Ashish Bagwari - Böcker

Deep Learning Approaches for Healthcare Data Analysis and Decision Making demystifies complex data-driven technologies, providing a clear framework for integrating advanced analytics into healthcare practices. With a focus on practical applications, the authors present a comprehensive digital transformation methodology that empowers readers to tackle the multifaceted challenges of healthcare data management. By leveraging deep learning techniques, readers will learn to analyze vast datasets, identify critical patterns, and develop predictive models that enhance diagnosis and treatment strategies while ensuring compliance with stringent data regulations. The book also addresses the pressing need for ethical AI practices, emphasizing patient privacy and data security. Real-world case studies illustrate how to implement personalized healthcare solutions and foster interdisciplinary collaboration, breaking down silos in knowledge and practice. Moreover, it explores innovative business models for sustainable AI integration, offering actionable insights for healthcare providers. This resource equips professionals with the tools to drive innovation, improve patient outcomes, and navigate the complexities of digital transformation in healthcare, making it a must-read for anyone at the intersection of technology and healthcare.Integrates deep learning and AI into healthcare practices, addressing data management and workflow optimizationIllustrates practical examples in the successful application of deep learning techniques in various healthcare settingsProvides insights into developing and implementing predictive models to enhance diagnosis and treatment strategiesIdentifies and addresses biases in predictive models to enhance trust and accountability in AI-driven decisionsPresents tools and methodologies for managing and analyzing large healthcare datasets to derive meaningful insights and improve decision-making

This book written for students of electronics and communication, students of computer science and communications engineers addresses topics such as Introduction of CRN, Advanced spectrum sensing techniques, Cooperative sensing techniques, Distributed sensing techniques, Issues in advanced sensing techniques, and Applications of 5G Networks. It provides new algorithms, explores recent results, and evaluates the performance of technologies in use in this area. It also provides new research topics and sensing techniques related to 5G networks for researchers.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.Features:Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subjectDiscusses advanced data gathering and sharing/ distributed sensing techniques with its business applicabilityIncludes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow chartsProvides pervasive background knowledge including both wireless communications and WSNsCovers wireless networks as well as sensor network models in detailThis book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.Features:Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subjectDiscusses advanced data gathering and sharing/ distributed sensing techniques with its business applicabilityIncludes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow chartsProvides pervasive background knowledge including both wireless communications and WSNsCovers wireless networks as well as sensor network models in detailThis book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also includes:Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications.Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing.Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0.Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability.Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.

Cognitive radio is 5-G technology, comes under IEEE 802.22 WRAN (Wireless Regional Area Network) standards. It is currently experiencing rapid growth due to its potential to solve many of the problems affecting present-day wireless systems. The foremost objective of "Introduction to Cognitive Radio Networks and Applications" is to educate wireless communication generalists about cognitive radio communication networks. Written by international leading experts in the field, this book caters to the needs of researchers in the field who require a basis in the principles and the challenges of cognitive radio networks.

color: black;">This book constitutes the refereed proceedings of the Second International Conference on Emerging Wireless Technologies and Sciences, ICEWTS 2024, held in Uttarakhand, India, during October 6–7, 2024. The 8 full papers and 4 short papers included in this book were carefully reviewed and selected from 430 submissions.

This book focuses on conceptual frameworks that are helpful in understanding the basics of electronics – what the feedback system is, the principle of an oscillator, the operational working of an amplifier, and other relevant topics. It also provides an overview of the technologies supporting electronic systems, like OP-AMP, transistor, filter, ICs, and diodes. It consists of seven chapters, written in an easy and understandable language, and featuring relevant block diagrams, circuit diagrams, valuable and interesting solved examples, and important test questions. Further, the book includes up-to-date illustrations, exercises, and numerous worked examples to illustrate the theory and to demonstrate their use in practical designs.