Internet Of Behavior Based Computational Intelligence For Smart Education

Internet of Behavior-Based Computational Intelligence for Smart Education Systems

Smart education is transforming the way we teach and learn by incorporating advanced technologies that enhance interactivity, personalization, and efficiency. By integrating digital tools, data analytics, and innovative strategies, it can better address the needs of 21st-century learners. Central to this shift is the Internet of Behavior (IoB). As IoT grows, it collects behavior-related data and combines that data analysis with intelligent systems. While this fusion enhances personalized learning, it also raises important concerns about privacy, security, and ethics. Internet of Behavior-Based Computational Intelligence for Smart Education Systems explores the intricacies of how IoB and computational intelligence are transforming education. It offers practical insights and theoretical foundations on how behavioral data can create smarter, more adaptive learning environments. Targeting educators, technologists, and researchers, the book delves into the latest developments at the intersection of technology and education, guiding readers toward more personalized, efficient, and effective learning systems.

INTERNET OF BEHAVIOR-BASED COMPUTATIONAL INTELLIGENCE FOR SMART EDUCATION.

Federated Learning for Neural Disorders in Healthcare 6.0

This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders. This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning Offers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examples It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.