Sustainable Computing and Optimization

OPTIMIZATION TECHNIQUES IN ENGINEERING The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained. This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I-Soft Computing and Evolutionary-Based Optimization; and Part II-Decision Science and Simulation-Based Optimization, which contains application-based chapters. Readers and users will find in the book: • An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics; • An in-depth treatment of contributions to optimal learning and optimizing engineering systems; • Maps out the relations between optimization and other mathematical topics and disciplines; • A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems. Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.

The book analyzes the combination of intelligent data analytics with the intricacies of biological data that has become a crucial factor for innovation and growth in the fast-changing field of bioinformatics and biomedical systems. Intelligent Data Analytics for Bioinformatics and Biomedical Systems delves into the transformative nature of data analytics for bioinformatics and biomedical research. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to improve results in the healthcare sector. With the exponential growth of data in these domains, the book explores how computational intelligence and advanced analytic techniques can be harnessed to extract insights, drive informed decisions, and unlock hidden patterns from vast datasets. From genomic analysis to disease diagnostics and personalized medicine, the book aims to showcase intelligent approaches that enable researchers, clinicians, and data scientists to unravel complex biological processes and make significant strides in understanding human health and diseases. This book is divided into three sections, each focusing on computational intelligence and data sets in biomedical systems. The first section discusses the fundamental concepts of computational intelligence and big data in the context of bioinformatics. This section emphasizes data mining, pattern recognition, and knowledge discovery for bioinformatics applications. The second part talks about computational intelligence and big data in biomedical systems. Based on how these advanced techniques are utilized in the system, this section discusses how personalized medicine and precision healthcare enable treatment based on individual data and genetic profiles. The last section investigates the challenges and future directions of computational intelligence and big data in bioinformatics and biomedical systems. This section concludes with discussions on the potential impact of computational intelligence on addressing global healthcare challenges. Audience Intelligent Data Analytics for Bioinformatics and Biomedical Systems is primarily targeted to professionals and researchers in bioinformatics, genetics, molecular biology, biomedical engineering, and healthcare. The book will also suit academicians, students, and professionals working in pharmaceuticals and interpreting biomedical data.

The book provides essential insights and practical tools needed to effectively navigate the evolving landscape of scholarly research, helping enhance the understanding of publication trends, citation impacts, and collaboration networks across multiple fields. Bibliometric Analyses in Data-Driven Decision-Making offers a comprehensive guide to researchers, academics, and practitioners interested in utilizing bibliometric analysis to understand and navigate the dynamic landscape of the increasingly vital field of data-driven decision-making and its applications across many areas. It provides insights into growth, impact, and trends within the field, using bibliometric tools and methodologies. This volume adopts a pragmatic approach, balancing theoretical concepts with practical applications of data-driven decision-making models through the perspectives of bibliometric analyses using real-world examples, case studies, and step-by-step guides. The reader will find the book: • Gives practical guidance on conducting bibliometric analyses across a range of applications for data-driven decision-making; • Illustrates the application of bibliometric tools in the field with real-world case studies; • Provides in-depth coverage of various bibliometric indicators and metrics; • Explores emerging trends and challenges in bibliometric analysis; • Provides a comprehensive overview of software and tools available for bibliometric research. Audience Librarians and Information professionals involved in research management, knowledge discovery, and the evaluation of scholarly communication, as well as professionals in industries reliant on cutting-edge research and development, technology assessment, and innovation. Also, a range of researchers and scholars seeking how to apply bibliometric analysis to assess the impact of their work, and advanced insights into bibliometric metrics, collaboration networks, and research trends.

The book explores the latest quantum computing research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, artificial intelligence-based devices, computer technology, and their solutions. Future quantum machines will exponentially boost computing power, creating new opportunities for improving cybersecurity. Both classical and quantum-based cyberattacks can be proactively identified and stopped by quantum-based cybersecurity before they harm. Complex math-based problems that support several encryption standards could be quickly solved by using quantum machine learning. This comprehensive book examines how quantum machine learning and quantum computing are reshaping cybersecurity, addressing emerging challenges. It includes in-depth illustrations of real-world scenarios and actionable strategies for integrating quantum-based solutions into existing cybersecurity frameworks. A range of topics are examined, including quantum-secure encryption techniques, quantum key distribution, and the impact of quantum computing algorithms. Additionally, it talks about machine learning models and how to use machine learning to solve problems. Through its in-depth analysis and innovative ideas, each chapter provides a compilation of research on cutting-edge quantum computer techniques, like blockchain, quantum machine learning, and cybersecurity. Audience This book serves as a ready reference for researchers and professionals working in the area of quantum computing models in communications, machine learning techniques, IoT-enabled technologies, and various application industries such as finance, healthcare, transportation and utilities.

This book provides a roadmap for creating customized, precision drug delivery systems and advancing personalized medicine by introducing the revolutionary, transformative role of 3D printing technology in medicine. The convergence of advanced manufacturing techniques with the complexities of pharmaceutical formulation has opened new frontiers of innovation, enabling unprecedented precision in drug delivery, tailored therapies, and complex dosage designs. From the creation of personalized drug formulations designed to meet the specific needs of individual patients to the development of complex, multi-functional drug delivery systems, the chapters within this volume explore a wide array of applications. This book is a groundbreaking resource that delves into the transformative role of 3D printing technology in the pharmaceutical field. Offering insights into customized and precision drug delivery systems, it highlights the synergy between advanced manufacturing techniques and pharmaceutical sciences. The book provides a balanced blend of theory and practical applications, presenting a roadmap for researchers and professionals to integrate 3D printing into their pharmaceutical processes. It discusses the role of innovative materials, cutting-edge technologies, and regulatory perspectives while highlighting the challenges and future trends in the field. By offering contributions from distinguished experts, the book serves as a comprehensive reference for understanding the potential of 3D printing to revolutionize drug delivery and advance personalized medicine. Readers will find this volume: • Explores cutting-edge 3D printing applications in pharmaceutical sciences, emphasizing precision and innovation; • Covers practical and theoretical insights into customized drug delivery systems using 3D printing; • Includes detailed case studies and emerging trends for academic and industrial professionals; • Features contributions from leading experts and researchers in the field. Audience Pharmaceutical scientists, academics, industrial researchers, formulation chemists, biotechnology professionals, and students specializing in pharmaceutical technology, drug delivery systems, and biopharmaceuticals.

This essential guide provides a comprehensive overview of how to leverage cutting-edge technology and circular economy principles to create resilient, profitable, and sustainable supply chains in an era of unprecedented environmental and technological change. In an age defined by technological breakthroughs and environmental challenges, this book delves into the transformative intersection of sustainability, logistics, and supply chain management. It offers a comprehensive exploration of how cutting-edge technologies like artificial intelligence, machine learning, and big data analytics are revolutionizing logistics operations. By optimizing routes, reducing emissions, and enhancing resource utilization, these tools empower businesses to embrace smarter, greener practices that align with today's sustainability goals. Beyond technology, the book addresses critical challenges, such as navigating the complexities of implementing sustainable solutions, overcoming operational resistance, and ensuring economic viability. It offers actionable strategies to foster resilience in supply chains, especially in the face of global disruptions such as pandemics and disruption to global trade. With a strong focus on circular economy principles, it provides practical frameworks for integrating reuse, recycling, and efficient waste management into supply chains, all while balancing environmental consciousness with economic imperatives. Rich in insights and real-world applications, this book is an essential resource for supply chain professionals, sustainability advocates, policymakers, and researchers. Whether you're looking to innovate your operations, enhance resilience, or contribute to a sustainable future, this book equips you with the knowledge and tools to lead the way in modern logistics. Readers will find the book: Offers a comprehensive analysis of how sustainability, artificial intelligence, and the circular economy are reshaping modern logistics and supply chain management; Provides actionable strategies to overcome challenges such as high implementation costs and operational resistance in adopting green logistics practices; Explores resilience strategies to mitigate supply chain vulnerabilities exposed by global disruptions, including pandemics; Highlights cutting-edge applications of AI, big data, and machine learning to optimize logistics operations for efficiency and reduced environmental impact; Delivers practical insights into integrating circular economy principles and fostering collaboration between industry, policymakers, and researchers. Audience Academics, researchers, supply chain and logistics professionals, sustainability advocates, technology specialists, and policymakers focused on sustainability and efficiency in the supply chain.

The world is experiencing an unprecedented period of change and growth through all the electronic and technological developments and everyone on the planet has been impacted. What was once 'science fiction', today it is a reality. This book explores the world of many of once unthinkable advancements by explaining current technologies in great detail. Each chapter focuses on a different aspect-Machine Vision, Pattern Analysis and Image Processing-Advanced Trends in Computational Intelligence and Data Analytics-Futuristic Communication Technologies-Disruptive Technologies for Future Sustainability. The chapters include the list of topics that spans all the areas of smart intelligent systems and computing such as: Data Mining with Soft Computing, Evolutionary Computing, Quantum Computing, Expert Systems, Next Generation Communication, Blockchain and Trust Management, Intelligent Biometrics, Multi-Valued Logical Systems, Cloud Computing and security etc. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.

This state-of-the-art book offers cutting-edge optimization techniques and practical decision-making frameworks essential for enhancing the efficiency and reliability of sustainable energy systems, making it an invaluable resource for researchers, policymakers, and energy professionals. Optimization in Sustainable Energy: Methods and Applications brings together valuable knowledge, methods, and practical examples to help scholars, researchers, professionals, and policymakers address the growing challenges of optimizing sustainable energy. This volume covers a range of topics, including mathematical models, heuristic algorithms, renewable resource management, and energy storage optimization. Each chapter explores a different aspect of sustainable energy, providing both theoretical understanding and practical guidance. The volume explores challenges and opportunities surrounding the integration of multi-criteria decision-making techniques in energy planning, highlighting insights on environmental, economic, and social factors influencing the strategic allocation of resources. The use of evolutionary algorithms, machine learning, and metaheuristics to optimize energy storage, distribution, and optimization are also discussed. The transition towards sustainable energy is at the forefront of global priorities, driven by the urgent need to mitigate climate change, reduce carbon emissions, and enhance energy security. As countries and industries increasingly prioritize renewable sources like wind, solar, and hydroelectric power, the complexity of optimizing these systems becomes a critical challenge. Optimization in Sustainable Energy: Methods and Applications, is a comprehensive exploration of cutting-edge methodologies used to enhance the efficiency, reliability, and performance of sustainable energy systems. Audience Research scholars, academics, students, policymakers, and industry experts in mechanical engineering, electrical engineering, and energy science.

This book provides a comprehensive exploration of the exciting intersection between technology and biology and delves into the principles, applications, and future directions of IoT in the realm of bioelectronics; it serves as both an introduction for those new to the field and as a detailed reference for experienced professionals seeking to deepen their knowledge. The rapid convergence of technology and biology heralds a new era of evolution in the Internet of Things (IoT), a transformative force enabling interconnected devices to communicate and operate with unparalleled synergy. This is particularly true in the groundbreaking field of bioelectronics, where the fusion of biological systems with electronic devices and IoT is reshaping the landscape of bioelectronics, promising to open up new frontiers in healthcare, diagnostics, and personalized medicine. This timely book explores the numerous ways in which IoT-enabled bioelectronic devices are used to monitor and enhance human health, from wearable sensors that track vital signs to implantable devices that can communicate with healthcare providers in real time. One central theme of this book is the transformative impact of IoT on healthcare. By enabling continuous, remote monitoring of patients, IoT technologies are not only improving the accuracy of diagnostics but also making healthcare more accessible and personalized. The book also addresses the critical issues of securing health records on the internet, which are of paramount importance as we increasingly rely on interconnected devices to collect and transmit sensitive health information. Additional attention is paid to the future directions of IoT in bioelectronics and the integration of innovative areas, such as artificial intelligence, machine learning, and big data analytics, in driving the development of ever more sophisticated and capable bioelectronic systems. Audience The target audience includes professionals, researchers, academics, and students involved in various fields related to bioelectronics, IoT, healthcare, biotechnology, engineering, and related disciplines.