In the current era, there are many environmental and energy challenges facing the agricultural sector, which negatively impact the climate, business, industry, and society. Through modern technologies and engineering, however, these challenges are now able to be met with solutions. There is a need for research in this area so that the industry can continue to be sustainable. Human Agro-Energy Optimization for Business and Industry presents research on humanized optimization approaches for smart energy and the agro-business industry. It is a critical scholarly resource that examines the efficient use of modern smart farming and renewable energy sources, which have a positive impact on sustainable development. Covering topics such as biomass characterization, energy efficiency, and sustainable development, this premier reference source is an essential resource for agricultural scientists, engineers, government officials, software developers, managers, business leaders, executive officers, students and educators of higher education, librarians, researchers, and academicians.

The emergence of a plethora of water contaminants as a result of industrialization has introduced complexity to water treatment processes. Such complexity may not be easily resolved using deterministic approaches. Artificial intelligence (AI) has found relevance and applications in almost all sectors and academic disciplines, including water treatment and management. AI provides dependable solutions in the areas of optimization, suspect screening or forensics, classification, regression, and forecasting, all of which are relevant for water research and management. Artificial Intelligence Applications in Water Treatment and Water Resource Management explores the different AI techniques and their applications in wastewater treatment and water management. The book also considers the benefits, challenges, and opportunities for future research. Covering key topics such as water wastage, irrigation, and energy consumption, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Through a combination of rapid technological advancement and the ongoing digital revolution, the role of Human Resources (HR) in shaping organizational trajectories has seen unprecedented growth. The amalgamation of digital HR technologies and the advent of Industry 5.0 pose both exceptional opportunities and formidable challenges, especially for developing economies grappling with resource constraints and skill gaps. These nations stand at a crossroads, where leveraging digital HR technologies becomes imperative for bolstering their competitive edge in the global arena. The book Convergence of Human Resources Technologies and Industry 5.0 undertakes a comprehensive exploration of the impacts, implementation, and repercussions of digital HR technologies within the framework of Industry 5.0 in developing economies. Bridging the gap between theory and practice, it employs a comprehensive approach encompassing theoretical frameworks, empirical investigations, and practical insights from both academia and industry. By offering tangible takeaways, and approaches, it equips readers to adeptly harness the power of digital HR technologies, enabling organizations to thrive in the era of Industry 5.0. Designed for HR professionals, executives, managers, researchers, policymakers, and students, this book delves into critical topics such as understanding the notion of Industry 5.0 in developing economies, exploring the transformative potential of digital HR technologies, and addressing challenges associated with their implementation.

The Handbook of Research on AI and ML for Intelligent Machines and Systems offers a comprehensive exploration of the pivotal role played by artificial intelligence (AI) and machine learning (ML) technologies in the development of intelligent machines. As the demand for intelligent machines continues to rise across various sectors, understanding the integration of these advanced technologies becomes paramount. While AI and ML have individually showcased their capabilities in developing robust intelligent machine systems and services, their fusion holds the key to propelling intelligent machines to a new realm of transformation. By compiling recent advancements in intelligent machines that rely on machine learning and deep learning technologies, this book serves as a vital resource for researchers, graduate students, PhD scholars, faculty members, scientists, and software developers. It offers valuable insights into the key concepts of AI and ML, covering essential security aspects, current trends, and often overlooked perspectives that are crucial for achieving comprehensive understanding. It not only explores the theoretical foundations of AI and ML but also provides guidance on applying these techniques to solve real-world problems. Unlike traditional texts, it offers flexibility through its distinctive module-based structure, allowing readers to follow their own learning paths.

Integrating electric vehicles (EVs) into power distribution systems presents significant challenges, particularly concerning power source dependability and grid stability. The distribution system, a critical element of the power system, is susceptible to failures and power outages exacerbated by the extensive adoption of EVs. Additionally, managing the administration, monitoring, and control of power systems in the context of EV integration is a complex and daunting task for energy experts. A Sustainable Future with E-Mobility: Concepts, Challenges, and Implementations offers a comprehensive solution to these challenges. It explores infrastructure frameworks, planning strategies, control strategies, and software applications for integrating EVs with power distribution systems, focusing on innovative grid developments. By providing insights into architectural reconfiguration, restoration strategies, power quality control, and regulatory aspects, the book equips students, researchers, academicians, policymakers, and industry experts with the knowledge needed to achieve a secure, resilient, and efficient integration of EVs into distribution networks.

Image processing integrates and extracts data from photos for a variety of uses. Applications for image processing are useful in many different disciplines. A few examples include remote sensing, space applications, industrial applications, medical imaging, and military applications. Imaging systems come in many different varieties, including those used for chemical, optical, thermal, medicinal, and molecular imaging. To extract the accurate picture values, scanning methods and statistical analysis must be used for image analysis. Thrust Technologies’ Effect on Image Processing provides insights into image processing and the technologies that can be used to enhance additional information within an image. The book is also a useful resource for researchers to grow their interest and understanding in the burgeoning fields of image processing. Covering key topics such as image augmentation, artificial intelligence, and cloud computing, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

The interaction of various service models, including edge computing and cloud computing, are quickly changing to better support microservices. This intricate weave of technology and information sharing is necessary to build systems that run faster and more efficiently. The interplay between these computing methods and microservices is emerging as the field of Osmotic Computing. Experts can now embark on an intellectual journey into data-driven exploration and ingenuity with the guidance of the book, Advanced Applications in Osmotic Computing. As ethical considerations become rising concerns, the potential biases, privacy encumbrances, and equitable conundrums of osmotic computing are investigated. This book offers judicious strategies to navigate these quandaries conscientiously, adding a layer of responsibility to the discourse. Within these pages, the very fabric of understanding in IoT, Cloud, Edge, Fog, and Machine Learning is redefined, marking a pivotal shift in the paradigm of technological comprehension. This book is an epicenter for the latest evolutions in osmotic computing, unfurling unconventional methodologies that shape the trajectory of data-driven decision-making. Readers will plunge into the theoretical bedrock, simultaneously witnessing pragmatic applications that adeptly bridge the schism between the theoretical constructs and pragmatic realization. The intended audience is multifaceted, encompassing data scientists, machine learning engineers, researchers, academics, educators, students, industry practitioners, interdisciplinary experts, and technology and business leaders.