Optimizing Intelligent Systems For Cross Industry Application
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Optimizing Intelligent Systems for Cross-Industry Application
Intelligent systems, powered by artificial intelligence (AI) and machine learning, offer transformative benefits across diverse sectors, from healthcare and finance to manufacturing and agriculture. By refining these systems to be more adaptable, scalable, and informative, industries can solve complex business problems and streamline operations. Effective research into technical challenges across intelligent system application is necessary to prioritize their development and impact in industries, such as crop analysis, disease diagnosis, or traffic management. Optimizing Intelligent Systems for Cross-Industry Application explores the challenges and opportunities associated with intelligent technology integration in various sectors, including agriculture, medicine, healthcare, computer engineering, business management, and environmental research. It presents solutions for the effective use of intelligent systems within their respective industries. This book covers topics such as human resources, smart cities, and crop productivity, and is a useful resource for computer engineers, agriculturalists, business owners, healthcare professionals, environmentalists, researchers, scientists, and academicians.
Machine Learning, Predictive Analytics, and Optimization in Complex Systems
Author: John Joseph, Ferdin Joe
language: en
Publisher: IGI Global
Release Date: 2025-06-27
The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists.
Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World
The rapid evolution of AI, data science, and emerging technologies is reshaping industries, economies, and societal interactions on an unprecedented scale. These advancements drive innovation in areas such as healthcare, finance, transportation, and environmental sustainability, offering transformative solutions while also raising critical ethical and regulatory challenges. As AI systems become more integrated into processes, multidisciplinary collaboration is essential to ensure transparency, fairness, and accountability. The responsible development and deployment of AI will determine its long-term impact, shaping a future where technology aligns with human values and social progress. Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World examines the technical basis, ethical issues, social repercussions, and prospects of AI-driven progress. It guides readers through the complex and ever-changing world of AI-driven transformation, preparing them to contribute to a future where technology serves humanity responsibly. Covering topics such as blockchain, machine learning, and social media, this book is an excellent resource for academicians, researchers, industry professionals, policymakers, business leaders, entrepreneurs, students, educators, and more.