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| Title | Advancements In Machine Learning For Iot: Ai-Driven Optimization And Security |
|---|---|
| ID_Doc | 6593 |
| Authors | Rao G.S.; Yuvaraj S.A.; Kondapi N.R.; Kumari A.R.; Palepu N.R.; Bharathi C.R.; Arulananth T.S.; Ebinezer M.J.D. |
| Year | 2025 |
| Published | Journal of Information Systems Engineering and Management, 10, 17 |
| DOI | http://dx.doi.org/10.52783/jisem.v10i17s.2787 |
| Abstract | With an emphasis on AI-driven optimisation and security improvements, this paper explores current developments in machine learning (ML) methodologies and their applications in the Internet of Things (IoT). The proliferation of IoT devices has made effective data management and strong security measures more important than ever. IoT systems can enhance their decision-making and performance by utilising machine learning algorithms, especially those that use supervised, unsupervised, and reinforcement learning. The study looks at several optimisation techniques that improve operational efficiencies in sectors like manufacturing, healthcare, and smart cities, such as resource allocation and predictive maintenance. It also looks at how anomaly detection, intrusion prevention systems, and behaviour-based authentication techniques are some ways that machine learning improves IoT security. The study does, however, also address important issues like scalability, data protection, and integrating machine learning models across various IoT ecosystems. In the end, this study demonstrates how machine learning can be used to build IoT settings that are smarter, more effective, and safer, opening the door for further innovation and advancement in this quickly changing industry. Copyright © 2024 by Author/s and Licensed by JISEM. |
| Author Keywords | AI-Driven Optimization; Anomaly Detection; Behavioral Authentication; Data Privacy; Energy Management; Intrusion Prevention; IoT Security; Machine Learning; Neural Networks; Predictive Maintenance; Resource Allocation; Scalability |
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