Amit Kumar Tyagi – författare

Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes.

Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR.

Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics Presents a roadmap of future trends suitable for innovative Data Science research and practice Includes topics such as Blockchain technology for securing data at end user/server side Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues.Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy.FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc.Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues.Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy.FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc.Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.

Blockchain technology is an emerging distributed, decentralized architecture and computing paradigm, which has accelerated the development and application of cloud, fog and edge computing; artificial intelligence; cyber physical systems; social networking; crowdsourcing and crowdsensing; 5g; trust management and finance; and other many useful sectors.Nowadays, the primary blockchain technology uses are in information systems to keep information secure and private. However, many threats and vulnerabilities are facing blockchain in the past decade such 51% attacks, double spending attacks, etc. The popularity and rapid development of blockchain brings many technical and regulatory challenges for research and academic communities. The main goal of this book is to encourage both researchers and practitioners of Blockchain technology to share and exchange their experiences and recent studies between academia and industry.The reader will be provided with the most up-to-date knowledge of blockchain in mainstream areas of security and privacy in the decentralized domain, which is timely and essential (this is due to the fact that the distributed and p2p applications are increasing day-by-day, and the attackers adopt new mechanisms to threaten the security and privacy of the users in those environments). This book provides a detailed explanation of security and privacy with respect to blockchain for information systems, and will be an essential resource for students, researchers and scientists studying blockchain uses in information systems and those wanting to explore the current state of play.

Blockchain technology is an emerging distributed, decentralized architecture and computing paradigm, which has accelerated the development and application of cloud, fog and edge computing; artificial intelligence; cyber physical systems; social networking; crowdsourcing and crowdsensing; 5g; trust management and finance; and other many useful sectors.Nowadays, the primary blockchain technology uses are in information systems to keep information secure and private. However, many threats and vulnerabilities are facing blockchain in the past decade such 51% attacks, double spending attacks, etc. The popularity and rapid development of blockchain brings many technical and regulatory challenges for research and academic communities. The main goal of this book is to encourage both researchers and practitioners of Blockchain technology to share and exchange their experiences and recent studies between academia and industry.The reader will be provided with the most up-to-date knowledge of blockchain in mainstream areas of security and privacy in the decentralized domain, which is timely and essential (this is due to the fact that the distributed and p2p applications are increasing day-by-day, and the attackers adopt new mechanisms to threaten the security and privacy of the users in those environments). This book provides a detailed explanation of security and privacy with respect to blockchain for information systems, and will be an essential resource for students, researchers and scientists studying blockchain uses in information systems and those wanting to explore the current state of play.

The wide gap between the existing security solutions and the actual practical deployment in smart manufacturing, smart home, and remote environments (with respect to wireless robotics) is one of the major reasons why we require novel strategies, mechanisms, architectures, and frameworks. Furthermore, it is also important to access and understand the different level of vulnerabilities and attack vectors in Wireless Sensor Network (WSN) and Wireless Robotics. This book includes an in-depth explanation of a secure and dependable Wireless Robotics (WR) architecture, to ensure confidentiality, authenticity, and availability. Features Blockchain technology for securing data at end/server side Emerging technologies/networking, like Cloud, Edge, Fog, etc., for communicating and storing data (securely). Various open issues, challenges faced in this era towards wireless robotics, including several future research directions for the future. Several real world’s case studies are included Chapters on ethical concerns and privacy laws, i.e., laws for service providers Security and privacy challenges in wireless sensor networks and wireless roboticsThe book is especially useful for academic researchers, undergraduate students, postgraduate students, and industry researchers and professionals.

The wide gap between the existing security solutions and the actual practical deployment in smart manufacturing, smart home, and remote environments (with respect to wireless robotics) is one of the major reasons why we require novel strategies, mechanisms, architectures, and frameworks. Furthermore, it is also important to access and understand the different level of vulnerabilities and attack vectors in Wireless Sensor Network (WSN) and Wireless Robotics. This book includes an in-depth explanation of a secure and dependable Wireless Robotics (WR) architecture, to ensure confidentiality, authenticity, and availability. Features Blockchain technology for securing data at end/server side Emerging technologies/networking, like Cloud, Edge, Fog, etc., for communicating and storing data (securely). Various open issues, challenges faced in this era towards wireless robotics, including several future research directions for the future. Several real world’s case studies are included Chapters on ethical concerns and privacy laws, i.e., laws for service providers Security and privacy challenges in wireless sensor networks and wireless roboticsThe book is especially useful for academic researchers, undergraduate students, postgraduate students, and industry researchers and professionals.

Human-Centric Integration of 6G-Enabled Technologies for Modern Society: Fundamentals, Applications, Analysis and Challenges serves as a comprehensive reference, addressing the information needs of professionals by providing deep information about the fundamentals and applications of 6G, enabling them to make informed decisions in the dynamic landscape of advanced communication technologies. In the 23 chapters, this book introduces the reader to the 6G technology, the evolution of wireless communication, and the integration of artificial intelligence; provides the use cases and applications of 6G technology and the insights into the challenges, future trends, and emerging technologies; and includes the applications of 6G technology in remote healthcare services, patient monitoring, and medical diagnostics.Human-Centric Integration of 6G-Enabled Technologies for Modern Society: Fundamentals, Applications, Analysis and Challenges redefines the way we connect, communicate, and collaborate with emerging technologies in this smart era of 6G technology. The title benefits from a collective wealth of knowledge and perspectives. This diversity enriches the content, providing readers with insights from various angles, setting it apart from publications authored or edited by a limited number of individuals.It discusses both the like fundamental concepts, diverse applications and analytical methodologies, as the challenges that come with the development and deployment of 6G-enabled technologies It is designed to address the latest developments in 6G technology, offering a forward-looking perspective on emerging trends It ensures that readers receive up-to-date information and insights into the rapidly evolving landscape of next-generation wireless communication

Human- Centric Integration of Next Generation Data Science and Blockchain Technology: Advancing Society 5.0 Paradigms focuses on the current technological landscape, addressing the evolving integration of data science and blockchain within the context of Society 5.0. This comprehensive resource explains the convergences between data science, blockchain, and the human-centric vision of Society 5.0, while also filling the gap in understanding and navigating this transformative intersection with recent shifts towards more decentralized and data-driven paradigms.The book introduces the concept of Society 5.0, examining the historical context, and outlines the evolving technological landscape shaping our interconnected future. It discusses the fundamental principles of data science, from data collection and preprocessing to exploratory data analysis and explains the transformative impact of data science and blockchain across industries such as healthcare, finance, education, and transportation. This book is essential to understanding and shaping the future of technology and society from decentralized solutions to predictive analytics/ emerging technologies.Addresses the evolving integration of data science and blockchain within the context of Society 5.0Introduces the basic architecture and taxonomy of blockchain technologyExplores the future urban lives under the concept of “Society 5.0”, characterized by the key phrases of data-driven society and knowledge-intensive societyOffers a firm foundation and understanding of recent advancements in various domains such as data analytics, neural networks, computer vision, and robotics, along with practical solutions to existing problems in fields such as healthcare, manufacturing industries, security, and infrastructure management

Smart Hospital Technology with Advanced Sensors: Innovations in Patient Care explores the transformative role of advanced sensor systems in modern healthcare environments. This edited book presents a comprehensive overview of smart hospital technologies, emphasizing the integration of AI, IoT, and automation to enhance patient care and hospital operations. The book covers a wide range of topics including wearable and implantable sensors, AI-driven diagnostics, robotic surgery, smart ICUs, and cybersecurity in healthcare. It also addresses emerging trends such as blockchain for medical data, ethical AI, and the convergence of smart hospitals with smart cities, supported by global case studies and expert contributions. Designed for students, researchers, and academics in bioinformatics, this book offers foundational knowledge, current applications, and future directions in digital healthcare. It serves as a valuable resource for understanding the challenges and opportunities in building intelligent, data-driven hospital systems that improve outcomes and redefine patient experiences.Provides a comprehensive foundation in smart hospital systems, covering core technologies such as AI, IoT, robotics, and advanced sensors used in modern healthcarePresents globally diverse case studies and expert insights that illustrate real-world applications, challenges, and innovations in digital patient care and hospital automationSupports academic research and curriculum development by offering structured chapters with analytical methods, ethical considerations, and future trends in intelligent healthcare systems

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logisticsThe text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logisticsThe text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

Nowadays, the latest technologies can be found not only in healthcare and space application but also in hybrid supercars. Supercars and hypercars require high-performance materials with high strength, high stiffness, and light weight. For higher performance, car engines now become stronger but smaller and with lower fuel consumption (with cleaner exhaust). Currently, the automotive industry involves batch production, but in the near future, personalized and individualized automobiles with low and limited quantities can be fabricated in smart factories, which integrate all companies working in the supply chain, from manufacturing to marketing and services. In this regard, future automobiles in smart cities become more personalized (single user, limited version, personal spare parts), safer, and smarter.Blockchain technology is the key to these future perspectives toward intelligent automobiles without any risk of safety, accident, security, theft, or traffic jam. In the current industry, blockchain technology can explore the interconnection of blockchain with other innovative technologies and trends, such as the Internet of Things (IoT) and artificial intelligence (AI), and analyzes the potential to transform business processes and whole industries if these innovations are applied jointly.In the case of the manufacturing sector, manufacturing can provide a high return on investment. It was reported that $1 of investment in manufacturing can create ~$2.5 of economic activity. In addition, smart products should be fabricated from smart materials via the intelligent manufacturing system framework. In smart production, if the products and machines are integrated, embedded, or otherwise equipped with smart sensors and devices, the system can immediately collect the current operating parameters and predict the product quality and then communicate the optimal parameters to machines in the production line. For smart city applications, the global smart cities market size is expected to grow from USD 410.8 billion in 2020 to USD 820.7 billion by 2025 at a compound annual growth rate (CAGR) of 14.8%. For smart city applications, blockchain technology can build on decentralization, immutability, and consensus characteristics.Additionally, intelligent wireless sensor networks can provide big information to monitor and manage the city’s regular operations and services, including traffic and transportation systems, street lighting systems, power plants, water supply networks, waste management, libraries, hospitals, schools, universities, etc. A blockchain-based distributed framework can be used for automobiles in the smart city. This framework can include a novel miner node selection algorithm for the blockchain-based distributed network architecture.This book explores how blockchain technology can be used in the automotive industry from smart manufacturing to the smart city.

Industry 4.0 is a revolutionary concept that aims to enhance productivity and profitability in various industries through the implementation of smart manufacturing techniques. This book discusses the profound impact of Industry 4.0, which involves the seamless integration of digital technologies into manufacturing processes within the realm of industrial engineering.Industry 4.0, Smart Manufacturing, and Industrial Engineering: Challenges and Opportunities thoroughly examines the intricate facets of Industry 4.0 and Smart Manufacturing, offering a comprehensive overview of the challenges and opportunities that this paradigm shift presents to industrial engineers. It provides practical insights and strategies to help professionals navigate the complexities of this evolving landscape. Fundamental components of Industry 4.0 and Smart Manufacturing, ranging from the incorporation of sensors and data analytics to the deployment of cyber-physical systems and the promotion of sustainable practices are covered in detail. The book addresses the obstacles and prospects brought about by Industry 4.0 in the digital age and offers solutions to issues such as data security, interoperability, and workforce preparedness.The book sheds light on how Industry 4.0 combines various disciplines, including engineering technology, data science, and management. It serves as a valuable resource for researchers, undergraduate and postgraduate students, as well as professionals operating in the field of industrial engineering and related domains.

Industry 4.0 is a revolutionary concept that aims to enhance productivity and profitability in various industries through the implementation of smart manufacturing techniques. This book discusses the profound impact of Industry 4.0, which involves the seamless integration of digital technologies into manufacturing processes within the realm of industrial engineering.Industry 4.0, Smart Manufacturing, and Industrial Engineering: Challenges and Opportunities thoroughly examines the intricate facets of Industry 4.0 and Smart Manufacturing, offering a comprehensive overview of the challenges and opportunities that this paradigm shift presents to industrial engineers. It provides practical insights and strategies to help professionals navigate the complexities of this evolving landscape. Fundamental components of Industry 4.0 and Smart Manufacturing, ranging from the incorporation of sensors and data analytics to the deployment of cyber-physical systems and the promotion of sustainable practices are covered in detail. The book addresses the obstacles and prospects brought about by Industry 4.0 in the digital age and offers solutions to issues such as data security, interoperability, and workforce preparedness.The book sheds light on how Industry 4.0 combines various disciplines, including engineering technology, data science, and management. It serves as a valuable resource for researchers, undergraduate and postgraduate students, as well as professionals operating in the field of industrial engineering and related domains.

Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries.Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning.Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.

Quantum computing and algorithms are set to revolutionize information processing. Covering such topics, Quantum Computing: The Future of Information Processing explains its principles, practical applications, and future implications in a clear and accessible manner. The book strives to simplify the essential concepts and practical applications of quantum computing. Its aim is to help students and researchers to apply quantum computing to advance AI and machine learning, cybersecurity, and blockchain. With its emphasis on practical applications, the book covers how quantum computing is changing such fields as:FinanceMedicineBuilt environmentNetworking and communicationsWith extensive real-world case studies and practical implementation guidance, the book is a guide for those seeking to understand how quantum computing is applied in various industries. Its in-depth exploration of quantum computing covers both foundational principles and advanced applications in a single resource, saving readers the need to purchase multiple books. Finally, the book focuses on the future of information processing so that students and researchers can anticipate and prepare for the transformative impact of quantum computing.

AI and Blockchain in Smart Grids: Fundamentals, Methods, and Applications examines the cutting-edge solution that combines artificial intelligence (AI), blockchain technology, and digital twin concepts to innovate the management and optimization of electrical power distribution. This innovative approach enhances the resilience, efficiency, and security of electricity grids while providing real-time insights for grid operators and stakeholders. The book covers such key elements as using:Digital twins in smart grids to gather real-time data from various grid componentsAI-powered analytics to process the data generated by digital twins and to analyze this information to detect patterns, predict grid failures, and recommend adjustments to enhance a grid's performanceBlockchain-based security to ensure the secure and transparent management of data within a smart grid, especially a tamper-resistant ledger to store information related to energy production, distribution, and consumptionDecentralized data sharing to allow grid data to be shared securely among various stakeholders, including utilities, regulators, and consumersGrid optimization techniques to improve electricity distribution, reduce energy waste, and balance supply and demand efficientlySelect real-world case studies and practical examples demonstrate how AI and blockchain are currently being applied to enhance grid management, energy distribution, and sustainability. By explaining to researchers, academics, and students how AI and blockchain can revolutionize electricity distribution and make grids smarter, more secure, and environmentally friendly, the book points to a future where grid operators, regulators, and consumers will benefit from real-time data and a resilient, efficient energy ecosystem.

Quantum computing and algorithms are set to revolutionize information processing. Covering such topics, Quantum Computing: The Future of Information Processing explains its principles, practical applications, and future implications in a clear and accessible manner. The book strives to simplify the essential concepts and practical applications of quantum computing. Its aim is to help students and researchers to apply quantum computing to advance AI and machine learning, cybersecurity, and blockchain. With its emphasis on practical applications, the book covers how quantum computing is changing such fields as:FinanceMedicineBuilt environmentNetworking and communicationsWith extensive real-world case studies and practical implementation guidance, the book is a guide for those seeking to understand how quantum computing is applied in various industries. Its in-depth exploration of quantum computing covers both foundational principles and advanced applications in a single resource, saving readers the need to purchase multiple books. Finally, the book focuses on the future of information processing so that students and researchers can anticipate and prepare for the transformative impact of quantum computing.

Blockchain and Digital Twin Applications in Smart Agriculture presents scenarios, solutions, applications to optimize crop management, improve resource allocation, and enhance sustainability in modern farming practices. It examines how blockchain and digital twin technology could enable farmers and stakeholders to create virtual, data-driven replicas of their agricultural operations while leveraging the security and transparency of blockchain technology. Highlights of the book include:An immutable blockchain ledger for storing and sharing agricultural data securelyDecentralized data management to share data among such stakeholders as farmers, suppliers, distributors, and consumersSupply chain transparency to ensure product authenticity, reduce food fraud, and protect consumersSolutions to improve the allocation of such resources as water, fertilizer, and labor to reduce costs and improve productivity.The book helps researchers and academics develop solutions that can transition the agricultural industry into a more data-driven, sustainable, and transparent future. It explains how creating digital twins of agricultural operations and securing data through blockchain can enhance the ability to make informed decisions, reduce waste, and ensure that agriculture practices are more efficient and environmentally responsible. Featuring practical applications and case studies, the book provides a roadmap to farming that is smart, data-driven, and more.

In the recent decade, industry 4.0 and its uses have attracted huge attention from the scientific community for doing today's task effectively. This book provides a comprehensive guide to industrial robotics. It covers the history, structure, applications, tools, and programming of robotic systems. The book delves into fundamental principles, including robot mechanics, motion control, and coordinate systems. It also explores advanced technologies like machine vision, AI, mobile robots, digital twins, and VR in industrial environments. The text is designed for students, engineers, and professionals, offering insights into designing, deploying, and maintaining robotic systems in modern factories, emphasizing their role in Industry 4.0.

Today blockchain has been used in many finance and non-finance applications (as the concept evolved with a use in the finance sector). In the future, next-generation blockchain will provide a future society which will be built with transparency, automation, and secure digital interaction. With integrating AI, quantum-resistant cryptography, edge computing, and IoT, etc., these advanced enhancements will provide trusted ecosystems where data moves safely across smart cities, autonomous systems, and global digital markets. We will see decentralized identity, seamless machine-to-machine communication, and scalable green consensus models which will support many sectors towards sustainable growth. As immersive technologies like AR, VR, and the metaverse will expand, next-gen blockchain will provide the backbone for verified assets, ethical data use, and resilient infrastructures that empower an interconnected, intelligent world.

AI is expected to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support initiatives and lifelong learning, but educational institutions and companies need to take changing technology into account. Moving towards AI supported by digital tools requires a major shift in the concept of learning, expertise, and its business applications.The book explores the concepts of advancements in teaching and learning. It comprises the latest research on AI and how it is changing learning and education. The book focuses on the opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. It also deals with ethical challenges related to learning and education, while connecting human learning and machine learning. It will be of use to a range of readers, including researchers, AI users, companies, and policy makers.

Today Privacy and Security in AI-Driven Mental Health Apps has expanding landscape of mental health technology. For that, we need to concentrate on the essential topics of privacy and security in AI-powered mental health apps. As the use of AI-driven solutions in mental health treatment advances, questions about the ethical management of sensitive data, user privacy, and system vulnerabilities have surfaced as serious challenges. This book gives a complete investigation of these difficulties, addressing how privacy-preserving approaches and secure systems might maintain trust, openness, and ethical integrity in mental health applications. The book is organized to cover a broad variety of subjects. It starts with an introduction to mental health applications and their integration with AI technology, followed by a full discussion on the sorts of data acquired, including sensitive behavioral, physiological, and psychological data. It then analyzes privacy threats, such as data breaches and abuse of personal information, and security problems including cyberattacks and flaws in AI systems. This book has some chapters which cover advanced solutions, including encryption approaches, differential privacy, blockchain integration, and federated learning, as well as the importance of regulatory frameworks like GDPR and HIPAA (including case studies of real-world applications to exhibit both achievements and failures in privacy and security). The last parts cover future directions, ethical issues, and suggestions for developers, physicians, and politicians. With contributions from specialists across disciplines, this book serves as important collection for understanding the convergence of AI, mental health, and data security, seeking to stimulate innovation while respecting user rights.

The Medical Internet of Things (MIoT) has redefined healthcare by interconnecting devices, systems, and applications to monitor, analyze, and manage patient data in real time. MIoT leverages wearable devices, sensors, and interconnected medical equipment, enabling remote health monitoring, precision medicine, and improved patient outcomes. By seamlessly integrating patient data from various sources, MIoT leads to early diagnosis, treatment optimization, and personalized healthcare interventions. With this interconnectedness, healthcare professionals gain continuous access to patient vitals, enhancing diagnostic accuracy and response times, particularly in remote and underserved areas. This real-time data flow also facilitates predictive analytics, helping pre-empt adverse events and reducing hospital readmissions. However, MIoT also raises significant challenges, including concerns about data privacy, security, and the risk of cyber threats. The vast amounts of sensitive patient information shared across networks make privacy safeguards and robust security mechanisms essential to protect patient confidentiality and data integrity.The potential of MIoT to revolutionize healthcare is vast, offering unprecedented opportunities for improved patient care and operational efficiency. As MIoT continues to evolve, balancing innovation with privacy and security will be crucial to fostering trust, ensuring compliance, and maximizing the benefits of this technology in healthcare.

In today’s era, the integration of cutting-edge technologies like Artificial Intelligence (AI), Digital Twin technology, and Big Data Analytics, is transforming Blockchain-enabled IoT ecosystems. Today AI enhances predictive intelligence and decision-making, while Digital Twins create real-time virtual replicas of physical systems for monitoring and simulation. Big Data analytics processes massive IoT-generated data streams to extract actionable insights whereas Blockchain technology ensures data integrity, transparency, and secure decentralized communication among devices. Together, these technologies enable smart, autonomous, and trustworthy systems across domains such as healthcare, smart cities, and industrial automation. This convergence improves efficiency, resilience, and scalability in next-generation IoT applications for modern society.

In the era rapid technological advancement, the transportation sector stands at the cusp of a transformative era. Human-Centric Intelligent Transportation Systems (HC-ITS) represent a significant leap forward, emphasizing the integration of human factors into the design, implementation, and operation of transportation networks. This book explores the multifaceted dimensions of HC-ITS, highlighting their potential to revolutionize transportation. The foundation of HC-ITS lies in recognizing the importance of human needs, behaviors, and safety. By prioritizing these elements, HC-ITS aims to create more efficient, accessible, and safer transportation environments. This approach not only enhances user experience but also fosters a sustainable and inclusive transportation ecosystem.This book provides a detailed overview of HC-ITS, beginning with the fundamental principles that underpin this innovative approach. It then delves into the core concepts, exploring how user-centered design, human-machine interaction, and advanced data analytics converge to create intelligent transportation solutions. The practical applications of HC-ITS are vast and varied. The volume examines their implementation in smart traffic management, public transportation systems, autonomous vehicles, and initiatives aimed at improving pedestrian and cyclist safety. Through detailed case studies and real-world examples, it illustrates how HC-ITS are being deployed to address current transportation challenges and enhance the quality of urban life.Key features:Explores fundamentals of human-centric ITS technologies comprehensively, including AI personalization and sensor fusion for user safety.Provides diverse applications of human-centric ITS, such as adaptive urban mobility and personalized routing.Provides an in-depth analysis of human-centric ITS performance metrics, like traffic flow optimization and accessibility.Includes real-world case studies, used for emission reductions currently.Reviews challenges and future directions of HC-ITS, focusing on privacy, equity, and multimodal integration.

The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems.We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications.AudienceResearchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.

Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.