Kah Phooi Seng - Böcker

Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach provides a comprehensive guide for public health authorities, researchers and health professionals in psychological health. The book takes a unique approach by exploring how Artificial Intelligence (AI) and Machine Learning (ML) based solutions can assist with monitoring, detection and intervention for mental health at an early stage. Chapters include computational approaches, computational models, machine learning based anxiety and depression detection and artificial intelligence detection of mental health.

With the increase in number of natural disasters and the ongoing pandemic, people are experiencing uncertainty, leading to fear, anxiety and depression, hence this is a timely resource on the latest updates in the field.

Examines the datasets and algorithms that can be used to detect mental disorders Covers machine learning solutions that can help determine the precautionary measures of psychological health problems Highlights innovative AI solutions and bi-statistics computation that can strengthen day-to-day medical procedures and decision-making

Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications provides a central source of reference on visual information processing in wireless sensor network environments and its technology, application, and society issues. This book is an important resource for researchers and academics working in the interdisciplinary domains of wireless sensor network technology and multimedia technology and its related areas, which include image processing, pervasive computing, embedded systems, and computer networks.

This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments.

Traditional wireless sensor networks (WSNs) capture scalar data such as temperature, vibration, pressure, or humidity. Motivated by the success of WSNs and also with the emergence of new technology in the form of low-cost image sensors, researchers have proposed combining image and audio sensors with WSNs to form wireless multimedia sensor networks (WMSNs). This introduces practical and research challenges, because multimedia sensors, particularly image sensors, generate huge amounts of data to be processed and distributed within the network, while sensor nodes have restricted battery power and hardware resources. This book describes how reconfigurable hardware technologies such as field-programmable gate arrays (FPGAs) offer cost-effective, flexible platforms for implementing WMSNs, with a main focus on developing efficient algorithms and architectures for information reduction, including event detection, event compression, and multicamera processing for hardware implementations. The authors include a comprehensive review of wireless multimedia sensor networks, a complete specification of a very low-complexity, low-memory FPGA WMSN node processor, and several case studies that illustrate information reduction algorithms for visual event compression, detection, and fusion.  The book will be of interest to academic researchers, R&D engineers, and computer science and engineering graduate students engaged with signal and video processing, computer vision, embedded systems, and sensor networks.