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Synergizing Machine Learning and Artificial Intelligence in Internet of Things Environments: Transformative Applications and Future Direction
0
Zitationen
3
Autoren
2026
Jahr
Abstract
The fusion of Machine Learning (ML) with Artificial Intelligence (AI) for the Internet of Things (IoT) ecosystem has emerged as a transformative approach to harnessing data-driven insights from interconnected devices. This paper brings the synergistic relationship among ML, AI, and IoT, emphasizing technologies that enhance decision-making, automate processes, and improve user experiences across various applications, from smart cities to healthcare. The main concepts of the IoT are examined, including its architecture, components, and processes for generating data. The contributions of ML and AI to the analysis of the considerable amounts of data generated by IoT devices are noted, amidst this the techniques of supervised learning, unsupervised learning, deep learning, and reinforcement learning are elaborated. The difficulties presented by this integration are discussed, such as data privacy issues, security concerns, and limited computing capabilities of edge devices. The paper then addresses the role of model interpretability and transparency, especially for situations in sensitive fields such as healthcare or autonomous systems. Additionally, the case studies demonstrating successful implementations of AI and ML in IoT environments exhibit the ways that both technologies have advanced in a range of applications related to predictive maintenance, energy monitoring and management, and personalized healthcare solutions. This work concludes with thinking forward about trends and what possibilities may lie ahead at the crossing of ML, AI, and IoT, highlighting the need for meaningful frameworks to support ethical and responsible deployment of ML and AI. Overall, our synthesis supports the potential of using ML and AI to attempt transformative change in IoT environments with the construction of new paradigms aimed at efficiency, sustainability, and improving the quality of life.
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