jist-202605192320260519232533CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-143711220251351Md. ObaidurRahamanMohammod AbulKashemSovonChakrabortyShakib MahmudDipto112202523224210.61882/jist.47968.13.51.232http://jist.ir/en/Article/47968http://jist.ir/en/Article/Download/47968http://jist.ir/en/Article/Download/47968http://jist.ir/en/Article/Download/47968http://jist.ir/en/Article/Download/47968http://jist.ir/en/Article/Download/47968http://jist.ir/en/Article/Download/47968http://jist.ir/en/Article/Download/47968[1] J. Heaney, J. Buick, M. U. Hadi, and N. Soin, “Internet of Things-based ECG and vitals healthcare monitoring system,” Micromachines, vol. 13, no. 12, p. 2153, Dec. 2022, https://doi.org/10.3390/mi13122153. [2] J.-T. Huang, J. Zhang, W. Wang, P. He, Y. Su, and M. R. Lyu, “AEON: A method for Automatic evaluation of NLP test cases,” arXiv (Cornell University), Jan. 2022, doi: 10.48550/arxiv.2205.06439. [3] Md. S. Azam, Md. A. Raihan, and H. K. Rana, “An experimental study of various machine learning approaches in heart disease prediction,” International Journal of Computer Applications, vol. 175, no. 21, pp. 16–21, Sep. 2020, doi: 10.5120/ijca2020920741. [4] X. Bao and Y. Deng, “Processing of Cardiac Signals for Health Monitoring and Early Detection of Heart Diseases,” Ph.D. dissertation, King’s College London, 2023. [5] Sunny, Jithin S., C. Pawan K. Patro, Khushi Karnani, Sandeep C. Pingle, Feng Lin, Misa Anekoji, Lawrence D. Jones, Santosh Kesari, and Shashaanka Ashili. "Anomaly detection framework for wearables data: A perspective review on data concepts, data analysis algorithms and prospects." Sensors 22, no. 3 (2022): 756, https://doi.org/10.3390/s22030756. [6] A. Belhani, H. Semira, R. Kheddara, and G. Hassis, “Implementation of uplink and downlink non-orthogonal multiple access (NOMA) on Zynq FPGA device,” Journal of Information Systems and Telecommunication (JIST), vol. 4, no. 44, p. 269, 2023. [7] M. A. Serhani, H. T. E. Kassabi, H. Ismail, and A. N. Navaz, “ECG Monitoring Systems: review, architecture, processes, and key challenges,” Sensors, vol. 20, no. 6, p. 1796, Mar. 2020, doi: 10.3390/s20061796. [8] V. Vijayan, J. P. Connolly, J. Condell, N. McKelvey, and P. Gardiner, “Review of Wearable Devices and data collection Considerations for connected Health,” Sensors, vol. 21, no. 16, p. 5589, Aug. 2021, doi: 10.3390/s21165589. [9] J. S and C. M. B. M. J, “Convolutional Neural Networks for Medical Image Segmentation and Classification: A review,” Journal of Information Systems and Telecommunication (JIST), vol. 11, no. 44, pp. 347–358, Dec. 2023, doi: 10.61186/jist.37936.11.44.347. [10] R. Patra, M. Bhattacharya, and S. Mukherjee, “IoT-Based Computational Frameworks in Disease Prediction and Healthcare Management: Strategies, challenges, and potential,” in Studies in computational intelligence, 2021, pp. 17–41. doi: 10.1007/978-981-15-9897-5_2. [11] Q. Abbas and A. Alsheddy, “Driver Fatigue Detection Systems using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative analysis,” Sensors, vol. 21, no. 1, p. 56, Dec. 2020, doi: 10.3390/s21010056. [12] H. E. Bays, C. F. Kirkpatrick, K. C. Maki, P. P. Toth, R. T. Morgan, J. Tondt, S. M. Christensen, D. L. Dixon, and T. A. Jacobson, “Obesity, dyslipidemia, and cardiovascular disease: A joint expert review from the Obesity Medicine Association and the National Lipid Association 2024,” Journal of Clinical Lipidology, vol. 18, no. 3, pp. e320–e350, 2024. [13] S. Yin, N. Xue, C. You, Y. Guo, P. Yao, Y. Shi, T. Liu, “Wearable physiological multi-vital sign monitoring system with medical standard,” IEEE Sensors Journal, vol. 21, no. 23, pp. 27157–27167, 2021. doi: 10.1016/j.jacl.2024.04.001. [14] Yang, Y., Bränn, E., Zhou, J., Wei, D., Bergstedt, J., Fang, F., ... & Lu, D. (2025). Premenstrual disorders and risk of cardiovascular diseases. Nature Cardiovascular Research, 1–10. https://doi.org/10.1038/s44161-025-00456-9. [15] T. Shaown, I. Hasan, Md. M. R. Mim, and Md. S. Hossain, “IoT-based portable ECG monitoring system for smart healthcare,” 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), May 2019, doi: 10.1109/icasert.2019.8934622. [16] B. Chong, J. Jayabaskaran, S. M. Jauhari, S. P. Chan, R. Goh, M. T. W. Kueh, et al., “Global burden of cardiovascular diseases: projections from 2025 to 2050,” European Journal of Preventive Cardiology, vol. zwae281, 2024. [17] M. O. Rahman, M. A. Kashem, A.-A. Nayan, M. F. Akter, F. Rabbi, M. Ahmed, and M. Asaduzzaman, “Internet of Things (IoT) based ECG system for rural health care,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 12, no. 6, 2021. doi: 10.14569/IJACSA.2021.0120653. [18] Y. Zhao, X. Yang, Y. Du, L. Chen, J. Dong, T. Hu, N. Sun et al., “Global cardiovascular disease burden attributable to particulate matter pollution, 1990–2021: An analysis of the global burden of disease study 2021 and forecast to 2045,” BMC Cardiovasc. Disord., vol. 25, no. 1, pp. 1–14, 2025 https://doi.org/10.1186/s12872-025-04724-6. [19] Kelters, I. R., Koop, Y., Young, M. E., Daiber, A., & van Laake, L. W. (2025). Circadian rhythms in cardiovascular disease. European Heart Journal, 46(36), 3532–3545. https://doi.org/10.1093/eurheartj/ehae455. [20] M. L. Sahu, M. Atulkar, M. K. Ahirwal, and A. Ahamad, “IoT-enabled cloud-based real-time remote ECG monitoring system,” Journal of Medical Engineering & Technology, vol. 45, no. 6, pp. 473–485, May 2021, doi: 10.1080/03091902.2021.1921870. [21] N. A. Nayan, R. Jaafar, and N. S. Risman, “Development of respiratory rate estimation technique using electrocardiogram and photoplethysmogram for continuous health monitoring,” Bulletin of Electrical Engineering and Informatics, vol. 7, no. 3, pp. 487–494, 2018. doi: 10.1007/s40846-022-00700-z. [22] A. Ghosh, A. Raha, and A. Mukherjee, “Energy-Efficient IoT-Health Monitoring System using Approximate Computing,” Internet of Things, vol. 9, p. 100166, Jan. 2020, doi: 10.1016/j.iot.2020.100166. [23] S. M. Ahsanuzzaman, T. Ahmed, and Md. A. Rahman, “Low cost, portable ECG monitoring and alarming system based on deep learning,” 2017 IEEE Region 10 Symposium (TENSYMP), pp. 316–319, Jan. 2020, doi: 10.1109/tensymp50017.2020.9231005. [24] B. Patil, V. Rajan, and P. Patani, “Wearable fetal ECG monitoring system from abdominal electrocardiography recording,” Journal of Pharmaceutical Negative Results, pp. 2383–2393, 2022. doi: 10.5555/jpnr.2022.2383. [25] S. Chakraborty, M. B. U. Talukdar, P. Sikdar, and J. Uddin, “An Efficient Sentiment Analysis Model for Crime Articles’ Comments using a Fine-tuned BERT Deep Architecture and Pre-Processing Techniques,” Journal of Information Systems and Telecommunication (JIST), vol. 12, no. 45, pp. 1–11, Mar. 2024, doi: 10.61186/jist.38322.12.45.1. [26] N. Xiao, W. Yu, and X. Han, “Wearable heart rate monitoring intelligent sports bracelet based on Internet of things,” Measurement, vol. 164, p. 108102, Jun. 2020, doi: 10.1016/j.measurement.2020.108102. [27] A. Badr, A. Badawi, A. Rashwan, and K. Elgazzar, “XBeats: a Real-Time Electrocardiogram monitoring and analysis system,” Signals, vol. 3, no. 2, pp. 189–208, Apr. 2022, doi: 10.3390/signals3020013. [28] N. A. Nayan and H. Ab Hamid, “Evaluation of patient electrocardiogram datasets using signal quality indexing,” Bulletin of Electrical Engineering and Informatics, vol. 8, no. 2, pp. 519–526, 2019. [29] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. Van Der Laak, B. Van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017. doi: 10.1016/j.media.2017.07.005. [30] U. N. Cobrado, S. Sharief, N. G. Regahal, E. Zepka, M. Mamauag, and L. C. Velasco, “Access control solutions in electronic health record systems: A systematic review,” Informatics in Medicine Unlocked, vol. 49, p. 101552, Jan. 2024, doi: 10.1016/j.imu.2024.101552. [31] L. Pérez-Lombard, J. Ortiz, and C. Pout, “A review on buildings energy consumption information,” Energy and Buildings, vol. 40, no. 3, pp. 394–398, Mar. 2007, doi: 10.1016/j.enbuild.2007.03.007. [32] S. T. Aarthy and J. L. Mazher Iqbal, "A novel deep learning approach for early detection of cardiovascular diseases from ECG signals," Medical Engineering & Physics, vol. 125, p. 104111, Mar. 2024, doi: 10.1016/j.medengphy.2024.104111. [33] S. Begum, E. Ghousia, E. Priyadarshi, S. Pratap, S. Kulshrestha, and V. Singh, “Automated detection of abnormalities in ECG signals using deep neural network,” Biomedical Engineering Advances, vol. 5, 100066, 2023.JaspreetKaur112202525626510.61882/jist.48051.13.51.256http://jist.ir/en/Article/48051http://jist.ir/en/Article/Download/48051http://jist.ir/en/Article/Download/48051http://jist.ir/en/Article/Download/48051http://jist.ir/en/Article/Download/48051http://jist.ir/en/Article/Download/48051http://jist.ir/en/Article/Download/48051http://jist.ir/en/Article/Download/48051[1] Kumar, A., Bharti, S., & Gupta, M. (2019). FBMC vs. OFDM: 5G mobile communication system. International Journal of Systems, Control and Communications, 10(3), 250-264. DOI:10.5815/ijwmt.2023.05.01. [2] Haykin, S., Thomson, D. J., & Reed, J. H. (2009). Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97(5), 849-877. DOI:10.1109/JPROC.2009.2015711. [3] Kumar, Arun, et al. "NOMA based CR for qam-64 and qam-256." Egyptian Informatics Journal 21.2 (2020): 67-71. DOI:10.5815/ijwmt.2023.05.01. [4] Kumar, Arun, and P. Nandha Kumar. "OFDM system with Cyclostationary feature detection spectrum sensing." ICTExpress 5.1 (2019): 21-25. DOI:10.1016/j.icte.2018.01.007. [5] Liang, Y. C., Chen, K. C., Li, G. Y., & Mahonen, P. (2011). Cognitive radio networking and communications: An overview. IEEE transactions on vehicular technology, 60(7), 3386-3407. DOI: 10.1109/TVT.2011.2158673. [6] Datla, D., Wyglinski, A. M., & Minden, G. J. (2009). A spectrum surveying framework for dynamic spectrum access networks. IEEE [7]Transactions on Vehicular Technology, 58(8), 4158-4168. DOI: 10.1109/TVT.2009.2025380. [8]Goldsmith, A., Jafar, S. A., Maric, I., & Srinivasa, S. (2009). Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proceedings of the IEEE, 97(5), 894-914. DOI: 10.1109/JPROC.2009.2015717. [9] Tumuluru, V. K., Wang, P., & Niyato, D. (2011). A novel spectrum-scheduling scheme for multichannel cognitive radio network and performance analysis. IEEE Transactions on Vehicular Technology, 60(4), 1849-1858. DOI: 10.1109/TVT.2011.2117951. [10] Navrátil, P. A., Childs, H., Fussell, D. S., & Lin, C. (2013). Exploring the spectrum of dynamic scheduling algorithms for scalable distributed-memoryray tracing. IEEE transactions on visualization and computer graphics, 20(6), 893-906. DOI: 10.1109/TVCG.2013.261. [11] Nandhakumar, P., & Kumar, A. (2016). Analysis of OFDM system with energy detection spectrum sensing. Indian J. Sci. Technol, 9(16), 1-6. DOI: 10.17485/ijst/2016/v9i16/92224. [12] Saberali, S. A., & Beaulieu, N. C. (2014, October). Matched-filter detection of the presence of MPSK signals. In 2014 International Symposium on Information Theory and its Applications (pp. 85-89). IEEE.DOI:0.1109/ISITA.2014.7001426. [13] Kim, K., Akbar, I. A., Bae, K. K., Um, J. S., Spooner, C. M., & Reed, J. H. (2007, April). Cyclostationary approaches to signal detection and classification in cognitive radio. In 2007 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks (pp. 212-215). IEEE. DOI: 10.1109/DYSPAN.2007.39. [14] Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer networks, 50(13), 2127-2159. DOI: 10.1016/j.comnet.2006.05.001. [15] Tu, C. C., & Champagne, B. (2009). Subspace-based blind channel estimation for MIMO-OFDM systems with reduced time averaging. IEEE Transactions on Vehicular Technology, 59(3), 1539-1544.DOI: 10.1109/TVT.2009.2036728. [16] Zhou, Y., Wang, Y., Wang, T., Zhang, K., & Zhang, W. (2011, May). Iterative inter-cell interference coordination in MU-MIMO systems. In 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE. DOI: 10.1109/VETECS.2011.5956799. [17] Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5G be?. IEEE Journal on selected areas in communications, 32(6), 1065-1082. DOI: 10.1109/JSAC.2014.2328098. [18] Srinu, S., & Sabat, S. L. (2013). Cooperative wideband sensing based on cyclostationary features with multiple malicious user elimination. AEU-International Journal of Electronics and Communications, 67(8), 702-707. DOI: 10.1016/j.aeue.2013.01.002. [19] Quan, Z., Cui, S., Sayed, A. H., & Poor, H. V. (2008). Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE transactions on signal processing, 57(3), 1128-1140. DOI: 10.1109/TSP.2008.2008540. [20] Na, D., & Choi, K. (2019). DFT spreading-based low PAPR FBMC with embedded side information. IEEE Transactions on Communications, 68(3), 1731-1745. DOI: 10.1109/TCOMM.2019.2952549. [21] He, Z., Zhou, L., Chen, Y., & Ling, X. (2018). Low-complexity PTS scheme for PAPR reduction in FBMC-OQAM systems. IEEE Communications Letters, 22(11), 2322-2325. DOI: 10.1109/LCOMM.2018.2863908. [22] Kumar, A., & Gupta, M. (2018). A review on activities of fifth generation mobile communication system. Alexandria Engineering Journal, 57(2), 1125-1135. DOI: 10.1016/j.aej.2017.06.014. [23] Bairagi, A. K., et al. (2020). Coexistence mechanism between eMBB and uRLLC in 5G wireless networks. IEEE transactions on communications, 69(3), 1736-1749. DOI: 10.1109/TCOMM.2020.3046635. [24] Mounir, M., et al. (2021). A novel hybrid precoding-companding technique for peak-to-average power ratio reduction in 5G and beyond. Sensors, 21(4), 1410. DOI: 10.3390/s21041410. [25] Kumar, A., et al. (2021). An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms. Computers, Materials & Continua, 69(3). DOI: 10.32604/cmc.2021.019092. [26] Miah, M. S., Schukat, M., & Barrett, E. (2020). Sensing and throughput analysis of a MU-MIMO based cognitive radio scheme for the Internet of Things. Computer communications, 154, 442-454. DOI: 10.1016/j.comcom.2020.02.040. [27] Ramamoorthy, R., et al. (2022). Analysis of cognitive radio for lte and 5g waveforms. Computer Systems Science & Engineering, 43(3).DOI: 10.32604/csse.2022.019943. [28] Pham, Q. V., et al. (2021). Swarm intelligence for next-generation networks: Recent advances and applications. Journal of Network and Computer Applications, 191, 103141. DOI: 10.1016/j.jnca.2021.103141. [29] Bala Kumar, D., & Nanda Kumar, S. (2024). Block chain-enabled cooperative spectrum sensing in 5G and B5G cognitive radio via massive multiple-input multiple-output non orthogonal multiple access. Results in Engineering, 24, 102840.DOI: 10.1016/j.rineng.2024.102840. [30] Salameh, H. B., & Hussienat, A. (2025). ML-Driven Feature-Based Spectrum Sensing for NOMA Signal Detection in Spectrum-agile IoT Networks under Fading Channels. IEEE Sensors Journal. DOI: 10.1109/JSEN.2025.3524968. [31] Zhai, Q., Dong, L., Cheng, W., Li, Y., & Liu, P. (2023). Joint optimization for active IRS-aided multicluster NOMA systems. IEEE Systems Journal, 17(4), 6691-6694. DOI: 10.1109/JSYST.2022.3228965.KaebehYaeghoobiMahsaBakhshandeh N112202524325510.61882/jist.48580.13.51.243http://jist.ir/en/Article/48580http://jist.ir/en/Article/Download/48580http://jist.ir/en/Article/Download/48580http://jist.ir/en/Article/Download/48580http://jist.ir/en/Article/Download/48580http://jist.ir/en/Article/Download/48580http://jist.ir/en/Article/Download/48580http://jist.ir/en/Article/Download/48580[1] A. Mahdavi and A. Ghaffari, “Embedding Virtual Machines in Cloud Computing based on Big Bang–Big Crunch Algorithm,” Journal of Information Systems & Telecommunication (JIST), p. 305, 2019. [2] N. Fernando, S. W. Loke, and W. Rahayu, “Mobile cloud computing: A survey,” Future Generation Computer Systems, vol. 29, no. 1, pp. 84–106, Jan. 2013, doi: 10.1016/J.FUTURE.2012.05.023. [3] Md. G. R. Alam, M. M. Hassan, Md. Z. Uddin, A. S. Almogren, and G. Fortino, “Autonomic computation offloading in mobile edge for IoT applications,” Future Gener. Comput. Syst., vol. 90, pp. 149–157, 2019, [Online]. Available: https://api.semanticscholar.org/CorpusID:52899499. [4] P. Boopathy et al., “Deep learning for intelligent demand response and smart grids: A comprehensive survey,” Comput Sci Rev, vol. 51, p. 100617, Feb. 2024, doi: 10.1016/J.COSREV.2024.100617. [5] I. Abdullaev, N. Prodanova, K. A. Bhaskar, E. L. Lydia, S. Kadry, and J. Kim, “Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning,” Computers, Materials and Continua, vol. 76, no. 2, pp. 1463–1477, Aug. 2023, doi: 10.32604/CMC.2023.038417. [6] H. Naseri, S. Azizi, and A. Abdollahpouri, “BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers,” Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 27, p. 175, 2020. [7] D. Seddiki, F. J. Maldonado Carrascosa, S. García Galán, M. Valverde Ibáñez, T. Marciniak, and N. Ruiz Reyes, “Enhanced virtual machine migration for energy sustainability optimization in cloud computing through knowledge acquisition,” Computers and Electrical Engineering, vol. 119, p. 109506, Oct. 2024, doi: 10.1016/J.COMPELECENG.2024.109506. [8] L.-D. Chou, H.-F. Chen, F.-H. Tseng, H.-C. Chao, and Y.-J. Chang, “DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization,” IEEE Syst J, vol. 12, no. 2, pp. 1554–1565, 2018, doi: 10.1109/JSYST.2016.2596299. [9] H. Wang, S. Cao, H. Li, L. Yan, Z. Guo, and Y. Gao, “Multi-objective joint optimization of task offloading based on MADRL in internet of things assisted by satellite networks,” Computer Networks, vol. 254, p. 110801, Dec. 2024, doi: 10.1016/J.COMNET.2024.110801. [10] T. Tsokov and H. Kostadinov, “Dynamic network-aware container allocation in Cloud/Fog computing with mobile nodes,” Internet of Things, vol. 26, p. 101211, Jul. 2024, doi: 10.1016/J.IOT.2024.101211. [11] C. You, K. Huang, H. Chae, and B.-H. Kim, “Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading,” IEEE Trans Wirel Commun, vol. 16, no. 3, pp. 1397–1411, 2017, doi: 10.1109/TWC.2016.2633522. [12] Q. Wang, S. Guo, J. Liu, and Y. Yang, “Energy-efficient computation offloading and resource allocation for delay-sensitive mobile edge computing,” Sustainable Computing: Informatics and Systems, vol. 21, pp. 154–164, Mar. 2019, doi: 10.1016/J.SUSCOM.2019.01.007. [13] S. C. Ghoshal et al., “VESBELT: An energy-efficient and low-latency aware task offloading in Maritime Internet-of-Things networks using ensemble neural networks,” Future Generation Computer Systems, vol. 161, pp. 572–585, Dec. 2024, doi: 10.1016/J.FUTURE.2024.07.034. [14] S. Yang, D. Kwon, H. Yi, Y. Cho, Y. Kwon, and Y. Paek, “Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing,” IEEE Trans Mob Comput, vol. 13, no. 11, pp. 2648–2660, 2014, doi: 10.1109/TMC.2014.2307293. [15] X. Xu, Q. Huang, X. Yin, M. Abbasi, M. R. Khosravi, and L. Qi, “Intelligent Offloading for Collaborative Smart City Services in Edge Computing,” IEEE Internet Things J, vol. 7, no. 9, pp. 7919–7927, Sep. 2020, doi: 10.1109/JIOT.2020.3000871. [16] T. Tang, C. Li, and F. Liu, “Collaborative cloud-edge-end task offloading with task dependency based on deep reinforcement learning,” Comput Commun, vol. 209, pp. 78–90, Sep. 2023, doi: 10.1016/J.COMCOM.2023.06.021. [17] Y. Miao, G. Wu, M. Li, A. Ghoneim, M. Al-Rakhami, and M. S. Hossain, “Intelligent task prediction and computation offloading based on mobile-edge cloud computing,” Future Generation Computer Systems, vol. 102, pp. 925–931, Jan. 2020, doi: 10.1016/J.FUTURE.2019.09.035. [18] M. Du, Y. Wang, K. Ye, and C. Xu, “Algorithmics of Cost-Driven Computation Offloading in the Edge-Cloud Environment,” IEEE Transactions on Computers, vol. 69, no. 10, pp. 1519–1532, 2020, doi: 10.1109/TC.2020.2976996. [19] L. Tan, Z. Kuang, J. Gao, and L. Zhao, “Energy-Efficient Collaborative Multi-Access Edge Computing via Deep Reinforcement Learning,” IEEE Trans Industr Inform, vol. 19, no. 6, pp. 7689–7699, Jun. 2023, doi: 10.1109/TII.2022.3213603. [20] A. Shakarami, A. Shahidinejad, and M. Ghobaei-Arani, “An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach,” Journal of Network and Computer Applications, vol. 178, p. 102974, Mar. 2021, doi: 10.1016/J.JNCA.2021.102974. [21] S. Zhong, S. Guo, H. Yu, and Q. Wang, “Cooperative service caching and computation offloading in multi-access edge computing,” Computer Networks, vol. 189, p. 107916, Apr. 2021, doi: 10.1016/J.COMNET.2021.107916. [22] G. Peng, H. Wu, H. Wu, and K. Wolter, “Constrained Multiobjective Optimization for IoT-Enabled Computation Offloading in Collaborative Edge and Cloud Computing,” IEEE Internet Things J, vol. 8, no. 17, pp. 13723–13736, 2021, doi: 10.1109/JIOT.2021.3067732. [23] Z. N. Samani and M. R. Khayyambashi, “Reliable resource allocation and fault tolerance in mobile cloud computing,” Journal of Information Systems and Telecommunication (JIST), vol. 7, no. 2, pp. 96–109, 2019. [24] J. Long, Y. Luo, X. Zhu, E. Luo, and M. Huang, “Computation offloading through mobile vehicles in IoT-edge-cloud network,” EURASIP J Wirel Commun Netw, vol. 2020, no. 1, p. 244, 2020, doi: 10.1186/s13638-020-01848-5. [25] L. Bracciale, M. Bonola, P. Loreti, G. Bianchi, R. Amici, and A. Rabuffi, “CRAWDAD dataset roma/taxi (v. 2014-07-17),” 2014.AliBaydounAhmed SZekri112202521023110.61882/jist.49070.13.51.210http://jist.ir/en/Article/49070http://jist.ir/en/Article/Download/49070http://jist.ir/en/Article/Download/49070http://jist.ir/en/Article/Download/49070http://jist.ir/en/Article/Download/49070http://jist.ir/en/Article/Download/49070http://jist.ir/en/Article/Download/49070http://jist.ir/en/Article/Download/49070[1] P. M. Mell and T. Grance, “The NIST definition of cloud computing,” Gaithersburg, MD, 2011. doi: 10.6028/NIST.SP.800-145. [2] D. Bliedy, S. Mazen, and E. Ezzat, “Datacentre Total Cost of Ownership (TCO) Models : A Survey,” International Journal of Computer Science, Engineering and Applications, vol. 8, no. 2/3/4, pp. 47–62, 2018, doi: 10.5121/ijcsea.2018.8404. [3] T. Benson, A. Akella, and D. A. Maltz, “Network traffic characteristics of data centers in the wild,” Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC, pp. 267–280, 2010, doi: 10.1145/1879141.1879175. [4] L. Zhou, C. H. Chou, L. N. Bhuyan, K. K. Ramakrishnan, and D. Wong, “Joint server and network energy saving in data centers for latency-sensitive applications,” Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, pp. 700–709, 2018, doi: 10.1109/IPDPS.2018.00079. [5] K. Bilal et al., “A survey on Green communications using Adaptive Link Rate,” Cluster Comput, vol. 16, no. 3, pp. 575–589, Jul. 2013, doi: 10.1007/s10586-012-0225-8. [6] A. C. Orgerie, M. D. De Assuncao, and L. Lefevre, “A survey on techniques for improving the energy efficiency of large-scale distributed systems,” ACM Comput Surv, vol. 46, no. 4, 2014, doi: 10.1145/2532637. [7] M. H. Ferdaus, M. Murshed, R. N. Calheiros, and R. Buyya, Network-aware virtual machine placement and migration in cloud data centers, no. May. 2015. doi: 10.4018/978-1-4666-8213-9.ch002. [8] A. Hammadi and L. Mhamdi, “A survey on architectures and energy efficiency in Data Center Networks,” Comput Commun, vol. 40, pp. 1–21, 2014, doi: 10.1016/j.comcom.2013.11.005. [9] K. Bilal et al., “A taxonomy and survey on Green Data Center Networks,” Future Generation Computer Systems, vol. 36, pp. 189–208, Jul. 2014, doi: 10.1016/j.future.2013.07.006. [10] R. W. Ahmad, A. Gani, S. H. A. Hamid, M. Shiraz, A. Yousafzai, and F. Xia, “A survey on virtual machine migration and server consolidation frameworks for cloud data centers,” Journal of Network and Computer Applications, vol. 52, pp. 11–25, 2015, doi: 10.1016/j.jnca.2015.02.002. [11] F. L. Pires and B. Baran, “A virtual machine placement taxonomy,” Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, no. July, pp. 159–168, 2015, doi: 10.1109/CCGrid.2015.15. [12] M. Masdari, S. S. Nabavi, and V. Ahmadi, “An overview of virtual machine placement schemes in cloud computing,” Journal of Network and Computer Applications, vol. 66, pp. 106–127, 2016, doi: 10.1016/j.jnca.2016.01.011. [13] H. Talebian et al., Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues, vol. 23, no. 2. Springer US, 2020. doi: 10.1007/s10586-019-02954-w. [14] H. Zhuang and B. Esmaeilpour Ghouchani, “Virtual machine placement mechanisms in the cloud environments: a systematic review,” Kybernetes, vol. 50, no. 2, pp. 333–368, 2021, doi: 10.1108/K-09-2019-0635. [15] L. Helali and M. N. Omri, “A survey of data center consolidation in cloud computing systems,” 2021. doi: 10.1016/j.cosrev.2021.100366. [16] A. Sumathi, … B. K.-T. J. of, and undefined 2023, “Advancements in Energy-Efficient Virtual Machine Placement Survey for Cloud Computing,” Researchgate.Net, no. February, 2024, doi: 10.13140/RG.2.2.17918.36164. [17] N. Rana et al., “A systematic literature review on contemporary and future trends in virtual machine scheduling techniques in cloud and multi-access computing,” Front Comput Sci, vol. 6, 2024, doi: 10.3389/fcomp.2024.1288552. [18] J. Zou, K. Wang, K. Zhang, and M. Kassim, “Perspective of virtual machine consolidation in cloud computing: a systematic survey,” Telecommun Syst, p. 11235, 2024, doi: 10.1007/s11235-024-01184-9. [19] S. R. Swain, A. Parashar, A. K. Singh, and C. Nan Lee, “An Energy Efficient Virtual Machine Placement Scheme for Intelligent Resource Management at Cloud Data Center,” in OCIT 2023 - 21st International Conference on Information Technology, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 65–70. doi: 10.1109/OCIT59427.2023.10430915. [20] S. Kumar, S. Mittal, and M. Singh, “Active VM Placement Approach Based on Energy Efficiency in Cloud Environment,” in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, 2022, pp. 35–46. doi: 10.1007/978-981-19-1018-0_4. [21] Z. Li, K. Lin, S. Cheng, L. Yu, and J. Qian, “Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers,” J Grid Comput, vol. 20, no. 4, 2022, doi: 10.1007/s10723-022-09631-0. [22] H. Xing, J. Zhu, R. Qu, P. Dai, S. Luo, and M. A. Iqbal, “An ACO for energy-efficient and traffic-aware virtual machine placement in cloud computing,” Swarm Evol Comput, vol. 68, no. November 2021, p. 101012, 2022, doi: 10.1016/j.swevo.2021.101012. [23] D. Dabhi and D. Thakor, “Utilisation-aware VM placement policy for workload consolidation in cloud data centres,” International Journal of Communication Networks and Distributed Systems, vol. 28, no. 6, pp. 704–726, 2022, doi: 10.1504/ijcnds.2022.126224. [24] E. I. Elsedimy, M. Herajy, and S. M. M. Abohashish, “Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter,” 2025. doi: 10.1007/s00521-024-10872-1. [25] K. Lu, R. Yahyapour, P. Wieder, C. Kotsokalis, E. Yaqub, and A. I. Jehangiri, “QoS-aware VM placement in multi-domain service level agreements scenarios,” IEEE International Conference on Cloud Computing, CLOUD, no. April 2014, pp. 661–668, 2013, doi: 10.1109/CLOUD.2013.112. [26] T. Renugadevi, K. Geetha, K. Muthukumar, and Z. W. Geem, “Optimized energy cost and carbon emission-aware virtual machine allocation in sustainable data centers,” Sustainability (Switzerland), vol. 12, no. 16, pp. 1–27, 2020, doi: 10.3390/SU12166383. [27] S. Rawas, A. Zekri, and A. El Zaart, “Power and Cost-Aware Virtual Machine Placement in Geo-Distributed Data Power and Cost-aware Virtual Machine Placement in Geo-distributed Data Centers,” no. March, 2018, doi: 10.5220/0006696201120123. [28] G. P. Maskare and S. Sharma, “The Hybrid ACO, PSO, and ABC Approach for Load Balancing in Cloud Computing,” vol. 10, 2023, Accessed: May 07, 2025. [Online]. Available: www.jetir.org. [29] M. H. Kim, J. Y. Lee, S. A. Raza Shah, T. H. Kim, and S. Y. Noh, “Min-max exclusive virtual machine placement in cloud computing for scientific data environment,” Journal of Cloud Computing, vol. 10, no. 1, pp. 1–17, Dec. 2021, doi: 10.1186/S13677-020-00221-7/FIGURES/12. [30] M. Koubàa, R. Regaieg, A. S. Karar, M. Nadeem, and F. Bahloul, “A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search,” Telecom, vol. 5, no. 4, pp. 1309–1331, 2024, doi: 10.3390/telecom5040065. [31] X. Zheng and Y. Xia, “Exploring mixed integer programming reformulations for virtual machine placement with disk anti-colocation constraints,” Performance Evaluation, vol. 135, 2019, doi: 10.1016/j.peva.2019.102035. [32] S. Yang, P. Wieder, R. Yahyapour, S. Trajanovski, and X. Fu, “Reliable Virtual Machine Placement and Routing in Clouds,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 10, pp. 2965–2978, 2017, doi: 10.1109/TPDS.2017.2693273. [33] A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, 2012, doi: 10.1016/j.future.2011.04.017. [34] J. Wang, J. Yu, R. Zhai, X. He, and Y. Song, “GMPR: A Two-Phase Heuristic Algorithm for Virtual Machine Placement in Large-Scale Cloud Data Centers,” IEEE Syst J, vol. 17, no. 1, pp. 1419–1430, Mar. 2023, doi: 10.1109/JSYST.2022.3187971. [35] S. Jangiti, V. Vijayakumar, and V. Subramaniyaswamy, “Hybrid best-fit heuristic for energy efficient virtual machine placement in cloud data centers,” EAI Endorsed Transactions on Energy Web, vol. 7, no. 26, pp. 1–7, 2020, doi: 10.4108/eai.13-7-2018.162689. [36] R. Keshri and D. P. Vidyarthi, “Communication-aware, energy-efficient VM placement in cloud data center using ant colony optimization,” International Journal of Information Technology (Singapore), vol. 15, no. 8, pp. 4529–4535, Dec. 2023, doi: 10.1007/S41870-023-01531-0/METRICS. [37] N. Donyagard Vahed, M. Ghobaei-Arani, and A. Souri, “Multiobjective virtual machine placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments: A comprehensive review,” International Journal of Communication Systems, vol. 32, no. 14, 2019, doi: 10.1002/dac.4068. [38] A. S. Abohamama and E. Hamouda, “A hybrid energy–Aware virtual machine placement algorithm for cloud environments,” Expert Syst Appl, vol. 150, p. 113306, 2020, doi: 10.1016/j.eswa.2020.113306. [39] A. M. Baydoun and A. S. Zekri, “Network-, Cost-, and Renewable-Aware Ant Colony Optimization for Energy-Efficient Virtual Machine Placement in Cloud Datacenters,” Future Internet, vol. 17, no. 6, p. 261, Jun. 2025, doi: 10.3390/fi17060261. [40] S. Talwani et al., “Machine-Learning-Based Approach for Virtual Machine Allocation and Migration,” Electronics (Switzerland), vol. 11, no. 19, 2022, doi: 10.3390/electronics11193249. [41] S. Rawas, A. Zekri, and A. El-Zaart, “LECC: Location, energy, carbon and cost-aware VM placement model in geo-distributed DCs,” Sustainable Computing: Informatics and Systems, vol. 33, 2022, doi: 10.1016/j.suscom.2021.100649. [42] A. Jumnal and S. M. Dilip Kumar, “Optimal VM placement approach using fuzzy reinforcement learning for cloud data centers,” in Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, Institute of Electrical and Electronics Engineers Inc., Feb. 2021, pp. 29–35. doi: 10.1109/ICICV50876.2021.9388424. [43] H. Padmanaban, “Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure,” Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, vol. 3, no. 1, pp. 1–26, Apr. 2024, doi: 10.60087/JAIGS.VOL03.ISSUE01.P26. [44] H. A. Alharbi, T. E. H. Elgorashi, A. Q. Lawey, and J. M. H. Elmirghani, “The Impact of Inter-Virtual Machine Traffic on Energy Efficient Virtual Machines Placement,” in 2019 IEEE Sustainability through ICT Summit, StICT 2019, 2019. doi: 10.1109/STICT.2019.8789381. [45] F. kamoun-abid, H. Frikha, A. Meddeb-Makhoulf, and F. Zarai, “Allocation of virtual machine in a cloud environment based on machine learning,” Res Sq, Jan. 2023, doi: 10.21203/RS.3.RS-2483861/V1. [46] N. Tziritas, T. Loukopoulos, S. Khan, C. Z. Xu, and A. Zomaya, “A communication-aware energy-efficient graph-coloring algorithm for VM placement in clouds,” Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCo, pp. 1684–1691, 2018, doi: 10.1109/SmartWorld.2018.00286. [47] S. Sadegh, K. Zamanifar, P. Kasprzak, and R. Yahyapour, “A two-phase virtual machine placement policy for data-intensive applications in cloud,” Journal of Network and Computer Applications, vol. 180, no. December 2020, p. 103025, 2021, doi: 10.1016/j.jnca.2021.103025. [48] J. Gedeon, M. Stein, L. Wang, and M. Mühlhäuser, “On Scalable In-Network Operator Placement for Edge Computing”. [49] T. Huang, W. Huang, B. Zhang, W. Chen, and X. Pan, “Optimizing energy consumption in centralized and distributed cloud architectures with a comparative study to increase stability and efficiency,” Energy Build, vol. 333, 2025, doi: 10.1016/j.enbuild.2025.115454. [50] S. S. Nabavi, S. S. Gill, M. Xu, M. Masdari, and P. Garraghan, “TRACTOR: Traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization,” International Journal of Communication Systems, vol. 35, no. 1, pp. 1–20, 2022, doi: 10.1002/dac.4747. [51] S. Azizi, M. Shojafar, J. Abawajy, and R. Buyya, “GRVMP: A Greedy Randomized Algorithm for Virtual Machine Placement in Cloud Data Centers,” IEEE Syst J, vol. 15, no. 2, pp. 2571–2582, 2020, doi: 10.1109/jsyst.2020.3002721. [52] W. Wei, H. Gu, W. Lu, T. Zhou, and X. Liu, “Energy Efficient Virtual Machine Placement with an Improved Ant Colony Optimization over Data Center Networks,” IEEE Access, vol. 7, pp. 60617–60625, 2019, doi: 10.1109/ACCESS.2019.2911914. [53] S. Bani-Ahmad, S. Sa’adeh, S. Bani-Ahmad, and S. Sa’adeh, “Scalability of the DVFS Power Management Technique as Applied to 3-Tier Data Center Architecture in Cloud Computing,” Journal of Computer and Communications, vol. 5, no. 1, pp. 69–93, Dec. 2016, doi: 10.4236/JCC.2017.51007. [54] J. Masoudi, B. Barzegar, and H. Motameni, “Energy-Aware Virtual Machine Allocation in DVFS-Enabled Cloud Data Centers,” IEEE Access, vol. 10, pp. 3617–3630, 2022, doi: 10.1109/ACCESS.2021.3136827. [55] “ElasticTree: Saving Energy in Data Center Networks,” in Proceedings of the 7th USENIX Symposium on Networked Systems Design and Implementation, San Jose, CA, USA, Apr. 2010. [56] S. Xiao, Y. Cui, X. Wang, Z. Yang, S. Yan, and L. Yang, “Traffic-aware Virtual Machine Migration in Topology-adaptive DCN,” Proceedings - International Conference on Network Protocols, ICNP, vol. 2016-December, Dec. 2016. [57] A. Akbari, A. Khonsari, and S. M. Ghoreyshi, “Thermal-aware virtual machine allocation for heterogeneous cloud data centers,” Energies (Basel), vol. 13, no. 11, 2020, doi: 10.3390/en13112880. [58] J. Lin, W. Lin, W. Wu, W. Lin, and K. Li, “Energy-aware virtual machine placement based on a holistic thermal model for cloud data centers,” Future Generation Computer Systems, vol. 161, pp. 302–314, 2024, doi: 10.1016/j.future.2024.07.020. [59] S. Omer, S. Azizi, M. Shojafar, and R. Tafazolli, “A priority, power and traffic-aware virtual machine placement of IoT applications in cloud data centers,” Journal of Systems Architecture, vol. 115, no. April, 2021, doi: 10.1016/j.sysarc.2021.101996. [60] A. K. Singh, S. R. Swain, D. Saxena, and C. N. Lee, “A Bio-Inspired Virtual Machine Placement Toward Sustainable Cloud Resource Management,” IEEE Syst J, vol. 17, no. 3, pp. 3894–3905, 2023, doi: 10.1109/JSYST.2023.3248118. [61] H. F. Farimani, S. R. K. Tabbakh, D. Bahrepour, and R. Ghaemi, “Reallocation of virtual machines to cloud data centers reduce service level agreement violation and energy consumption using the FMT method,” Journal of Information Systems and Telecommunication, vol. 7, no. 4, pp. 316–325, 2019. [62] F. Alharbi, Y. C. Tian, M. Tang, W. Z. Zhang, C. Peng, and M. Fei, “An Ant Colony System for energy-efficient dynamic Virtual Machine Placement in data centers,” Expert Syst Appl, vol. 120, pp. 228–238, 2019, doi: 10.1016/j.eswa.2018.11.029. [63] S. Mashhadi Moghaddam, M. O’Sullivan, C. Walker, S. Fotuhi Piraghaj, and C. P. Unsworth, “Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers,” Future Generation Computer Systems, vol. 106, pp. 221–233, 2020, doi: 10.1016/j.future.2020.01.008. [64] A. Kamalinia and A. Ghaffari, “Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms,” Journal of Information Systems and Telecommunication, vol. 4, no. 16, pp. 1–10, 2017, doi: 10.1007/s11277-017-4839-2. [65] S. Sadegh, K. Zamanifar, P. Kasprzak, and R. Yahyapour, “A two-phase virtual machine placement policy for data-intensive applications in cloud,” Journal of Network and Computer Applications, vol. 180, p. 103025, Apr. 2021, doi: 10.1016/J.JNCA.2021.103025. [66] Y. Fan, H. Ding, L. Wang, and X. Yuan, “Green latency-aware data placement in data centers,” Computer Networks, vol. 110, pp. 46–57, 2016, doi: 10.1016/j.comnet.2016.09.015. [67] S. Farzai, M. H. Shirvani, and M. Rabbani, “Communication-Aware Traffic Stream Optimization for Virtual Machine Placement in Cloud Datacenters with VL2 Topology,” no. May, 2021. [68] A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers,” Concurrency Computation Practice and Experience, vol. 24, no. 13, pp. 1397–1420, 2012, doi: 10.1002/cpe.1867. [69] S. Fang, R. Kanagavelu, B. S. Lee, C. H. Foh, and K. M. M. Aung, “Power-efficient virtual machine placement and migration in data centers,” Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013, pp. 1408–1413, 2013, doi: 10.1109/GreenCom-iThings-CPSCom.2013.246. [70] S. Georgiou, K. Tsakalozos, and A. Delis, “Exploiting network-topology awareness for VM placement in IaaS clouds,” in Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013, 2013, pp. 151–158. doi: 10.1109/CGC.2013.30. [71] “Data center network architectures.” [Online]. Available: https://en.wikipedia.org/wiki/Data_center_network_architectures. [72] C. Guo et al., “BCube: A high performance, server-centric network architecture for modular data centers,” Computer Communication Review, vol. 39, no. 4, pp. 63–74, 2009, doi: 10.1145/1594977.1592577. [73] L. Gyarmati and T. A. Trinh, “Scafida: A scale-free network inspired data center architecture,” 2010. doi: 10.1145/1880153.1880155. [74] A. Singla, C. Y. Hong, L. Popa, and P. B. Godfrey, “Jellyfish: Networking data centers randomly,” Proceedings of NSDI 2012: 9th USENIX Symposium on Networked Systems Design and Implementation, pp. 225–238, 2012. [75] M. C. Çavdar, I. Korpeoglu, and Ö. Ulusoy, “A Utilization Based Genetic Algorithm for virtual machine placement in cloud systems,” Comput Commun, vol. 214, pp. 136–148, Jan. 2024, doi: 10.1016/J.COMCOM.2023.11.028. [76] K. Lacurts, S. Deng, A. Goyal, and H. Balakrishnan, “Choreo: Network-Aware Task Placement for Cloud Applications,” 2013, doi: 10.1145/2504730.2504744.HeshamtAsadiMahmood AlborziHessam Zandhessami112202518920910.61882/jist.49180.13.51.189http://jist.ir/en/Article/49180http://jist.ir/en/Article/Download/49180http://jist.ir/en/Article/Download/49180http://jist.ir/en/Article/Download/49180http://jist.ir/en/Article/Download/49180http://jist.ir/en/Article/Download/49180http://jist.ir/en/Article/Download/49180http://jist.ir/en/Article/Download/49180[1] J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider, and A. Wahab, “A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions,” Electronics (Basel), vol. 9, no. 7, p. 1177, Jul. 2020, doi: 10.3390/electronics9071177. [2] N. Mishra and S. Pandya, “Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review,” IEEE Access, vol. 9, pp. 59353–59377, 2021, doi: 10.1109/ACCESS.2021.3073408. [3] S. A. Bakhsh, M. A. Khan, F. Ahmed, M. S. Alshehri, H. Ali, and J. Ahmad, “Enhancing IoT network security through deep learning-powered Intrusion Detection System,” Internet of Things, vol. 24, p. 100936, Dec. 2023, doi: 10.1016/j.iot.2023.100936. [4] V. Gugueoth, S. Safavat, and S. Shetty, “Security of Internet of Things (IoT) using federated learning and deep learning — Recent advancements, issues and prospects,” ICT Express, vol. 9, no. 5, pp. 941–960, Oct. 2023, doi: 10.1016/j.icte.2023.03.006. [5] M. Macas, C. Wu, and W. Fuertes, “A survey on deep learning for cybersecurity: Progress, challenges, and opportunities,” Computer Networks, vol. 212, p. 109032, Jul. 2022, doi: 10.1016/j.comnet.2022.109032. [6] A. S. Dina, A. B. Siddique, and D. Manivannan, “A deep learning approach for intrusion detection in Internet of Things using focal loss function,” Internet of Things, vol. 22, p. 100699, Jul. 2023, doi: 10.1016/j.iot.2023.100699. [7] B. Alabsi, M. Anbar, and S. Rihan, “CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks,” Sensors, vol. 23, no. 14, p. 6507, Jul. 2023, doi: 10.3390/s23146507. [8] C. Alex, G. Creado, W. Almobaideen, O. A. Alghanam, and M. Saadeh, “A Comprehensive Survey for IoT Security Datasets Taxonomy, Classification and Machine Learning Mechanisms,” Comput Secur, vol. 132, p. 103283, Sep. 2023, doi: 10.1016/j.cose.2023.103283. [9] S.-M. Tseng, Y.-Q. Wang, and Y.-C. Wang, “Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset,” Future Internet, vol. 16, no. 8, p. 284, Aug. 2024, doi: 10.3390/fi16080284. [10] A. S. Ahanger, S. M. Khan, F. Masoodi, and A. O. Salau, “Advanced intrusion detection in internet of things using graph attention networks,” Sci Rep, vol. 15, no. 1, p. 9831, Mar. 2025, doi: 10.1038/s41598-025-94624-8. [11] H. Asadi, M. Alborzi, and H. Zandhessami, “Enhancing IoT Security: A Comparative Analysis of Hybrid Hyperparameter Optimization for Deep Learning-Based Intrusion Detection Systems,” Journal of Information Systems and Telecommunication (JIST), vol. 12, no. 47, pp. 183–196, Nov. 2024, doi: 10.61186/jist.46793.12.47.183. [12] I. Ullah and Q. H. Mahmoud, “Design and Development of RNN Anomaly Detection Model for IoT Networks,” IEEE Access, vol. 10, pp. 62722–62750, 2022, doi: 10.1109/ACCESS.2022.3176317. [13] M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simul Model Pract Theory, vol. 101, p. 102031, May 2020, doi: 10.1016/j.simpat.2019.102031. [14] A. Tchernykh et al., “Scalable Data Storage Design for Nonstationary IoT Environment With Adaptive Security and Reliability,” IEEE Internet Things J, vol. 7, no. 10, pp. 10171–10188, Oct. 2020, doi: 10.1109/JIOT.2020.2981276. [15] Y. Li, Y. Zuo, H. Song, and Z. Lv, “Deep Learning in Security of Internet of Things,” IEEE Internet Things J, vol. 9, no. 22, pp. 22133–22146, Nov. 2022, doi: 10.1109/JIOT.2021.3106898. [16] S. M. Kasongo, “A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework,” Comput Commun, vol. 199, pp. 113–125, Feb. 2023, doi: 10.1016/j.comcom.2022.12.010. [17] B. Madhu, M. Venu Gopala Chari, R. Vankdothu, A. K. Silivery, and V. Aerranagula, “Intrusion detection models for IOT networks via deep learning approaches,” Measurement: Sensors, vol. 25, p. 100641, Feb. 2023, doi: 10.1016/j.measen.2022.100641. [18] R. Zhao et al., “A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things,” IEEE Internet Things J, vol. 9, no. 12, pp. 9960–9972, 2022, doi: 10.1109/JIOT.2021.3119055. [19] S. U. Jan, S. Ahmed, V. Shakhov, and I. Koo, “Toward a Lightweight Intrusion Detection System for the Internet of Things,” IEEE Access, vol. 7, pp. 42450–42471, 2019, doi: 10.1109/ACCESS.2019.2907965. [20] A. Heidari and M. A. Jabraeil Jamali, “Internet of Things intrusion detection systems: a comprehensive review and future directions,” Cluster Comput, vol. 26, no. 6, pp. 3753–3780, Dec. 2023, doi: 10.1007/s10586-022-03776-z. [21] D. Musleh, M. Alotaibi, F. Alhaidari, A. Rahman, and R. M. Mohammad, “Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT,” Journal of Sensor and Actuator Networks, vol. 12, no. 2, p. 29, Mar. 2023, doi: 10.3390/jsan12020029. [22] A. Kumar, K. Abhishek, M. R. Ghalib, A. Shankar, and X. Cheng, “Intrusion detection and prevention system for an IoT environment,” Digital Communications and Networks, vol. 8, no. 4, pp. 540–551, Aug. 2022, doi: 10.1016/j.dcan.2022.05.027. [23] S. Alosaimi and S. M. Almutairi, “An Intrusion Detection System Using BoT-IoT,” Applied Sciences, vol. 13, no. 9, p. 5427, Apr. 2023, doi: 10.3390/app13095427. [24] M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simul Model Pract Theory, vol. 101, p. 102031, May 2020, doi: 10.1016/j.simpat.2019.102031. [25] T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Computers and Electrical Engineering, vol. 99, p. 107810, Apr. 2022, doi: 10.1016/j.compeleceng.2022.107810.ArmanRezasoltaniAmir MohammadKhaniAli Husseinzadeh KashanShahram AgahFatemeh Agah112202516517610.61882/jist.49352.13.51.165http://jist.ir/en/Article/49352http://jist.ir/en/Article/Download/49352http://jist.ir/en/Article/Download/49352http://jist.ir/en/Article/Download/49352http://jist.ir/en/Article/Download/49352http://jist.ir/en/Article/Download/49352http://jist.ir/en/Article/Download/49352http://jist.ir/en/Article/Download/49352[1] M. A. Konerman et al., “Machine learning models to predict disease progression among veterans with hepatitis C virus,” PLOS ONE, vol. 14, no. 1, p. e0208141, Jan. 2019, doi: https://doi.org/10.1371/journal.pone.0208141. [2] Ahmet Ercan Topcu, Ersin Elbasi, and Yehia Ibrahim Alzoubi, “Machine Learning-Based Analysis and Prediction of Liver Cirrhosis,”Jul.2024,doi:https://doi.org/10.1109/tsp63128.2024.10605929. [3] E. B. Tapper and N. D. Parikh, “Diagnosis and Management of Cirrhosis and Its Complications: A Review,” JAMA, vol. 329, no. 18, pp. 1589–1602, May 2023, doi: https://doi.org/10.1001/jama.2023.5997. [4] R. Wei et al., “Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning,” vol. 35, pp. 124–132, Sep. 2018, doi: https://doi.org/10.1016/j.ebiom.2018.07.041. [5] C. Labenz et al., “Structured Early detection of Asymptomatic Liver Cirrhosis: Results of the population-based liver screening program SEAL,” Journal of Hepatology, vol. 77, no. 3,pp.695–701,Sep.2022,doi: https://doi.org/10.1016/j.jhep.2022.04.009. [6] E. Forte et al., “Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression,” Molecular & Cellular Proteomics, pp. 100876–100876, Nov. 2024, doi: https://doi.org/10.1016/j.mcpro.2024.100876. [7] Varshni Premnath and Shanthi Veerappapillai, “Unveiling miRNA–Gene Regulatory Axes as Promising Biomarkers for Liver Cirrhosis and Hepatocellular Carcinoma,” ACS Omega, vol. 9, no. 44, pp. 44507–44521, Oct. 2024, doi: https://doi.org/10.1021/acsomega.4c06551. [8] L. Wang et al., “Impact of Asymptomatic Superior Mesenteric Vein Thrombosis on the Outcomes of Patients with Liver Cirrhosis,” Thrombosis and Haemostasis, vol. 122, no. 12, pp. 2019–2029, Sep. 2022, doi: https://doi.org/10.1055/s-0042-1756648. [9] Md. Nahid Hasan, T. Ahmed, Md. Ashik, Md. Jahid Hasan, Tahaziba Azmin, and J. Uddin, “An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest,” Journal of Information Systems and Telecommunication (JIST), vol. 11, no. 42, pp. 163–175, Jun. 2023, doi: https://doi.org/10.52547/jist.34246.11.42.163. [10] Sudiksha Kottachery Kamath, Sanjeev Kushal Pendekanti, and D. Rao, “LivMarX: An Optimized Low-Cost Predictive Model Using Biomarkers for Interpretable Liver Cirrhosis Stage Classification,” IEEE Access, vol. 12, pp. 92506–92522,Jan.2024,doi:https://doi.org/10.1109/access.2024.3422451. [11] I. Hanif and M. M. Khan, “Liver Cirrhosis Prediction using Machine Learning Approaches,” 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 2022, doi: https://doi.org/10.1109/uemcon54665.2022.9965718. [12] D. Bhardwaj, G. Kaur, and G. L. Babu, “Improving Prognostic Prediction of Cirrhosis Using an Optimized Ensemble Machine Learning Approach,” pp. 1–6, Aug. 2024, doi: https://doi.org/10.1109/ciscon62171.2024.10695979. [13] Bhanu Prakash K, Vennela D, Dhana Lakshmi N, and Siva Priyanka S, “Stage Prediction of Liver Cirrhosis Disease using Machine Learning,” pp. 1–6, Aug. 2024, doi: https://doi.org/10.1109/icecsp61809.2024.10698096. [14] Rauf Jamadar, Harsh Uike, and Vaishali Jabade, “Cirrhosis Disease Prediction Using Machine Learning,” pp. 515–520, Dec.2023,doi:https://doi.org/10.1109/icacctech61146.2023.00090. [15] Tejasv Singh Sidana, S. Singhal, S. Gupta, and R. Goel, “Liver Cirrhosis Stage Prediction Using Machine Learning: Multiclass Classification,” Lecture notes in networks and systems, pp. 109–129, Nov. 2022, doi: https://doi.org/10.1007/978-981-19-3679-1_9. [16] Arif Mudi Priyatno and Triyanna Widiyaningtyas, “A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 9, no. 2, pp. 196–207,Feb.2024,doi: https://doi.org/10.33480/jitk.v9i2.5015. [17] S. I. Khan and A. S. M. L. Hoque, “SICE: an improved missing data imputation technique,” Journal of Big Data, vol. 7, no. 1, Jun. 2020, doi: https://doi.org/10.1186/s40537-020-00313-w. [18] S. Jeganathan, A. R. Lakshminarayanan, S. Parthasarathy, A. Abdul Azeez Khan, and K. J. Sathick, “OptCatB: Optuna Hyperparameter Optimization Model to Forecast the Educational Proficiency of Immigrant Students based on CatBoost Regression,” Journal of Internet Services and Information Security, vol. 14, no. 3, pp. 111–132, Aug. 2024, doi: https://doi.org/10.58346/jisis.2024.i2.008. [19] F. Fazel and B. Foing, “Evaluating Classification Algorithms: Exoplanet Detection using Kepler Time Series Data,” arXiv (CornellUniversity),Feb.2024,doi: https://doi.org/10.48550/arxiv.2402.15874. [20] Fedesoriano, “Cirrhosis Prediction Dataset,” www.kaggle.com.https://www.kaggle.com/fedesoriano/cirrhosis-prediction-dataset. [21] V. Thambawita et al., “An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning Applied to Gastrointestinal Tract Abnormality Classification,” ACM Transactions on Computing for Healthcare, vol. 1, no. 3, pp. 1–29, Jul. 2020, doi: https://doi.org/10.1145/3386295. [22] P. J. Muhammad Ali, “Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements,” ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, vol. 10, no. 1, pp. 85–91, Jun. 2022, doi: https://doi.org/10.14500/aro.10955. [23] K. K, U. K, S. A, and A. Kumar, “Predicting Student Performance for Early Intervention using Classification Algorithms in Machine Learning,” Journal of Information Systems and Telecommunication (JIST), vol. 9, no. 36, pp. 226–235,Oct.2021,doi: https://doi.org/10.52547/jist.9.36.226. [24] T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A Next-generation Hyperparameter Optimization Framework,” arXiv (Cornell University), Jul. 2019, doi: https://doi.org/10.48550/arxiv.1907.10902. [25] I. D. Mienye and N. Jere, “A Survey of Decision Trees: Concepts, Algorithms, and Applications,” IEEE access, pp. 1–1,Jan.2024,doi: https://doi.org/10.1109/access.2024.3416838. [26] A. Jafarnejad, A. Rezasoltani, and A. M. Khani, "Comparative Analysis of Machine Learning Algorithms in Predicting Jumps in Stock Closing Price: Case Study of Iran Khodro Using NearMiss and SMOTE Approaches," Iranian Journal of Finance, vol. 9, no. 3, pp. 27–54, 2025, doi: 10.30699/ijf.2025.491324.1496. [27] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, no. 1, pp. 3–42, Mar. 2006, doi: https://doi.org/10.1007/s10994-006-6226-1. [28] G. Biau and B. Cadre, “Optimization by gradient boosting,” arXiv.org, Jul. 17, 2017. https://arxiv.org/abs/1707.05023 (accessed Apr. 24, 2024). [29] Y. Ding, H. Zhu, R. Chen, and R. Li, “An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification,” Applied Sciences, vol. 12, no. 12, p. 5872, Jun. 2022, doi: https://doi.org/10.3390/app12125872. [29] C. Starbuck, "Logistic regression," in Springer eBooks, pp. 223–238, 2023. doi: 10.1007/978-3-031-28674-2_12. [30] A. Jafarnejad Chaghoshi, A. Rezasoltani, and A. M. Khani, "Unleashing the Power of Ensemble Learning: Predicting National Ranks in Iran’s University Entrance Examination," Industrial Management Journal, vol. 16, no. 3, pp. 457–481, 2024, doi: 10.22059/imj.2024.381521.1008178. [31] G. Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” hal.science, Dec. 04, 2017. https://hal.science/hal-03953007 (accessed Mar. 27, 2023). [32] A. V. Dorogush, V. Ershov, and A. Gulin, “CatBoost: gradient boosting with categorical features support,” arXiv.org, Oct. 24, 2018. https://arxiv.org/abs/1810.11363. [33] Motiei, M., Khani, A. M., & Beyrami, S. (2021). The effect of green supply chain and green human resource management on environmental performance: The mediating role of green innovation. Logistics Thought, 20(77), 165–197. https://doi.org/10.22034/lot.2021.96691. [34] A. Jafarnjad, A. Rezasoltani, and A. M. Khani, "Analyzing and Predicting Hiring Decisions Using Machine Learning and Deep Learning," Journal of Public Administration, vol. 17, no. 2, pp. 295–327, 2025, doi: 10.22059/jipa.2025.390322.3649. [35] Jafarnejad Chaghoshi, A., Khani, A. M., & Rezasoltani, A. (2024). Risk modeling in banking services for the blind using fuzzy FMEA and graph neural network (GNN). Journal of Industrial Management Perspective, 14(4), 223–255. https://doi.org/10.48308/jimp.14.4.223. [36] P.J.Beslin Pajila, B. Gracelin. Sheena, A. Gayathri, J. Aswini, M. Nalini, and Siva Subramanian R, “A Comprehensive Survey on Naive Bayes Algorithm: Advantages, Limitations and Applications,” Sep. 2023, doi: https://doi.org/10.1109/icosec58147.2023.10276274. [37] J. Kasubi, M. D. Huchaiah, I. Gad, and M. K. Hooshmand, “A Comparison Analysis of Conventional Classifiers and Deep Learning Model for Activity Recognition in Smart Homes based on Multi-label Classification,” Journal of Information Systems and Telecommunication (JIST), vol. 12,no46pp127–137,Jun.2024,doi: https://doi.org/10.61186/jist.36294.12.46.127. [38] A. Rezasoltani, A. Jafarnejad, and A. M. Khani, "A voting-based hybrid machine learning model for predicting backorders in the supply chain," Journal of Decisions and Operations Research, vol. 10, no. 1, pp. 194–213, 2025, doi: 10.22105/dmor.2025.511401.1924.AbdallahMaitiMohamedHaniniAbdallahAbarda112202517718810.61882/jist.49725.13.51.177http://jist.ir/en/Article/49725http://jist.ir/en/Article/Download/49725http://jist.ir/en/Article/Download/49725http://jist.ir/en/Article/Download/49725http://jist.ir/en/Article/Download/49725http://jist.ir/en/Article/Download/49725http://jist.ir/en/Article/Download/49725http://jist.ir/en/Article/Download/49725[1].KrawczykB, B. (2016). “Learning from imbalanced data: Open challenges and future directions”. Published in Progress in Artificial Intelligence, V5(4), pp 221-232. [2].Haixiang, G., and al. (2017). “Learning from class-imbalanced data: Review of methods and applications”. Published in Expert Systems with Applications, v73, pp 220-239. [3].LemaîtreG., Nogueira, F., and Aridas, C. K(2017). « Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning”. Published in Journal of Machine Learning Research, v18(17), pp1-5. [4].BrancoP., Torgo, L., andRibeiro, R. P2019). A survey of predictive modeling on imbalanced domains. ACM Computing Surveys, v49(2), pp1-50. [5].He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284. [6].ChawlaN. V. et al2002). SMOTE: Synthetic Minority Over-sampling Technique. Published in Journal of Artificial Intelligence Research, 16, 321-357. [7].Kaur, H. et al. (2019). A systematic review on imbalanced data challenges in machine learning: Applications and solutions. Published in ACM computing surveys (CSUR), 52(4), 1-36. [8].Abdullah, A. A., Mohammed, N. S., Khanzadi, M., Asaad, S. M., Abdul, Z. K., & Maghdid, H. S. (2025). In-depth Analysis on Machine Learning Approaches: Techniques, Applications, and Trends. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 13(1), 190-202. [9].Sabr, S. S., Mustafa, N. S., Omar, T. S., Rasool, S. H., Omer, N. A., Hamad, D. S., ... & Maghdid, H. S. (2025). A Comprehensive Part-of-Speech Tagging to Standardize Central-Kurdish Language: A Research Guide for Kurdish Natural Language Processing Tasks. arXiv preprint arXiv:2504.19645. [10].Kaur, H., Pannu, H. S., and Malhi, A. K. (2019). A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM computing surveys (CSUR), 52(4), 1-36. [11]. LinC. C., Yen, S. J., and Lee, Y. S2017). On combining SMOTE with under-sampling: An experimental study on class imbalance problem. Published in Information Sciences, v371, 123-137. [12].YangC., at al. (2024). Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data. Published in Journal of big data, v11(1), 7. [13].Loffredo, E., Pastore, M., Cocco, S., & Monasson, R. (2024). Restoring balance: principled under/oversampling of data for optimal classification. arXiv preprint arXiv:2405.09535. [14].Buda , M. , Maki, A., and Mazurowski, M. A. (2018). “A systematic study of the class imbalance problem in convolutional neural networks”. Neural Networks, 106, pp 249-259. [15].Leevy, J. L., Khoshgoftaar, T. M., Bauder, R. A., & Seliya, N. (2018). A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1), 1-30. [16].Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. Advances in Intelligent Computing, 878-887. [17].Liu, Y., Wu, T., & Yan, P. (2020). Balancing imbalanced data using adaptive synthetic sampling with feature selection. Computational Intelligence and Neuroscience, 2020, 1-11. [18].Longadge, R., & Dongre, S. (2013). Class imbalance problem in data mining review. arXiv preprint arXiv:1305.1707. [19].Brownlee, J. (2020). Imbalanced classification with Python: better metrics, balance skewed classes, cost-sensitive learning. Machine Learning Mastery. [20].Yadav, S., & Bhole, G. P. (2020, December). Handling imbalanced dataset classification in machine learning. In 2020 IEEE Pune Section International Conference (PuneCon) (pp. 38-43). IEEE. [21].Liu, L., Wu, X., Li, S., Li, Y., Tan, S., & Bai, Y. (2022). Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection. BMC Medical Informatics and Decision Making, 22(1), 82. [22].Maiti, A., Abarda, A., & Hanini, M. (2022, October). A New Hybrid Artificial Intelligence Model for Diseases Identification. In The Proceedings of the International Conference on Smart City Applications (pp. 825-836). Cham: Springer International Publishing. [23].He, H., Garcia, E. A. (2009). “Learning from imbalanced data”. In IEEE Transactions on knowledge and data engineering, 21(9), pp1263-1284. [24].Tanha, J., Abdi, Y., Samadi, N., Razzaghi, N., & Asadpour, M. (2020). Boosting methods for multi-class imbalanced data classification : an experimental review. Journal of Big data, 7, 1-47. [25].Pawara, P., Okafor, E., Groefsema, M., He, S., Schomaker, L. R., & Wiering, M. A. (2020). One-vs-One classification for deep neural networks. Pattern Recognition, 108, 107528. [26].Brownlee, J. (2020). One-vs-rest and one-vs-one for multi-class classification. Machine Learning Mastery. [27].LiQ., SongY., ZhangJ., and ShengV. S2020). « Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering”. Published in Expert Systems with Applications, in147, p113--152. [28].Chakraborty, S., & Dey, L. (2024). Multi-class Classification. In Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance: Theory and Practices (pp. 51-76). Singapore : Springer Nature Singapore. [29].Diabetic Retinopathy Detection data set, in kaggle.com/c/diabetic-retinopathy-detection/data. [30].Maiti , A., Abarda, A., Hanini, M., and Oussous, A. (2024). ”An Optimal Model Combining SqueezeNet and Machine Learning Methods for Lung Disease Diagnosis. Current Medical Imaging, 20(1). [31].Khan, A. A., Chaudhari, O., & Chandra, R. (2024). A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Systems with Applications, 244, 122778.DOI : 10.1016/j.eswa.2023.122778. [32].Araf, I., Idri, A., & Chairi, I. (2024). Cost-sensitive learning for imbalanced medical data: A review. Artificial Intelligence Review, 57(4), 80.DOI : 10.1007/s10462-023-10652-8. [33].Vargas, W. de, Schneider Aranda, J. A., dos Santos Costa, R., da Silva Pereira, P. R., & Victória Barbosa, J. L. (2023). Imbalanced data preprocessing techniques for machine learning: A systematic mapping study. Knowledge and Information Systems, 65(1), 31-57.DOI : 10.1007/s10115-022-01772-8. [34].Liang, G., & Zhang, C. (2012). A Comparative Study of Sampling Methods and Algorithms for Imbalanced Time Series Classification. In AI 2012: Advances in Artificial Intelligence (pp. 637–648). Springer.DOI : 10.1007/978-3-642-35101-3_54. [35].Soleimani, M., & Mirshahzadeh, A. S. (2023). Multi-class classification of imbalanced intelligent data using deep neural network. EAI Endorsed Transactions on AI and Robotics, 2, 1-10.DOI : 10.4108/airo.7998. [36].Chakraborty, S., & Dey, L. (2024). Applications of Multi-objective, Multi-label, and Multi-class Classifications. In Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance: Theory and Practices (pp. 135-164). Singapore: Springer Nature Singapore. DOI : 10.1007/978-981-97-9622-9.