Manoj Diwakar – författare

Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources.Provides a complete overview of the challenges and pain points in implementing AI-driven diagnostics and disease prediction within the healthcare industryExplores various edge computing applications in healthcare and the use of hardware acceleration for AI applicationsPresents a comprehensive resource that bridges the gap between AI and healthcare

Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support examines how to fuse imaging, genomics, electronic health records, and wearable sensor data into clinically actionable insights. As healthcare data becomes increasingly diverse and voluminous, there is a pressing need for integrative methodologies that preserve information across modalities while maintaining interpretability and safety. Current resources either focus on single-modality AI or domain-specific applications, leaving practitioners with fragmented guidance. This volume defines a cohesive, data-centric framework for multimodal predictive diagnostics and clinical decision support, addressing methodological foundations, reproducible pipelines, and real-world translation challenges.Integrates multimodal AI across imaging, genomics, and clinical dataPresents case studies on predictive diagnostics and decision supportAddresses explainability, fairness, and regulatory compliance challenges

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.

Surgical robotics and computer-assisted surgery represent a rapidly evolving interdisciplinary field that integrates robotics, artificial intelligence, medical imaging, sensing technologies, and human–machine interaction to enhance surgical precision and safety. These technologies enable minimally invasive procedures, improved visualization, real-time decision support, and enhanced dexterity beyond human limitations. As healthcare systems increasingly adopt digital and robotic solutions, computer-assisted surgery is transforming clinical workflows, surgical outcomes, and patient care standards worldwide.This book presents a comprehensive overview of the fundamentals, technologies, and recent advances in surgical robotics and computer-assisted surgery and provides a thorough understanding of the fundamentals and advances in surgical robotics and computer-assisted surgery. It covers core concepts, system architectures, control mechanisms, imaging integration, AI-assisted decision support, and emerging robotic platforms. Through theoretical insights, practical case studies, and clinical applications, the book bridges engineering principles with medical practice, highlighting current challenges, ethical considerations, and future directions in robotic-assisted surgical systems. This edited book provides a comprehensive overview of the latest advancements and applications in surgical robotics. This book covers the fundamental principles, technologies, and clinical applications of robotic surgery.The book is intended for researchers, graduate students, surgeons, biomedical engineers, and industry professionals working in surgical robotics, medical imaging, healthcare technology, and computer-assisted intervention systems.

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 explains medical image processing and analysis using deep learning algorithms to analyze medical data. It focuses on the latest achievements and developments in applying this analysis to medical imaging, clinical, and other healthcare applications.The book covers among other areas:Image acquisition and formation.Computer-aided diagnosis.Image classification.Feature extraction.Image enhancement/segmentation.Medical image processing issues such as segmentation, visualization, registration, and navigation may seem to be distinct, yet they are all intertwined in the process of resolving clinical bottlenecks. Using deep learning algorithms, researchers were able to achieve record-breaking performance and set the bar for future research. Due to the extensive quantity of medical imaging data of CT scan, ultrasound, and MRI, there is widespread use of machine learning, specifically deep learning, to discover specific patterns on such data. Such large data is well quantified by deep learning models. Deep learning is now being utilized, customized, and particularly developed for medical image analysis, as opposed to when it was first introduced to the community. Having learned more about the techniques, researchers have come up with innovative ideas for combining artificial intelligence (AI) with neural networks to solve difficult issues like medical image reconstruction. The key features of this book are:Machine learning and deep learning applications.Medical imaging applications.Feature extraction and analysis.Medical image classification, segmentation, recognition, and registration.Medical image analysis and enhancement.Handling medical image dataset.