D. Sivabalaselvamani – författare

Advanced AI and Data Science Applications explores how the latest developments in artificial intelligence (AI) and data science are transforming diverse domains. The book blends theory and practice to serve as a roadmap to help readers understand how these cutting-edge technologies are revolutionizing practices across various fields. By providing a mix of theoretical insights and practical implementations, the book offers a holistic understanding of advanced AI and data science applications. Highlights of the book include:Metaheuristic optimization techniques for solving complex AI model training challengesThe impact of AI and data science on urban developmentImplementing AI for enhanced cybersecurity in industrial control systemsA comparative study of traditional and AI-based methods for English speech recognitionTemporal dependency modeling in real-time data streams using a deep learning modelPredictive analytics for financial fraud detection and risk managementData science in manufacturing for cost reduction and efficiencyAI-driven agricultural analyticsFeaturing such advanced modeling techniques as predictive modeling, simulation, and optimization algorithms, the book presents innovative solutions that emphasize benefits and practicality. With its emphasis on interdisciplinary applications, it showcases successful projects that underscore the synergy between AI and data science domains, empowering readers to harness the power of innovation for enhanced problem-solving and efficiency in interdisciplinary realms.

Advanced AI and Data Science Applications explores how the latest developments in artificial intelligence (AI) and data science are transforming diverse domains. The book blends theory and practice to serve as a roadmap to help readers understand how these cutting-edge technologies are revolutionizing practices across various fields. By providing a mix of theoretical insights and practical implementations, the book offers a holistic understanding of advanced AI and data science applications. Highlights of the book include: Metaheuristic optimization techniques for solving complex AI model training challenges The impact of AI and data science on urban development Implementing AI for enhanced cybersecurity in industrial control systems A comparative study of traditional and AI-based methods for English speech recognition Temporal dependency modeling in real-time data streams using a deep learning model Predictive analytics for financial fraud detection and risk management Data science in manufacturing for cost reduction and efficiency AI-driven agricultural analytics Featuring such advanced modeling techniques as predictive modeling, simulation, and optimization algorithms, the book presents innovative solutions that emphasize benefits and practicality. With its emphasis on interdisciplinary applications, it showcases successful projects that underscore the synergy between AI and data science domains, empowering readers to harness the power of innovation for enhanced problem-solving and efficiency in interdisciplinary realms.