Intelligent Technologies for Scientific Research and Engineering – Volume 4 | 22.53 MB
Title: Advanced Technologies for Science and Engineering (Volume 4) Intelligent Technologies for Scientific Research and Engineering
Author: S. Kannadhasan, R. Nagarajan & Kaushik Pal
Category: Nonfiction, Computers, Advanced Computing, Artificial Intelligence
Language: English | 479 Pages | ISBN: 3030067777
Description:
Advanced Technologies for Science and Engineering – Vol. 4 is a comprehensive compilation of cutting-edge research at the intersection of artificial intelligence, data analytics, and advanced engineering applications. The book focuses on leveraging intelligent technologies to solve complex, real-world problems in today’s data-driven landscape.
Comprising thirty-seven peer-reviewed chapters, the volume blends theoretical foundations with practical implementations across diverse domains such as healthcare informatics, smart grids, energy forecasting, remote sensing, recommender systems, and climate modelling. It highlights innovative models, optimisation techniques, and scalable frameworks, supported by real-world case studies from industries including telecommunications, banking, e-commerce, and social media.
Key Features
Extensive coverage of AI, machine learning, deep learning, and big data analytics
46 peer-reviewed chapters combining theory with real-world applications
Multidisciplinary topics, including healthcare, energy, climate science, and smart systems
Focus on hybrid models, optimisation methods, and scalable frameworks
Emphasis on contemporary challenges such as data privacy, sustainability, and intelligent automation
Advanced Technologies for Science and Engineering – Vol. 4 is a comprehensive compilation of cutting-edge research at the intersection of artificial intelligence, data analytics, and advanced engineering applications. The book focuses on leveraging intelligent technologies to solve complex, real-world problems in today’s data-driven landscape.
Comprising thirty-seven peer-reviewed chapters, the volume blends theoretical foundations with practical implementations across diverse domains such as healthcare informatics, smart grids, energy forecasting, remote sensing, recommender systems, and climate modelling. It highlights innovative models, optimisation techniques, and scalable frameworks, supported by real-world case studies from industries including telecommunications, banking, e-commerce, and social media.
Key Features
Extensive coverage of AI, machine learning, deep learning, and big data analytics
46 peer-reviewed chapters combining theory with real-world applications
Multidisciplinary topics, including healthcare, energy, climate science, and smart systems
Focus on hybrid models, optimisation methods, and scalable frameworks
Emphasis on contemporary challenges such as data privacy, sustainability, and intelligent automation
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