ML-Driven Performance Optimization
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P115Keywords:
Machine Learning, Performance Optimization, Predictive Modeling, Reinforcement Learning, Adaptive Algorithms, Resource Utilization, System EfficiencyAbstract
Machine Learning (ML) has currently become a paradigm shifting technology of enhancing performance of the system in numerous areas like computing, networking, and industrial processes. In the present paper, the author presents the profound study of the performance optimization with the help of ML that is carried out with references to the methods that are grounded on the predictive modelling, reinforcement learning, and adaptive algorithms to maximize performance and resource utilization. The paper examines how ML can be applied to the dynamic environment to identify bottlenecks automatically to streamline the workflow and real-time alteration of strategies. It dwells upon the theoretical deliberations and the practical implementation with particular accent given on the impact of ML on the performance measures (latency, throughput, energy efficiency, and reliability). Large-scale deployment challenges have also been discussed in the paper, and the state of the art methodologies have been reviewed as well as a framework to assess approaches based on ML-based optimisation. Using both experimental findings and case-studies, the article demonstrates that the ML algorithms can enhance the performance level of systems, reduce the operating costs, and become more effective in decision-making
References
[1] Lin, Y. (2023). Optimization and use of cloud computing in big data science. Computing, Performance and Communication Systems, 7(1), 119-124.
[2] Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160.
[3] Mazzei, D., & Ramjattan, R. (2022). Machine learning for industry 4.0: a systematic review using deep learning-based topic modelling. Sensors, 22(22), 8641.
[4] Rahman, M. S., Ghosh, T., Aurna, N. F., Kaiser, M. S., Anannya, M., & Hosen, A. S. (2023). Machine learning and internet of things in industry 4.0: A review. Measurement: Sensors, 28, 100822.
[5] Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2020, 191.
[6] Rane, N. L., Kaya, Ö., & Rane, J. (2024). Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0. Deep Science Publishing.
[7] Rahman, M. A., Shahrior, M. F., Iqbal, K., & Abushaiba, A. A. (2025). Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration. Automation, 6(3), 37.
[8] Liu, X., Qi, H., Jia, S., Guo, Y., & Liu, Y. (2025). Recent Advances in Optimization Methods for Machine Learning: A Systematic Review. Mathematics, 13(13), 2210.
[9] Capacho, J. W. V., Pérez-Zuñiga, G., & Rodriguez-Urrego, L. (2025). Diagnostic analysis and performance optimization of scalable computing systems in the context of industry 4.0. Sustainable Computing: Informatics and Systems, 45, 101067.
[10] Jawad, Z. N., & Balázs, V. (2024). Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review. Beni-Suef University Journal of Basic and Applied Sciences, 13(1), 4.
[11] Barbierato, E., & Gatti, A. (2024). The challenges of machine learning: A critical review. Electronics, 13(2), 416.
[12] Ahmed, I. (2025). Navigating Ethics And Risk In Artificial Intelligence Applications Within Information Technology: A Systematic Review. American Journal of Advanced Technology and Engineering Solutions, 1(01), 579-601.
[13] Pathak, A. R. (2025). Highlights on Utilizing Machine Learning for High Performance Computing Systems. Procedia Computer Science, 258, 1242-1253.
[14] Sage: Practical & Scalable ML-Driven Performance Debugging in Microservices, Online. https://www.csl.cornell.edu/~delimitrou/papers/2021.asplos.sage.pdf
[15] Velesaca, H. O., Holgado-Terriza, J. A., & Gutierrez-Guerrero, J. M. (2025). Industrial Process Automation Through Machine Learning and OPC-UA: A Systematic Literature Review. Electronics, 14(18), 3749.
[16] Yan, Z., Zhang, J., Zhang, R., Liu, Z., Luo, G., Shen, Z., & Shen, Q. (2025). Machine Learning-Driven
[17] Optimization for Multi-Performance Trade-Offs in
[18] Triply Periodic Minimal Surface Lattice Metamaterials. Thin-Walled Structures, 114040.
[19] Reza, M. F. (2023). Machine learning enabled solutions for design and optimization challenges in networks-on-chip based multi/many-core architectures. ACM Journal on Emerging Technologies in Computing Systems, 19(3), 1-26.
[20] Osterhage, W. (2013). Computer Performance Optimization. Springer-Verlag.
[21] Wang, G. G., Cai, X., Cui, Z., Min, G., & Chen, J. (2017). High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Transactions on Emerging Topics in Computing, 8(1), 20-30.
[22] Perera, A. T. D., Wickramasinghe, P. U., Nik, V. M., & Scartezzini, J. L. (2019). Machine learning methods to assist energy system optimization. Applied energy, 243, 191-205.
[23] Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2019). A survey of optimization methods from a machine learning perspective. IEEE transactions on cybernetics, 50(8), 3668-3681.
[24] Li, Z., O'Brien, L., Zhang, H., & Cai, R. (2012, December). A factor framework for experimental design for performance evaluation of commercial cloud services. In 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings (pp. 169-176). IEEE.
[25] Tekale, K. M., & Rahul, N. (2022). AI and Predictive Analytics in Underwriting, 2022 Advancements in Machine Learning for Loss Prediction and Customer Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 95-113. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P111
[26] Tekale, K. M., Enjam, G. R., & Rahul, N. (2023). AI Risk Coverage: Designing New Products to Cover Liability from AI Model Failures or Biased Algorithmic Decisions. International Journal of AI, BigData, Computational and Management Studies, 4(1), 137-146. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I1P114
[27] Tekale, K. M. (2024). AI Governance in Underwriting and Claims: Responding to 2024 Regulations on Generative AI, Bias Detection, and Explainability in Insurance Decisioning. International Journal of AI, BigData, Computational and Management Studies, 5(1), 159-166. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P116
[28] Tekale, K. M. (2022). Claims Optimization in a High-Inflation Environment Provide Frameworks for Leveraging Automation and Predictive Analytics to Reduce Claims Leakage and Accelerate Settlements. International Journal of Emerging Research in Engineering and Technology, 3(2), 110-122. https://doi.org/10.63282/3050-922X.IJERET-V3I2P112
[29] Tekale, K. M., & Enjam, G. reddy. (2023). Advanced Telematics & Connected-Car Data. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 124-132. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P114
[30] Tekale, K. M. (2024). Generative AI in P&C: Transforming Claims and Customer Service. International Journal of Emerging Trends in Computer Science and Information Technology, 5(2), 122-131. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I2P113
[31] Tekale, K. M. T., & Enjam, G. reddy . (2022). The Evolving Landscape of Cyber Risk Coverage in P&C Policies. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 117-126. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P113
[32] Tekale, K. M., & Rahul, N. (2023). Blockchain and Smart Contracts in Claims Settlement. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 121-130. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P112
[33] Tekale, K. M., Rahul, N., & Enjam, G. reddy. (2024). EV Battery Liability & Product Recall Coverage: Insurance Solutions for the Rapidly Expanding Electric Vehicle Market. International Journal of AI, BigData, Computational and Management Studies, 5(2), 151-160. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P115
[34] Tekale, K. M. (2023). Cyber Insurance Evolution: Addressing Ransomware and Supply Chain Risks. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 124-133. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P113
[35] Tekale, K. M., & Enjam, G. R. (2024). AI Liability Insurance: Covering Algorithmic Decision-Making Risks. International Journal of AI, BigData, Computational and Management Studies, 5(4), 151-159. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P116
[36] Tekale , K. M. (2023). AI-Powered Claims Processing: Reducing Cycle Times and Improving Accuracy. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 113-123. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P113
[37] Tekale, K. M., & Rahul, N. (2024). AI Bias Mitigation in Insurance Pricing and Claims Decisions. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 138-148. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P113
