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The industrial landscape is changing rapidly, and so is global society. This change is driven by the growing adoption of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies. IIoT and AI are transforming the way industrial engineering is done, enabling new levels of automation, productivity, and efficiency. However, as IIoT and AI become more pervasive in the industrial world, they also offer new security risks that must be addressed to ensure the reliability and safety of critical systems.Industrial Internet of Things Security: Protecting AI-Enabled Engineering Systems in Cloud and Edge Environments provides a comprehensive guide to IIoT security, covering topics such as network architecture, risk management, data security, and compliance. It addresses the unique security challenges that the cloud and edge environments pose, providing practical guidance for securing IIoT networks in these contexts. It includes numerous real-world case studies and examples, providing readers with practical insights into how IIoT security and AI-enabled industrial engineering are being implemented in various industries. Best practices are emphasized for the readers to ensure the reliability, safety, and security of their systems while also learning the latest developments in IIoT security for AI-enabled industrial engineering systems in this rapidly evolving field.By offering step-by-step guidance for the implantation process along with best practices, this book becomes a valuable resource for practitioners and engineers in the areas of industrial engineering, IT, computer engineering, and anyone looking to secure their IIoT network against cyber threats.

In a world grappling with resource depletion and environmental degradation, this work empowers readers to foster sustainable urban environments, while promoting a deeper understanding of the intricate relationship between new technologies and cutting-edge economic theory. A major highlight is the presentation of a diverse range of real-world best practices. These case studies of successful integrations of Smart Cities and a Circular Economies identify the key factors that contribute to their triumph. The takeaways equip policymakers and urban planners with practical solutions for addressing the challenges of sustainable urban developments. From policy and regulatory hurdles to technological advancements, financing and investment opportunities to collaboration and stakeholder engagement, the authors offer valuable guidance for leaders and decision makers seeking to surmount barriers to integration. In sum, Smart Cities and Circular Economy is a timely, indispensable resource offering practical solutions and insights, championing the creation of sustainable and liveable urban environments. Whether you are a policymaker, urban planner, business leader, researcher, or student, this is an essential tool for advancing sustainable urban development in our rapidly changing world.

This new book delves into how modern technologies—i.e., global positioning systems (GPS), unmanned aerial vehicles (drones), image processing methods, artificial intelligence, machine learning, and deep learning—are being used to make agriculture more farmer-friendly and more economically profitable. The volume focuses on the use of smart sensors, actuators, and decision support systems to provide intelligent data about crop health and for monitoring for yield prediction, soil quality, nutrition requirement prediction, etc. The authors discuss robotic-based innovations in agriculture, soft computing methodologies for crop forecasting, machine learning techniques to classify and identify plant diseases, deep convolutional neural networks for recognizing nutrient deficiencies, and more.

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.

This new volume provides detailed insight into supply chain management (SCM) and supply chain performance measurement (SCPM) in the textile industry. The book provides a comprehensive set of performance sub-criteria for the evaluation of SCPM, offering the information needed to understand, implement, and evaluate the supply chain performance of the textile industry and its cyclic processes.Using the Delphi method (a process used to arrive at a group opinion or decision by surveying a panel of experts), the authors studied several companies of diverse sizes and applied an analytical hierarchy process to establish crucial performance measurement criteria and sub-criteria. Around 80 textile industry personnel were contacted, including general managers, factory managers, managers, assistant managers, executives, and supervisors involved in various functions related to textile supply chains to discuss procurement, production, and distribution alternatives. The authors employed the multi-criteria decision-making technique, i.e. analytical hierarchy process, for executing pair-wise comparison and establishing priority weights of criteria, sub-criteria, and for estimating the contribution of supply chain cyclic processes to overall supply chain performance.

LASER-ASSISTED MACHINING This unique book develops exhaustive engineering perceptions of different laser-assisted techniques, reviews the engineering context of different laser fabrication techniques, and describes the application of laser-assisted fabrication techniques. Lasers are essential in the area of material processing because they can produce coherent beams with little divergence. The fabrication process known as surface cladding includes joining (soldering, welding), material removal (laser-aided drilling, cutting, etc.), deformation (extrusion, bending), and material addition. Some remarkable advantages of laser-assisted material development include faster processing rates and preservation of essential alloying components. However, the lack of widespread understanding of various material phenomena and how laser parameters affect them prevents the technology from being widely accepted on an industrial scale. Among the subjects Laser-Assisted Machining covers include high-powered lasers in material processing applications, laser-based joining of metallic and non-metallic materials, direct laser cladding, laser surface processing, laser micro and nano processing, emerging laser materials processing techniques, solid-state lasers, laser cutting, drilling and piercing, laser welding, laser bending or forming, laser cleaning, laser automation and in-process sensing, femtosecond laser micromachining, laser-assisted micro-milling/grinding, laser-assisted jet electrochemical micro-machining, laser-assisted water jet micro-machining, hybrid laser-electrochemical micromachining process, quill and nonreciprocal ultrafast laser writing, laser surface engineering, ultrashort pulsed laser surface texturing, laser interference patterning systems, laser interference lithography, laser-guided discharge texturing. Audience The book will be used by researchers in the fields of manufacturing technology and materials science as well as engineers and high-level technicians for a better understanding of various innovative and novel techniques to cope with the need of micromachining, as well as microfabrication industries for successful implementation of microproduct manufacturing.

Optimization for advanced manufacturing processes in fabrication is becoming increasingly important around the world across a range of industries because of its improved productivity, capabilities, efficacy, and sustainability characteristics.This new book explains how to optimize various industrial systems and processes utilizing new significant statistical and evolutionary methodologies. Statistical, multi-criteria decision-making, and evolutionary strategies for single- and multi-objective optimization are covered here, with relevant theoretical details, salient characteristics, implementation processes, effectiveness studies, and results in manufacturing.The chapters present a variety of advanced manufacturing processes and optimization techniques. Processes are described with basic explanations, characteristics, and applications in various technological and biomedical domains. The chapters address the processes prediction of mechanical properties, design and development of tools, impact of power supply, increasing machining precision and efficiency, genetic algorithm optimization, additive manufacturing, and much more.Providing a comprehensive study of advanced manufacturing processes, this volume is a valuable reference for engineers and R&D researchers involved in optimization for advanced manufacturing processes as well as for academics and postgraduate-level students in mechanical engineering, manufacturing engineering, and machining processes.

Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities delves into the transformative potential of artificial and cognitive computing in the realm of healthcare systems, maintaining a specific emphasis on sustainability. By exploring the integration of advanced technologies in smart cities, the authors examine and discuss how AI and cognitive computing can be harnessed to enhance healthcare delivery. The book provides focused navigation through innovative solutions and strategies that contribute to the creation of sustainable healthcare ecosystems within the dynamic environment of smart cities. From optimizing resource utilization to improving patient outcomes, this comprehensive exploration provides insight for readers with an interest in the future of healthcare within the era of intelligent urban development.

This book showcases research, theoretical, and validated work associated with digital finance to enhance the quality of a more sustainable environment. The primary objective of Fintech for ESG and the Circular Economy is to evaluate how fintech advancements and sustainable practices effectively drive transformative and sustainable change. It aims to motivate individuals with ideas and discussions to promote financial technology and foster creativity for a more sustainable and equitable future. The book delves into the intersection of technology and sustainability, offering insights into how big data, machine learning, and blockchain technology are transforming ESG practices and the circular economy. It highlights the potential of FinTech to drive sustainable finance, explores the current role of cyber ecosystems and digital currencies in sustainable finance, and examines the intricate legal landscape surrounding green finance in India. The book also discusses the influence of ESG factors on financial decision-making and the integration of sustainability metrics into financial analysis, supported by examples of how companies and investors are adopting these practices. Additionally, the book explores the role of financial institutions in enabling crowdfunding platforms, particularly in African markets, and provides case studies that demonstrate their impact on financial inclusion and entrepreneurship. It also analyzes the legal implications of blockchain and smart contracts within FinTech, the convergence of digital business models with ESG principles, and the role of digital currencies in promoting financial inclusion and sustainable economic growth, particularly in India. The book concludes with an exploration of tokenomics and its potential to incentivize sustainable behaviors, and examines how digital finance innovations in Tanzania are improving financial inclusion by overcoming barriers to financial services for the unbanked population. Audience This book will be of interest to scholars, researchers, academicians, and students of commerce, banking, finance, sustainability, and financial technology. This book is helpful for business professionals, researchers, and technical industry workers in finance and artificial intelligence.

This book pioneers the synergy between state-of-the-art edge computing technologies and the power of operations research. It comprehensively explores real-world applications, demonstrating how various operations'research techniques enhance edge computing's efficiency, reliability and resource allocation. Innovative solutions for dynamic task scheduling, load balancing and data management, all tailored to the unique challenges of edge environments, are displayed. Starting with operation research methodologies with foundations, applications and research challenges in edge computing and an overview of digital education, this book continues with an exploration of applications in the health sector using IoT, intelligent payment procedures and performance measurement of edge computing, using edge computing and operation research. Smart or AI-based applications are also explored further on and the book ends with insight into ultralightweight and security protocols with solutions for IoT using blockchain.

Bridging the gap between the need for micro elements and the profitable microfabrication of goods, this new book provides an informative overview of the electro-micromachining and microfabrication processes, varieties, and important applications. Opening with an overview of a variety of micromachining technologies, with an emphasis on nontraditional approaches and recent advances in each, the volume discusses the ultrasonic micromachining processes for producing a variety of micro-shapes, such as micro-holes, micro-slots, and micro-walls, as well as assisted hybrid micromachining with ultrasonic vibration of the tool or workpiece, all which help to improve precision and to advance research. Computer-aided design and computer-aided manufacturing dental micromachining technologies are discussed. Micro-electrical discharge machining, laser micro grooving, and laser micromachining are among the advanced micro-manufacturing processes addressed as well. The volume also covers the use of an electrochemical micromachining method to improve micro texturing and the use of nano-additives to enhance MQL and micromachining process optimization.

HYBRID MICROMACHINING and MICROFABRICATION TECHNOLOGIES The book aims to provide a thorough understanding of numerous advanced hybrid micromachining and microfabrication techniques as well as future directions, providing researchers and engineers who work in hybrid micromachining with a much-appreciated orientation. The book is dedicated to advanced hybrid micromachining and microfabrication technologies by detailing principals, techniques, processes, conditions, research advances, research challenges, and opportunities for various types of advanced hybrid micromachining and microfabrication. It discusses the mechanisms of material removal supported by experimental validation. Constructional features of hybrid micromachining setup suitable for industrial micromachining applications are explained. Separate chapters are devoted to different advanced hybrid micromachining and microfabrication to design and development of micro-tools, which is one of the most vital components in advanced hybrid micromachining, and which can also be used for various micro and nano applications. Power supply, and other major factors which influence advanced hybrid micromachining processes, are covered and research findings concerning the improvement of machining accuracy and efficiency are reported.

COMPUTATIONAL INTELLIGENCE IN SUBSTAINABLE RELIABILITY ENGINEERING The book is a comprehensive guide on how to apply computational intelligence techniques for the optimization of sustainable materials and reliability engineering. This book focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in reliability engineering, material design, and manufacturing to ensure sustainability. Computational Intelligence in Sustainable Reliability Engineering unveils applications of different models of evolutionary algorithms in the field of optimization and solves the problems to help the manufacturing industries. Some special features of this book include a comprehensive guide for utilizing computational models for reliability engineering, state-of-the-art swarm intelligence methods for solving manufacturing processes and developing sustainable materials, high-quality and innovative research contributions, and a guide for applying computational optimization on reliability and maintainability theory. The book also includes dedicated case studies of real-life applications related to industrial optimizations. Audience Researchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design.

This book comprises select proceedings of the 1st International Conference on Computational Intelligence for Engineering and Management Applications (CIEMA - 2022). This book emphasizes applications of computational intelligence including machine intelligence, data analytics, and optimization algorithms for solving fundamental and advanced engineering and management problems. This book serves as a valuable resource for researchers, industry professionals, academicians, and doctoral scholars in engineering, production, thermal, materials, design, computer engineering, natural sciences, and management who work on computational intelligence. The book also serves researchers who are willing to use computational intelligence algorithms in real-time applications.

Blockchain is new-age technology used to track every transaction using cryptocurrency across servers linked in a peer-to-peer network, enabling transactions to be secure, transparent and reliable. Retaining an efficient, secure and patient-centric healthcare industry has never been so important, especially due to the damaging effects of the Covid-19 pandemic. The applicability of Blockchain in the healthcare domain can be seen as a remarkable opportunity for researchers and scientists to solve real-world problems. This book focuses on the fundamentals of Blockchain technology along with the methods of its integration with the healthcare industry. It also provides an enhanced understanding of Blockchain technology, AI and IoT across the various application areas of the healthcare industry. Furthermore, throughout the book, areas of relevant applications, such as patient data privacy protection, pharmaceutical supply chains and genomics are discussed.

Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.

Multi-criteria decision-making (MCDM) has gained vast popularity for its ability to help make decisions in the presence of various similar and conflicting choices.This new volume applies the MCDM theory to solving problems and challenges in manufacturing environments. It discusses using MCDM computational methods to evaluate and select the most optimal solution or method for real-world, real-time manufacutring engineering issues. It details the decision-making process in relation materials selection; identification, assessment, and evaluation of risk; sustainability assessment; selection of green suppliers; and more.The chapter authors demonstrate the application of myriad MCDM techniques in decision-making, including MADM (multiple attribute decision-making), DEA (data envelopment analysis), fuzzy TOPSIS (technique for order preference by similarities to ideal solution), fuzzy-VIKOR (multicriteria optimization and compromise solution); MOORA (multi-objective optimization on the basis of ratio analysis), EWM (entropy weight method), (AHP) analytic hierarchy process, TODIM (TOmada de Decisao Interativa Multicriterio), and others. The volume illustrates these MCDM models in several industries and industrial processes, including for experimental analysis and optimization of drilling of glass fiber reinforced plastic, in the textile industries, for selection of refrigerants for domestic applications, and others.

This book explores the significance, challenges and benefits of digital twin technologies; it focuses in particular on various architectures, applications and challenges in the implementation of digital twins to Machine Learning and Internet of Things capabilities. Through the analysis of smart city and smart manufacturing case studies, the book explores the benefits of digital technologies in the Industry 4.0 Era.

This book addresses the reliability, risk, and safety issues of real industrial systems with application of the latest reliability and risk-based modeling. Related topics such as maintenance decision-making and risk and safety modeling are also addressed with the implementation of decision-making techniques. The book provides real-life studies on industrial operations along with solutions. It discusses modeling and optimization of reliability and safety aspects in industry and covers reliability maintenance issues in process industries. The book goes on to present cost optimization, life cycle costing analysis, and multi-criteria decision making (MCDM) application for risk and safety analysis. Academic institutions, students, professionals, large companies involved in engineering sciences, research scholars, and investigators working in the domain of Reliability and Safety Engineering and its allied domains will find this book useful.

This volume helps to address the genuine 21st century need for advances in data science and computing technology. It provides an abundance of new research and studies on progressive and innovative technologies, including artificial intelligence, communication systems, cyber security applications, data analytics, Internet of Things (IoT), machine learning, power systems, VLSI, embedded systems, and much more. The book presents a variety of interesting and important aspects of data science and computing technologies and methodologies in a wide range of applications, including deep learning, DNA cryptography, classy fuzzy MPPT controller, driving assistance, and safety systems. Novel algorithms and their applications for solving cutting-edge computational and data science problems are included also for an interdisciplinary research perspective. The book addresses recent applications of deep learning and ANN paradigms, the role and impact of big data in the e-commerce and retail sectors, algorithms for load balancing in cloud computing, advances in embedded system based applications, optimization techniques using a MATLAB platform, and techniques for improving information and network security. Advances in Data Science and Computing Technology: Methodology and Applications provides a wealth of valuable information and food for thought on many important issues for data scientists and researchers, industry professionals, and faculty and students in the data and computing sciences.

FUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.

This book introduces the step-by-step processes involved in using MCDM methods, starting from problem formulation, model development, and criteria weighting to the final ranking of the alternatives. The authors explain the different MCDM methods that can be used in specific manufacturing environments. The book explains the conceptual frameworks of how these methods are applied with special focus on their applicability and usefulness. The authors begin with an introduction to multi-criteria decision-making, followed by explanations of 29 MCDM methods and their applications. The final sections of the book describe helpful normalization techniques and criteria weight measurement techniques. The collection of diverse range of manufacturing applications and case studies presented here will aid readers in applying cutting-edge MCDM methods to their own manufacturing projects. As both a research and teaching tool, this book encourages critical and logical thinking when applying MCDM methods for solving complex manufacturing decision-making problems.

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. Audience 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.

This book covers the applications of computational intelligence techniques in business systems and advocates how these techniques are useful in modern business operations. The book redefines the computational intelligence foundations, the three pillars - neural networks, evolutionary computation, and fuzzy systems. It also discusses emerging areas such as swarm intelligence, artificial immune systems (AIS), support vector machines, rough sets, and chaotic systems. The other areas have also been demystified in the book to strengthen the range of computational intelligence techniques such as expert systems, knowledge-based systems, and genetic algorithms. Therefore, this book will redefine the role of computational intelligence techniques in modern business system operations such as marketing, finance & accounts, operations, personnel management, supply chain management, and logistics. Besides, this book guides the readers through using them to model, discover, and interpret new patterns that cannot be found through statistical methods alone in various business system operations. This book reveals how computational intelligence can inform the design and integration of services, architecture, brand identity, and product portfolio across the entire enterprise. The book will provide insights into research gaps, open challenges, and unsolved computational intelligence problems. The book will act as a premier reference and instant material for all the users who are contributing/practicing the adaptation of computational intelligence modern techniques in business systems.

This new volume offers a variety of perspectives from investigators, industry professionals, stakeholders, and economic strategists that look at new ways of solving optimization problems related to different industrial sectors. Case studies relay how optimization methods deal with both real operative conditions in process industries and in service industries. The volume also explores emerging research areas toward the implementation of optimization algorithms for enhancement of system performance as well as system effectiveness. The book explores the role of optimization methods in engineering applications in industrial and mechanical engineering as well as in the fields of healthcare/medicine, food production, oil, textiles, energy, and agriculture. The volume offers new ways of solving optimization problems related to different industrial sectors, incorporating mathematical formulation for particular design problems and thus aiding the selection of the optimal design among many alternatives. It shows optimization methods that deal with actual operative conditions both in process and in service industries. A unique advantage of this volume is its wide range of topics in different engineering domains using novel mathematical modeling-based optimization methods for solving the real-life problems. The array of examples and case studies of the effective use of optimization in diverse areas of engineering include healthcare analysis and monitoring (fetal phonocardiography), medical device design (3D printing design for protheses), agriculture/farming (monitoring climate conditions), environmental science (waste management), automotive and aeronautic design, industrial manufacturing, solar energy, and more. Key features: Presents case studies on optimization problems related to industry Discusses case studies on operations management practices optimization Provides an overview of design optimization Highlights case studies on process optimization Assesses different techniques for handling engineering problems This valuable book will be useful for researchers, scientists, faculty, and students involved or interested in the field of optimization engineering in industrial design. Indexed in SCOPUS.

This book covers the emerging applications of different computational and optimization techniques in order to achieve a sustainable agriculture. A sustainable agricultural management requires tools in providing integrated, area-specifi c, and interpreted prediction or forecasting and guidance in every aspect in agriculture.

INTELLIGENT DECISION SUPPORT SYSTEMS FOR SMART CITY APPLICATIONS This book provides smart city frameworks to address new difficulties by adding new features and allowing the city environment to react to collected data and information to increase the efficiency and sustainability of services for inhabitants. Making a smart city is an emerging strategy to mitigate the problems generated by urban population growth and rapid urbanization. This book aims to provide a better understanding of the concept of smart cities and the application of an intelligent decision support system. Based on the analysis of existing information there are eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. This book will focus on the application of the decision support system in managing these eight crucial aspects of smart cities. The intent in writing this book was also to provide a source that covers the stage-by-stage integration of the four key areas involving planning, physical infrastructure, ICT infrastructure, and deploying the smart solutions necessary for city transformation. With this as the motivation, “Decision Support Systems for Smart City Applications” provides the application of an intelligent decision support system for effectively and efficiently managing the transformation process, which can aid various supply chain stakeholders, academic researchers, and related professionals in building smart cities. Various chapters of this book are expected to support practicing managers during the implementation of smart solutions for city transformation. Audience This book is aimed at both academics and practitioners alike in the fields of intelligent computing, decision support systems, the manufacturing industry, supply chain managers, stakeholders, policymakers, and other technical and administrative personnel.

Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilizing well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.

This book addresses the different problems, practices, challenges and opportunities in sustainable resource management with the help of decision-making techniques to showcase the relevance of computational modelling approaches in sustainable management and Industry 4.0. It aims to address the inherent complexity of managing ecosystems, particularly with respect to involvement of multi-stakeholders, lack of information and uncertainties. Critical analyses are made to point out the need for, and propose a call to, a new way of thinking about sustainable resource management. This book will be useful for academicians, researchers, and industrialists in the field of industrial and production engineering.

This book discusses the use of machine vision and technologies in specific engineering case studies and focuses on how machine vision techniques are impacting every step of industrial processes and how smart sensors and cognitive big data analytics are supporting the automation processes in Industry 4.0 applications. Industry 4.0, the Fourth Industrial Revolution, combines traditional manufacturing with automation and data exchange. Machine vision is used in the industry for reliable product inspections, quality control, and data capture solutions. It combines different technologies to provide important information from the acquisition and analysis of images for robot-based inspection and guidance. Features Presents a comprehensive guide on how to use machine vision for Industry 4.0 applications, such as analysis of images for automated inspections, object detection, object tracking, and more Includes case studies of Robotics Internet of Things with its current and future applications in healthcare, agriculture, and transportation Highlights the inclusion of impaired people in the industry, for example, an intelligent assistant that helps deaf-mute individuals to transmit instructions and warnings in a manufacturing process Examines the significant technological advancements in machine vision for Industrial Internet of Things and explores the commercial benefits using real-world applications from healthcare to transportation Discusses a conceptual framework of machine vision for various industrial applications The book addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and postgraduate students, researchers, and anyone interested in the trends, development, and opportunities for machine vision for Industry 4.0 applications.

As transactions and other business functions move online and grow more popular every year, the finance and banking industries face increasingly complex data management and identity theft and fraud issues. AI can bring many financial and business functions to the next level, as systems using deep learning technologies are able to analyze patterns and spot suspicious behavior and potential fraud. In this volume, the focus is on the application of artificial intelligence in finance, business, and related areas. The book presents a selection of chapters presenting cutting-edge research on current business practices in finance and management. Topics cover the use of AI in e-commerce systems, financial services, fraud prevention, identifying loan-eligible customers, online business, Facebook social commerce, insurance industry, online marketing, and more.

The world is experiencing an unprecedented period of change and growth through all the electronic and technilogical 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 book addresses the problem of waste management by using multi-criteria decision-making (MCDM) methods. The authors discuss how to apply MCDM, a complex decision-making tool that involves both quantitative and qualitative factors, to develop strategies for effective waste management using various optimization models to rank alternatives, while also incorporating the concerns and needs of multiple stakeholders to find the most optimal decisions for various types of wastes. Typically, there does not exist a single optimal solution to waste problems; with help of MCDM, far better solutions can often be found and utilized to facilitate sustainable waste management techniques in various industries. This book provides unique, effective, and quick decision-making strategies for waste management. With the ever-increasing population and continuing human development, the problem of managing waste becomes increasingly essential, and this volume helps lead the way to finding sustainable solutions.

This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues.

Given the increasing need to optimize resources sustainably, decision-makers face challenges in analyzing and considering the numerous factors involved. This book makes an effort to present and concentrate on the challenges in decision-making processes for green and sustainable engineering. Through a collection of case studies such as evaluation of waste assessment and drainage system, sustainable building assessment, renewable energy selection, materials and manufacturing process optimization, and crop pattern influence in environmental and economic conditions, readers can learn how to apply cutting-edge Multiple-Criteria Decision Making (MCDM) methods in addressing complexities involved in the decision-making process.

Sustainability issues have gained more importance in contemporary globalization, pushing decision makers to find a systematic mathematical approach to conduct analyses of this real-world problem. The growing complexity in modern social-economics or engineering environments or systems has forced researchers to solve complicated problems by using multi-criteria decision-making (MCDM) approaches. However, traditional MCDM research mainly focuses on reaching the highest economic value or efficiency, and issues related to sustainability are still not closely explored. Advanced Multi-Criteria Decision Making for Addressing Complex Sustainability Issues discusses and addresses the challenges in the implementation of decision-making models in the context of green and sustainable engineering, criteria identification, quantification, comparison, selection, and analysis in the context of manufacturing, supply chain, transportation, and energy sectors. All academic communities in the areas of management, economics, business sciences, mechanical, and manufacturing technologies are able to use, apply, and implement the models presented in this book. It is intended for researchers, manufacturers, engineers, managers, industry professionals, academicians, and students.

The new book'Smart Green Energy Production'explores the innovative surfaces and Intersections between Intelligent Algorithms and Green Energy Technologies to advance and enhance sustainable energy solutions. This comprehensive guide covers state-of-the-art and future-oriented computational strategies for optimizing or optimally controlling green energy production and managing carbon dioxide emissions. Key topics also include the application of smart hybrid quantum computing, the efficiency of swarm intelligence, the scalability of cloud computing, as well as analytical, heuristic and sophisticated optimization and controlling techniques. This book provides a detailed analysis of how these technologies can be leveraged to create more efficient, cost-effective, as well as human-, environmentally and life-friendly energy systems, offering readers a thorough understanding of the future of sustainable energy generation, induction, production and consumption.

'Digital Transformation in Healthcare 5.0: IoT, AI, and Digital Twin'provides a comprehensive overview of the integration of cutting-edge technology with healthcare, from the Fourth Industrial Revolution (4IR) to the introduction of IoT, AI, and Digital Twin technologies. This in-depth discussion of the digital revolution expanding the healthcare industry covers a wide range of topics, including digital disruption in healthcare delivery, the impact of 4IR and Health 4.0, e-health services and applications, virtual reality's impact on accessible healthcare delivery, digital twins and dietary health technologies, big data analytics in healthcare systems, machine learning models for cost-effective healthcare delivery systems, affordable healthcare with machine learning, enhanced biomedical signal processing with machine learning, and data-driven AI for information retrieval of biomedical images.

This book focuses on computing for Industry 4.0 illustrating different domains with the purpose of integration with existing domains for automation of processes. It gives readers an idea about the various challenges and design structure for computing of Industry 4.0. The contents include contributions from experts in Cyber-Physical Systems (CPS), the Internet of Things (IoT), Industrial Internet of Things (IIoT), cloud computing, cognitive computing, and artificial intelligence across the world, contributing their knowledge to identify the different characteristics of the above domains.

We are delighted to present the proceedings of the 5th International Conference on Advances in Additive Manufacturing Technologies (ICAAMT 2023). This conference serves as a premier forum for researchers, practitioners, and industry experts to share their latest findings, innovations, and insights in the field of additive manufacturing. The rapid advancements and the increasing adoption of these technologies across various sectors underscore the importance of this gathering. The conference was held from November 27-29, 2023, in Chennai, India and organized by the Department of Mechanical Engineering, Chennai Institute of Technology, Chennai, India.

In this book, the role of Artificial Intelligence (AI), Internet of Things (IoT) and Blockchain in smart healthcare is explained through a detailed study of Artificial Neural Network, Fuzzy Set Theory, Intuitionistic Fuzzy Set, Machine Learning and Big Data technology.Industry 5.0 for Smart Healthcare Technologies: Utilizing Artificial Intelligence, Internet of Medical Things and Blockchain focuses on interesting applications of AI, promising advancements in IoT and important findings in Blockchain technology. When applied to smart healthcare technologies, Industry 5.0 offers numerous benefits that can revolutionize the healthcare industry. This book provides readers with insights and tools for enhanced patient care, remote patient monitoring, predictive analytics and early intervention of diseases, seamless data sharing and interoperability, telemedicine and virtual care, and a safer and more secure healthcare ecosystem. The authors examine novel computational algorithms for the processing of medical images, as well as novel algorithms for the processing of biosignals in detection of diseases. This book also explores systems for processing physiological parameters and discusses applications of AI techniques in the broader healthcare industry. The authors also investigate the importance of Augment Reality/Virtual Relatity (AR/VR) in the healthcare sector and examine the futuristic applications of Industry 5.0 in the healthcare sector.This book is intended for researchers and professionals working in interdisciplinary fields of computer engineering/science and healthcare. It will provide them with the tools to enhance diagnostics, optimize treatment plans, and empower patients to actively participate in their healthcare journey.

The recent pandemic has forced researchers to adapt technologies such as robotics and AI in the healthcare field. This book, Robotics and Automation in Healthcare: Advanced Applications, explores these new technologies by focusing on important issues related to the employment of robotics and automation in healthcare, such as in medical diagnosis, treatment, and surgery.The volume reviews wireless charging of implantable pacemakers, considers smart bot design for library building of medical colleges, and discusses strain distribution in biomechanical systems. Other topics included in the book are medical imaging, drone technology, 3D printing, and image processing techniques. The application and importance of actuators in medical devices, especially during surgery, is discussed, as are wearable devices for pre-identification of seizure development. The volume also looks at a decision support system for detection of suitable robots and early detection of diseases with the support of image processing techniques. The application of nano-robots in healthcare is also explored.Providing advanced information and insight into robotics, wearable devices, and applications of image processing in healthcare field, this volume will be helpful to those in communications and electronics engineering as well as those at the forefront of smart technology in healthcare.

As multicriteria decision-making (MCDM) continues to grow and evolve, machine learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design. Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is a comprehensive resource that bridges the gap between ML and MCDM. It offers a practical approach by demonstrating the application of ML and MCDM algorithms to real-world problems. Through case studies and examples, it showcases the effectiveness of these techniques in optimal design. The book also provides a comparative analysis of conventional MCDM algorithms and machine learning techniques, enabling readers to make informed decisions about their use in different scenarios. It also delves into emerging trends, providing insights into future directions and potential opportunities. The book covers a wide range of topics, including the definition of optimal design, MCDM algorithms, supervised and unsupervised ML techniques, deep learning techniques, and more, making it a valuable resource for professionals and researchers in various fields. Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is designed for professionals, researchers, and practitioners in engineering, computer science, sustainability, and related fields. It is also a valuable resource for students and academics who wish to expand their knowledge of machine learning applications in multicriteria decision-making. By offering a blend of theoretical insights and practical examples, this guide aims to inspire further research and application of machine learning in multidimensional decision-making environments.

The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics.Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic.This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing.Features: Presents cutting-edge manufacturing technologies and information to maximise product exchanges and production Discusses the improvement in service quality, product quality, and production effectiveness Conveys how a manufacturing company's competitiveness can increase if it can manage the turbulence and changes in the global market Presents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivity Covers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater quality Includes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline

This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2023, held in Hamirpur, India, during December 21–22, 2023. The 29 full papers included in this book were carefully reviewed and selected from 173 submissions. They were organized in topical sections as follows: Machine Learning; Image Processing; Network Security; and Data Sciences.

This book is divided into seven chapters that provide detailed explanations of blockchain technology and its applications in the healthcare industries. The first chapter covers the security of transactions using blockchain technology. Every transaction is recorded in a block, which acts as a record book and helps in maintaining transparency and security. Chapter 2 delves into the Smart Contract, Hash Function, and Coinbase Transaction Structure in depth. Chapter 3 investigates the current state of blockchain advancement and crypto applications across various regions. Chapter 4 looks at the electronic voting system that takes advantage of blockchain technology's high level of security. Chapter 5 talks about WEB3.0, which uses blockchain as a medium to work, reducing the factor of data loss or leaks, as blockchain is fundamentally unalterable making them more secure for data storage. Chapter 6 presents the views on healthcare data management based on blockchain technology as well as prospective study areas for exchanging medical data via blockchain. Chapter 7 discusses how to generate tokens through a step-by-step procedure.

This book is essential for anyone looking to understand how hyperautomation can revolutionize businesses by simplifying operations, reducing errors, and creating more intelligent and adaptable workplaces through the use of automation technologies such as artificial intelligence, machine learning, and robotic process automation. The use of automation technologies to simplify any and every activity conceivable in a business, allowing repeated operations to operate without manual intervention, is known as hyperautomation. Hyperautomation transforms current and old processes and equipment by utilizing artificial intelligence, machine learning, and robotic process automation. This digital transformation may assist a business in gaining cost and resource efficiency, allowing it to prosper in a more competitive environment. With the advancement of automation technologies, hyperautomation is becoming more prevalent. Companies are shifting their methods to create more human-centered and intelligent workplaces. This change has ushered in a new era for organizations that rely on technology and automation tools to stay competitive. Businesses may move beyond technology's distinct advantages to genuine digital agility and scale adaptability when all forms of automation operate together in close partnership. Automation tools must be simple to incorporate into the current technological stack while not requiring too much effort from IT. A platform must be able to plug and play with a wide range of technologies to achieve hyperautomation. The interdependence of automation technologies is a property that is connected to hyperautomation. Hyperautomation saves individuals time and money by reducing errors. Hyperautomation has the potential to create a workplace that is intelligent, adaptable, and capable of making quick, accurate decisions based on data and insights. Model recognition is used to determine what to do next and to optimize processes with the least amount of human engagement possible.

The text covers both basic and advanced techniques based on state performance systems and binary systems. The chapters will highlight reliability prediction, series-parallel, and complex modeling. It presents a dynamic reliability analysis of safety-critical systems using Petri nets, and dynamic resource allocation modeling of software with patching. The text illustrates a semi-Markov analysis of systems with a Weibull interface.This book: discusses in a comprehensive manner the reliability-centered maintenance modeling of electric vehicle systems; covers the reliability modeling of multi-state systems under the product development stage, and the reliability assessment of a multi-state degraded system; examines the role of nature-inspired techniques in the reliability optimization of systems; explores the practical challenges and solutions for RAMS management of train control systems; and showcases the methodology for the assessment of multi-state system reliability of traction electric drives, including overload modes. It is primarily written for graduate students and academic researchers in the fields of industrial engineering, systems engineering, manufacturing engineering, production engineering, mechanical engineering, and mathematics.

Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students.

This volume comprises of research papers presented at the 4th International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) organized by Department of Computer and Communication Engineering, Manipal University Jaipur, India during April 4 – 5, 2024. The book includes a collection of innovative ideas from researchers, scientists, academics, industry professionals and students. The book covers a variety of topics, such as artificial intelligence and computer vision, image processing and video analysis, applications and services of artificial intelligence and computer vision, interdisciplinary areas combining artificial intelligence and computer vision, and other innovative practices.