In the industry of manufacturing and design, one major constraint has been enhancing operating performance using less time. As technology continues to advance, manufacturers are looking for better methods in predicting the condition and residual lifetime of electronic devices in order to save repair costs and their reputation. Intelligent systems are a solution for predicting the reliability of these components; however, there is a lack of research on the advancements of this smart technology within the manufacturing industry. AI Techniques for Reliability Prediction for Electronic Components provides emerging research exploring the theoretical and practical aspects of prediction methods using artificial intelligence and machine learning in the manufacturing field. Featuring coverage on a broad range of topics such as data collection, fault tolerance, and health prognostics, this book is ideally designed for reliability engineers, electronic engineers, researchers, scientists, students, and faculty members seeking current research on the advancement of reliability analysis using AI.

"This book explores the theoretical and practical aspects of prediction methods using artificial intelligence and machine learning in the manufacturing field"--

How to minimize the global problem of e-waste KEY FEATURES ● Explore core concepts of Reliability Analysis, various smart models, different electronic components, and practical use of MATLAB. ● Cutting edge coverage on building intelligent systems for reliability analysis. ● Includes numerous techniques and methods to identify failure and reliability parameters. DESCRIPTION Intelligent Reliability Analysis using MATLAB and AI explains a roadmap to analyze and predict various electronic components’ future life and performance reliability. Deeply narrated and authored by reliability experts, this book empowers the reader to deepen their understanding of reliability identification, its significance, preventive measures, and various techniques. The book teaches how to predict the residual lifetime of active and passive components using an interesting use case on electronic waste. The book will demonstrate how the capacity of re-usability of electronic components can benefit the consumer to reuse the same component, with the confidence of successful operations. It lists key attributes and ways to design experiments using Taguchi’s approach, based on various acceleration factors. This book makes it easier for readers to understand reliability modeling of active and passive components using the Artificial Neural Network, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS). The book keeps you engaged with a systematic and detailed explanation of step-wise MATLAB-based implementation of electronic components. These explanations and illustrations will help the readers to predict fault and failure well before time. WHAT YOU WILL LEARN ● Optimize various acceleration factors for exploring the residual life of components experimentally. ● Design an intelligent model to predict the upcoming faults and failures of electronic components and make provision for timely replacement of the fault components. ● Design experiments using Taguchi’s approach. ● Understand reliability modeling of active and passive components using the Artificial Neural Network and Fuzzy Logic. WHO THIS BOOK IS FOR This book is for current and aspiring emerging tech professionals, researchers, students, and anyone who wishes to understand and diagnose the product life of electronic components using the power of artificial intelligence and various experimental techniques. TABLE OF CONTENTS 1. RELIABILITY FUNDAMENTALS 2. RELIABILITY MEASURES 3. REMAINING USEFUL LIFETIME ESTIMATION TECHNIQUES 4. INTELLIGENT MODELS FOR RELIABILITY PREDICTION 5. ACCELERATED LIFE TESTING 6. EXPERIMENTAL TESTING OF ACTIVE AND PASSIVE COMPONENTS 7. INTELLIGENT MODELING FOR RELIABILITY ASSESSMENT USING MATLAB

Cyber-physical systems (CPS) have emerged as a unifying name for systems where cyber parts (i.e., the computing and communication parts) and physical parts are tightly integrated, both in design and during operation. Such systems use computations and communication deeply embedded in and interacting with human physical processes as well as augmenting existing and adding new capabilities. As such, CPS is an integration of computation, networking, and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. Artificial Intelligence Paradigms for Smart Cyber-Physical Systems focuses on the recent advances in Artificial intelligence-based approaches towards affecting secure cyber-physical systems. This book presents investigations on state-of-the-art research issues, applications, and achievements in the field of computational intelligence paradigms for CPS. Covering topics that include autonomous systems, access control, machine learning, and intrusion detection and prevention systems, this book is ideally designed for engineers, industry professionals, practitioners, scientists, managers, students, academicians, and researchers seeking current research on artificial intelligence and cyber-physical systems.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

This book provides a comprehensive exploration of next-generation internet, distributed systems, and distributed computing, offering valuable insights into their impact on society and the future of technology. The use of distributed systems is a big step forward in IT and computer science. As the number of tasks that depend on each other grows, a single machine can no longer handle all of them. Distributed computing is better than traditional computer settings in several ways. Distributed systems reduce the risks of a single point of failure, making them more reliable and able to handle mistakes. Most modern distributed systems are made to be scalable, which means that processing power can be added on the fly to improve performance. The internet of the future is meant to give us freedom and choices, encourage diversity and decentralization, and make it easier for people to be creative and do research. By making the internet more three-dimensional and immersive, the metaverse could introduce more ways to use it. Some people have expressed negative things about the metaverse, and there is much uncertainty regarding its future. Analysts in the field have pondered if the metaverse will differ much from our current digital experiences, and if so, whether people will be willing to spend hours per day exploring virtual space while wearing a headset. This book will look at the different aspects of the next-generation internet, distributed systems, distributed computing, and their effects on society as a whole.

Increasing demand for smart and intelligent devices in human life with better sensing, communication and signal processing is increasingly pushing researchers and designers towards future design challenges based upon internet-of-things (IoT) applications. Several types of research have been done at the level of solid-state devices, circuits, and materials to optimize system performance with low power consumption. For suitable IoT-based systems, there are some key areas, such as the design of energy storage devices, energy harvesters, novel low power high-speed devices, and circuits. Uses of new materials for different purposes, such as semiconductors, metals, and insulators in different parts of devices, circuits, and energy sources, also play a significant role in smart applications of such systems. Emerging techniques like machine learning and artificial intelligence are also becoming a part of the latest developments in an electronic device and circuit design. This groundbreaking new book will, among other things, aid developing countries in updating their semiconductor industries in terms of IC design and manufacturing to avoid dependency on other countries. Likewise, as an introduction to the area for the new-hire or student, and as a reference for the veteran engineer in the field, it will be helpful for more developed countries in their pursuit of better IC design. It is a must have for any engineer, scientist, or other industry professional working in this area.

ICICS is a series of conferences initiated by School of Electronics and Electrical Engineering at Lovely Professional University. Looking at the response to the conference, the bi-annual conference now onwards will be annual. The 5th International Conference on Intelligent Circuits and Systems (ICICS 2023) will be focusing on intelligent circuits and systems for achieving the targets in Sustainable Development Goal (SDG) 3, identified as ‘Good Health and Wellbeing’ by United Nations (Refs: https://sdgs.un.org/goals/goal3, https://sdg-tracker.org/).

While cognitive informatics and natural intelligence are receiving greater attention by researchers, multidisciplinary approaches still struggle with fundamental problems involving psychology and neurobiological processes of the brain. Examining the difficulties of certain approaches using the tools already available is vital for propelling knowledge forward and making further strides. Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence is a collection of innovative research that examines the enhancement of human cognitive performance using emerging technologies. Featuring research on topics such as parallel computing, neuroscience, and signal processing, this book is ideally designed for engineers, computer scientists, programmers, academicians, researchers, and students.

As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.