This book contains a collection of research that discusses the latest ideas, applications, and technology related to smart systems, including medical applications, business intelligence, and intelligent-based education. In addition to some papers that shows how is artificial intelligence technologies deals with some problems related to environmental and sustainability.

This text discusses smart grid technologies including home energy management systems, demand management systems, source-side management systems and communication technologies for power supply management, and supervisory control and data acquisition. It further covers applications of rooftop solar PV panels, rooftop solar heating systems, solar streetlights, solar traffic signal systems, and electrical demand management for smart cities. This book: · Includes design and implementation of intelligent and smart techniques using artificial intelligence, the Internet of Things, and machine learning for the development of smart cities. · Covers important topics including smart grid power supply, energy management, smart transport system, smart buildings, and traffic management. · Provides smart solutions for waste management, traffic, parking, energy, and health care system. · Highlights renewable energy applications including rooftop solar PV panels, rooftop solar heating systems, solar traffic signal systems, and electrical demand management. · Presents MATLAB-based simulations and results for smart cities solutions. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, civil engineering, and environmental engineering.

Climate change, natural resource depletion, and unsustainable agricultural practices pose unprecedented challenges to our planet. The increasing environmental footprint of computer networks, communication systems, and other IT infrastructures exacerbates these issues, contributing significantly to energy consumption and greenhouse gas emissions. Without innovative solutions, these challenges will continue to escalate, threatening the sustainability of our planet for future generations. AI Applications for Business, Medical, and Agricultural Sustainability offers a comprehensive solution by harnessing the power of Artificial Intelligence (AI) and High-Performance Computing (HPC). This book is ideal for educators, environmentalists, industry professionals, researchers, and academics. By introducing new energy models, algorithms, and methodologies, the book provides a roadmap for developing next-generation computing and communication infrastructures that are environmentally sustainable.

This book comprehensively discusses the role of cloud computing in artificial intelligence‐based data‐driven systems and hybrid cloud computing for large data‐driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‐based frameworks and advanced computing techniques to deal with online/virtual systems. This book: • Covers the aspects of security, authentication, and prediction for data‐driven systems in heterogeneous environments. • Provides data‐driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‐making for real‐time problems. • Showcases deep learning‐based computer vision algorithms for enhanced pattern detection in different domains based on data‐centric approaches. • Examines the role of the Internet of Things and machine learning algorithms for data‐driven systems. • Highlights the applications of data‐driven systems and cloud computing in enhancing network performance. This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering.

Applying Computational Intelligence for Social Good: Track, Understand and Build a Better World, Volume 132 presents views on how Computational Intelligent and ICT technologies can be applied to ease or solve social problems by sharing examples of research results from studies of social anxiety, environmental issues, mobility of the disabled, and problems in social safety. Sample chapters in this release include Why is implementing Computational Intelligence for social good so challenging? Principles and its Application, Smart crisis management system for road accidents using Geo-Spacial Machine Learning Techniques, Residential Energy Management System (REMS) Using Machine Learning, Text-Based Personality Prediction using XLNet, and much more. - Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation for solving socially relevant problems - Focuses on Forecasting applications, Human Behavior and Critics response analysis in social forums, Healthcare monitoring Systems, Disaster Management, Industrial management, and most recently, Epidemics and Outbreaks - Brings together many different aspects of the current research on intelligence technologies, such as neural networks, support vector machines, fuzzy logic, and evolutionary computation