[28] Chen, C., Zhao, L., Wang, J., & Li, Y. (2024). Graph neural network based intrusion detection in software defined networking environments. Scientific Reports, 14, 65321. https://doi.org/10.1038/s41598-024-65321-7[29] Yaseen, Z. M., Hameed, A., & Karim, A. F. (2024). Hybrid autoencoder and convolutional neural network for intrusion detection in fog enabled IoT systems. IEEE Access, 12, 87102–87118. https://doi.org/10.1109/ACCESS.2024.3456712[30] Abbas, S., Alsubai, S., Ojo, S., Sampedro, G. A., Almadhor, A., Al Hejaili, A., & Bouazzi, I. (2024). An efficient deep recurrent neural network for detection of cyberattacks in realistic IoT environment. The Journal of Supercomputing, 80(10), 13557–13575.[31] Patel, D., Pillai, S., & Kumar, V. (2023). Deep reinforcement learning based anomaly detection for securing IoT edge networks. Neural Computing and Applications, 35(23), 17159–17175. https://doi.org/10.1007/s00521-023-08672-x[32] Rajabzadeh,M., Derakhshan-Barjoei,P. (2022). An Efficient Proxy-Based Message Authentication Framework in Vehicular Ad-hoc Networks. Journal of Communication Engineering, 11(1), 111-136. doi: 10.22070/jce.2024.18230.1255[33] Rajabzadeh Asaar, M., Derakhshan Barjoei, P. (2023). A New Protocol for Lightweight Anonymous Authentication with Leading Security in Wireless Sensor Networks Based on IoT. Intelligent Multimedia Processing and Communication Systems (IMPCS), vol. 4, no. 3, 2023, pp. 1-13. 10.71856/impcs.2023.903612[34] Kumar, P., Gupta, G. P. (2024). Hybrid deep learning based threat intelligence framework for securing Internet of Things. IEEE Transactions on Information Forensics and Security, 19, 882–895. https://doi.org/10.1109/TIFS.2023.3321500[35] Shahpar, Z., Badragheh, M. (2026). Presenting a Hybrid Model on Machine Learning and Principal Component Analysis for Action Detection in the Internet of Things. Journal of Information and Communication Technology, vol. 17, no. 66.[36] davami, F. , Derakhshan-Barjoei, P. and Shaviklou, N. (2026). Detecting abnormal nodes in IoT security using neural networks and graph theory. Tabriz Journal of Electrical Engineering, doi: 10.22034/tjee.2026.69361.5088[37] zeraatkarmoghaddam, m., ghayori, m. (2023). Improvement of intrusion detection system on Industrial Internet of Things based on deep learning using metaheuristic algorithms. Journal of Information and Communication Technology, vol. 15, no. 57, pp. 165-190.[38] Abdi, A., Salimi-Badr, A., Souzani, A.(2026). A Self-supervised Sensors’ Anomaly Detection Scheme in Industrial Control Systems based on Ensemble Deep Learning . Journal of Information and Communication Technology, vol. 17, no. 66.maryamnooraei HosseinShidelzade911202615717010.66224/jict.51676.18.68.157http://jour.aicti.ir/fa/Article/51676http://jour.aicti.ir/fa/Article/Download/51676http://jour.aicti.ir/fa/Article/Download/51676http://jour.aicti.ir/fa/Article/Download/51676http://jour.aicti.ir/fa/Article/Download/51676http://jour.aicti.ir/fa/Article/Download/51676http://jour.aicti.ir/fa/Article/Download/51676http://jour.aicti.ir/fa/Article/Download/51676[1] Moin, A., et al., A model-driven approach to machine learning and software modeling for the IoT: Generating full source code for smart Internet of Things (IoT) services and cyber-physical systems (CPS). Software and Systems Modeling, 2022. 21(3): p. 987-1014.[2] Dral, P.O., et al., MLatom 3: a platform for machine learning-enhanced computational chemistry simulations and workflows. Journal of Chemical Theory and Computation, 2024. 20(3): p. 1193-1213.[3] M Casares, P.A., et al., GradDFT. A software library for machine learning enhanced density functional theory. The Journal of Chemical Physics, 2024. 160(6).[4] Nooraei Abadeh, M., A semantic axiomatic design for integrity in IoT. Transactions on Emerging Telecommunications Technologies, 2024. 35(9): p. e5032. https://doi.org/10.1002/ett.5032.[5] Eyke, N.S., B.A. Koscher, and K.F. Jensen, Toward machine learning-enhanced high-throughput experimentation. Trends in Chemistry, 2021. 3(2): p. 120-132.[6] Snoeck, M. and Y. Wautelet, Agile MERODE: a model-driven software engineering method for user-centric and value-based development. Software and Systems Modeling, 2022. 21(4): p. 1469-1494.[7] Nooraei Abadeh, M. and M. Mirzaie, DiffPageRank: An efficient differential PageRank approach in MapReduce. The Journal of Supercomputing, 2021. 77(1): p. 188–211. https://doi.org/10.1007/s11227-020-03265-3.[8] Boumahdi, F., et al., MDA4SOA+ d: A new model driven architecture to supporting decision making in SOA. Journal of King Saud University-Computer and Information Sciences, 2023. 35(5): p. 101544.[9] Wang, Y., et al., Machine learning-enhanced flexible mechanical sensing. Nano-Micro Letters, 2023. 15(1): p. 55.[10] Liu, J., H. Hu, W. Xu, and D. Luo, Internet of things challenges and future scope for enhanced living environments, in Advances in Computers. 2024, Elsevier. p. 201-246.[11] Park, Y.-S., B.U. Park, and H.-J. Jeon, Advances in machine learning-enhanced nanozymes. Frontiers in Chemistry, 2024. 12: p. 1483986.[12] Kwon, H., Z.A. Ali, and B.M. Wong, Harnessing semi-supervised machine learning to automatically predict bioactivities of per-and polyfluoroalkyl substances (PFASs). Environmental Science & Technology Letters, 2023. 10(11): p. 1017-1022.[13] Albayati, M.G., et al., Semi-supervised machine learning for fault detection and diagnosis of a rooftop unit. Big Data Mining and Analytics, 2023. 6(2): p. 170-184.[14] Ahmad, E., Model-based system engineering of the Internet of Things: A bibliometric literature analysis. IEEE Access, 2023. 11: p. 50642-50658.[15] Alulema, D., et al., SI4IoT: A methodology based on models and services for the integration of IoT systems. Future Generation Computer Systems, 2023. 143: p. 132-151.[16] Marques, G., Editor, Internet of Things: Architectures for Enhanced Living Environments. Advances in Computers. Vol. 133. 2024, Cambridge, MA: Academic Press/Elsevier. [17] Javed, A.R., et al., Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects. Cities, 2022. 129: p. 103794.[18] Mardani Korani, Z., et al., Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review. Sensors, 2023. 23(3): p. 1458.[19] Moin, A. Data analytics and machine learning methods, techniques and tool for model-driven engineering of smart IoT services. in 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 2021. IEEE, p. 287–292.[20] Moin, A., et al., Enabling automated machine learning for model-driven AI engineering. arXiv preprint arXiv:2203.02927, 2022.[21] Berrouyne, I., et al., A model-driven methodology to accelerate software engineering in the internet of things. IEEE Internet of Things Journal, 2022. 9(20): p. 19757-19772.[22] Wang, H. and D.-Y. Yeung, A survey on Bayesian deep learning. ACM computing surveys (csur), 2020. 53(5): p. 1-37.[23] Moin, A., et al. Supporting AI engineering on the IoT edge through model-driven TinyML. in 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). 2022. IEEE.[24] Morin, B., N. Harrand, and F. Fleurey, Model-based software engineering to tame the IoT jungle. IEEE software, 2017. 34(1): p. 30-36.[25] Paszke, A., et al., Automatic differentiation in PyTorch, in NIPS 2017 Workshop on Autodiff: The Future of Gradient-Based Machine Learning Software and Techniques. 2017, Long Beach, CA, USA.[26] Atzori, L., A. Iera, and G. Morabito, The internet of things: A survey. Computer networks, 2010. 54(15): p. 2787-2805.[27] Bennett, K.P. and E. Parrado-Hernández, The interplay of optimization and machine learning research. The Journal of Machine Learning Research, 2006. 7: p. 1265-1281.[28] Nižetić, S., et al., Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of cleaner production, 2020. 274: p. 122877.[29] Aouedi, O., et al., A survey on intelligent Internet of Things: Applications, security, privacy, and future directions. IEEE Communications Surveys & Tutorials, 2025. 27(2): p. 1238–1292.[30] Moghaddasi, K. and M. Masdari, Blockchain-driven optimization of IoT in mobile edge computing environment with deep reinforcement learning and multi-criteria decision-making techniques. Cluster Computing, 2024. 27(4): p. 4385-4413.[31] Moin, A., S. Rössler, and S. Günnemann, ThingML+ augmenting model-driven software engineering for the Internet of Things with machine learning. arXiv preprint arXiv:2009.10633, 2020.[32] Rademacher, F., et al., Model-Driven Engineering of Microservice Architectures—The LEMMA Approach, in Ernst Denert Award for Software Engineering 2022: Practice Meets Foundations. 2024, Springer Nature Switzerland Cham. p. 105-147.[33] Verbruggen, C. and M. Snoeck, Practitioners’ experiences with model-driven engineering: a meta-review. Software and Systems Modeling, 2023. 22(1): p. 111-129.[34] Moghaddasi, K. and M. Masdari, Blockchain-driven optimization of IoT in mobile edge computing environment with deep reinforcement learning and multi-criteria decision-making techniques. Cluster Computing, 2024. 27(4): p. 4385–4413. [35] Steel Industry Energy Consumption Dataset. UCI Machine Learning Repository. Available: https://archive.ics.uci.edu/dataset/851/steel+industry+energy+consumption.[36] Shahpar, Z. and M. Badragheh, Presenting a hybrid model based on machine learning and principal component analysis for attack detection in the Internet of Things. Journal of Information and Communication Technology, 2026. 17(66–67): p. 178–188. Available from: https://jour.aicti.ir/en/Article/49811. امير هوشنگتاج فرalishafiee9112026728410.66224/jict.51720.18.68.72http://jour.aicti.ir/fa/Article/51720http://jour.aicti.ir/fa/Article/Download/51720http://jour.aicti.ir/fa/Article/Download/51720http://jour.aicti.ir/fa/Article/Download/51720http://jour.aicti.ir/fa/Article/Download/51720http://jour.aicti.ir/fa/Article/Download/51720http://jour.aicti.ir/fa/Article/Download/51720http://jour.aicti.ir/fa/Article/Download/51720[1] Donate, M. J., & De Pablo, J. D. S. (2015). The role of knowledge-oriented leadership in knowledge management practices and innovation. Journal of business research, 68(2), 360-370. [2] Boroujerdi, S., Hasani, K. and Delshab V. (2019)" Investigating the influence of knowledge management on organizational innovation in higher educational institutions" Emerald Publishing Limited[3] H.Boateng and F.G Agyemang, (2015( "The effects of knowledge sharing and knowledge application on service recovery performance", Journal of Business Information Review, Vol 32, Issue 2, [4] Hamdoun, M., Jabbour, C. J. C., & Othman, H. B. (2018). Knowledge transfer and organizational innovation: Impacts of quality and environmental management. Journal of cleaner production, 193, 759-770. [5] Hassan, N., & Raziq, A. (2019). Effects of knowledge management practices on innovation in SMEs. Management Science Letters, 9(7), 997-1008[6] Rauter, R., Globocnik, D., Perl-Vorbach, E., & Baumgartner, R. J. (2019). Open innovation and its effects on economic and sustainability innovation performance. Journal of Innovation & Knowledge, 4(4), 226-233. [7] Sintset Kenfac, G., Nekoumanesh, S., & Yang, M. (2013). Process Innovation: Impacts on Organization's Performance: A Qualitative Study of Four Swedish Municipalities.[8] Gloet, M., & Samson, D. (2016). Knowledge management and systematic innovation capability. International Journal of Knowledge Management (IJKM), 12(2), 54-72.[9] 9- Acharya, C., Ojha, D., Gokhale, R., and Patel, P. C. (2022). Managing information for innovation using knowledge integration capability: The role of boundary spanning objects. Int. J. Inf. Manage. 62, 102438. doi: 10.1016/j.ijinfomgt.2021.102438[10] H. Idrees., J. Hynek, J. Xu, A. Akbar and S. Jabeen, (2022) “Impact of knowledge management capabilities on new product development performance through mediating role of organizational agility and moderating role of business model innovation”, journal Frontiers in Psychology[11] Garcia-Perez, A., Ghio, A., Occhipinti, Z., and Verona, R. (2020). Knowledge management and intellectual capital in knowledge-based organisations: a review and theoretical perspectives. J. Knowledge Manage. 24, 1719–1754. doi: 10.1108/JKM-12-2019-0703[12] Abu Elnasr E. Sobaih, Hassane Gharbi, Mohamed A. Ben Abdallah, Osama H. Mahmoud Hassan (2025) "Unveiling the role of knowledge management effectiveness in university’s performance through administrative departments’ innovation" Journal of Open Innovation: Technology, Market, and Complexity.[13] Oztekin, A., Delen, D., Zaim, H., Turkyilmaz, A., & Zaim, S. (2015). The influence of knowledge management on financial and non-financial performance. Journal of Information & Knowledge Management, 14(02), 1550013. [14] Smith, J. Q., Jones, M. R., & Brown, C. D. (2020). Advancing Managerial Evolution and Resource Management in Contemporary Business Landscapes. Journal of Management Studies, 58, 1-25.[15] Zaim, H., Muhammed, S., and Tarim, M. (2019). Relationship between knowledge management processes and performance: critical role of knowledge utilization in organizations. Knowledge Manage. Res. Pract. 17, 24–38. doi: 10.1080/14778238.2018.1538669[16] Choi, S. Y., Lee, H., & Yoo, Y. (2023). The impact of information technology and transitive memory systems on knowledge sharing, application, and team performance: A field study. MIS quarterly, 855-870. [17] Khan, M. S., Saengon, P., Charoenpoom, S., Soonthornpipit, H., & Chongcharoen, D. (2021). The impact of organizational learning culture, workforce diversity and knowledge management on innovation and organization performance: A structural equation modeling approach. Human Systems Management (Preprint), 1-13. [18] Silvia, M.-L., & Juan-Gabriel, C.-N. (2014). Linking knowledge corridors to customer value through knowledge processes. Journal of Knowledge Management, 18(2), 342–365.[19] Costa, V., & Monteiro, S. (2016). Key knowledge management processes for innovation: A systematic literature review. VINE Journal of Information and Knowledge Management Systems, 46(3), 386–410.[20] Hamdoun, M., Chiappetta Jabbour, C. J., & Ben Othman, H. (2018). Knowledge transfer and organizational innovation: Impacts of quality and environmental management. Journal of Cleaner Production, 193, 759–770.[21] Li, C., Ashraf, S. F., Shahzad, F., Bashir, I., Murad, M., Syed, N., & Riaz, M. (2020). Influence of Knowledge Management Practices on Entrepreneurial and Organizational Performance: A Mediated-Moderation Model. Frontiers in Psychology, 11, 2862. [22] Susanty, A. I., Yuningsih, Y., & Anggadwita, G. (2019). Knowledge management practices and innovation performance: A study at Indonesian Government apparatus research and training center. Journal of Science and Technology Policy Management. [23] Nkararo, N., & Gitari, J. (2018). Influence of strategic knowledge management practices on organizational innovation in large scale manufacturing firms in Nakuru County, Kenya. International Journal of Management and Commerce Innovations, 5(2), 595-610. [24] Hussinki, H., Ritala, P., Vanhala, M., & Kianto, A. (2017). Intellectual capital, knowledge management practices and firm performance. Journal of intellectual capital. [25] طالعی‌فر، رضا و بخشی، نصراله و صادقی، حمیدرضا. (1400).تأثیر شیوه‌های مدیریت منابع انسانی، یادگیری سازمانی، فرهنگ سازمانی و قابلیت‌های مدیریت دانش بر عملکرد سازمانی؛ یک چارچوب مبتنی بر مفاهیم،پنجمین کنفرانس بین‌المللی چشم‌اندازهای نوین در مدیریت، حسابداری و کارآفرینی، تهران.[26] باقرزاده، محمد و پورفخری، منصوره. (1399).بررسی و تبیین نقش میانجی کاربرد دانش در رابطه بین شیوه‌ای مدیریت دانش و نوآوری (مورد مطالعه: شرکت ملی صنایع پتروشیمی)،ششمین کنفرانس ملی پژوهش‌های کاربردی در مدیریت، حسابداری و اقتصاد سالم در بانک، بورس و بیمه، تهران[27] ابوالحسنی زراعتکار، مهدیه و سلاجقه، سنجر. (1398).بهبود عملکرد نوآوری از طریق کسب دانش با در نظر گرفتن نقش تعدیل‌کننده حفظ کارکنان و شیوه‌های مدیریت منابع انسانی (مورد مطالعه: سازمان تأمین اجتماعی شهر کرمان)،دومین همایش ملی علوم مدیریت ، اقتصاد و بانکداری اسلامی،تهران.[28] مولودیان، هادی و راه‌چمنی، رضا. (1397).بررسی نقش رهبری مبتنی بر دانش در شیوه‌های مدیریت دانش و نوآوری،اولین همایش ملی مدیریت و اقتصاد با رویکرد اقتصاد مقاومتی، مشهد..[29] مرادی، عبدالله، یزدانی، سیروان و حیدری، چیمن. (1395). مطالعه رابطه رهبری دانشگرا و عملکرد نوآوری محصول با تأکید بر نقش واسطه‌ای شیوه‌های مدیریت دانش،چهارمین همایش علمی پژوهشی یافته‌های نوین علوم مدیریت، کارآفرینی و آموزش ایران، تهران.[30] Ode, E., & Ayavoo, R. (2020). The mediating role of knowledge application in the relationship between knowledge management practices and firm innovation. Journal of Innovation & Knowledge, 5(3), 210-218ShahrzadSedaghatmehdiAzadimotlaghamir hoseinjahangir9112026224310.66224/jict.51852.18.68.22http://jour.aicti.ir/fa/Article/51852http://jour.aicti.ir/fa/Article/Download/51852http://jour.aicti.ir/fa/Article/Download/51852http://jour.aicti.ir/fa/Article/Download/51852http://jour.aicti.ir/fa/Article/Download/51852http://jour.aicti.ir/fa/Article/Download/51852http://jour.aicti.ir/fa/Article/Download/51852http://jour.aicti.ir/fa/Article/Download/51852[1] H.-Z. Xu et al., “Quafu-Qcover: Explore combinatorial optimization problems on cloud-based quantum computers,” Chinese Phys. B, vol. 33, no. 5, p. 50302, 2024.[2] A. Mahapatra, R. Pradhan, S. K. Majhi, and K. Mishra, “Quantum ML-Based Cooperative Task Orchestration in Dew-Assisted IoT Framework,” Arab. J. Sci. Eng., vol. 50, no. 15, pp. 11975–12002, 2025, doi: 10.1007/s13369-024-09661-8.[3] M. I. Hossain, S. A. Sumon, H. M. Hasan, F. Akter, M. B. Badhon, and M. N. U. Islam, “Quantum-edge cloud computing: A future paradigm for iot applications,” arXiv Prepr. arXiv2405.04824, 2024.[4] S. Akter, M. B. Badhon, M. K. I. Bhuiyan, H. M. Hasan, F. Akter, and M. N. U. Islam, “Quantum-edge cloud computing for iot: Bridging the gap between cloud, edge, and quantum technologies,” Edge, Quantum Technol. (September 29, 2024), 2024.[5] B. Narang, H. Virwani, and N. Jain, “Comprehensive Review on the Synergies and Challenges of Quantum Computing, Cloud Computing, and Fog Computing in New Era,” in Proceedings of the 5th International Conference on Information Management \& Machine Intelligence, in ICIMMI ’23. New York, NY, USA: Association for Computing Machinery, 2024. doi: 10.1145/3647444.3652433.[6] Y. Mahmoudi, N. Zioui, and H. Belbachir, “A new quantum-inspired clustering method for reducing energy consumption in IOT networks,” Internet of Things, vol. 20, p. 100622, 2022, doi: https://doi.org/10.1016/j.iot.2022.100622.[7] S. Jannu et al., “Energy Efficient Quantum-Informed Ant Colony Optimization Algorithms for Industrial Internet of Things,” IEEE Trans. Artif. Intell., vol. 5, no. 3, pp. 1077–1086, 2024, doi: 10.1109/TAI.2022.3220186.[8] K. Zanbouri, H. M. R. Al-Khafaji, N. J. Navimipour, and Ş. Yalçın, “A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm,” IEEE Multimed., vol. 30, no. 3, pp. 74–86, 2023, doi: 10.1109/MMUL.2023.3247522.[9] T. Hasan et al., “Edge Caching in Fog‐Based Sensor Networks through Deep Learning‐Associated Quantum Computing Framework,” Comput. Intell. Neurosci., vol. 2022, no. 1, p. 6138434, 2022.[10] T. A. Ahanger, F. Dahan, U. Tariq, and I. Ullah, “Quantum inspired task optimization for IoT edge fog computing environment,” Mathematics, vol. 11, no. 1, p. 156, 2022.[11] M. Bhatia, S. K. Sood, and S. Kaur, “Quantum-based predictive fog scheduler for IoT applications,” Comput. Ind., vol. 111, pp. 51–67, 2019.[12] S. N. Ghorpade, M. Zennaro, B. S. Chaudhari, R. A. Saeed, H. Alhumyani, and S. Abdel-Khalek, “Enhanced differential crossover and quantum particle swarm optimization for IoT applications,” IEEE Access, vol. 9, pp. 93831–93846, 2021.[13] S. Khan, N. Younas, M. Alhussein, W. J. Khan, M. S. Anwar, and K. Aurangzeb, “Quantum Inspired Adaptive Resource Management Algorithm for Scalable and Energy Efficient Fog Computing in Internet of Things (IoT).,” Comput. Model. Eng. Sci., vol. 142, no. 3, 2025.[14] M. Hayeri Khyavi, “Emerging technologies in future generations of high performance computing: introduction, taxonomy and future research directions,” Journal of Information and Communication Technology, vol. 55, no. 55, p. 26, 2023.[15] M. Esnaashari, F. Ehsani Boshla, M. Dehyadegari, and B. Samadi, “High Performance Computing: Next Generation Requirements and Research Axes,” Journal of Information and Communication Technology, vol. 51, no. 51, p. 209, 2022.[16] M. Azimzadeh, A. Rezaee, S. Jafarali Jassbi, and M. Esnaashari, “Community-Based Multi-Criteria Placement of Applications in the Fog Environment,” Journal of Information and Communication Technology, vol. 59, no. 59, p. 17, 2024.[17] A. G. Delavar, R. Akraminejad, and S. Mozafari, “WSTMOS: A Method For Optimizing Throughput, Energy, And Latency In Cloud Workflow Scheduling,” Journal of Information and Communication Technology, vol. 57, no. 57, p. 62, 2023.[18] Z. Abbasi, S. Fatahi, and M. J. Ershadi, “An approach to prioritize quality dimensions of based on cloud computing using Multiple Criteria Decision Making method,” Journal of Information and Communication Technology, vol. 53, no. 53, p. 224, 2023.AmirHosseinpour qholamrezarezaeialibadiezadeh911202612110.66224/jict.52221.18.68.1http://jour.aicti.ir/fa/Article/52221http://jour.aicti.ir/fa/Article/Download/52221http://jour.aicti.ir/fa/Article/Download/52221http://jour.aicti.ir/fa/Article/Download/52221http://jour.aicti.ir/fa/Article/Download/52221http://jour.aicti.ir/fa/Article/Download/52221http://jour.aicti.ir/fa/Article/Download/52221http://jour.aicti.ir/fa/Article/Download/52221[1] U. Shrivastava, L. Ofstein, D. Golhar. “ Direct and Indirect Effects of ICT Infrastructure, Skills, and Use on Entrepreneurship: A Cross-Country Empirical Investigation’’. Journal of Global Information Management, Volume 29 ,Issue 6 , November-December 2021[2] پازری، م؛ حقیقی نصب، م؛ عدالتیان شهریاری، ج.«ارائه مدل ارزش آفرینی در اکوسیستم کارآفرینی فناوری اطلاعات و ارتباطات». دو فصلنامه علمی فناوری اطلاعات و ارتباطات ایران، سال سیزدهم، شاره های49و50. 1400[3] K. Afawubo, Y. A Noglo,. ‘ ICT and entrepreneurship: A comparative analysis of developing, emerging and developed countries’. Technological Forecasting and Social Change, 175, 2021[4] R. S Morano, Jacomossi, R. R., Barrichello, A., & Feldmann, P. R. ‘The Interdependence between ease of doing business, innovation, and competitiveness of nations. BAR-Brazilian Administration Review’, 20(2), e220103. 2023[5] T. Li, ‘’Engagement in International Entrepreneurship: Interactive Effects of Resource-based Factors and Institutional Environments’’. Journal of Global Entrepreneurship Research, 9(1), 1–17. 2019[6] A.V Stel, Carree, M., Thurik, R.,’’ The effect of entrepreneurial activity on national economic growth. Small Business Economics’’, 24, 311-321. 2005[7] S. Djankov, La Porta, R., Lopez-de-Silanes, F., Shleifer, A.,’’The regulation of entry. The Quarterly Journal of Economics’’, 117(1), 1–37. 2002[8] M. V .Alderete,’’ ICT Incidence on the Entrepreneurial Activity at Country Level’’. International Journal of Entrepreneurship and Small Business, 21(2), 183–201, 2014[9] M. V .Alderete, ‘’Mobile Broadband: a Key Enabling Technology for Entrepreneurship?’’ Journal of Small Business Management, 55(2), 254-269.2017[10] M.D .Giudice, Straub, D.,’’ IT and entrepreneurism: an on-again, off-again love affair or a marriage? MIS Quarterly’’, 35 (4), 3–7. 2011[11] WEF. (2016). Available at: http://www3.weforum.org/docs/GITR2016/WEF_GITR_Full_Report.pdf[12] V. Stel, Storey, & Thurik. ‘’ The Effect of Business Regulations on Nascent and Young Business Entrepreneurship’’. Small Business Economics, 28(2-3).2007[13] A.R .Thurik, Carree, M.A., Stel A.V., Audretsch, D.B.’’ Does self-employment reduce unemployment?’’ Journal of Business Venturing, 23 (6), 673-686.2007[14] J.-H .Keppler, ‘’Barriers to Entry: Abolishing the Barriers to Understanding’’. Journal of Public Finance and Public Choice, 27(2–3), 99–124. 2009[15] T. Canare,’’The Effect of Ease of Doing Business on Firm Creation’’. Annals of Economics and Finance, 19(2), 555-584.2018[16] دقتی، ع؛ یعقوبی، ن؛ کمالیان، ا؛ دهقانی، م. «ارائه الگوی توسعه مرحله‌ای حکمرانی شبکه‌ای با استفاده از فراترکیب». فصلنامه مدیریت دولتی. دوره 11. شماره 2. 1398[17] C. Wyborn C, Louder E, Harrison J, Montambault J, Montana J, Ryan M, Bednarek A, Nesshöver C, Pullin A. "Understanding the Impacts of Research Synthesis". Environmental Science & Policy. 86: 72–84.2018[18] C.L. Shook, Ketchen, D. J., Hult, G. T. M., & Kacmar, K. M.’’An assessment of the use of structural equation modeling in strategic management research’’. Strategic Management Journal, 25(4), 397–404. 2004[19] K.A. Bollen,’’ Structural Equations with Latent Variables’’. Wiley-Interscience.1989[20] M. Sandelowski, & Barroso, J. ‘’Handbook for Synthesizing Qualitative Research’’. New York: Springer Publishing Company.2007[21] J. Guo, Zhiming Cheng, Ben Zhe Wang, ‘’Internet development and entrepreneurship, China Economic Review’’, Available online 11 September 2024[22] C. Chaves-Vargas Joana , Ribes-Giner Gabriela, Moya-Clemente Ismael , ‘’Effect of the economic, social and technological factors on sustainable entrepreneurship over time’’, Journal of Business Research 173 .2024[23] A.Lado Ignacio Castro Abancens ,Ana M. Moreno Menéndez• José C. Casillas. “Traditional and digital entrepreneurial ecosystems: a framework of differences and similarities”. Journal of Global Entrepreneurship Research. 14:35, 2024[24] E. Lungu Anca, Mircea Radu Georgescu, ‘’Digital Entrepreneurship: An empirical Approach on Students’ Perceptions and Attitudes, Procedia Computer Science 237 ,544–551, 2024[25] Y. Uma Shankar , Shweta Vyas, Kanchan4, Indrajit Ghosal and Ajay Kumar Yadav. “Impact of entrepreneurial leadership, Social media, digital technology, Entrepreneurial orientation and innovation on business performance in the handicraft sector: Talent management as mediating construct”. Journal of Innovation and Entrepreneurship, 13-72, 2024[26] D. Cong Doanh, Ngoc Diep Do , Huy Nhuong Bui, Tuan Vu Chu, Thanh Van Pham. “ICT skills, entrepreneurial self-perceived creativity, and digital entrepreneurship: Insights from the stimulus-organism-response model”, Thinking Skills and Creativity 54 .101646,2024[27] C. Kreiterling, “Digital innovation and entrepreneurship: a review of challenges in competitive markets”, Journal of Innovation and Entrepreneurship. 12-49,2023[28] J.G. Tuwei , Korir Michael, Komen Joyce. “The Moderating Effect of Transformational Leadership on the Relationship Between Entrepreneurial Orientation and Performance of Manufacturing Firms in Nairobi County”. International Journal of Entrepreneurship and Business Innovation (IJEBI). Volume 6, Issue 1, 68-86, 2023[29] Y.J Wu , Chih-Hung Yuan, ID and Chia-I Pan. “Entrepreneurship Education: An Experimental Study with Information and Communication Technology”. Sustainability, 10, 691; 2018[30] S. Gomes, J.M. Lopes. “ICT Access and Entrepreneurship in the Open Innovation Dynamic Context: Evidence from OECD Countries. Open Innov”. Technol. Mark. Complex, 8, 102. 2022[31] T. Ben Hassen. “The entrepreneurship ecosystem in the ICT sector in Qatar: local advantages and constraints”. Journal of Small Business and Enterprise Development. Volume 27, Issue 2, Pages 177-195, 6 March 2020[32] D. Pandit, Sh. R. Tiwari. “Examining Entrepreneurial Intention in Higher Education: An Exploratory Study of College Students in India.” The Journal of Entrepreneurship 27(1), March 2018[33] N. Fallah Haghighi, Hojatollah Hajihoseini, Ghasem Ramezanpour Nargesi, Masoud Bijani. Gap analysis of current and desired states of entrepreneurship development components in the field of ICTs in Iran. Technology in Society. Volume 54, Pages 101-110, August 2018[34] O. Blessing Onyeka. Emeasoba Nneka Charity. “Influence of entrepreneurship skills acquisition on job creation among business education undergraduates in anambra state. Entrepreneurship studies”, nnamdi azikiwe university, awka. June 2023[35] M. Yunis, Tarhini, A., & Kassar, A. “The Role of ICT and Innovation in Enhancing Organizational Performance: The Catalysing Effect of Corporate Entrepreneurship”. Journal of Business Research, 88, 344–356. 2018 [36] F. Zhang, Dahui Li. “Regional ICT access and entrepreneurship: Evidence from China”.Information & Management. Volume 55, Issue 2, Pages 188-198, March 2018[37] Y. Muhammed. Kabiru Jinjiri Ringim. Kabiru Maitama Kura. “Moderating Role of Perceived Social Support on the Relationship Between Entrepreneurship Education and Entrepreneurial Intention.” Journal of Entrepreneurship and Business 8(1):104-119, July 2021[38] X. Neumeyer. “Inclusive High-Growth Entrepreneurial Ecosystems: Fostering Female Entrepreneurs' Participation in Incubator and Accelerator Programs”. IEEE Transactions on Engineering Management PP(99):1-10, April 2020[39] S. Asongu. Jacinta Chikaodi Nwachukwu. “Openness, ICT and entrepreneurship in sub-Saharan Africa”. Information Technology and People. February 2018[40] A. Zenebe, Alsaaty, F., & Anyiwo, D. “Relationship between individual’s entrepreneurship intention, and adoption and knowledge of information technology and its applications: An empirical study”. Journal of Small Business and Entrepreneurship, 30(3), 215–232.2017[41] B. Spigel. “The Relational Organization of Entrepreneurial Ecosystems”. Entrepreneurship Theory and Practice 41(1), June 2015[42] B. Feld. “Startup Communities: Building an Entrepreneurial Ecosystem in Your City, 2nd Edition”. ISBN: 978-1-119-61779-2, June 2020[43] F. Hashim, Zaini Amir, Norizan Abd Razak. Empowering rural women entrepreneurs with ICT skills: An impact study. Procedia- Social and Behavioral Sciences. Volume 15, Pages 3369-3373,2011[44] D. Isenberg, “How to start an entrepreneurial revolution”. John Wiley & Sons. 2014[45] A. Petkova, Rindova, V., & Gupta, A. “No News Is Bad News: Sensegiving Activities, Media Attention, and Venture Capital Funding of New Technology Organizations”. Organization Science, 24(3), 865–888. 2013[46] Y. Rosseel, “lavaan: An R Package for Structural Equation Modeling’’. Journal of Statistical Software, 48(1), 1–36. 2012[47] H. Ansari Samani, Mirzapour, M., & Dalvandi, H. “The Effect of Political Risk on Entrepreneurship: Evidence from Selected Countries of the World”. Innovation Economic Ecosystem Studies, 3(4), 13-36. 10, 2023[48] M. E. Porter, “The Five Competitive Forces That Shape Strategy”. Harvard Business Review, 2008[49] D. Noah Afees, David Oladipo, “Does ICT Adoption Moderate the Impact of Entrepreneurship on Economic Growth in Africa?”, Adm. Sci., 15(3), 88,2025 MohammadFazeliMohsenRajiMohammad MojtabaFazeli911202611512910.66224/jict.52801.18.68.115http://jour.aicti.ir/fa/Article/52801http://jour.aicti.ir/fa/Article/Download/52801http://jour.aicti.ir/fa/Article/Download/52801http://jour.aicti.ir/fa/Article/Download/52801http://jour.aicti.ir/fa/Article/Download/52801http://jour.aicti.ir/fa/Article/Download/52801http://jour.aicti.ir/fa/Article/Download/52801http://jour.aicti.ir/fa/Article/Download/52801[1] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.[2] Alfriehat N., Anbar M., Aladaileh M., Hasbullah I., et al., “RPL based attack detection approaches in IoT networks: review and taxonomy,” Artificial Intelligence Review, vol. 57, art. 248, Aug. 2024[3] T. Winter et al., RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks, RFC 6550, IETF, 2012[4] M. Emeç and M. H. Özcanhan, “ROUT-4-2023: RPL based routing attack dataset for IoT,” IEEE Dataport, doi: 10.21227/3mbe-5j70, 2023.[5] K. O. A. Alimi, K. Ouahada, A. M. M. Abu-Mahfouz, S. Rimer, and O. A. Alimi, “Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things,” Journal of Sensor and Actuator Networks (JSAN), vol. 11, no. 3, article 32, July 2022. DOI: 10.3390/jsan11030032[6] Abdelouahid Derhab, Arwa Aldweesh, Ahmed Z Emam, and Farrukh Aslam Khan. 2020. Intrusion detection system for internet of things based on temporal convolution neural network and efficient feature engineering. Wireless Communications and Mobile Computing 2020 (2020), 1–16. [7] Mohamed Abdel-Basset, Hossam Hawash, Ripon K Chakrabortty, and Michael J Ryan. 2021. Semi-supervised spatiotemporal deep learning for intrusions detection in IoT networks. IEEE Internet of Things Journal 8, 15 (2021), 12251–12265.[8] Y. Guan, M. Noferesti, and N. Ezzati-Jivan, “CNN-BiLSTM-Based Classification of RPL Attacks in IoT Smart Grid Networks (Industry Track),” in Proc. ACM/IFIP/USENIX Middleware Conf., Bologna, Italy, Dec. 2023[9] A. Etheridge and V. Anu, RPL Attack Detection in IoT Environments: An Ensemble Approach, in Proc. IEMTRONICS 2024 – International IoT, Electronics and Mechatronics Conference, Lecture Notes in Electrical Engineering, vol. 1228, Springer, 2025, pp. 113–122[10] M. Osman, J. He, N. Zhu, and F. M. M. Mokbal, “An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach,” Ad Hoc Netw., vol. 152, 103331, Oct. 2023[11] Mustafa Qahatan Alsudani, Salah H. Abbdal Reflish, Kohbalan Moorthy, and Myasar Mundher Adnan. 2023. A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system. Materials Today: Proceedings 80 (2023), [12] A. Chougule, R. Mane, K. Bhattacharjee, and S. Mehta, “Ensemble Learning Based Intrusion Detection System for RPL-Based IoT Networks,” in Demystifying AI and ML for Cyber-Threat Intelligence, A. Chougule and R. Mane, Eds. Cham: Springer, 2025, pp. 27–37, doi: 10.1007/978-3-031-90723-4_3.LeilaMansourifarSorooshGhazinoori911202621522710.66224/jict.52827.18.68.215http://jour.aicti.ir/fa/Article/52827http://jour.aicti.ir/fa/Article/Download/52827http://jour.aicti.ir/fa/Article/Download/52827http://jour.aicti.ir/fa/Article/Download/52827http://jour.aicti.ir/fa/Article/Download/52827http://jour.aicti.ir/fa/Article/Download/52827http://jour.aicti.ir/fa/Article/Download/52827http://jour.aicti.ir/fa/Article/Download/52827[1] D. Acemoglu, “The Simple Macroeconomics of AI”, NBER, 2024, WP.32487.[2] United Nations Environment Programme, “AI has an environmental problem. Here’s what the world can do about that,” 13/11/2025. [Online]. Available: https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about. [Accessed: Aug. 18, 2026].[3] K. M. Pratt, "9 Key Benefits of AI for Business", TechTarget, [Online], 05/12/2025. Available: https://www.techtarget.com/searchenterpriseai/feature/6-key-benefits-of-AI-for-business [Accessed: May. 5, 2026]. [4] Y. Qin, Z. Xu, X. Wang and M. Skare, "Artificial intelligence and economic development: An evolutionary investigation and systematic review". Journal of the Knowledge Economy, 2022, 15(1), 1736-70.[5] A. Widuto, “AI and the energy sector”, European Parliamentary Research Service, Tech. Rep, Jul. 2025.[6] D. D'Ambrosio, H. Hopewell, V. Jacamon, A. Martinos and N. Salmon, "Energy and AI", International Energy Agency (IEA), Tech. Rep. 2025. Available: https://www.iea.org/reports/energy-and-ai [Accessed: May. 3, 2026]. [7] L. Yee, M. Chui, R. Roberts, and S. Smit, “Technology Trends Outlook 2025”, McKinsey & Company, Tech. Rep. 2025.[8] M. Tadeo, “EU needs true single market to stay competitive, Nobel prize winner Aghion tells Euronews,” Euronews, Dec. 03, 2025. [Online]. Available: https://www.euronews.com/my-europe/2025/12/03/eu-needs-true-single-market-to-stay-competitive-nobel-prize-winner-aghion-tells-euronews, [Accessed: May. 22, 2026]. [9] M. Comunale and A. A. Manera, "The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions," IMF Working Papers 2024/065, International Monetary Fund, 2024.[10] C. Alonso C. A. Berg, S. Siddharth Kothari, CH. Papageorgiou, S. Rehman, "Will the AI revolution cause a great divergence?", Journal of Monetary Economics, 2022, Volume 127.[11] A. Nagamatsu, Y. Nagamatsu, CH. Watanabe, "The new paradigm of informal economies under GAI driven innovation", Telecom 2025, 6:39[12] M. Emami, A.A Haghdoost, V. Yazdi‑Feyzabadi and M. H. Mehrolhassani, "Drivers, uncertainties, and future scenarios of the Iranian health system", BMC Health Services Research, 2022, 22:1402.[13] A. Ahmadzadeh Ghasab, M. Heydari and M. Arab, "Foresight for Internationalization of Medical Sciences Universities in Iran in the 2040 horizon", European Journal of Futures Research, 2025, 13:2.[14] S. Ch. Pandey, N. Virmani, Dimple Bhandari and Sandeep Jagtap, "A sustainability-oriented KPI framework for digital twin adoption in the sugar industry: ISM–MICMAC approach", Journal of Agribusiness in Developing and Emerging Economies, 2025, e-ISSN: 2044-0847.[15] M. Soares, A. do Paço, A. Braga and A. Arantes, "Modelling the Barriers to Reverse Logistics for Sustainable Supply Chains: A Combined ISM and MICMAC Analysis Approach", Sustainability, 2025, 17:9375.[16] A. Agrawal, JS. Gans, A. Goldfarb A, "Exploring the impact of artificial intelligence: Prediction versus judgment",Information Economics and Policy". 2019 Jun 1;47:1-6.[17] S. Pramono, M. Ulkhaq and Z. Aulia, "Analysing the barriers of reverse logistics implementation: A case study", 2022, IOP Conf. Ser.: Mater. Sci. Eng. 1072 012063.[18] L. Yee, M. Chui, R. Roberts, and M. Issler, “Technology Trends Outlook 2024”, Tech. Rep. 2025. FahimehSalimi Kouchiabolghasemsarabadani911202622824910.66224/jict.52968.18.68.228http://jour.aicti.ir/fa/Article/52968http://jour.aicti.ir/fa/Article/Download/52968http://jour.aicti.ir/fa/Article/Download/52968http://jour.aicti.ir/fa/Article/Download/52968http://jour.aicti.ir/fa/Article/Download/52968http://jour.aicti.ir/fa/Article/Download/52968http://jour.aicti.ir/fa/Article/Download/52968http://jour.aicti.ir/fa/Article/Download/52968[1] O. Benfeldt and J. S. Persson, "Semiotic mediation in data governance: Towards valuing data as assets", Information and Organization, vol. 35, no. 3, p. 100588, 2025. doi: 10.1016/j.infoandorg.2025.100588.[2] R. Abraham, J. Schneider, and J. vom Brocke, "Data governance: A conceptual framework, structured review, and research agenda", International Journal of Information Management, vol. 49, pp. 424–438, 2019.[3] V. Khatri and C. V. Brown, "Designing data governance", Communications of the ACM, vol. 53, no. 1, p. 148, Jan. 2010. doi: 10.1145/1629175.1629210.[4] R. Mahanti, “Data Governance Success: Growing and Sustaining Data Governance”. Singapore: Springer Nature, 2021. doi: 10.1007/978-981-16-5086-4.[5] M. Johnston, "Are you prepared for your next data disaster?", Network World Asia, vol. 13, no. 2, 2016.[6] C. J. Sifter, "Establishing a data governance center of excellence within your bank", New Jersey Banker, pp. 24–25, 2017.[7] D. Paparova, M. Aanestad, P. Vassilakopoulou, and M. K. Bahus, "Data governance spaces: The case of a national digital service for personal health data", Information and Organization, vol. 33, no. 1, p. 100451, 2023.[8] J. Parra-Moyano and K. Schmedders, "What managers need to know about data exchanges", MIT Sloan Management Review, vol. 61, no. 4, pp. 39–44, 2020.[9] K. Bližnák, M. Munk, and A. Pilková, “A systematic review of recent literature on data governance (2017–2023),” IEEE Access, vol. 12, pp. 149875–149888, 2024, doi: 10.1109/ACCESS.2024.3476373.[10] DAMA International, “DAMA-DMBOK: Data Management Body of Knowledge”, 2nd ed. Basking Ridge, NJ, 2017.[11] Er. Kritika, "An overarching guide to data governance", in Creating and Sustaining an Information Governance Program, K. Helge and C. A. Rookey, Eds. IGI Global, 2024, pp. 283–307. doi: 10.4018/979-8-3693-0472-3.ch015.[12] D. Sargiotis, "Roles and responsibilities in data governance: Building an effective team", Data Governance: A Guide. Springer Nature Switzerland, 2024, pp. 279–303. doi: 10.1007/978-3-031-67268-2_8.[13] M. Jagals and E. Karger, “Inter-organizational data governance: A literature review,” in Proceedings of the 29th European Conference on Information Systems (ECIS 2021), 2021, Research Paper 57.[14] M. E. Kaminski and G. Malgieri, “Impacted stakeholder participation in AI and data governance,” Yale Journal of Law & Technology, vol. 27, pp. 247–326, 2025.[15] N. Chukwurah, A. B. Ige, C. Idemudia, and V. I. Adebayo, “Strategies for engaging stakeholders in data governance: Building effective communication and collaboration,” Open Access Research Journal of Multidisciplinary Studies, vol. 8, no. 1, pp. 57–67, 2024, doi: 10.53022/oarjms.2024.8.1.0045.[16] S. Rosenbaum, "Data governance and stewardship: Designing data stewardship entities and advancing data access", Health Services Research, vol. 45, no. 5, pp. 1442–1455, 2010.[17] National Institute of Standards and Technology, “NIST Big Data Interoperability Framework: Volume 1 – Definitions”, NIST, USA, 2015.[18] B. Elyasi, M. Hasaninasab, F. Salimi-Kouchi, and M. Hosseinpour, “Data Governance: Foundations for Implementing Data Governance” [in Persian]. Tehran, Iran: Avaye Ghalam, 2020.[19] R. Lebaea, Y. Roshe, S. Ntontela, and B. A. Thango-Mabizela, "The role of data governance in ensuring system success and long-term IT performance: A systematic review", Preprints.org, 2024. doi: 10.20944/preprints202410.1841.v1.[20] Project Management Institute, “A Guide to the Project Management Body of Knowledge (PMBOK Guide)”, 6th ed. Newtown Square, PA, USA: PMI, 2017.[21] R. R. Koilakonda, "Implementing data governance frameworks for enhanced decision making", International Journal of Science and Research, vol. 13, no. 6, pp. 1239–1243, 2024. doi: 10.21275/SR24618105346.[22] M. de Mildt, S. Van Damme, S. Verbrugge, and D. Colle, “A conceptual deep dive into data ecosystems: A systematic literature review,” Information Systems and e-Business Management, 2026, doi: 10.1007/s10257-026-00719-8.[23] D. Lis and B. Otto, “Data governance in data ecosystems—Insights from organizations,” in Proceedings of the 26th Americas Conference on Information Systems (AMCIS 2020), 2020, Paper 12.[24] D. Lis and B. Otto, “Towards a taxonomy of ecosystem data governance,” in Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS), 2021, pp. 6067–6076, doi: 10.24251/HICSS.2021.733.[25] C. A. Bassi and S. N. Alves-Souza, "Challenges to implementing effective data governance: A literature review", In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023), vol. 3, 2023, pp. 17–28.[26] C. Tang, “The Data Industry: The Business and Economics of Information and Big Data”. Hoboken, NJ, USA: Wiley, 2016.[27] T. van den Broek and A. F. van Veenstra, “Modes of governance in inter-organizational data collaborations,” in Proceedings of the 23rd European Conference on Information Systems (ECIS 2015), 2015, Paper 188.[28] A. Mendelow, "Stakeholder mapping", Proceedings of the 2nd International Conference on Information Systems, Cambridge, MA, USA, 1991.[29] J. M. Bryson, "What to do when stakeholders matter: Stakeholder identification and analysis techniques", Public Management Review, vol. 6, no. 1, pp. 21–53, 2004.[30] R. K. Mitchell, B. R. Agle, and D. J. Wood, "Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts," Academy of Management Review, vol. 22, no. 4, pp. 853–886, 1997.[31] A. Ahmadi, R. Kerachian, R. Rahimi, and M. J. E. Skardi, "Comparing and combining social network analysis and stakeholder analysis for natural resource governance", Environmental Development, vol. 32, p. 100451, 2019.[32] J. J. Proksik, F. Brugger, H. Cossa, L. Chavane & E. Macete, "Using Q-methodology for policy research and stakeholder engagement to strengthen public health in large-scale mining in Mozambique," Sustainable Development, vol. 33, pp. 1298–1314, 2025. doi: 10.1002/sd.3175. DelaramKianiHaniehNaderi911202617119710.66224/jict.53399.18.68.171http://jour.aicti.ir/fa/Article/53399http://jour.aicti.ir/fa/Article/Download/53399http://jour.aicti.ir/fa/Article/Download/53399http://jour.aicti.ir/fa/Article/Download/53399http://jour.aicti.ir/fa/Article/Download/53399http://jour.aicti.ir/fa/Article/Download/53399http://jour.aicti.ir/fa/Article/Download/53399http://jour.aicti.ir/fa/Article/Download/53399[1] T. Guan, F. Liu, X. Wu, R. Xian, Z. Li, X. Liu, X. Wang, L. Chen, F. Huang, Y. Yacoob, D. Manocha, and T. Zhou, “HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models,” Mar. 25, 2024, arXiv:2310.14566. doi: 10.48550/arXiv.2310.14566.[2] Y. Li, Y. Du, K. Zhou, J. Wang, W. X. Zhao, and J.-R. Wen, “Evaluating Object Hallucination in Large Vision-Language Models,” Oct. 26, 2023, arXiv: arXiv:2305.10355. doi: 10.48550/arXiv.2305.10355.[3] A. Seth, D. Manocha, and C. Agarwal, “Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models,” Mar. 13, 2025, arXiv: arXiv:2412.20622. doi: 10.48550/arXiv.2412.20622.[4] Z. Liu, Y. Nie, Y. Tan, X. Yue, Q. Cui, C. Wang, X. Zhu, and B. Zheng, “Safety Alignment for Vision Language Models,” May 22, 2024, arXiv:2405.13581. doi: 10.48550/arXiv.2405.13581.[5] W. Luo, S. Ma, X. Liu, X. Guo, and C. Xiao, “JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks,” Nov. 24, 2024, arXiv: arXiv:2404.03027. doi: 10.48550/arXiv.2404.03027.[6] Z. Yang, J. Fan, A. Yan, E. Gao, X. Lin, T. Li, K. Mo, and C. Dong, “Distraction is All You Need for Multimodal Large Language Model Jailbreaking,” Jun. 17, 2025, arXiv:2502.10794. doi: 10.48550/arXiv.2502.10794.[7] C. Jiang, H. Jia, W. Ye, M. Dong, H. Xu, M. Yan, J. Zhang, and S. Zhang, “Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models,” in Proceedings of the 32nd ACM International Conference on Multimedia, Melbourne, VIC, Australia: ACM, Oct. 2024, pp. 525–534. doi: 10.1145/3664647.3680576.[8] Q. Cao, J. Cheng, X. Liang, and L. Lin, “VisDiaHalBench: A Visual Dialogue Benchmark For Diagnosing Hallucination in Large Vision-Language Models,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Bangkok, Thailand: Association for Computational Linguistics, 2024, pp. 12161–12176. doi: 10.18653/v1/2024.acl-long.658.[9] S. Schrodi, D. T. Hoffmann, M. Argus, V. Fischer, and T. Brox, “Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models,” Apr. 16, 2025, arXiv: arXiv:2404.07983. doi: 10.48550/arXiv.2404.07983.[10] S. Yin, C. Fu, S. Zhao, T. Xu, H. Wang, D. Sui, Y. Shen, K. Li, X. Sun, and E. Chen, “Woodpecker: Hallucination Correction for Multimodal Large Language Models,” Science China Information Sciences, vol. 67, no. 12, p. 220105, Dec. 2024. doi: 10.1007/s11432-024-4251-x.[11] V. Rawte, A. Mishra, A. Sheth, and A. Das, “Defining and quantifying visual hallucinations in vision-language models,” in Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025), 2025, pp. 501–510.[12] W. Liang, Y. Zhang, Y. Kwon, S. Yeung, and J. Zou, “Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning,” Oct. 19, 2022, arXiv: arXiv:2203.02053. doi: 10.48550/arXiv.2203.02053.[13] C. Yi, Y.-H. He, D.-C. Zhan, and H.-J. Ye, “Bridge the Modality and Capability Gaps in Vision-Language Model Selection,” May 18, 2025, arXiv: arXiv:2403.13797. doi: 10.48550/arXiv.2403.13797.[14] X. Liu, Y. Zhu, J. Gu, Y. Lan, C. Yang, and Y. Qiao, “MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models,” Jun. 19, 2024, arXiv: arXiv:2311.17600. doi: 10.48550/arXiv.2311.17600.[15] Z. Liu, Y. Nie, Y. Tan, J. Liu, X. Yue, Q. Cui, C. Wang, X. Zhu, and B. Zheng, “PSA-VLM: Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment,” 2024, arXiv:2411.11543. doi: 10.48550/arXiv.2411.11543.[16] L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, and T. Liu, “A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions,” 2023, arXiv:2311.05232. doi: 10.48550/arXiv.2311.05232.[17] Z. Bai, P. Wang, T. Xiao, T. He, Z. Han, Z. Zhang, and M. Z. Shou, “Hallucination of Multimodal Large Language Models: A Survey,” Apr. 01, 2025, arXiv:2404.18930. doi: 10.48550/arXiv.2404.18930.[18] Z. Yang, X. Luo, D. Han, Y. Xu, and D. Li, “Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key,” 2025, arXiv. doi: 10.48550/ARXIV.2501.09695.[19] P. Kaul, Z. Li, H. Yang, Y. Dukler, A. Swaminathan, C. J. Taylor, and S. Soatto, “THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models,” Apr. 03, 2025, arXiv:2405.05256. doi: 10.48550/arXiv.2405.05256.[20] X. Fan, X. Wang, H. Wei, X. Zhang, and D. Zhao, “Toward a Stable, Fair, and Comprehensive Evaluation of Object Hallucination in Large Vision-Language Models,” in Advances in Neural Information Processing Systems 37, Vancouver, BC, Canada: Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2024, pp. 111406–111431. doi: 10.52202/079017-3538.[21] M. Ye-Bin, N. Hyeon-Woo, W. Choi, and T.-H. Oh, “BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models,” Jul. 18, 2024, arXiv: arXiv:2407.13442. doi: 10.48550/arXiv.2407.13442.[22] M. Ye, X. Rong, W. Huang, B. Du, N. Yu, and D. Tao, “A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations,” Feb. 14, 2025, arXiv: arXiv:2502.14881. doi: 10.48550/arXiv.2502.14881.[23] E. Shayegani, Y. Dong, and N. Abu-Ghazaleh, “Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models,” Oct. 10, 2023, arXiv: arXiv:2307.14539. doi: 10.48550/arXiv.2307.14539.[24] W. Hu, S. Gu, Y. Wang, and R. Hong, “VideoJail: Exploiting Video-Modality Vulnerabilities for Jailbreak Attacks on Multimodal Large Language Models,” presented at the ICLR 2025 Workshop on Building Trust in Language Models and Applications, Mar. 2025. Accessed: Feb. 03, 2026. [Online]. Available: https://openreview.net/forum?id=fSAIDcPduZ[25] H. Zhong, Q. Teng, B. Zheng, G. Chen, Y. Tan, Z. Liu, J. Liu, W. Su, X. Zhu, B. Zheng, and K. Zhang, “Towards Visualization-of-Thought Jailbreak Attack against Large Visual Language Models,” presented at the Thirty-ninth Annual Conference on Neural Information Processing Systems, Oct. 2025. Accessed: Feb. 03, 2026. [Online]. Available: https://openreview.net/forum?id=5P5YgohyBZ.[26] S. S. Ghosal, S. Chakraborty, V. Singh, T. Guan, M. Wang, A. Velasquez, A. Beirami, F. Huang, D. Manocha, and A. S. Bedi, “Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment,” Jun. 14, 2025, arXiv:2411.18688. doi: 10.48550/arXiv.2411.18688.[27] B. Chen, X. Lyu, L. Gao, J. Song, and H. T. Shen, “SafePTR: Token-Level Jailbreak Defense in Multimodal LLMs via Prune-then-Restore Mechanism,” Dec. 03, 2025, arXiv: arXiv:2507.01513. doi: 10.48550/arXiv.2507.01513.[28] W. You, Z. Li, H. Guo, X. Liu, W. Wang, X. Dong, and G. Wang, “MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attacks,” Mar. 24, 2025, arXiv:2503.19134. doi: 10.48550/arXiv.2503.19134.[29] L. Zhu, D. Ji, T. Chen, P. Xu, J. Ye, and J. Liu, “IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding,” 2024, arXiv:2402.18476. doi: 10.48550/arXiv.2402.18476.[30] Y. Shu, H. Lin, Y. Liu, Y. Zhang, G. Zeng, Y. Li, Y. Zhou, S.-N. Lim, H. Yang, and N. Sebe, “When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding,” Oct. 07, 2025, arXiv:2506.05551. doi: 10.48550/arXiv.2506.05551.[31] X. Liu, M. Luo, A. Chatterjee, H. Wei, C. Baral, and Y. Yang, “Investigating VLM Hallucination from a Cognitive Psychology Perspective: A First Step Toward Interpretation with Intriguing Observations,” arXiv preprint arXiv:2507.03123, 2025.[32] E. Shayegani, Y. Dong, and N. Abu-Ghazaleh, “Jailbreak in Pieces: Compositional Adversarial Attacks on Multi-Modal Language Models,” Oct. 10, 2023, arXiv:2307.14539. doi: 10.48550/arXiv.2307.14539.[33] L. He, Z. Chen, Z. Shi, T. Yu, J. Shao, and L. Sheng, “Systematic Reward Gap Optimization for Mitigating VLM Hallucinations,” Nov. 24, 2025, arXiv:2411.17265. doi: 10.48550/arXiv.2411.17265.[34] X. Lyu, B. Chen, L. Gao, J. Song, and H. T. Shen, “Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization,” Apr. 01, 2025, arXiv: arXiv:2405.15356. doi: 10.48550/arXiv.2405.15356.[35] Y. Shu, H. Lin, Y. Liu, Y. Zhang, G. Zeng, Y. Li, Y. Zhou, S.-N. Lim, H. Yang, and N. Sebe, “TextHalu-Bench and Semantic Hallucination Mitigation for Scene Text Understanding in Large Multimodal Models,” 2025, arXiv:2506.05551. doi: 10.48550/arXiv.2506.05551.[36] Z. Sun, S. Shen, S. Cao, H. Liu, C. Li, Y. Shen, C. Gan, L.-Y. Gui, Y.-X. Wang, Y. Yang, K. Keutzer, and T. Darrell, “Aligning Large Multimodal Models with Factually Augmented RLHF,” Sep. 25, 2023, arXiv:2309.14525. doi: 10.48550/arXiv.2309.14525.[37] Y. Xie, G. Li, X. Xu, and M.-Y. Kan, “V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization,” Nov. 05, 2024, arXiv: arXiv:2411.02712. doi: 10.48550/arXiv.2411.02712.[38] Z. Yang, X. Luo, D. Han, Y. Xu, and D. Li, “Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key,” Mar. 03, 2025, arXiv: arXiv:2501.09695. doi: 10.48550/arXiv.2501.09695.[39] N. Jiang, A. Kachinthaya, S. Petryk, and Y. Gandelsman, “Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations,” Feb. 10, 2025, arXiv: arXiv:2410.02762. doi: 10.48550/arXiv.2410.02762.[40] X. Chen, Z. Ma, X. Zhang, S. Xu, S. Qian, J. Yang, D. F. Fouhey, and J. Chai, “Multi-Object Hallucination in Vision-Language Models,” Oct. 31, 2024, arXiv:2407.06192. doi: 10.48550/arXiv.2407.06192. Seyed Mehdi HosseiniMohammad Ali PourminaAhmadFakharianMehdi Khatir9112026859910.66224/jict.53541.18.68.85http://jour.aicti.ir/fa/Article/53541http://jour.aicti.ir/fa/Article/Download/53541http://jour.aicti.ir/fa/Article/Download/53541http://jour.aicti.ir/fa/Article/Download/53541http://jour.aicti.ir/fa/Article/Download/53541http://jour.aicti.ir/fa/Article/Download/53541http://jour.aicti.ir/fa/Article/Download/53541http://jour.aicti.ir/fa/Article/Download/53541[1] D Kaur, A Anwar, I Kamwa, S Islam, SM Muyeen. "A Bayesian deep learning approach with convolutional feature engineering to discriminate cyber-physical intrusions in smart grid systems", - IEEE, 2023 - ieeexplore.ieee.org[2] F. Brancati, D. Mongelli, F. Mariotti P. Lollini. "A cybersecurity risk assessment methodology for industrial automation control systems". volume 24, article number 76, (2025).[3] C. Alcaraz and S. Zeadally, "Critical infrastructure protection: Requirements and challenges for the 21st century," International Journal of Critical Infrastructure Protection, vol. 8, pp. 53-66, 2015.[4] National Institute of Standards and Technology (NIST), "Guide to industrial control systems security," NIST Special Publication 800-82 Rev. 3, 2011.[5] J. P. Farwell and R. Rohozinski, "Stuxnet and the future of cyber war," Survival, vol. 53, no. 1, pp. 23-40, 2011.[6] R. M. Lee, M. J. Assante, and T. Conway, "Analysis of the cyber attack on the Ukrainian power grid," Electricity Information Sharing and Analysis Center (E-ISAC), 2016.[7] C. Krauss, "Colonial Pipeline hack reveals critical infrastructure vulnerabilities," The New York Times, May 13, 2021.[8] National Institute of Standards and Technology (NIST), "Guide for conducting risk assessments," NIST Special Publication 800-30 Rev. 1, 2012.[9] Y. Cherdantseva, P. Burnap, A. Blyth, P. Eden, K. Jones, H. Soulsby, and K. Stoddart, "A review of cyber security risk assessment methods for SCADA systems," Computers & Security, vol. 56, pp. 1-27, 2016.[10] W. Wang, Z. Lu, and T. Chen, "Cybersecurity challenges in industrial control systems," International Journal of Critical Infrastructure Protection, vol. 9, pp. 52-80, 2015.[11] Bhamare, D., Zolanvari, M., Erbad, A., Jain, R., Khan, K. and Meskin, N., 2020. Cybersecurity for industrial control systems: A survey. computers & security, 89, p.101677. [12] International Organization for Standardization (ISO/IEC), "Information technology -- Security techniques -- Methodology for IT security evaluation," ISO/IEC Standard 18045, 2022.[13] Hu, C.L., Wang, L., Chen, M.L. and Pei, C., 2024. A real-time interactive decision-making and control framework for complex cyber-physical-human systems. Annual Reviews in Control, 57, p.100938.[14] Patel, R., 2023. Automated Threat Detection and Risk Mitigation for ICS (Industrial Control Systems) Employing Deep Learning in Cybersecurity Defence. Int. J. Curr. Eng. Technol, 13(06), pp.584-591. [15] Ali, B.S., Ullah, I., Al Shloul, T., Khan, I.A., Khan, I., Ghadi, Y.Y., Abdusalomov, A., Nasimov, R., Ouahada, K. and Hamam, H., 2024. ICS-IDS: application of big data analysis in AI-based intrusion detection systems to identify cyberattacks in ICS networks. The Journal of Supercomputing, 80(6), pp.7876-7905.[16] Barua, S., Gao, X., Pasman, H. and Mannan, M.S., 2016. Bayesian network based dynamic operational risk assessment. Journal of Loss Prevention in the Process Industries, 41, pp.399-410.[17] Almaiah, M.A., Yelisetti, S., Arya, L., Babu Christopher, N.K., Kaliappan, K., Vellaisamy, P., Hajjej, F. and Alkdour, T., 2023. A novel approach for improving the security of IoT–medical data systems using an enhanced dynamic Bayesian network. Electronics, 12(20), p.4316.[18] Abdulhamid, A., Kabir, S., Ghafir, I. and Lei, C., 2024, January. Reliability Assessment of IoT-enabled Systems using Fault Trees and Bayesian Networks. In International Conference on Advances in Distributed Computing and Machine Learning (pp. 267-277). Singapore: Springer Nature Singapore.MehrshadGoljarianVaniaShayestehfarMohammad MahdiMoeini MaeneshAliFotowat-Ahmady911202614815610.66224/jict.53559.18.68.148http://jour.aicti.ir/fa/Article/53559http://jour.aicti.ir/fa/Article/Download/53559http://jour.aicti.ir/fa/Article/Download/53559http://jour.aicti.ir/fa/Article/Download/53559http://jour.aicti.ir/fa/Article/Download/53559http://jour.aicti.ir/fa/Article/Download/53559http://jour.aicti.ir/fa/Article/Download/53559http://jour.aicti.ir/fa/Article/Download/53559[1] M. A. Scrugli, D. Loi, L. Raffo, and P. Meloni, “A runtime-adaptive cognitive iot node for healthcare monitoring,” in Proceedings of the 16th ACM International Conference on Computing Frontiers, ser. CF ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 350–357. [Online]. Available: https://doi.org/10.1145/3310273.3323160[2] M. S. Thaler, The only EKG book you’ll ever need. Wolters Kluwer Health/Lippincott Williams et Wilkins, 2012.[3] U. B. Baloglu, M. Talo, O. Yildirim, R. S. Tan, and U. R. Acharya, “Classification of myocardial infarction with multi-lead ecg signals and deep cnn,” Pattern Recognition Letters, vol. 122, pp. 23–30, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S016786551930056X[4] Özal Yildirim, “A novel wavelet sequence based on deep bidirectional lstm network model for ecg signal classification,” Computers in Biology and Medicine, vol. 96, pp. 189–202, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0010482518300738[5] S. Mehta and A. Aneja, “Enhanced ecg signal analysis using hybrid cnn-lstm and augmentation techniques,” in 2025 International Conference on Automation and Computation (AUTOCOM), 2025, pp. 1657–1661.[6] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.[7] A. Natarajan, Y. Chang, S. Mariani, A. Rahman, G. Boverman, S. Vij, and J. Rubin, “A wide and deep transformer neural network for 12-lead ecg classification,” in 2020 Computing in Cardiology, 2020, pp. 1–4.[8] S. Osowski and T. H. Linh, “Ecg beat recognition using fuzzy hybrid neural network,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 11, pp. 1265–1271, 2001.[9] S. Tan, S. Ortiz-Gagné, N. Beaudoin-Gagnon, P. Fecteau, A. Courville, Y. Bengio, and J. P. Cohen, “Icentia11k Single Lead Continuous Raw Electrocardiogram Dataset,” PhysioNet, Apr. 2022, version 1.0. [Online]. Available: https://doi.org/10.13026/kk0v-r952[10] F. Khodami, “Investigating the effect of motion artifacts on the impedance between chest electrodes and skin,” M.S. thesis, Sharif University of Technology, Tehran, Iran, 2021.[11] A. Zhang, X. Yang, T. Li, M. Dou, and H. Yang, “Classification method of ecg signals based on ranet,” Cardiovascular Engineering and Technology, vol. 15, no. 5, pp. 561–571, Oct 2024. [Online]. Available: https://doi.org/10.1007/s13239-024-00730-5[12] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no. 56, pp. 1929–1958, 2014. [Online]. Available: http://jmlr.org/papers/v15/srivastava14a.html[13] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2017. [Online]. Available: https://arxiv.org/abs/1412.6980[14] J. Johnson and T. Khoshgoftaar, “Survey on deep learning with class imbalance,” Journal of Big Data, vol. 6, p. 27, 03 2019.[15] J. Pan and W. J. Tompkins, “A real-time qrs detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. BME-32, no. 3, pp. 230–236, 1985.[16] V. Tihonenko, A. Khaustov, S. Ivanov, and A. Rivin, “St.-petersburg institute of cardiological technics 12-lead arrhythmia database,” 2007. [Online]. Available: https://physionet.org/content/incartdb/[17] G. Moody and R. Mark, “The impact of the mit-bih arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001.[18] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “The mit-bih long term database,” 1992. [Online]. Available: https://physionet.org/content/ltdb/[19] M. Kapsecker, M. C. Möller, and S. M. Jonas, “Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder,” Computers in Biology and Medicine, vol. 184, p. 109422, Jan. 2025. [Online]. Available: http://dx.doi.org/10.1016/j.compbiomed.2024.109422[20] H. Hamami, Y. Solewicz, D. Zur, Y. Kleerekoper, and J. A. Behar, “upvc-net: A universal premature ventricular contraction detection deep learning algorithm,” IEEE Transactions on Biomedical Engineering, p. 1–10, 2026. [Online]. Available: http://dx.doi.org/10.1109/TBME.2026.3655531[21] M. Kapsecker and S. M. Jonas, “Cross-device federated unsupervised learning for the detection of anomalies in single-lead electrocardiogram signals,” PLOS Digital Health, vol. 4, no. 4, p. e0000793, Apr. 2025. [Online]. Available: http://dx.doi.org/10.1371/journal.pdig.0000793[22] K. A. Purtova, V. A. Moskalenko, L. A. Smirnov, G. V. Osipov, and N. Y. Zolotykh, “Problem analysis of the experiment formulation correctness in the task of ecg diagnostics using machine learning methods,” SSRN preprint, no. 4947212, 2024.[23] K. Tateno and L. Glass, “A method for detection of atrial fibrillation using rr intervals,” in Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163), 2000, pp. 391–394MonaJami PourElaheKhatibi Noori911202619821410.66224/jict.53813.18.68.198http://jour.aicti.ir/fa/Article/53813http://jour.aicti.ir/fa/Article/Download/53813http://jour.aicti.ir/fa/Article/Download/53813http://jour.aicti.ir/fa/Article/Download/53813http://jour.aicti.ir/fa/Article/Download/53813http://jour.aicti.ir/fa/Article/Download/53813http://jour.aicti.ir/fa/Article/Download/53813http://jour.aicti.ir/fa/Article/Download/53813[1] Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005.[2] Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2025). Integrating AI and learning analytics for data-driven pedagogical decisions and personalized interventions in education. Technology, knowledge and learning, 1-31.[3] Birgün, M. (2026). Integrating AI into Qur'an learning: Technical advances and pedagogical gaps. Social Sciences & Humanities Open, 13, 102499.[4] کاظمی راد، ام البنین، جامی پور، مونا و فقیهی، فاطمه، " واکاوی نقش هوش مصنوعی در حوزه حقوق: رویکردی اکتشافی"، دوفصلنامه فناوری اطلاعات و ارتباطات ایران، دوره 17، شماره 63، صص 1-18. [5] Wang, T., & Cheng, E. C. K. (2021). An investigation of barriers to Hong Kong K-12 schools incorporating Artificial Intelligence in education. Computers and Education: Artificial Intelligence, 2, 100031.[6] Shaikh, A. A., Kumar, A., Jani, K., Mitra, S., García-Tadeo, D. A., & Devarajan, A. (2022). The role of machine learning and artificial intelligence for making a digital classroom and its sustainable impact on education during COVID-19. Materials Today: Proceedings, 56, 3211-3215.[7] Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International journal of educational technology in higher education, 18(1), 54.[8] Demandsage. (2026). 77 AI in Education Statistics 2026 (Global Trends & Facts), Available at: https://www.demandsage.com/ai-in-education-statistics/ (Accessed 17 April 2026). [9] Hu, C., Li, F., Wang, S., Gao, Z., Pan, S., & Qing, M. (2025). The role of artificial intelligence in enhancing personalized learning pathways and clinical training in dental education. Cogent Education, 12(1), 2490425.[10] Hadyaoui, A., & Cheniti-Belcadhi, L. (2026). Scalable adaptive assessment framework for collaborative eLearning: a service-oriented AI approach. Smart Learning Environments, 13(1), 8.[11] Yetik, S. S. (2025). The Impact of Artificial Intelligence on University Teaching Processes: An Analysis Based on Faculty Perspectives. Kastamonu Education Journal, 33(3), 448-468. [12] Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049. [13] Hassan, A. (2026). Leveraging artificial intelligence (AI) to enhance student engagement and academic performance in higher education. Education and Information Technologies, 1-27.[14] Wang, Q. (2026). Construction and Empirical Analysis of an Artificial Intelligence-Based Educational Assessment Model. International Journal of Information and Communication Technology Education (IJICTE), 22(1), 1-16.[15] Tlili, A., Saqer, K., Salha, S., & Huang, R. (2025). Investigating the effect of artificial intelligence in education (AIEd) on learning achievement: A meta-analysis and research synthesis. Information Development, 41(3), 825-842.[16] Kassenkhan, A. M., Moldagulova, A. N., & Serbin, V. V. (2025). Gamification and artificial intelligence in education: A review of innovative approaches to fostering critical thinking. IEEE Access.[17] Trujillo, F., Pozo, M., & Suntaxi, G. (2025). Artificial Intelligence in Education: A Systematic Literature Review of Machine Learning Approaches in Student Career Prediction. Journal of Technology and Science Education, 15(1), 162-185.[18] Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84.[19] Saputra, I., Astuti, M., Sayuti, M., & Kusumastuti, D. (2023). Integration of Artificial Intelligence in Education: Opportunities, Challenges, Threats and Obstacles. A Literature Review. The Indonesian journal of computer science, 12(4).[20] Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International journal of educational technology in higher education, 16(1), 39.[21] Liang, J., Stephens, J. M., & Brown, G. T. (2025, April). A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment. In Frontiers in Education (Vol. 10, p. 1522841). Frontiers Media SA.[22] Ait Baha, T., El Hajji, M., Es-Saady, Y., & Fadili, H. (2024). The impact of educational chatbot on student learning experience. Education and Information Technologies, 29(8), 10153-10176.[23] Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161-182.[24] Su, J., & Zhong, Y. (2022). Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Computers and Education: Artificial Intelligence, 3, 100072.Abduljabbar, I. A., & Abdullah, S. M. (2022). An evolutionary algorithm for solving academic courses timetable scheduling problem. Baghdad Science Journal, 19(2), 11.[25] Marrella, D., Anttila, T., Ryhänen, J., Miller, R., Liu, B., & Liverneaux, P. (2026). The balance between artificial and human intelligence in clinical practice. Journal of Hand Surgery (European Volume), 51(6), 685-697.[26] Bešinović, N., De Donato, L., Flammini, F., Goverde, R. M., Lin, Z., Liu, R., ... & Vittorini, V. (2021). Artificial intelligence in railway transport: Taxonomy, regulations, and applications. IEEE Transactions on Intelligent Transportation Systems, 23(9), 14011-14024.[27] Goel, A. K., & Polepeddi, L. (2018). Jill Watson. Learning engineering for online education: Theoretical contexts and design-based examples. Routledge.[28] Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial intelligence in education: AIEd for personalised learning pathways. Electronic Journal of e-learning, 20(5), 639-653.[29] Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1(3), 0028.[30] Murphy, R. F. (2019). Artificial intelligence applications to support K-12 teachers and teaching. Rand Corporation, 10(1), 1-20.[31] Karamuk, E. (2025). The automation trap: Unpacking the consequences of over-reliance on AI in education and its hidden costs. In Pitfalls of AI integration in education: Skill obsolescence, misuse, and bias (pp. 147-170). IGI Global Scientific Publishing.[32] George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196.Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167.[33] Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167.[34] Lima, G. D. O., Costa, J. A., Dorça, F. A., & Araújo, R. D. (2026). An AI‐Supported Pedagogical Architecture to Foster Self‐Regulated Learning in Virtual Environments. Computer Applications in Engineering Education, 34(1), e70118.[35] Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54(5), 977-997.[36] Li, X., & Jiang, Y. (2024). Artificial intelligence in education: Opportunities, current status, and prospects. Geographical Research Bulletin, 3, 146-174.[37] Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000366994.pdf (Accessed 25 June 2026).[38] Yurt, E., & Kasarci, I. (2024). A Questionnaire of Artificial Intelligence Use Motives: A contribution to investigating the connection between AI and motivation. International Journal of Technology in Education, 7(2).[39] Tan, X., Cheng, G., & Ling, M. H. (2025). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers and Education: Artificial Intelligence, 8, 100355.[40] Liang, J., Wang, L., Luo, J., Yan, Y., & Fan, C. (2023). The relationship between student interaction with generative artificial intelligence and learning achievement: serial mediating roles of self-efficacy and cognitive engagement. Frontiers in psychology, 14, 1285392.[41] Liu, X., & Zhong, B. (2025). Integrating generative Artificial Intelligence into student learning: A systematic review from a TPACK perspective. Educational Research Review, 100741.[42] Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia journal of mathematics, science and technology education, 19(8), em2307.[43] Dong, L., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers and Education: Artificial Intelligence, 8, 100400.[44] Hwang, S. (2022). Examining the effects of artificial intelligence on elementary students’ mathematics achievement: A meta-analysis. Sustainability, 14(20), 13185.[45] Oran, B. B. (2023). Correlation between artificial intelligence in education and teacher self-efficacy beliefs: A review. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, (34), 1354-1365.[46] Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social science & medicine, 292, 114523.[47] Castro-Schez, J. J., Glez-Morcillo, C., Albusac, J., & Vallejo, D. (2021). An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning. Information Sciences, 544, 446-468.[48] Jin, S. H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1), 37.[49] Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied sciences, 13(5), 3056.[50] Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068.[51] Embarak, O. H. (2022). Internet of Behaviour (IoB)-based AI models for personalized smart education systems. Procedia Computer Science, 203, 103-110.[52] Muangprathub, J., Boonjing, V., & Chamnongthai, K. (2020). Learning recommendation with formal concept analysis for intelligent tutoring system. Heliyon, 6(10).[53] Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., ... & Gašević, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3, 100075.[54] Qin, Y., Huang, Z., Yu, J., Qing, P., Lui, S., Liu, R., ... & Hu, N. (2022). Practice-based learning using smart class: a competency-based model in undergraduate radiology education. Academic Radiology, 29(1), 150-157.[55] Abduljabbar, I. A., & Abdullah, S. M. (2022). An evolutionary algorithm for solving academic courses timetable scheduling problem. Baghdad Science Journal, 19(2), 11.[56] Ghaffar, A., Din, I. U., Tariq, A., & Zafar, M. H. (2025). Hybridization and artificial intelligence in optimizing university examination timetabling problem: A systematic review. Review of Education, 13(2), e70071.[57] Gao, P. P., Nagel, A., & Biedermann, H. (2020). Categorization of Educational Technologies as Related to. Pedagogy in basic and higher education: Current developments and challenges, 167.[58] Yim, I. H. Y., & Su, J. (2025). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 12(1), 93-131.[59] Bh, M. P., Yellapurkar, S., Chengappa, K., & Pentapati, K. C. (2026). Personalized Learning With Artificial Intelligence in Dental Education: An Integrative Review. International Journal of Dentistry, 2026(1), 8928166.[60] Amin, M. R. M., Ismail, I., & Sivakumaran, V. M. (2025). Revolutionizing education with artificial intelligence (AI)? Challenges, and implications for open and distance learning (ODL). Social Sciences & Humanities Open, 11, 101308.[61] Katalinic, A., Slavuj, V., & Jaksic, D. (2026). Artificial Intelligence in online education: A systematic review of its impact on learner engagement and satisfaction. Education sciences, 16(3), 389.[62] Brada, A., & Dahmani, F. (2024). Artificial intelligence technologies and their significance in enhancing the quality of adaptive e-learning. Journal of Science and Knowledge Horizons, 4(02), 30-47.[63] Ahmed, S., Rahman, M. S., Kaiser, M. S., & Hosen, A. S. (2025). Advancing personalized and inclusive education for students with disability through artificial intelligence: perspectives, challenges, and opportunities. Digital, 5(2), 11.