Hazra Imran – författare

Sustainable AI Techniques for Real-Time Risk Monitoring offers a comprehensive examination of energy-efficient artificial intelligence approaches for hazard detection in smart environments. The book begins by identifying the limitations of traditional AI models —particularly their high computational and energy demands — and introduces the concept of Green AI as a sustainable alternative. It systematically presents key methodologies, including lightweight deep learning architectures, model optimization techniques, and the integration of edge and fog computing. In addition, it explores advanced paradigms such as federated learning and bio-inspired computing to enable scalable and resource-efficient real-time monitoring systems.The book further elaborates on practical applications across diverse domains, including fire hazard detection, industrial safety, environmental monitoring, and smart healthcare systems. It also examines how secure and decentralized technologies—such as blockchain—enhance the reliability of IoT-based hazard detection frameworks. The concluding section outlines future research directions, emphasizing renewable-powered IoT infrastructures and the ethical, legal, and societal implications of Green AI.Overall, this book serves as a valuable resource for academics, researchers, and practitioners striving to develop sustainable, reliable, and energy-conscious intelligent safety systems.

Sustainable AI Techniques for Real-Time Risk Monitoring offers a comprehensive examination of energy-efficient artificial intelligence approaches for hazard detection in smart environments. The book begins by identifying the limitations of traditional AI models —particularly their high computational and energy demands — and introduces the concept of Green AI as a sustainable alternative. It systematically presents key methodologies, including lightweight deep learning architectures, model optimization techniques, and the integration of edge and fog computing. In addition, it explores advanced paradigms such as federated learning and bio-inspired computing to enable scalable and resource-efficient real-time monitoring systems.The book further elaborates on practical applications across diverse domains, including fire hazard detection, industrial safety, environmental monitoring, and smart healthcare systems. It also examines how secure and decentralized technologies—such as blockchain—enhance the reliability of IoT-based hazard detection frameworks. The concluding section outlines future research directions, emphasizing renewable-powered IoT infrastructures and the ethical, legal, and societal implications of Green AI.Overall, this book serves as a valuable resource for academics, researchers, and practitioners striving to develop sustainable, reliable, and energy-conscious intelligent safety systems.

The book 'Text Mining Approaches for Biomedical Data' delves into the fascinating realm of text mining in healthcare. It provides an in-depth understanding of how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing healthcare research and patient care. The book covers a wide range of topics such as mining textual data in biomedical and health databases, analyzing literature and clinical trials, and demonstrating various applications of text mining in healthcare.This book is a guide for effectively representing textual data using vectors, knowledge graphs, and other advanced techniques. It covers various text mining applications, building descriptive and predictive models, and evaluating them. Additionally, it includes building machine learning models using textual data, covering statistical and deep learning approaches.This book is designed to be a valuable reference for computer science professionals, researchers in the biomedical field, and clinicians. It provides practical guidance and promotes collaboration between different disciplines. Therefore, it is a must-read for anyone who is interested in the intersection of text mining and healthcare.