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.

In today's context of intricate global challenges, encompassing climate crises, resource scarcity, and social disparities, the imperative for sustainable development has never been more pressing. While academic scholars and researchers are instrumental in crafting solutions, they often grapple with the intricate balance between theoretical concepts and practical implementation. This gap impedes the transformation of innovative ideas into tangible societal progress, leaving a void where effective real-world strategies for cross-industry sustainability should flourish. "Fostering Cross-Industry Sustainability With Intelligent Technologies" seeks to bridge this divide. This book is more than just a collection of pages; it serves as a roadmap for those determined to make a tangible impact. It brings together a diverse group of esteemed experts from various disciplines, offering a comprehensive spectrum of actionable insights, all grounded in the ethical imperatives of inclusivity and environmental responsibility. Anchored in the United Nations Sustainable Development Goals (SDGs), this volume serves as a guiding star, channeling theoretical expertise into practical solutions. For academic scholars, scientists, innovators, and students alike, Fostering Cross-Industry Sustainability With Intelligent Technologies is the definitive guidepost. It fosters a profound understanding of the real-world implications of research, promoting interdisciplinary collaborations that transcend conventional boundaries. This comprehensive book presents a wealth of sustainable science and intelligent technology applications, all while emphasizing the importance of ethics and societal impact. With visionary insights woven throughout its pages, it calls upon humanity to envision a future where challenges transform into opportunities, and sustainable development becomes an attainable reality.

"This book offers the latest research within the field of HAIS, surveying the broad topics and collecting case studies, future directions, and cutting edge analyses, investigating biologically inspired algorithms such as ant colony optimization and particle swarm optimization"--

The rise of Artificial Intelligence (AI) amidst the backdrop of a world that is changing at lightning speed presents a whole new set of challenges. One of our biggest hurdles is more transparency in AI solutions. It's a complex issue, but one that we need to address if we want to ensure that the benefits of AI are accessible to everyone. Across diverse sectors such as healthcare, surveillance, and human-computer interaction, the inability to understand and evaluate AI's decision-making processes hinders progress and raises concerns about accountability. Cross-Industry AI Applications is a groundbreaking solution to illuminate the mysteries of AI-driven human behavior analysis. This pioneering book addresses the necessity of transparency and explainability in AI systems, particularly in analyzing human behavior. Compiling insights from leading experts and academics offers a comprehensive exploration of state-of-the-art methodologies, benchmarks, and systems for understanding and predicting human behavior using AI. Each chapter delves into novel approaches and real-world applications, from facial expressions to gesture recognition, providing invaluable knowledge for scholars and professionals alike.

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering

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.

Technological Advancements in Data Processing for Next Generation Intelligent Systems presents an in-depth exploration of cutting-edge data processing technologies that drive the development of next-generation intelligent systems in the context of the digital transformation era. This comprehensive book delves into the role data plays as a critical asset for organizations across diverse industries, and how recent technological breakthroughs have unlocked unprecedented potential for handling vast data volumes and real-time analysis. The book begins by providing a thorough overview of novel technologies such as artificial intelligence (AI) or machine learning (ML), edge computing, federated learning, quantum computing, and more. These revolutionary technologies, when integrated with big data frameworks, in-memory computing, and AI/ML algorithms, have transformed data processing capabilities, enabling the creation of intelligent systems that fuel innovation, optimize operations, and deliver personalized experiences. The ultimate aim of this integration is to empower devices with the ability to make autonomous intelligent decisions, maximizing computing power. This book serves as a valuable resource for research scholars, academicians, and industry professionals working towards the future advancement of optimized intelligent systems and intelligent data processing approaches. The chapters encompass a wide range of topics, including architecture and frameworks for intelligent systems, applications in diverse domains, cloud-based solutions, quantum processing, federated learning, in-memory data processing, real-time stream processing, trustworthy AI for Internet of Things (IoT) sensory data, and more.