Amita Nandal – författare

In an effort to investigate the revolutionary potential of augmented reality (AR) and virtual reality (VR) technologies in the field of mechanical and medical emergency management, Virtual Reality, Real Emergency was written. This book seeks to educate readers on how AR and VR may transform response to emergencies, boost training, and facilitate better decision-making under pressure. It aims to equip professionals and hobbyists alike to use these immersive technologies for saving lives and minimizing calamities by analyzing real-world applications, cutting-edge breakthroughs, and practical insights.The ground-breaking book Virtual Reality, Real Emergency: Integrating AR/VR in Computing and Medical Crisis Management has two goals: to teach and to give readers agency in times of crisis. Our goal is to educate both industry experts and casual observers on the revolutionary possibilities of AR and VR in the high-stakes arenas of mechanical and medical emergency response. The gap between theory and practice is something we want to close by providing real-world insights, practical counsel, and examples of achievement. Our goal is to educate the public on the potential life-saving benefits of AR and VR technology, demystify these tools for the general reader, and provide concrete measures for incorporating them into existing emergency response procedures.This book not only celebrates the technological advances that have been accomplished but also calls for more cooperation to ensure that these innovations are implemented in a way that puts people’s lives first during emergencies.

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment.This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision.Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions.The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.