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Study on Integrating Machine Learning Algorithms in Modern Electronic Device Design
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Author(s): Gopikrishna Pasam (Department of Engineering, University of Technology and Applied Sciences, Oman), Swetha Kannepally (Department of Electrical and Electronics Engineering, SRKR Engineering College, India), Nellore Manoj Kumar (Department of Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India), Parveen Sharma (School of Mechanical Engineering, Lovely Professional University, Phagwara, India)and Prashant Kumar Shukla (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India)
Copyright: 2025
Pages: 24
Source title:
Integrating Artificial Intelligence Into the Energy Sector
Source Author(s)/Editor(s): Abdelkader Mohamed Sghaier Derbali (Taibah University, Saudi Arabia)
DOI: 10.4018/979-8-3693-7112-1.ch020
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Abstract
This chapter explores the integration of Machine Learning (ML) algorithms in the design of modern electronic devices, highlighting their transformative impact on performance, functionality, and user experience. With the proliferation of smart devices, ML algorithms enable adaptive learning, predictive maintenance, and real-time decision-making, significantly enhancing device efficiency. The chapter delves into key ML techniques such as neural networks, reinforcement learning, and support vector machines, discussing their applications in optimizing hardware design, energy management, and user interfaces. Additionally, it examines the challenges of implementing ML in constrained environments, including power limitations and computational overhead. This chapter provides a comprehensive guide for engineers, researchers, and developers to utilize machine learning in electronic device innovation, driving the evolution of intelligent, self-learning systems. Keywords: Machine Learning, Electronic Device Design, Smart Devices, Neural Networks, Reinforcement Learning, Support Vector Machines, Hardware Optimization, Energy Management, Adaptive Learning
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