Emerging Trends in Computation Intelligence and Disruptive Technologies

Demystifying Emerging Trends in Green Technology explores the transformative intersection of computational intelligence, disruptive technologies, and green innovations. This volume offers insights into diverse fields such as blockchain, IoT, artificial intelligence, machine learning, and sustainable development. Each chapter presents cutting-edge research and practical solutions addressing environmental sustainability, energy efficiency, and eco-friendly technologies. With contributions from leading researchers, this book discusses advancements like blockchain-based security, green marketing, smart waste management, sustainable agriculture, and innovative healthcare solutions. It emphasizes the role of interdisciplinary approaches in driving a greener and smarter future. Key Features: - Integration of AI, IoT, and blockchain in sustainable systems - Applications in healthcare, agriculture, energy, and environmental science - Practical and innovative solutions for real-world challenges - Insights into future trends in green technology and disruptive innovation Readership: Ideal for academics, researchers, professionals, and enthusiasts seeking to advance green technology practices.

Demystifying Emerging Trends in Machine Learning (Volume 2) offers a deep dive into emerging and trending topics in the field of machine learning (ML). This edited volume showcases several machine learning methods for a variety of tasks. A key focus of this volume is the application of text classification for cybersecurity, E-commerce, sentiment analysis, public health and web content analysis. The 49 chapters highlight a wide variety of machine learning methods including SVNs, K-Means Clustering, CNNs, DCNNs, among others. Each chapter includes accessible information through summaries, discussions and reference lists. This comprehensive volume is essential for students, researchers, and professionals eager to understand the emerging trends reshaping machine learning today. Readership Scholars and professionals interested in machine learning trends and research.