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Mathematical Formulas For Industrial and Mechanical Engineering serves the needs of students and teachers as well as professional workers in engineering who use mathematics. The contents and size make it especially convenient and portable. The widespread availability and low price of scientific calculators have greatly reduced the need for many numerical tables that make most handbooks bulky. However, most calculators do not give integrals, derivatives, series and other mathematical formulas and figures that are often needed. Accordingly, this book contains that information in an easy way to access in addition to illustrative examples that make formulas clearer. Students and professionals alike will find this book a valuable supplement to standard textbooks, a source for review, and a handy reference for many years.

Covers mathematics formulas needed for Industrial and Mechanical Engineering Quick and easy to use reference and study Includes practical examples and figures to help quickly understand concepts

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with a focus on technologies that enable the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery, and consumption. Specific sections explore ways to implement CPS effectively and appropriately and cover digitalization of agriculture, digital computers to assist the processes of agriculture with digitized data and allied technologies, including AI, Computer Vision, Big data, Block chain, and IoT. Other sections cover Agri 4.0 and how it can digitalize, estimate, plan, predict, and produce the optimum agricultural inputs and outputs required for commercial purposes. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices, and government policies in Agri 4.0.

Addresses one of the most complex applications of CPSDescribes various technologies, covering CPS in agriculture from precision agriculture to smart supply chain managementFocuses on the digital framework, tools, and systems capable of supporting Agri 4.0

In an era where Artificial Intelligence (AI) is revolutionizing healthcare, Explainable AI in Healthcare Imaging for Precision Medicine addresses the critical need for transparency, trust, and accountability in AI-driven medical technologies. As AI becomes an integral part of clinical decision-making, especially in imaging and precision medicine, the question of how AI reaches its conclusions grows increasingly significant. This book explores how Explainable AI (XAI) is transforming healthcare by making AI systems more interpretable, reliable, and transparent, empowering clinicians and enhancing patient outcomes.Through a comprehensive examination of the latest research, real-world case studies, and expert insights, this book delves into the application of XAI in medical imaging, disease diagnosis, treatment planning, and personalized care. It discusses the technical methodologies behind XAI, the challenges and opportunities of its integration into healthcare, and the ethical and regulatory considerations that will shape the future of AI-assisted medical decisions.Key areas of focus include the role of XAI in improving diagnostic accuracy in fields such as radiology, pathology, and genomics and its potential to enhance collaboration between AI systems, healthcare professionals, and patients. The book also highlights practical applications of XAI in personalized medicine, showing how explainable models help tailor treatments to individual patients, and discusses how XAI can contribute to reducing bias and improving fairness in medical decision-making.Written by leading experts in AI, healthcare, and precision medicine, Explain[S3G1] able AI in Healthcare Imaging for Precision Medicine is an essential resource for researchers, clinicians, students, and policymakers. Whether you are looking to stay at the forefront of AI innovations in healthcare or seeking to understand how explainability can build trust in AI systems, this book provides the insights and knowledge needed to navigate the evolving landscape of AI in medicine. It invites readers to explore how XAI can revolutionize healthcare and precision medicine, shaping a future where AI is both powerful and trustworthy.

Provides step-by-step procedures to build a digital human modelAssists in validating predicted human motion using simulations and experimentsOffers formulation optimization features for dynamic human motion prediction

Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applicationsCombines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologiesProvides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book

Future of Transportation Logistics and Disaster Management with Drones: Opportunities and Challenges discusses the important aspects and recent technological advancements of future drones, the challenges faced in current practices, and their advanced applications. It will take readers through essential components of drone systems such as methodologies, modeling, design, statistics, implementation, and maintenance. It explains the use of real-life technologies such as smart devices, virtual systems, IoT-based technologies, AI-centric solutions, smart sensors, and telecom networks to train and evaluate simple-to-complex models for building drone systems. This edited book provides all the relevant concepts, theoretical frameworks, emerging technologies, analytics techniques, and the latest empirical research findings in smart cities, transportation logistics, disaster management, smart devices, smart solutions, and more. It describes the future intelligent and resilient transportation systems backed with the Internet of Vehicle Things, the problems faced by Big Data Analytics, and their optimization techniques for inhouse supply-chain management. It also establishes a framework for handover management in the Internet of Drone-based networks. It raises and addresses the concerns surrounding the role of 5G communication for handling communication in drones, its related security, and legal aspects. The perspective of all stakeholders is discussed using case studies of existing applications at the global level, including industrial use cases. This book is a valuable reference for professionals who want to improve their understanding of the strategic role of trust at different levels of the information and knowledge society, at the level of global communication, economy, education, and organizations as actors in drone systems. By the end of this book, readers will have the ability to design an end-to-end solution for Transportation Logistics and Disaster Management with the aid of smart drones.

Future of Internet of Bio-nano Things in Personalized Healthcare: Applications and Challenges explores the core principles of nanomaterials and the sensing paradigms essential for medical devices based on nano-sensors technology, examining the latest theoretical and practical developments in Internet of Bio-nano Things (IoBNT) for health applications. This book explores how IoBNT facilitates the detection and treatment of diseases within the human body through communication between an in-body network of nanodevices and an external control unit. Readers will discover the transformative benefits of IoBNT, which integrates information and communication technologies with medical and nano-bio sensor devices to enhance patient care. The book highlights advances in telemedicine, including highly functional electronic medical devices like implantable and wearable devices engineered through nanotechnology. It details real-time case studies on smart drug delivery, edge computing, and personalized healthcare applications to provide valuable insights into optimizing network performance and utilization in personalized healthcare. The comprehensive exploration of machine learning algorithms and communication protocols further equips readers with the knowledge to design and implement end-to-end IoBNT solutions.Presents models based on edge computing, communication protocols, machine learning, smart drug delivery, disease detection mechanism, and bio-nano sensor-based applications, among othersIncludes wearable sensors, nano sensors and IoT applications that are enabling Internet of Bio-Nano Things system initiatives worldwideElaborates the different bio-computing techniques of intelligent systems from a practical point of view: solving common life problems

Reliability Analysis and Modeling for Complex Systems is a crucial resource for engineers and technologists grappling with modern challenges. As technology advances and safety concerns mount, the complexity of systems like autonomous vehicles and critical infrastructure demands innovative reliability assessment methods. This book bridges theory and practice, offering practical solutions for professionals navigating the intricate world of reliability engineering. Through real-world case studies and interdisciplinary insights, it equips readers to address the multifaceted challenges of ensuring dependability in today's interconnected technological landscape.Dives deeply into advanced probabilistic modeling and analysis techniques tailored for complex systems. This content addresses the needs of reliability engineers and researchers who seek to apply cutting-edge methods to their projectsIncludes interdisciplinary perspectives, such as human factors and cyber-physical systems, which is essential for addressing the real-world challenges the target audience faces. This approach will help readers tackle complex system reliability from multiple anglesReal-world case studies that bridge theory and practice, helping practitioners and academics understand how to apply reliability analysis to complex systems in various domains

Adaptive AI in Sensor Informatics: Methods, Applications, and Implications explores the growing need for efficient, interpretable, and reliable adaptive AI systems tailored to wireless sensor networks. The book highlights how adaptive AI strengthens collaboration between humans and artificial intelligence by enabling transparent decision-making processes. Aimed at academics, professionals, and students, it provides an accessible yet thorough guide to understanding the intersection of adaptive AI and sensor informatics, focusing on practical implementation and the development of models that are both trustworthy and user-friendly. Readers will gain insight into the essential role adaptive AI plays in advancing wireless sensor networks across various sectors.The book also examines the unique challenges and opportunities that arise when deploying adaptive AI in real-world sensor environments. It offers actionable advice for designing AI models that comply with regulations and support user confidence, especially in areas such as healthcare, environmental monitoring, smart cities, and industrial automation.. Draws on the latest research and methods to provide valuable insight into the efficiency of AI-based systems, particularly within the realm of wireless sensors and related domains. Explores and explains the critical role played by adaptive AI and sensor informatics in healthcare, finance, and autonomous vehicles, where the synergy of AI and sensor data plays a pivotal role. Presents relevant case studies, practical demonstrations, and empirical evidence to substantiate the efficacy of AI-enabled sensor systems

Quantum Cryptography and Annealing for Securing Industrial IoT focuses on the rapidly evolving field of quantum security solutions for Industrial Internet of Things (IIoT) platforms, emphasizing the critical intersection of quantum cryptography, post-quantum cryptography, and their practical applications in IIoT. The book’s primary objective is to drive advancements that significantly intersect quantum cryptography in securing IIoT devices, elevate secure IIoT infrastructures, and optimize the overall delivery. Distinguishing itself by prioritizing practical applications, it offers a nuanced perspective on how technological integrations in quantum cryptosystems are actively employed in real-world scenarios. The authors meticulously examine the role of quantum cryptosystems in the design, analysis, and optimization of IIoT-specific hardware, covering their resilience to physical and side-channel attacks and evaluating performance. This book strikes a balance between theoretical concepts and practical applications, providing insights into the challenges and solutions encountered in applying quantum cryptographical principles to IIoT engineering problems.Presents clear, concise, and reader-friendly quantum cryptographical concepts through the use of accessible language, diagrams, and examples to enhance the learning experienceIncludes real-world examples and case studies, demonstrating the practical applications of quantum cryptographical concepts in the design and development of industrial robots and IIoT devicesCaters to a diverse audience with backgrounds in mathematics, engineering, cybersecurity, quantum computing, and industrial engineering, making the content accessible to a broad range of readers

Neuromorphic Computing for Brain Computer Interfaces: Enhanced Synergies in Mind and Machine examines practical situations where interpretability is crucial, going beyond theoretical considerations and providing case studies and examples to illustrate how Brain Computer Interfaces could be implemented in the real world. The book encompasses novel concepts, cutting-edge research, frameworks, and tools that facilitate comprehension of neuromorphic computing models. It is an ideal, comprehensive reference for professionals, researchers, and students who want to grasp the fundamental concepts and most recent developments in brain-computer interfaces (BCIs) and neuromorphic computing.Provides an in-depth study of the principles, technology, and applications that drive neuromorphic computing and brain-computer interfacesOffers an interdisciplinary approach that includes cognitive science, neuroscience, artificial intelligence, and biomedical engineeringPresents actionable insights and methodologies that can be applied to real-world challenges by presenting practical implementations and case studies

This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain.• Focuses on virtual machine placement and migration techniques for cloud data centres• Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services• Includes application of placement techniques for quality of service, performance, and reliability improvement• Explores data centre resource management, load balancing and orchestration using machine learning techniques• Analyses dynamic and scalable resource scheduling with a focus on resource managementThe reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.

This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain.• Focuses on virtual machine placement and migration techniques for cloud data centres• Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services• Includes application of placement techniques for quality of service, performance, and reliability improvement• Explores data centre resource management, load balancing and orchestration using machine learning techniques• Analyses dynamic and scalable resource scheduling with a focus on resource managementThe reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.

This book delves into the history of biometrics, the different systems that have been developed to date, problems that have arisen from these systems, the necessity of AI-based biometrics systems, different AI techniques developed to date (including machine learning, deep learning, natural language processing, and pattern recognition), their potential uses and applications, security and privacy issues in AI-based Biometric systems, current trends in AI-based biometrics, and presents case studies of AI-based biometrics.

This book delves into the history of biometrics, the different systems that have been developed to date, problems that have arisen from these systems, the necessity of AI-based biometrics systems, different AI techniques developed to date (including machine learning, deep learning, natural language processing, and pattern recognition), their potential uses and applications, security and privacy issues in AI-based Biometric systems, current trends in AI-based biometrics, and presents case studies of AI-based biometrics.

This book explores the application of Data and Model Centric AI for Industry 5.0 Solutions, vital in the evolving manufacturing landscape. It delves into data-centric AI, focusing on data collection, processing, and analysis for informed decision-making and operational optimisation. Additionally, it examines model-centric AI, showcasing advanced machine learning models for predictive maintenance and demand forecasting, adapting to dynamic conditions. Through practical insights, it illustrates AI's role in enhancing Industry 5.0 processes, offering valuable knowledge for professionals and scholars alike.This book:Emphasis on compact and robust solutions, addressing challenges in resource-constrained environments.Explains how the most recent advances in artificial technology have opened the way for new breakthroughs in the supply chain industry, cloud computing, IoT based system etc.Discusses demand Forecasting AI models analyze historical sales data and external factors to predict future product demand, optimizing inventory management and production planning.Present models for collecting, processing, and analyzing large volumes of data from various sources, such as sensors, machines, production processes, and the supply chain.Explain developing and deploying sophisticated machine learning models to predict outcomes, optimize processes, and adapt to changing conditions in Industry 5.0 environments.The reference book is a valuable resource for researchers and scholars interested in AI for Industry 5.0 Solutions.

This book demystifies the concept of AI-driven creativity with an in-depth understanding of the underlying principles, algorithms, and applications of generative AI. It begins with a coverage of AI fundamentals, followed by a technical discussion including detailed explanations of model architectures, training processes, and optimisation techniques. It explores case studies showcasing how generative AI is reshaping industries such as entertainment, marketing, design, and healthcare. The book also tackles ethical considerations, addressing issues related to bias, transparency, and responsible AI development.Features:Provides deep understanding of generative AI, including in-depth coverage of various generative models such as GANs, VAEs, and RNNs.Includes practical, real-world examples of how generative AI is applied in different industries.Dedicates significant attention to ethical considerations.Equips readers with the knowledge and tools to build, train, and deploy their own generative AI systems.Offers thorough explanations of AI algorithms, neural network architectures, and training processes.This book is aimed at graduate students and researchers in computer engineering, AI and ML.

Currently, computational intelligence approaches are utilised in various science and engineering applications to analyse information, make decisions, and achieve optimisation goals. Over the past few decades, various techniques and algorithms have been created in disciplines such as genetic algorithms, artificial neural networks, evolutionary algorithms, and fuzzy algorithms. In the coming years, intelligent optimisation algorithms are anticipated to become more efficient in addressing various issues in engineering, scientific, medical, space, and artificial satellite fields, particularly in early disease diagnosis. A metaheuristic in computer science is designed to discover optimisation algorithms capable of solving intricate issues. Metaheuristics are optimisation algorithms that mimic biological behaviours of animals or birds and are utilised to discover the best solution for a certain problem. A meta-heuristic is an advanced approach used by heuristics to tackle intricate optimisation problems. A metaheuristic in mathematical programming is a method that seeks a solution to an optimisation problem. Metaheuristics utilise a heuristic function to assist in the search process. Heuristic search can be categorised as blind search or informed search. Meta-heuristic optimisation algorithms are gaining popularity in various applications due to their simplicity, independence from data trends, ability to find optimal solutions, and versatility across different fields.Recently, many nature-inspired computation algorithms have been utilised to diagnose people with different diseases. Nature-inspired methodologies are now widely utilised across several fields for tasks such as data analysis, decision-making, and optimisation. Techniques inspired by nature are categorised as either biology-based or natural phenomena-based. Bioinspired computing encompasses various topics in computer science, mathematics, and biology in recent years. Bio-inspired computer optimisation algorithms are a developing method that utilises concepts and inspiration from biological development to create new and resilient competitive strategies. Bio-inspired optimisation algorithms have gained recognition in machine learning and deep learning for solving complicated issues in science and engineering. Utilising BIAs learning methods with machine learning and deep learning shows great promise for accurately classifying medical conditions.This book explores the historical development of bio-inspired algorithms and their application in machine learning and deep learning models for disease diagnosis, including COVID-19, heart diseases, cancer, diabetes and some other diseases. It discusses the advantages of using bio-inspired algorithms in disease diagnosis and concludes with research directions and future prospects in this field.

Currently, computational intelligence approaches are utilised in various science and engineering applications to analyse information, make decisions, and achieve optimisation goals. Over the past few decades, various techniques and algorithms have been created in disciplines such as genetic algorithms, artificial neural networks, evolutionary algorithms, and fuzzy algorithms. In the coming years, intelligent optimisation algorithms are anticipated to become more efficient in addressing various issues in engineering, scientific, medical, space, and artificial satellite fields, particularly in early disease diagnosis. A metaheuristic in computer science is designed to discover optimisation algorithms capable of solving intricate issues. Metaheuristics are optimisation algorithms that mimic biological behaviours of animals or birds and are utilised to discover the best solution for a certain problem. A meta-heuristic is an advanced approach used by heuristics to tackle intricate optimisation problems. A metaheuristic in mathematical programming is a method that seeks a solution to an optimisation problem. Metaheuristics utilise a heuristic function to assist in the search process. Heuristic search can be categorised as blind search or informed search. Meta-heuristic optimisation algorithms are gaining popularity in various applications due to their simplicity, independence from data trends, ability to find optimal solutions, and versatility across different fields.Recently, many nature-inspired computation algorithms have been utilised to diagnose people with different diseases. Nature-inspired methodologies are now widely utilised across several fields for tasks such as data analysis, decision-making, and optimisation. Techniques inspired by nature are categorised as either biology-based or natural phenomena-based. Bioinspired computing encompasses various topics in computer science, mathematics, and biology in recent years. Bio-inspired computer optimisation algorithms are a developing method that utilises concepts and inspiration from biological development to create new and resilient competitive strategies. Bio-inspired optimisation algorithms have gained recognition in machine learning and deep learning for solving complicated issues in science and engineering. Utilising BIAs learning methods with machine learning and deep learning shows great promise for accurately classifying medical conditions.This book explores the historical development of bio-inspired algorithms and their application in machine learning and deep learning models for disease diagnosis, including COVID-19, heart diseases, cancer, diabetes and some other diseases. It discusses the advantages of using bio-inspired algorithms in disease diagnosis and concludes with research directions and future prospects in this field.

This book provides a platform for research on deep generative models, with an emphasis on its healthcare applications. The book addresses the unanswered questions that stop these approaches from making a huge difference in real-world clinical practice. The goal of this book is to bring together a wide range of methodologies which are using generative models in health care-related contexts. The book leverages the recent methodological advancements in deep generative models to address critical health-care challenges across all data-types, paving the way for their practical integration into the healthcare system and elevate their impact on the future of healthcare.

The convergence of AI and the metaverse is poised to revolutionize healthcare and other industries. Healthcare has undergone rapid transformation through the adoption of AI technologies, ranging from machine learning for diagnostics to AI-driven drug discovery and personalized treatments. Simultaneously, the development of immersive virtual environments, including the metaverse, has opened new frontiers in virtual interaction, training, and simulation. At the same time, the pandemic has accelerated the shift toward remote healthcare services such as telemedicine, highlighting the potential for virtual care ecosystems that transcend physical limitations. As AI becomes more sophisticated, it is now capable of creating virtual hospitals, digital twins, and AI-driven monitoring tools that offer unprecedented levels of patient care. The metaverse, often associated with gaming and entertainment, is evolving into a platform where healthcare services can be delivered in immersive, interactive, and real-time virtual environments. This combination of AI-powered healthcare in the metaverse promises a future where patients can receive personalized, continuous care regardless of location.This book explores the emerging field of AI-powered healthcare in virtual environments and provide readers with an understanding of how this technological integration will reshape the future of medical care. By combining the potential of AI with the immersive capabilities of the metaverse, this book aims to offer a roadmap for the future of healthcare, emphasizing how virtual health ecosystems can improve diagnostics, treatment, and patient engagement.This book will serve as a reference for researchers, scholars, and industry professionals in different fields related to healthcare, metaverse and artificial intelligence. It can also be a hands-on resource for students interested in the fields of metaverse and healthcare applications using artificial intelligence.

This book demystifies the concept of AI-driven creativity with an in-depth understanding of the underlying principles, algorithms, and applications of generative AI. It begins with a coverage of AI fundamentals, followed by a technical discussion including detailed explanations of model architectures, training processes, and optimisation techniques. It explores case studies showcasing how generative AI is reshaping industries such as entertainment, marketing, design, and healthcare. The book also tackles ethical considerations, addressing issues related to bias, transparency, and responsible AI development.Features:Provides deep understanding of generative AI, including in-depth coverage of various generative models such as GANs, VAEs, and RNNs.Includes practical, real-world examples of how generative AI is applied in different industries.Dedicates significant attention to ethical considerations.Equips readers with the knowledge and tools to build, train, and deploy their own generative AI systems.Offers thorough explanations of AI algorithms, neural network architectures, and training processes.This book is aimed at graduate students and researchers in computer engineering, AI and ML.