Deepak Garg – 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 aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.

Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

The book aims to develop methodologies and throw light on the advances in empirical research of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.

Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.

This book constitutes the proceedings of the 15th International Conference on Information Systems Security, ICISS 2019, held in Hyderabad, India, in December 2019.The 13 revised full papers and 4 short papers presented in this book together with 4 abstracts of invited talks were carefully reviewed and selected from 63 submissions.The papers cover topics such as: smart contracts; formal techniques; access control; machine learning; distributed systems; cryptography; online social networks; images and cryptography.

This book caters to a wide range of readers, including professionals in the healthcare and IT sectors, as well as security practitioners. This resource provides valuable perspectives on the risks and difficulties currently faced by the healthcare industry and presents practical recommendations for effectively managing these risks and enhancing security and reliability. This book is beneficial for anyone seeking to enhance their understanding of the risks, security, and reliability challenges encountered by the healthcare industry. The provided information offers a comprehensive overview of the issues at hand and provides recommendations for mitigating risks and enhancing security and stability.

This book constitutes the refereed proceedings of the 14th International Conference on Advanced Computing, IACC 2024, held in Bali, Indonesia, during December 13–14, 2024.

The 17 full papers presented in this book were carefully reviewed and selected from 89 submissions. They focus on topics such as: Advancements in Artificial Intelligence and Machine Learning; Applications of Large Language Models; AI in Healthcare and Medical Imaging; AI for Disaster Management and Renewable Energy and Challenges and Ethical Considerations in AI.