Deepak Kumar Jain - Böcker

AI-Driven Human-Machine Interaction for Biomedical Engineering: Concepts, Applications, and Methodologies offers a comprehensive examination of the intricate relationship between humans and machines, particularly through the transformative lens of artificial intelligence (AI). As AI technologies rapidly evolve, understanding their implications for human-machine interaction (HMI) has become essential across various domains, especially healthcare. Structured into well-defined chapters, the book begins with an introduction to AI-driven HMI, laying the groundwork for understanding its significance in sustainable healthcare and beyond. Subsequent chapters explore critical topics such as machine learning principles, advanced biomedical data classification methods, and the role of AI in telemedicine.Readers will delve into cutting-edge techniques, from deep learning to non-invasive computer vision, while also examining the implications of these technologies across industries. Each chapter equips readers with actionable insights and highlights emerging trends, ethical considerations, and the future of AI in HMI, ensuring a well-rounded perspective on this dynamic field. This is an invaluable resource for researchers, academics, and students in the fields of Biomedical Engineering, Computer Science, Data Science, Artificial Intelligence, and Healthcare Technology.Offers practical insights into AI-driven methodologies for enhanced human-machine collaboration in healthcare and beyondProvides foundational knowledge of machine learning principles applicable across diverse industriesEquips readers with cutting-edge techniques for biomedical data classification and analysisAddresses ethical considerations and emerging trends in AI applications for informed decision-makingFacilitates innovation by bridging theoretical concepts with real-world applications in human-machine interaction

Taking a broad, multidisciplinary approach to the ethical, legal, and practical dimensions of developing AI, the book puts key issues such as bias, transparency, accountability, and privacy at center stage. From computer science through law and ethics into policy, it lays down the roadmap on which developers, data scientists, and policymakers could bring AI technologies properly attuned to societal values. This book can be read as a resource for those who want to be able to guide the development and deployment of responsibly controlled AI systems.

Gain a competitive edge in the semiconductor industry with this essential guide, which provides the practical insights and machine learning techniques needed to optimize the fabrication of hybrid nanodevices for integrated circuits. Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning explores the intersection of advanced manufacturing techniques and machine learning applications in the field of nanotechnology, specifically focusing on hybrid nanodevices for integrated circuits. This book provides a comprehensive understanding of how machine learning algorithms and techniques can optimize the fabrication processes of hybrid nanodevices, improving their efficiency, reliability, and performance in integrated circuit applications. The book begins with an introduction to the fundamentals of hybrid nanodevice fabrication and the role of machine learning in enhancing these processes. It then delves into various machine learning algorithms and models used for process optimization, quality control, and predictive maintenance in integrated circuit fabrication. Case studies and practical examples illustrate real-world applications of machine learning in improving yield, reducing costs, and accelerating time-to-market for hybrid nanodevices. It also addresses the pressing need for a comprehensive guide on machine learning applications in nanodevice fabrication. It provides researchers, engineers, and industry professionals with practical insights for implementing machine learning techniques to tackle challenges such as variability reduction, defect detection, and process optimization. By bridging the gap between theory and practice, the book equips readers with the knowledge and tools necessary to leverage machine learning for a competitive advantage in the semiconductor industry.

The book explores the complete system perspective, underlying theories, modelling, and the applications of pattern recognition in Healthcare Recommender System.

This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders.This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features:● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection.● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders.● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.