Call for Chapters: Combining Visual Intelligence and Federated Learning in Smart Healthcare

Editors

Manisha Guduri, University of Louisiana at Lafayette, United States
Chinmay Chakraborty, Birla Institute of Technology, India
Martin Margala, University of Louisiana at Lafayette, United States

Call for Chapters

Proposals Submission Deadline: April 21, 2024
Full Chapters Due: July 14, 2024
Submission Date: July 14, 2024

Introduction

Smart devices in healthcare can interact with the environment by gathering, processing, interpreting, storing, and retrieving information originated from sensors, neuromorphic analog circuits, robots, and other data retrieving sources through explainable AI, Internet of Things, Gestural technology, federated learning. These systems can exploit Visual Languages to improve communication with people in real-life scenarios, such as intelligent devices which Recognize patterns easily, sharpens the perception.

Objective

The objective of this book aims to implement emerging technology to transforms images from the original grayscale and reveals hidden patterns, into new well-defined structured patterns empowering research community, and computer analytics to remove indicative uncertainty. Such languages, combined with intelligent, experience-based, healthcare systems, fall in the area of Visual Intelligence, empowering people to understand how machines process the data smart healthcare devices. Moreover, Visual Intelligence comprises all the processes enabling machines to analyze and interpret the data. The combination of smart healthcare and Visual Intelligence with federated learning has given rise to exciting new applications in fields as diverse as healthcare, education, marketing, games, and automotive.

Target Audience

1. Researchers in AI and Healthcare
2. Graduate students in Computer Science
3. Medical Students

Recommended Topics

Anchored at healthcare applications, Visual Intelligence focuses on research areas that connect visual information processing with intelligence, federated learning, and the Internet of things, promoting their integration and exciting new developments. Analog VLSI circuits for the perception of visual motion is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics, and computational neuroscience.

This special issue invites research papers/letters/ review articles from all over the globe to publish leading international research on the theories, practices, and experiences in the field of smart healthcare for visual intelligence with federated learning. Through smart healthcare for visual intelligence, it is expected to improve teaching and learning practices.

• Man-machine interface design for human visual motion perception systems.
• Gestural technology in smart healthcare
• Integrating explain-ability in Artificial Intelligence for data visualization.
• AI and FL techniques for enhanced accessibility systems
• Visual learning techniques for smart healthcare
• AI Tools for health Informatics
• Aesthetic computing
• Decision model visualization for human behavior analysis in human-robot interaction
• Medical IoT for healthcare
• Knowledge based recommended systems for visual analytics.
• Neuromorphic analog VLSI systems for visual motion sensing
• Neuromorphic sensory systems for visual motion
• Federated learning for visual intelligence
• Face detection and recognition, face anti-spoofing, face landmark detection and parsing.
• Deeplearning techniques in healthcare

Submission Procedure

Researchers and practitioners are invited to submit on or before April 21, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by May 5, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by July 14, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Combining Visual Intelligence and Federated Learning in Smart Healthcare. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

April 21, 2024: Proposal Submission Deadline
May 5, 2024: Notification of Acceptance
July 14, 2024: Full Chapter Submission
August 25, 2024: Review Results Returned
September 22, 2024: Final Acceptance Notification
September 29, 2024: Final Chapter Submission



Inquiries

Manisha Guduri
University of Louisiana at Lafayette
manisha.guduri@louisiana.edu

Chinmay Chakraborty
Birla Institute of Technology
chinmay.chakraborty@gmail.com

Martin Margala
University of Louisiana at Lafayette
martin.margala@louisiana.edu

Classifications


Computer Science and Information Technology; Education; Medicine and Healthcare; Physical Sciences and Engineering
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