Ben Othman Soufiene – författare

This book covers the fundamentals, applications, algorithms, protocols, emerging trends, problems, and research findings in the field of AI and IoT in smart healthcare. It includes case studies, implementation and management of smart healthcare systems using AI. Chapters focus on AI applications in Internet of Healthcare Things, provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and AI, with the real-world examples. This book is aimed at Researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.Features:Focus on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare servicesDiscusses artificial intelligence applications, experiments, core concepts, and cutting-edge themesDemonstrates new approaches to analyzing medical data and identifying ailments using AI to improve overall quality of lifeIntroduces fundamental concepts for designing the Internet of Healthcare Things solutionsIncludes pertinent case studies and applicationsThis book is aimed at researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.

This book covers the fundamentals, applications, algorithms, protocols, emerging trends, problems, and research findings in the field of AI and IoT in smart healthcare. It includes case studies, implementation and management of smart healthcare systems using AI. Chapters focus on AI applications in Internet of Healthcare Things, provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and AI, with the real-world examples. This book is aimed at Researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.Features:Focus on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare servicesDiscusses artificial intelligence applications, experiments, core concepts, and cutting-edge themesDemonstrates new approaches to analyzing medical data and identifying ailments using AI to improve overall quality of lifeIntroduces fundamental concepts for designing the Internet of Healthcare Things solutionsIncludes pertinent case studies and applicationsThis book is aimed at researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.

Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples.Features:Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathologyCovers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.

Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples.Features:Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathologyCovers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.

This book looks at the integration of blockchain technology in healthcare settings, focusing on its potential to address security and privacy concerns of medical applications. From fragmented electronic health records (EHRs) to data breaches and interoperability issues, innovative solutions are necessary to unlock the full potential of health information and prevent the recurrence of such issues.Blockchain offers a promising framework for addressing these challenges. Its decentralized, tamper-resistant nature holds the key to building trust and transparency in healthcare data management. By leveraging blockchain technology, secure, interoperable systems empower patients to take control of their health information while facilitating a seamless collaboration among healthcare providers. Throughout this book, the authors explore the fundamental principles of blockchain technology and its applications within the healthcare landscape. From EHRs and patient consent management to pharmaceutical supply chains and clinical research, this book examines how blockchain can drive efficiency, enhance security, and ultimately improve patient outcomes.This book is intended for a broad audience, including healthcare professionals, patients, policymakers, and anyone interested in the intersection of technology and healthcare.

This book looks at the integration of blockchain technology in healthcare settings, focusing on its potential to address security and privacy concerns of medical applications. From fragmented electronic health records (EHRs) to data breaches and interoperability issues, innovative solutions are necessary to unlock the full potential of health information and prevent the recurrence of such issues.Blockchain offers a promising framework for addressing these challenges. Its decentralized, tamper-resistant nature holds the key to building trust and transparency in healthcare data management. By leveraging blockchain technology, secure, interoperable systems empower patients to take control of their health information while facilitating a seamless collaboration among healthcare providers. Throughout this book, the authors explore the fundamental principles of blockchain technology and its applications within the healthcare landscape. From EHRs and patient consent management to pharmaceutical supply chains and clinical research, this book examines how blockchain can drive efficiency, enhance security, and ultimately improve patient outcomes.This book is intended for a broad audience, including healthcare professionals, patients, policymakers, and anyone interested in the intersection of technology and healthcare.

The volume explores a wide-range of applications of computer vision in the field of healthcare. Computer vision, an interdisciplinary field that combines image processing, pattern recognition, and artificial intelligence, has the potential to revolutionize healthcare by enabling automated analysis and interpretation of medical images and videos. This chapter will provide an overview of the current advancements and potential future directions of computer vision applications in healthcare. Specifically, the book examines the application of computer vision techniques in various healthcare domains, including diagnostic imaging, surgical interventions, clinical decision support, and healthcare management. It reviews the challenges and limitations in implementing computer vision systems in real-world healthcare settings and discusses potential solutions.Overall, this book aims to shed light on the current and potential applications of computer vision in healthcare. It will summarize the state-of-the-art techniques, their benefits, challenges, and implications, while also discussing potential ethical considerations. The objective is to provide healthcare professionals, researchers, and practitioners with an understanding of the transformative impact of computer vision in the healthcare sector and inspire further research and development in this exciting field.

This book explores the applications of machine learning techniques in the healthcare industry to optimize decision-making processes. The book delves into the ways in which machine learning can be used to analyze large and complex healthcare data sets, such as electronic health records, medical imaging, and wearable device data, to extract valuable insights and improve patient care.

HealthTech Horizons provides a concise yet comprehensive view of how cutting-edge technologies are transforming healthcare. Covering AI and machine learning in diagnostics, deep learning architectures (CNNs, RNNs, GANs) for genomics and imaging, synthetic data augmentation, and optimization algorithms (MRMR, EHO, HHWO) for complex biomedical datasets, this book bridges research with real-world applications.It also examines the disruptive rise of deepfake technologies and the metaverse in telemedicine, surgical training, and immersive patient care, while addressing the ethical and computational challenges of integrating these tools responsibly. Designed for researchers, clinicians, and innovators, HealthTech Horizons is both a technical reference and a roadmap for the next era of smart, ethical, and intelligent healthcare systems.The digital revolution in healthcare is no longer on the horizon—it’s here. HealthTech Horizons dives deep into the convergence of biomedical science and advanced computational techniques, offering a research-driven perspective on how technology is redefining modern medicine. This book explores:- Artificial intelligence and machine learning in diagnostics, treatment planning, and personalized medicine.- Deep learning architectures (CNNs, RNNs, GANs) applied to genomics, medical imaging, and biomarker discovery.- Synthetic data augmentation and generative adversarial networks (GANs) for enhancing predictive accuracy.- Feature selection and optimization algorithms (e.g., MRMR, EHO, HHWO) for high-dimensional biomedical datasets.- Deepfake technologies and their dual role in healthcare innovations and security threats.- Metaverse applications in telemedicine, surgical simulation, and immersive patient care.- Ethical and computational challenges in deploying AI responsibly in clinical practice.Bridging research and practice, this book is an indispensable resource for data scientists, bioinformaticians, clinicians, and healthcare innovators seeking to understand—and shape—the next frontier of smart healthcare systems. HealthTech Horizons is not just about the future; it is a roadmap for leveraging algorithms, data, and virtual ecosystems to create resilient, ethical, and intelligent healthcare solutions.