This new volume explores a plethora of blockchain-based solutions for big data and IoT applications, looking at advances in real-world applications in several sectors, including higher education, cybersecurity, agriculture, business and management, healthcare and biomedical science, construction and project management, smart city development, and others. Chapters explore emerging technology to combat the ever-increasing threat of security to computer systems and offer new architectural solutions for problems encountered in data management and security. The chapters help to provide a high level of understanding of various blockchain algorithms along with the necessary tools and techniques. The novel architectural solutions in the deployment of blockchain presented here are the core of the book.

Provides a technical introduction to deepfakes, its benefits, and the potential harms Presents practical approaches of creation and detection of deepfakes using Deep Learning (DL) Techniques Draws attention towards various challenging issues and societal impact of deepfakes with their existing solutions Includes research analysis in the domain of DL fakes for assisting the creation and detection of deepfakes applications Discusses future research directions with emergence of deepfakes technology

Convolutional neural networks (CNNs), a type of deep neural network that has become dominant in a variety of computer vision tasks, in recent few years has attracted interest across a variety of domains due to their high efficiency at extracting meaningful information from visual imagery. Convolutional neural networks (CNNs) excel at a wide range of machine learning and deep learning tasks. As sensor-enabled internet of things (IoT) devices pervade every aspect of modern life, it is becoming increasingly critical to run CNN inference, a computationally intensive application, on resource-constrained devices. Through this edited volume we aim to provide a structured presentation of CNN enabled IoT applications in vision, speech, and natural language processing. This book discusses a variety of CNN techniques and applications, including but not limited to, IoT enabled CNN for speech de-noising, a smart app for visually impaired people, disease detection, ECG signal analysis, weather monitoring, texture analysis, etc. Unlike other books on the market, this book covers the tools, techniques, and challenges associated with the implementation of CNN algorithms, computation time, and the complexity associated with reasoning and modelling various types of data. We have included CNN's current research trends and future directions.

The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers

New technologies such as artificial intelligence, blockchain, the Internet of Things (IoT), etc. are redefining business processes around the world at a rapid rate and resulting in both great opportunities and challenges for businesses. Though these technologies are extensively being used in developed countries, emerging economies are also not far behind. Disruptive Technologies in International Business advances the understanding of technological applications in business within an international paradigm. With its in-depth discussions of diverse topics such as the global value chain (GVC), environmental risk management, IoT, Surface Mobility, and anime, the book argues that technologies offer many advantages but there are accompanying risks, challenges, and disadvantages as well. The need of the hour is to address the impact of these technologies on the environment, society, and economy of the world. This book offers a collage of insights on how these technologies can potentially change the playing field in businesses and countries and contribute to the betterment of society. This book will provide business practitioners, international organizations, government officials, and policy makers with inspiration and new leads toward more efficient systems, policies, and operational frameworks in our increasingly technology-driven society.

Transportation typically entails crucial “life-death” choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.

Transportation typically entails crucial "life-death" choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.

The concept of Internet of Things has silently existed since the late nineteenth century but in the current decade expectations and excitement has peaked. However not many have understood the profound change that it can usher in. How big this change can be and how it can transform our working!! This book aims to bring in this realization with illustrative and practical case studies with comprehensive concepts. From beginners to practitioners in the field of academics or industry, it serves as a comprehensive yet easy to comprehend source of information on the multiple facets of IoT. Simplistic but comprehensive introduction of the facets of primarily the industrial IoT Practical adoption cases explaining the Core technology stack and business applications Comprehensive view of current technologies which complete the IoT delivery ecosystem, followed by overview of IoT enabled new business models. Realistic view of how industrial firms can evolve into the next stage of maturity along with determinants influencing this transformation since manufacturing is envisioned to be a key segment to adopt and benefit from IoT. Detailed analysis of IoT benefits for the universal triad- energy management, logistics optimization and distribution channel management. A full-fledged case study on Adoption of Green manufacturing using IoT. Real world example of gauging End User perception using different models which is important for a successful adoption of IoT. A futuristic visionary view of IoT as comprehended based on evolution of technology and platforms, and finally analysis of the extremely crucial concepts of security, privacy and governance.

This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example-a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices. Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

With the emergence of smart technology and automated systems in today's world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.

With the emergence of smart technology and automated systems in today's world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.

As technology weaves itself more tightly into everyday life, socio-economic development has become intricately tied to these ever-evolving innovations. Technology management is now an integral element of sound business practices, and this revolution has opened up many opportunities for global communication. However, such swift change warrants greater research that can foresee and possibly prevent future complications within and between organizations. The Handbook of Research on Engineering Innovations and Technology Management in Organizations is a collection of innovative research that explores global concerns in the applications of technology to business and the explosive growth that resulted. Highlighting a wide range of topics such as cyber security, legal practice, and artificial intelligence, this book is ideally designed for engineers, manufacturers, technology managers, technology developers, IT specialists, productivity consultants, executives, lawyers, programmers, managers, policymakers, academicians, researchers, and students.

The recent advancements in the machine learning paradigm have various applications, however, it has shown significant results in the field of medical data analysis. The results are highly accurate and are comparable to human experts. The various research has proved the high accuracy of deep learning algorithms and has become a standard choice for analysing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Researchers in industry, hospitals, and academia have published hundreds of scientific contributions in this area during a pandemic. This book is an ideal and relevant source of content for data science and healthcare professionals who want to delve into complex deep learning algorithms, calibrate models, and improve the predictions of the trained model on medical imaging. Primary audiences for this book are professionals and researchers in the fields of data science, machine learning, deep learning, and AI. Also academicians, healthcare professionals, or anyone who may have a keen interest in how the machine and deep learning algorithms are helping in the identification of solutions to medical sensor/image data analysis, event detection, segmentation, and abnormality detection, object/lesion classification, organ/region/landmark localization, object/lesion detection, organ/substructure segmentation, lesion segmentation, and medical image registration. The variety of readers in the fields of government, consulting, healthcare professionals, as well as the readers from all the social strata, can also be benefited from this book to improve understanding of the cutting-edge theory, technologies, methodologies, and applications of deep Learning algorithms for medical care.

The book introduces a variety of latest techniques designed to represent, enhance, and empower multi-disciplinary approaches of geographic information system (GIS), artificial intelligence (AI), deep learning (DL), machine learning, and cloud computing research in healthcare. It provides a unique compendium of the current and emerging use of geospatial data for healthcare and reflects the diversity, complexity, and depth and breadth of this multi-disciplinary area. This book addresses various aspects of how smart healthcare devices can be used to detect and analyze diseases. Further, it describes various tools and techniques to evaluate the efficacy, suitability, and efficiency of geospatial data for health-related applications. It features illustrative case studies, including future applications and healthcare challenges. This book is beneficial for computer science and engineering students and researchers, medical professionals, and anyone interested in using geospatial data in healthcare. It is also intended for experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious to learn about this important subject. The book presents an effort to draw how we can build health-related applications using geospatial big data and their subsequent analysis.