Manisha Guduri – författare

Federated Intelligence in Medicine: AI-Driven Robotics for Secure and Intelligent Healthcare Systems explores the rapidly evolving field where medical robotics intersects with cutting-edge artificial intelligence and secure data-sharing technologies. Covering a comprehensive range of topics, the book begins with foundational knowledge in medical robotics and Generative AI, progressing to advanced concepts such as federated learning and architectural modeling tailored for AI-driven robotic systems. Readers will explore algorithms enhancing surgical robotics, remote robotic surgeries, and human-robot interaction. The text also tackles AI-driven diagnostic robots, privacy preservation through homomorphic encryption, and differential privacy and emerging medical imaging techniques integrated with robotics.Later chapters examine the synergy of large language models, agent AI, and edge AI with robotic technologies, providing both theoretical frameworks and practical case studies that illuminate real-world applications and challenges. This book is an invaluable resource for researchers and academicians engaged in artificial intelligence, robotics, and medical technology. It also serves undergraduate and graduate students in Biomedical Engineering, Electronics and Electrical Engineering, and Computer Science and Engineering.Explores the integration of Generative AI and Federated Learning for adaptive and secure medical robotic systemsProvides practical case studies that demonstrate real-world applications in surgical and diagnostic roboticsAddresses privacy preservation techniques, including homomorphic encryption and differential privacy for healthcare data securityCovers emerging trends in AI-driven medical imaging and human-robot interaction technologiesBridges theoretical concepts with implementation strategies for researchers, professionals, and students in medical technology

The aim of this book is to identify some of the challenges that need to be addressed to accelerate the deployment and adoption of smart health technologies for ubiquitous healthcare access especially in wearable devices. These wearable devices may include pacemakers, defibrillators, RFID devices, assistive devices for the visually impaired, magnifiers, and talking assistants. It also explores how Internet of Things (IoT) and big data technologies can be combined with these wearable devices to provide better healthcare solutions. Features:Focuses on real-time implementation of deep and machine learning techniques as well as novel algorithms for smart healthcare.Explores innovative challenges and solutions to complex problems in assistive devices with Medical 4.0 technologies.Presents an overview of challenges in the design of medical wearable devices.Discusses different techniques on VLSI for medical devices.Includes a case study on an AI-tuned cardiac pacemaker.This book is aimed at graduate students and researchers in biomedical, electrical, computer engineering, and medical technologies.

The objective of the conference is to bring to focus the recent technological advancements across all the stages of data analysis including acquisition, processing, and communication. Advancements in acquisition sensors along with improved storage and computational capabilities, have stimulated the progress in theoretical studies and state-of-the-art real-time applications involving large volumes of data. This compels researchers to investigate the new challenges encountered, where traditional approaches are incapable of dealing with large, complicated new forms of data.

This new volume covers a diverse range of advances in intelligent systems that involve applications of AI and digital automation, such as machine learning, deep learning, sectional convolutional neural network models, and much more. It explores advances in predictive analysis, such as using AI for leukemia detection and other diseases, strategic decision-making models using data analytics, video object detection using convolution, CNN-based mask detection, medical image watermarking using RDWT-SVD-DCT, and more.