Computational Mathematics, Data Science and Artificial Intelligence
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Beschreibung
This book discusses the technical foundations and practical applications of data science, machine learning, artificial intelligence (AI) and business intelligence. By aligning its themes with key Sustainable Development Goals (SDGs)—including SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production) and SDG 16 (Peace, Justice and Strong Institutions)—the book underscores the transformative potential of intelligent data use in addressing global challenges.
Designed for decision-makers, analysts, researchers, policy leaders and professionals with a working knowledge of data analytics, the book provides both inspiration and technical depth to navigate the evolving landscape of data-driven innovation. It presents chapters that guide the development and deployment of data-driven solutions across diverse domains. Going beyond theory, chapters in the book focus on real-world challenges and innovations—ranging from plant disease detection and epidemiology to bio-inspired materials, antineoplastic drugs and Ebola response. Additional chapters examine the role of data in physiotherapy, brand management, social networking, sustainable production, graph theory and optimization strategies.
This book discusses the technical foundations and practical applications of data science, machine learning, artificial intelligence (AI), and business intelligence. By aligning its themes with key Sustainable Development Goals (SDGs)—including SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), and SDG 16 (Peace, Justice and Strong Institutions)—the book underscores the transformative potential of intelligent data use in addressing global challenges.
Designed for decision-makers, analysts, researchers, policy leaders, and professionals with a working knowledge of data analytics, the book provides both inspiration and technical depth to navigate the evolving landscape of data-driven innovation. It presents chapters that guide the development and deployment of data-driven solutions across diverse domains. Going beyond theory, chapters in the book focus on real-world challenges and innovations—ranging from plant disease detection and epidemiology to bio-inspired materials, antineoplastic drugs, and Ebola response. Additional chapters examine the role of data in physiotherapy, brand management, social networking, sustainable production, graph theory, and optimization strategies.
Presents comprehensive frameworks for modeling complex systems related to health, the environment, and urban planning Discusses data-driven strategies designed to optimize resource management, technological innovation, and promote equity Includes AI and computational methodologies to enhance resilience, economic growth, and environmental stewardship
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Kusum Deep
Themen in »Computational Mathematics, Data Science and Artificial Intelligence«
computational mathematics numerical analyis data science artificial intelligence optimization ICCMDSAI-2025 Fermatean fuzzy transportation problems Riemann Zeta function deep learning plant disease optimistic rollups physiotherapy traffic managemnt antineoplastic drugs SDG 2 (Zero Hunger)