Prasenjit Chatterjee – författare

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.