Digital Twin for Smart Manufacturing: Emerging Approaches and Applications provides detailed descriptions on how to integrate and optimize novel digital technologies for smart manufacturing. The book discusses digital twins, which combine the industrial internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. In addition, they provide an effective way to integrate technologies like cyber-physical systems into a smart manufacturing system, potentially optimizing the entire business process and operating procedure of the manufacturing firm. Drawing on the latest research, the book addresses the topics and technologies key to successful implementation of a smart manufacturing system, including augmented and virtual reality, big data and energy management. Broader subjects such as additive manufacturing and robotics are also covered in this context, covering every aspect of production.

Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks. Features: Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care. Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery. Summarizes several strategies, analyses, and optimization methods for patient healthcare. Focuses on drug discovery and development by cloud computing and data-driven research The targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.

This book presents a framework for integrating blended learning and massive open online courses (MOOCs) in the Indian education system. It argues that blended teaching and learning is the most suitable approach to education in a post-COVID-19 world. Drawing on case studies used in blended learning practices around the world, the book provides ample resources for beginners to improvise the spread of knowledge around information technology in higher education. It discusses various concepts such as flip learning in blended learning models and examines the self-assessment tools and structures it offers to institutions for building competencies. In addition to addressing the challenges and opportunities of adopting the digital mode of teaching, the book also offers techniques and concepts helpful for designing MOOCs. It covers concepts such as curriculum designing, content flow, teaching behavior, and evaluation patterns, which are important aspects of online teaching. An indispensable guide to navigating the shift from offline to online teaching, this book will be of interest to students, teachers, and researchers of education, education technology, digital education, and information technology. It will also be useful to policymakers, educational institutions, EdTech start-ups, NGOs in the education sector, and online education centers.

The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: Provides a smart traffic congestion avoidance system with an integrated fuel consumption model. Predicts traffic in short-term and regular. This is illustrated with a case study. Efficient Traffic light controller and deviation system in accordance with the traffic scenario. IoT based Intelligent Transport Systems in a Global perspective. Intelligent Traffic Light Control System and Ambulance Control System. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. Bunch of solutions and ideas for smart traffic development in smart cities. This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications. This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.

The future policing ought to cover the identification of new assaults, the disclosure of new ill-disposed patterns and forecast of any future vindictive patters from the accessible authentic information. Such keen information will bring about building clever advanced proof handling frameworks that will help cops investigate violations. Artificial Intelligence for Cyber Defence and Smart Policing will describe the best way of practising artificial intelligence for cyber defence and smart policing. Key Features: • Combines both AI for cyber defence and smart policing in one place • Covers novel strategies in future to help cybercrime examinations and police. • Discusses different AI models to fabricate more exact techniques • Elaborates on problematization and international issues. • Includes case studies and real-life examples. This book is primarily aimed at graduates, researchers and IT professionals. Business executives will also find this book helpful. S Vijayalakshmi is currently working as an Associate Professor in the Data Science Department in CHRIST (Deemed to be University), Pune, Lavasa Campus. She is having many academic portfolios associated with the current position. Her research area is on Image Processing and IoT. P Durgadevi is working as an Assistant Professor in the department of Computer Science and Engineering, GITAM University, Bengaluru. Her research interest is in medical image processing, Machine Learning and IoT. Lija Jacob is an Assistant Professor at the Dept. of Data Science, Christ [Deemed to be University],Pune Lavasa. She has more than 17 years of teaching experience and 8 years of research experience. Her research interests are Computer Vision, Machine learning, Deep Learning, etc. Balamurugan Balusamy is currently working as Professor in the School of Computing Sciences and Engineering at Galgotias University, Greater Noida, India. He has published 30+ books on various technologies and visited 15 plus countries for his technical course. He has several top-notch conferences in his resume and has published over 150 of quality journal, conference and book chapters combined.

This book is a reference on digital technology and its impact on sustainability, providing insight into sustainable practices globally. It focuses on the critical practices leading to sustainable initiatives among various organizations, IT infrastructure, communities, and government compliance. The book describes the green computing paradigms and the impact of a circular economy with a focus on sustainable practices in a post-pandemic world. Sustainable Digital Technologies: Trends, Impacts, and Assessments discusses the critical factors leading to sustainable initiatives in a global economy. It highlights the impact of digital technology and Industry 4.0 in today’s world. The book focuses on the role, responsibility, and the effect of the Internet of Things for digital sustainability and practices. It describes implementation strategies for green cloud computing and presents additional strategies for sustainable practices in a post-pandemic world. This publication is designed for use by technology development academicians, data scientists, industrial professionals, researchers, and students interested in uncovering the latest innovations in the field and the current research on problem-oriented processing techniques in sustainable and evolutionary computing applications with reduced energy channelization.

In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements. Digital Twin Technologies for Healthcare 4.0 discusses how the concept of the digital twin can be merged with other technologies, such as artificial intelligence (AI), machine learning (ML), big data analytics, IoT and cloud data management, for the improvement of healthcare systems and processes. The book also focuses on the various research perspectives and challenges in implementation of digital twin technology in terms of data analysis, cloud management and data privacy issues. With chapters on visualisation techniques, prognostics and health management, this book is a must-have for researchers, engineers and IT professionals in healthcare as well as those involved in using digital twin technology, AI, IoT & big data analytics for novel applications.

This book is a unique guide to the disruptions, innovations, and opportunities that technology provides the insurance sector and acts as an academic/industry-specific guide for creating operational effectiveness, managing risk, improving financials, and retaining customers. It also contains the current philosophy and actionable strategies from a wide range of contributors who are experts on the topic. It logically explains why traditional ways of doing business will soon become irrelevant and therefore provides an alternative choice by embracing technology. Practitioners and students alike will find value in the support for understanding practical implications of how technology has brought innovation and modern methods to measure, control, and evaluation price risk in the insurance business. It will help insurers reduce operational costs, strengthen customer interactions, target potential customers to provide usage-based insurance, and optimize the overall business. Retailers and industry giants have made significant strides in adopting digital platforms to deliver a satisfying customer experience. Insurance companies must adjust their business models and strategies to remain competitive and take advantage of technology. Insurance companies are increasingly investing in IT and related technologies to improve customer experience and reduce operational costs. Innovation through new technologies is a key driver of change in the financial sector which is often accompanied by uncertainty and doubt. This book will play a pivotal role in risk management through fraud detection, regulatory compliances, and claim settlement leading to overall satisfaction of customers.

At present, the technologies associated with artificial intelligence (AI) are reshaping human resource practices across industry. The adoption of AI technologies in human resource management is a key component for innovating and adapting while also focusing on sustainability and success in an ever-changing work environment. This book covers a wide range of techniques, from algorithms and conversational AI to decisional AI and machine learning. The book covers the significance of AI technology in human resources, the challenges faced in implementation, integrating AI into the HR process, the risks associated with implementation, and much more. From a laymen's point of view AI technology leverages the implementation of improvised actions based on decisions made in past. The decision is based on the recommended actions which are repetitive and follows a pattern in decision-making. Thus AI is important to streamline the HR processes and improve the efficacy of the organization.

This book brings together insights for cancer management from emerging sophisticated information and communication technologies such as artificial intelligence, data science, and big data analytics. It focuses on targeted disease treatment using big data analytics, providing information about targeted treatment in oncology, challenges and application of big data in cancer therapy. Featured topics include: Recent developments in the fields of artificial intelligence, machine learning, medical imaging, personalized medicine, computing and data analytics for improved patient care. Description of the application of big data with AI to discover new targeting points for cancer treatment. Summary of several risk assessments in the field of oncology using big data. Focus on prediction of doses in oncology using big data We are in the era of large-scale science. In oncology there is a huge number of data sets grouping information on cancer genomes, transcriptomes, clinical data, and more. The challenge of big data in cancer is to integrate all this diversity of data collected into a unique platform that can be analyzed, leading to the generation of readable files. The possibility of harnessing information from all the accumulated data leads to an improvement in cancer patient treatment and outcome. Solving the big data problem in oncology has multiple facets. Big data in Oncology: Impact, Challenges, and Risk Assessment brings together insights from emerging sophisticated information and communication technologies such as artificial intelligence, data science, and big data analytics for cancer management. The book is written for academics, research scholars, health care professionals, hospital management, pharmaceutical chemist, biomedical industry, software engineers and IT professionals.

This book presents a framework for integrating blended learning and massive open online courses (MOOCs) in the Indian education system. It argues that blended teaching and learning is the most suitable approach to education in a post-COVID-19 world. Drawing on case studies used in blended learning practices around the world, the book provides ample resources for beginners to improvise the spread of knowledge around information technology in higher education. It discusses various concepts such as flip learning in blended learning models and examines the self-assessment tools and structures it offers to institutions for building competencies. In addition to addressing the challenges and opportunities of adopting the digital mode of teaching, the book also offers techniques and concepts helpful for designing MOOCs. It covers concepts such as curriculum designing, content flow, teaching behavior, and evaluation patterns, which are important aspects of online teaching. An indispensable guide to navigating the shift from offline to online teaching, this book will be of interest to students, teachers, and researchers of education, education technology, digital education, and information technology. It will also be useful to policymakers, educational institutions, EdTech start-ups, NGOs in the education sector, and online education centers.

This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights.

The main aim of Healthcare 4.0: Health Informatics and Precision Data Management is to improve the services given by the healthcare industry and to bring meaningful patient outcomes by applying the data, information and knowledge in the healthcare domain. Features: * Improves the quality of health data of a patient * Presents a wide range of opportunities and renewed possibilities for healthcare systems * Gives a way for carefully and meticulously tracking the provenance of medical records * Accelerates the process of disease-oriented data and medical data arbitration * Brings meaningful patient health outcomes * Eradicates delayed clinical communications * Helps the research intellectuals to step down further toward the disease and clinical data storage * Creates more patient-centered services The precise focus of this handbook is on the potential applications and use of data informatics in healthcare, including clinical trials, tailored ailment data, patient and ailment record characterization and health records management.

Cognitive Computing for Internet of Medical Things (IoMT) offers a complete assessment of the present scenario, role, challenges, technologies, and impact of IoMT-enabled smart healthcare systems. It contains chapters discussing various biomedical applications under the umbrella of the IoMT. Key Features Exploits the different prospects of cognitive computing techniques for the IoMT and smart healthcare applications Addresses the significance of IoMT and cognitive computing in the evolution of intelligent medical systems for biomedical applications Describes the different computing techniques of cognitive intelligent systems from a practical point of view: solving common life problems Explores the technologies and tools to utilize IoMT for the transformation and growth of healthcare systems Focuses on the economic, social, and environmental impact of IoMT-enabled smart healthcare systems This book is primarily aimed at graduates, researchers and academicians working in the area of development of the application of the of the application of the IoT in smart healthcare. Industry professionals will also find this book helpful.

The book IoT and Big Data Analytics (IoT-BDA) for Smart Cities - A Global Perspective, emphasizes the challenges, architectural models, and intelligent frameworks with smart decisionmaking systems using Big Data and IoT with case studies. The book illustrates the benefits of Big Data and IoT methods in framing smart systems for smart applications. The text is a coordinated amalgamation of research contributions and industrial applications in the field of smart cities. Features: Provides the necessity of convergence of Big Data Analytics and IoT techniques in smart city application Challenges and Roles of IoT and Big Data in Smart City applications Provides Big Data-IoT intelligent smart systems in a global perspective Provides a predictive framework that can handle the traffic on abnormal days, such as weekends and festival holidays Gives various solutions and ideas for smart traffic development in smart cities Gives a brief idea of the available algorithms/techniques of Big Data and IoT and guides in developing a solution for smart city applications This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.

Legal Analytics: The Future of Analytics in Law navigates the crisscrossing of intelligent technology and the legal field in building up a new landscape of transformation. Legal automation navigation is multidimensional, wherein it intends to construct streamline communication, approval, and management of legal tasks. The evolving environment of technology has emphasized the need for better automation in the legal field from time to time, although legal scholars took long to embrace information revolution of the legal field. * Describes the historical development of law and automation. * Analyzes the challenges and opportunities in law and automation. * Studies the current research and development in the convergence of law, artificial intelligence, and legal analytics. * Explores the recent emerging trends and technologies that are used by various legal systems globally for crime prediction and prevention. * Examines the applicability of legal analytics in forensic investigation. * Investigates the impact of legal analytics tools and techniques in judicial decision making. * Analyzes deep learning techniques and their scope in accelerating legal analytics in developed and developing countries. * Provides an in-depth analysis of implementation, challenges, and issues in society related to legal analytics. This book is primarily aimed at graduates and postgraduates in law and technology, computer science, and information technology. Legal practitioners and academicians will also find this book helpful.

This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights.

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

The primary goal of this book is to address the issues faced by teachers in the adoption of digital tools into their teaching and their students learning. This book also addresses the issues confronting educators in the integration of digital technologies into their teaching and their students' learning. Such issues include a skepticism of the added value of technology to educational learning outcomes, the perception of the requirement to keep up with the fast pace of technological innovation, a lack of knowledge of affordable educational digital tools and a lack of understanding of pedagogical strategies to embrace digital technologies in their teaching. This book presents theoretical perspectives of learning and teaching today's digital students with technology and proposes a pragmatic and sustainable framework for teachers' professional learning to embed digital technologies into their repertoire of teaching strategies in a systematic, coherent and comfortable manner so that technology integration becomes an almost effortless pedagogy in their day-to-day teaching. Some of the objectives are given below: Shares valuable insights into the influence of technology on teaching and learning in higher education Provides deeper insights on higher education and sustainability interact Studies innovations from various perspectives Investigates how the educators and students apply the unique innovative and emotional dimensions in modern age of learning Provides a timely overview of changes in education reforms and policy research globally Evaluates the problematic relationship between globalization, the state, and education reforms.

Increased use of artificial intelligence (AI) is being deployed in many hospitals and healthcare settings to help improve health care service delivery. Machine learning (ML) and deep learning (DL) tools can help guide physicians with tasks such as diagnosis and detection of diseases and assisting with medical decision making. This edited book outlines novel applications of AI in e-healthcare. It includes various real-time/offline applications and case studies in the field of e-Healthcare, such as image recognition tools for assisting with tuberculosis diagnosis from x-ray data, ML tools for cancer disease prediction, and visualisation techniques for predicting the outbreak and spread of Covid-19. Heterogenous recurrent convolution neural networks for risk prediction in electronic healthcare record datasets are also reviewed. Suitable for an audience of computer scientists and healthcare engineers, the main objective of this book is to demonstrate effective use of AI in healthcare by describing and promoting innovative case studies and finding the scope for improvement across healthcare services.

The book collects the latest research and thinking from international experts on green computing and the smart city. The financial and environmental costs of energy are a concern in smart cities due to the high usage of computing, technology, security, IoT, communications, traffic, and other technologies. This book tackles this problem with a focus on computing, reporting on various approaches being taken worldwide, illustrated by several international case studies demonstrating these approaches. Researchers use this book as an up-to-date reference and engineers use it as a guide for the design and implementation of real solutions.

The purpose of this edited book is to provide the relevant technologies and case studies in a concise format that will simplify and streamline the processing of blockchain. The goal is for the contents of this book to change the way business transformations are conducting in economic and social systems. The book examines blockchain technology, the transaction attributes, and its footprint in various fields. It offers fundamentals and terminologies used in blockchain, architecture, and various consensus mechanisms that can be deployed in areas such as healthcare, smart cities, and supply chain management. The book provides a widespread knowledge into the deployment of security countermeasures that can be implemented for a blockchain network and enables the reader to consider the management of business processes and the implementation process in detail. The book highlights the challenges and provides various e-business case studies of security countermeasures. The book serves researchers and businesses by providing a thorough understanding of the transformation process using blockchain technology.

This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications.