Shabnam Kumari - Böcker

AI for Product Development explores the transformative role of artificial intelligence in reshaping modern industries. This book offers a comprehensive guide, spanning the evolution of AI in product innovation to practical applications, such as clustering techniques, human-autonomous vehicle interactions, and personalized healthcare solutions. With contributions from leading researchers, it covers explainable AI, real-world case studies, and ethical considerations in intelligent systems. The chapters delve into cutting-edge topics such as YOLO model variants, AI-driven emotion detection, and strategies for overcoming AI implementation challenges. Designed for researchers, professionals, and students, it bridges theory and practice, emphasizing AI's profound impact on product development and beyond.

Today Privacy and Security in AI-Driven Mental Health Apps has expanding landscape of mental health technology. For that, we need to concentrate on the essential topics of privacy and security in AI-powered mental health apps. As the use of AI-driven solutions in mental health treatment advances, questions about the ethical management of sensitive data, user privacy, and system vulnerabilities have surfaced as serious challenges. This book gives a complete investigation of these difficulties, addressing how privacy-preserving approaches and secure systems might maintain trust, openness, and ethical integrity in mental health applications. The book is organized to cover a broad variety of subjects. It starts with an introduction to mental health applications and their integration with AI technology, followed by a full discussion on the sorts of data acquired, including sensitive behavioral, physiological, and psychological data. It then analyzes privacy threats, such as data breaches and abuse of personal information, and security problems including cyberattacks and flaws in AI systems. This book has some chapters which cover advanced solutions, including encryption approaches, differential privacy, blockchain integration, and federated learning, as well as the importance of regulatory frameworks like GDPR and HIPAA (including case studies of real-world applications to exhibit both achievements and failures in privacy and security). The last parts cover future directions, ethical issues, and suggestions for developers, physicians, and politicians. With contributions from specialists across disciplines, this book serves as important collection for understanding the convergence of AI, mental health, and data security, seeking to stimulate innovation while respecting user rights.