Developments Towards Next Generation Intelligent Systems For Sustainable Development

The rapid proliferation of connected devices in our daily lives, from smart homes to industrial sensors, has led to an explosion of data that requires processing before it is useful to experts. However, modern devices often have limited resources, making it challenging to decode and utilize this data effectively. Additionally, the need for real-time decision-making further complicates this issue, as traditional data processing methods take far too long to be able to keep up with the required volume and speed. Developments Towards Next Generation Intelligent Systems for Sustainable Development offers a comprehensive solution to these challenges by integrating novel technologies such as AI, edge computing, federated learning, quantum computing, and more. The book shows how intelligent systems can maximize computing power by leveraging these technologies to process large volumes of data efficiently and autonomously and make real-time decisions. The proposed architectures and frameworks focus on real-time analysis, faster decision-making, enhanced privacy, and efficient data processing.

The rapid proliferation of connected devices in our daily lives, from smart homes to industrial sensors, has led to an explosion of data that requires processing before it is useful to experts. However, modern devices often have limited resources, making it challenging to decode and utilize this data effectively. Additionally, the need for real-time decision-making further complicates this issue, as traditional data processing methods take far too long to be able to keep up with the required volume and speed. Developments Towards Next Generation Intelligent Systems for Sustainable Development offers a comprehensive solution to these challenges by integrating novel technologies such as AI, edge computing, federated learning, quantum computing, and more. The book shows how intelligent systems can maximize computing power by leveraging these technologies to process large volumes of data efficiently and autonomously and make real-time decisions. The proposed architectures and frameworks focus on real-time analysis, faster decision-making, enhanced privacy, and efficient data processing.

This book discusses issues related to ICT, intelligent systems, data science, AI, machine learning, sustainable development and overall their impacts on sustainability. It provides an overview of the technologies of future. The book also discusses novel intelligent algorithms and their applications to move from a data-centric world to sustainable world. It includes research paradigms on sustainable development goals and societal impacts. The book provides an overview of cutting-edge techniques toward sustainability and ideas to help researchers who want to understand the challenges and opportunities of using smart management perspective for sustainable society. It serves as a reference to wide ranges of readers from computer science, data analysts, AI technocrats and management researchers.

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum Addresses the advanced field of renewable generation, from research, impact and idea development of new applications

The world is addressing the insistent challenge of climate change, and the need for innovative solutions has become paramount. In this period of technical developments, artificial intelligence (AI) has emerged as a powerful instrument with enormous prospects to combat climate change and other environmental subjects. AI's ability to process vast amounts of data, identify patterns, and make intelligent predictions offers unprecedented opportunities to tackle this global crisis. High-Performance Computing (HPC) or super-computing environments address these large and complex challenges with individual nodes (computers) working together in a cluster (connected group) to perform massive amounts of computing in a short period. Creating and removing these clusters is often automated in the cloud to reduce costs. Computer networks, communication systems, and other IT infrastructures have a growing environmental footprint due to significant energy consumption and greenhouse gas emissions. To address this seemingly self-defeating conundrum, and create a truly sustainable environment, new energy models, algorithms, methodologies, platforms, tools, and systems are required to support next-generation computing and communication infrastructures. Harnessing High-Performance Computing and AI for Environmental Sustainability navigates through AI-driven solutions from sustainable agriculture and land management to energy optimization and smart grids. It unveils how AI algorithms can analyze colossal datasets, offering unprecedented insights into climate modeling, weather prediction, and long-term climate trends. Integrating AI-powered optimization algorithms revolutionizes energy systems, propelling the transition towards a low-carbon future by reducing greenhouse gas emissions and enhancing efficiency. This book is ideal for educators, environmentalists, industry professionals, and researchers alike, and it explores the ethical dimensions and policies surrounding AI's contribution to environmental development.

Picture a world where autonomous systems operate continuously and intelligently, utilizing real-time data to make informed decisions. Such systems have the potential to revolutionize agriculture, urban infrastructure, and industrial automation. This transformation, often termed the Internet of Self-Sustaining Systems (IoSS), is a pivotal topic that demands academic attention and exploration. Addressing this critical issue head-on is The Convergence of Self-Sustaining Systems With AI and IoT, which offers an in-depth examination of this transformative convergence. It serves as a guiding light for academic scholars seeking to unravel the vast potential of self-sustaining systems coupled with AI and IoT. Inside its pages, readers will delve into AI-driven autonomous agriculture, eco-friendly transportation solutions, and intelligent energy management. Moreover, the book explores emerging technologies, security concerns, ethical considerations, and governance frameworks. Join us on this intellectual journey and position yourself at the forefront of the AI and IoT revolution that promises a sustainable, autonomous future.