ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.

ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.

RESOURCE MANAGEMENT IN ADVANCED WIRELESS NETWORKS Written and edited by a team of experts in the field, this exciting new volume provides a comprehensive exploration of cutting-edge technologies and trends in managing resources in advanced wireless networks. This groundbreaking new volume from Wiley-Scrivener discusses the challenges that are emerging while managing the resources in various wireless networking technologies. Initially, the evolution of wireless networking technologies is presented, focusing on the advantages of improving data rates and data reliability. The book then goes through the various architecture designs based on the network paradigms, along with the evolution of networks based on the trends in the telecommunication industry. Various salient features are highlighted in managing resources, and the role of routing strategies is addressed with regard to real-time applications. Covering resource management in wireless networks, various industries are covered, such as healthcare and financial services, but the ideas are useful across many industries. Whether for the veteran engineer, industry professional, or student, this is a must- have for any library.

The integration of artificial intelligence (AI), quantum computing, and semiconductor technology offers improved innovation to redefine computational power and capabilities. As AI drives advances in machine learning and data processing, quantum computing revolutionizes problem-solving with its ability to handle complex calculations at improved speeds. Advancements in semiconductor technology push the limits of processing efficiency and miniaturization. Continued exploration on this convergence may accelerate breakthroughs in various fields such as cryptography, material science, and healthcare. Integration of AI, Quantum Computing, and Semiconductor Technology explores the intersection of artificial intelligence (AI) and semiconductor technology within the context of quantum computing. It offers a comprehensive analysis of the current advancements, challenges, and potential applications resulting from this convergence. This book covers topics such as cyber security, healthcare monitoring, and machine learning, and is a useful resource for computer engineers, energy scientists, business owners, healthcare administrators, environmental scientists, academicians, and researchers.

Also contains brochures, directories, manuals, and programs from various College of Engineering student organizations such as the Society of Women Engineers and Tau Beta Pi.