Deepak Kumar Jain – författare

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.

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.

The book explores the complete system perspective, underlying theories, modelling, and the applications of pattern recognition in Healthcare Recommender System. Considering the interest of researchers and academicians, editors here aim to present this book in a multidimensional perspective that will be covering Healthcare Recommender Systems in depth, considering pattern recognition techniques using amalgamation of emerging technologies. It aims to cover all topics ranging from discussion of recommender system to efficient management to recent research challenges and issues. Editors aim to present the book in a self-sufficient manner and in order to achieve this, the book has been organized into various chapters.

The prime focus of the book is to explore the various issues, challenges, and research directions of pattern recognition in Healthcare Recommender Systems. The table of contents is designed in a manner so as to provide the reader with a broad list of its applications. Additionally, the book addresses the transformations in the area of Healthcare Recommender Systems. Thus, the book plans to discuss the recent research trends and advanced topics in the field of healthcare automation system which will be of interest to industry experts, academicians and researchers working in this area. Hence, the editors aim is to cover diversity in the domain while achieving completeness.

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.