Pawan Whig – författare

Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development.The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.

Quantum Learning: Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education explores the transformative intersection of three revolutionary technologies reshaping the future of learning. The concept of quantum learning provides a paradigm where quantum principles redefine machine learning models, enhance computational speed, and enable novel personalized education systems.The book integrates artificial intelligence (AI), quantum algorithms, and data-driven pedagogy to reimagine classrooms and cognitive processes. Readers will discover how quantum-inspired neural networks, quantum data analysis, and intelligent tutoring systems can revolutionize educational delivery. Through interdisciplinary research, the work translates complex quantum and AI concepts into practical educational applications, featuring case studies and real-world insights that demonstrate how quantum-enhanced intelligence can personalize learning and improve outcomes. The text covers both theoretical frameworks and practical implementation strategies, offering a blueprint for adaptive, scalable learning ecosystems.This wide-ranging book will appeal to a diverse audience of researchers, educators, technologists, and policymakers seeking to understand and shape the next generation of education innovation. By combining these domains into one book and using an accessible approach, it makes cutting-edge concepts comprehensible to both technical and non-technical readers, positioning it as an essential resource for anyone involved in educational technology, artificial intelligence research, or quantum computing applications in learning environments.

Quantum Learning: Bridging Artificial Intelligence, Quantum Computing, and Data Science in Education explores the transformative intersection of three revolutionary technologies reshaping the future of learning. The concept of Quantum Learning provides a paradigm where quantum principles redefine machine learning models, enhance computational speed, and enable novel personalized education systems.The book integrates AI, quantum algorithms, and data-driven pedagogy to reimagine classrooms and cognitive processes. Readers will discover how quantum-inspired neural networks, quantum data analysis, and intelligent tutoring systems can revolutionize educational delivery. Through interdisciplinary research, the work translates complex quantum and AI concepts into practical educational applications, featuring case studies and real-world insights that demonstrate how quantum-enhanced intelligence can personalize learning and improve outcomes. The text covers both theoretical frameworks and practical implementation strategies, offering a blueprint for adaptive, scalable learning ecosystems.This wide-ranging book will appeal to a diverse audience of researchers, educators, technologists, and policymakers seeking to understand and shape the next generation of education innovation. By combining these domains into one book and using an accessible approach, it makes cutting-edge concepts comprehensible to both technical and non-technical readers, positioning it as an essential resource for anyone involved in educational technology, artificial intelligence research, or quantum computing applications in learning environments.

This book constitutes the revised selected papers of the First International Conference, ICSD 2023, virtually held in Delhi, India, during July 15–16, 2023.The book comprises 31 full papers that were selected from a total of 129 submissions.

This book constitutes the refereed proceedings of the Second International Conference on Sustainable Development through Machine Learning, AI and IoT, ICSD 2024, held in Virtual Event, during April 27–28, 2024. The 38 full papers presented here were carefully reviewed and selected from 167 submissions.

This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals—and how they complement advances in generative, agentic, and physical AI—this book is an essential resource for understanding and leveraging their potential.The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation.Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data—complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains.GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged:Graph Convolutional Networks (GCNs): These networks learn from a node’s local neighborhood by aggregating information from adjacent nodes, updating the node’s representation in the process.Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data.GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.

This two-volume set CCIS 2887-2888 constitutes the refereed proceedings of the Third International Conference on Sustainable Development through Machine Learning, AI and IoT, ICSD 2025, held in New Delhi, India, during June 14-15, 2025.The 56 full papers include in these volumes were carefully reviewed and selected from 252 submissions. They provide a comprehensive overview of recent research trends and technological advancements in sustainable development enabled by machine learning, artificial intelligence, and IoT. The contributions cover a broad spectrum of application domains, including energy-efficient and intelligent systems, smart and sustainable cities, environmental monitoring and protection, agriculture and food systems, and innovative healthcare solutions.

This two-volume set CCIS 2887-2888 constitutes the refereed proceedings of the Third International Conference on Sustainable Development through Machine Learning, AI and IoT, ICSD 2025, held in New Delhi, India, during June 14-15, 2025.The 56 full papers include in these volumes were carefully reviewed and selected from 252 submissions. They provide a comprehensive overview of recent research trends and technological advancements in sustainable development enabled by machine learning, artificial intelligence, and IoT. The contributions cover a broad spectrum of application domains, including energy-efficient and intelligent systems, smart and sustainable cities, environmental monitoring and protection, agriculture and food systems, and innovative healthcare solutions.