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This book describes the state-of-the-art of chemoinformatics, bioinformatics, materials informatics and measurement/metrology informatics to develop drugs with desired activity or physicochemical properties and to optimize the functionality, efficacy, safety and quality of the compounds for drugs. Recently,'AI drug discovery', drug discovery research utilizing artificial intelligence technology such as machine learning, has attracted much attention. This book provides an overview of the four applied informatics fields and their applications in drug development for a wide spectrum of readers from learners to professional scientists in academia and industry. It focuses on the basic research stage of drug development with contributions from experts at the forefront of these fields. The authors hope that this book will be of assistance to explore new opportunities for collaboration between pharmaceutical science and informatics.

This book discusses the roles of the Internet of Things (IoT) and machine learning (ML) in smart health care, including the integration of cloud computing with IoT and ML for managing healthcare data. It presents the fundamentals and many applications of IoT and ML in different areas of smart health care. It deliberates upon security and privacy issues, including trust concerns about smart healthcare systems based on IoT and ML algorithms. This book is concluded by discussing challenges and future work directions in smart health care using IoT and ML, and it serves as a reference resource for researchers and practitioners in academia and industry.

This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field.

In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities.This textbook aims to live up to the now broad diversity of computer science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages and visualisation, the textbook is framed by history and an outlook. Students with a connection to sports science are given a comprehensive insight into computer science in sport, supported by a didactically sophisticated concept that makes it easy to convey the learning content. Numerous questions for self-testing underpin the learning effect and ensure optimal exam preparation. For advanced students, the in-depth discussion of time series data mining, artificial neural networks, convolution kernels, transfer learning and random forests offers additional value.

This book gathers the proceedings of the 11th International Conference on E-Health and Bioengineering, EHB 2023, held in hybrid form on November 9–10, 2023, in/from Bucharest, Romania. This first volume of a three-volume set reports on advances in medical devices and instrumentation, for a wide range of applications including medical diagnosis and therapy, rehabilitation, and medical data management. It also describes the use of artificial intelligence in medicine for detecting and modeling diseases, health monitoring, medical decision making, and related applications. All in all, this book offers extensive and timely information to researchers and professionals in bioengineering, health informatics and related interdisciplinary fields.

This book introduces the revolutionary use of AI in the field of cervical cancer detection. The book explores how advanced computer algorithms can analyze medical images and patient data to enhance early detection and accurate diagnosis of cervical cancer. The book starts by providing a comprehensive overview of cervical cancer, its risk factors, and the importance of early detection. It then delves into the fundamental concepts of artificial intelligence and its application in healthcare. Readers will gain a deeper understanding of how AI algorithms can'see'patterns in cervical cells and tissue, enabling the detection of abnormal cells and precancerous changes that may indicate the presence of cervical cancer. Drawing on the latest research and real-world case studies, the book showcases the various AI techniques used for cervical cancer screening, including the analysis of Pap smear and liquid-based cytology images. This book is an essential read for healthcare professionals, researchers, policymakers, and anyone interested in the intersection of AI and healthcare.

Advances in experimental technologies have given rise to tremendous amounts of biology data. This not only offers valuable sources of data to help understand biological evolution and functional mechanisms, but also poses challenges for accurate and effective data analysis. This book offers an essential introduction to the theoretical and practical aspects of association analysis, including data pre-processing, data mining methods/algorithms, and tools that are widely applied for computational biology. It covers significant recent advances in the field, both foundational and application-oriented, helping readers understand the basic principles and emerging techniques used to discover interesting association patterns in diverse and heterogeneous biology data, such as structure-function correlations, and complex networks with gene/protein regulation. The main results and approaches are described in an easy-to-follow way and accompanied by sufficientreferences and suggestions for future research. This carefully edited monograph is intended to provide investigators in the fields of data mining, machine learning, artificial intelligence, and bioinformatics with a profound guide to the role of association analysis in computational biology. It is also very useful as a general source of information on association analysis, and as an overall accompanying course book and self-study material for graduate students and researchers in both computer science and bioinformatics.

This book features selected papers from the International Conference on Communication and Applied Technologies (ICOMTA 2023), jointly organized by the Colombian Association of Journalism and Science Communication (Colombia), and the Benemerita Universidad Autonoma de Puebla (Mexico); and as collaborators at the Universidad de Málaga (Spain), Universidade de Vigo (Spain), Universidade de Santiago de Compostela-Equipo de Investigaciones Políticas (Spain), Red Internacional de Gestión de la Comunicación (XESCOM),the International Media Management Academic Association (IMMAA), Red de Investigadores en Comunicación de Ecuador (RICE), and Observatorio Interuniversitario de Medios Ecuatorianos (OIME), during 6 – 8 September 2023. It covers recent advances in the field of digital communication and processes, digital social media, software, big data, data mining and intelligent systems.

This book describes, extends, and illustrates the metrics of binary classification through worked examples.Worked examples based on pragmatic test accuracy study data are used in chapters to illustrate relevance to day-to-day clinical practice. Readers will gain an understanding of sensitivity and specificity and predictive values along with many other parameters.The contents are highly structured, and the use of worked examples facilitates understanding and interpretation.This book is a resource for clinicians in any discipline who are involved in the performance or assessment of test accuracy studies and professionals in the disciplines of machine learning or informatics wishing to gain insight into clinical applications of 2x2 tables.

In a world where communication is key to human connection, understanding, and learning from one another, the book investigates the rich and intricate world of sign languages, highlighting the fascinating complexities of visual-spatial languages and their unique role in bridging the gap between hearing and deaf communities through information and communication technology. The book takes a journey through the evolution of sign language processing tools while exploring the cutting-edge techniques used to decipher, analyze, and process them, from the fundamentals of sign language structure and the nuances of non-manual signals to the latest developments in computational linguistics (corpora design, annotation tools, and notation systems), sign language recognition, and machine/deep learning applications. With a mission to reveal the silent language of expression, the author provides a captivating and thought-provoking look into a world often overlooked yet teeming with life and meaning. Offering a comprehensive and engaging overview of the current state of sign language research as well as its prospects, this monograph is an introduction resource for computer scientists, linguistics, educators, academics and sign language interpreters alike. This monograph is an invitation to discover the incredible potential of sign languages to transform human communication, promote inclusivity, and shape the future of technology. The readers will gain a deeper appreciation of the beauty and complexity of sign languages through technology, and they will be inspired to embrace the power of visual communication in our increasingly diverse and interconnected world.

This book constitutes selected papers from the 20th European, Mediterranean, and Middle Eastern Conference, EMCIS 2023, which was held in Dubai, UAE, during December 11-12, 2023. EMCIS covers technical, organizational, business, and social issues in the application of information technology and is dedicated to the definition and establishment of Information Systems (IS) as a discipline of high impact for IS professionals and practitioners. It focuses on approaches that facilitate the identification of innovative research of significant relevance to the IS discipline following sound research methodologies that lead to results of measurable impact. The 43 papers presented in this volume were carefully reviewed and selected from a total of 126 submissions. They were organized in topical sections as follows: Part I: Metaverse; blockchain technology and applications; digital governance; healthcare information systems; artificial intelligence; Part II: Big data and analytics; digital services and social media; innovative research projects; managing information systems; smart cities.

This volume constitutes the revised selected papers of several workshops which were held in conjunction with the 5th International Conference on Process Mining, ICPM 2023, held in Rome, Italy, in October 23–27, 2023.The 38 revised full papers presented in this book were carefully reviewed and selected from 85 submissions. The book also contains one invited talk. ICPM 2023 presented the following six workshops:– 4th International Workshop on Event Data and Behavioral Analytics (EdbA)– 4th International Workshop on Leveraging Machine Learning in Process Mining (ML4PM)– 6th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H)– 8th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI)– 2nd International Workshop on Education Meets Process Mining (EduPM)– 2nd International Workshop on Collaboration Mining for Distributed Systems (COMINDS).

The book explores the convergence of healthcare and cutting-edge technology, making it a captivating subject for readers interested in future research. Smart healthcare with machine learning techniques offers a transformative paradigm that utilizes the power of new technology, data analytics, and interconnected devices to enhance the quality, efficiency, and accessibility of healthcare services. This involves leveraging Internet of Things (IoT) devices, wearable technology, and machine learning algorithms to monitor patient health, predict medical conditions, and offer personalized treatment recommendations. This innovative combination not only enhances diagnostics and treatment but also addresses the research challenges of healthcare access and delivery in an increasingly connected world. By exploring the synergy between smart healthcare and machine learning, the book helps to understand how these technologies can collaborate to revolutionize patient care and healthcare delivery. This book is an outcome with applications of future technologies to overcome the toughest humanitarian challenges from an engineering approach.

Dieses Buch präsentiert Fortschritte in der biomedizinischen Technologie. IoT und Machine Learning haben neue Ansätze im mobilen Gesundheitswesen ermöglicht, mit Fokus auf kontinuierlicher Überwachung kritischer Gesundheitssituationen. Intelligente Hybridisierung von IoT, drahtlosen Implantaten und Cloud-Computing wird derzeit von verschiedenen Einrichtungen entwickelt und getestet. Biomedizinische Signale und Bildmodalitäten werden nicht-invasiv erfasst und erfordern eine mehrkanalige Erfassung für wirksame Überwachung. Automatisierte Gesundheitssysteme basieren auf Signal- und Bildaufnahme, Vorverarbeitung, Merkmalsextraktion und Klassifikation. Das Buch beschreibt zeitgenössische Ansätze in der biomedizinischen Signalerfassung und -verarbeitung mit maschinellem und tiefem Lernen. Jedes Kapitel ist eigenständig und bietet eine umfassende Übersicht über Theorien, Algorithmen und Herausforderungen im Bereich moderner Gesundheitssysteme.

One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson's disease patients. This framework offers a new way to assess and monitor patients'motor deficits in a personalized and automated way, improving the efficiency and accuracy of diagnosis and treatment.Compared to other books on eHealth and Parkinson's disease, this book offers a unique perspective and solution to the challenges facing patients and healthcare providers. It combines state-of-the-art technology, such as wearable devices and deep learning algorithms, with clinical expertise to develop a personalized and efficient evaluation framework for Parkinson's disease patients.This book provides a roadmap for the integration of cutting-edge technology into clinical practice, paving the way for more effective and patient-centered healthcare. To understand this book, readers should have a basic knowledge of eHealth, IoT, deep learning, and Parkinson's disease. However, the book provides clear explanations and examples to make the content accessible to a wider audience, including researchers, practitioners, and students interested in the intersection of technology and healthcare.

Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records are often different because of the cost of obtaining information and the time spent consuming the information. In general, clinical data is unreliable and therefore the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue. Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on an MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information within the data. This is a beneficial clinical application of GAN because it can effectivelyprotect patient confidentiality. This book covers the application of GANs on medical imaging augmentation and segmentation.

This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders.This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features:● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection.● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders.● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.

Sentiment analysis deals with extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. Medical sentiment analysis refers to the identification and analysis of sentiments or emotions expressed in free-textual documents with a scope on healthcare and medicine. This fascinating problem offers numerous application areas in the domain of medicine, but also research challenges. The book provides a comprehensive introduction to the topic. The primary purpose is to provide the necessary background on medical sentiment analysis, ranging from a description of the notions of medical sentiment to use cases that have been considered already and application areas of relevance. Medical sentiment analysis uses natural language processing (NLP), text analysis and machine learning to realise the process of extracting and classifying statements regarding expressed opinion and sentiment. The book offers a comprehensive overview on existingmethods of sentiment analysis applied to healthcare resources or health-related documents. It concludes with open research avenues providing researchers indications which topics still have to be developed in more depth.

This book constitutes the proceedings of the 11th Workshop on Clinical Image-Based Procedures, CLIP 2022, which was held in conjunction with MICCAI 2022, in Singapore in September 2022. The 9 full papers included in this book were carefully reviewed and selected from 12 submissions. They focus on the applicability of basic research methods in the clinical practice by creating holistic patient models as an important step towards personalized healthcare.

This book includes selected peer-reviewed papers presented at the International Conference on Trends in Electronics and Health Informatics (TEHI 2022), held at University of Puebla, Puebla, México, during December 7–9, 2022. The book is broadly divided into five sections—artificial intelligence and soft computing, healthcare informatics, Internet of things and data analytics, electronics, and communications.

This book discusses a new process mining method along with a detailed comparison between different techniques that provide a complete vision of the process of data acquisition, data analysis, and data prediction.Process Mining Techniques for Managing and Improving Healthcare Systems offers a new framework for process learning which is probabilistic and enables the process to be learned in an accumulative manner. The steps of prediction modeling and building the required knowledge are highlighted throughout the book, along with a strong emphasis on the correlation between the healthcare domain and technology including the different aspects, such as: managing records, information, and procedures; early detection of diseases; and the improvement of accuracy in choosing the right treatment procedures.This reference provides a wealth of knowledge for practitioners, researchers, and students at the basic and intermediary levels working within the healthcare system, computer science, electronics and communications, as well as medical providers and also hospital management entities.

This book presents high-quality research papers presented at Congress on Smart Computing Technologies (CSCT 2022) organized by SAU Center for Research and Innovative Learning (SCRIL), South Asian University, India, from 3–4 December 2022. The book extensively covers recent research in algorithms for smart computing, AI and machine learning in smart computing, edge computing algorithms, adversarial networks and autoencoders, data visualization, data mining, data analytics, machine learning, game theory, high-performance computing, mobile and ubiquitous platforms for smart environments, cloud/edge/fog computing technologies for smart systems, Internet of Things (IoT) and industrial IoT technologies for smart systems, smart device and hardware, security, privacy, and economics in smart environments, big data, healthcare informatics, smart precision agriculture, smart transportation, social network analysis, and human–computer interaction.

This book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and sleeping. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalitiessuch as positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described. Each chapter starts with the importance, problemstatement and motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors'knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems.

This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through artificial intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You'll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you'll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity. Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare.What You Will LearnUnderstand the components required to facilitate precision health and personalized careApply and implement precision health systemsOvercome the challenges of delivering precision healthcare at scaleReconcile ethical and moral implications of delivering precision healthcareGain insight into the hurdles providers face while implementing precision healthcare Who This Book Is ForHealthcare professionals, clinicians, engineers, bioinformaticians, chief information officers (CIOs), and students

This book covers the latest research studies regarding Explainable Machine Learning used in multimedia-based healthcare applications. In this context, the content includes not only introductions for applied research efforts but also theoretical touches and discussions targeting open problems as well as future insights. In detail, a comprehensive topic coverage is ensured by focusing on remarkable healthcare problems solved with Artificial Intelligence. Because today's conditions in medical data processing are often associated with multimedia, the book considers research studies with especially multimedia data processing.

This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines. The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.

This book features selected papers from the International Conference on Communication and Applied Technologies (ICOMTA 2022), jointly organized by the Universidad del Rosario (Bogotá, Colombia) and the Universidad Politécnica Salesiana (Cuenca, Ecuador), and as collaborators at the University of Vigo (Galicia, Spain), the University of Santiago de Compostela-Political Research Team (Galicia, Spain), and the Network of Communication Researchers of Ecuador (RICE), during August, 31–September 2, 2022. It covers recent advances in the field of digital communication and processes, digital social media, software, big data, data mining, and intelligent systems.

This book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.

This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 20–22 October 2021. The book includes works related to the field of vision- and sensor-based human action or activity and behavior analysis and recognition. It covers human activity recognition (HAR), action understanding, gait analysis, gesture recognition, behavior analysis, emotion, and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition—both in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.

User care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the resident's activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care system in the literature to identify current practices for future research directions. Therefore, this book is aimed at a comprehensive review of data sources (e.g., sensors) with machine learning for various smart user care systems. To encourage the readers in the field, insights of practical essence of different machine learning algorithms with sensor data (e.g., publicly available datasets) are also discussed. Some code segments are also included to motivate the researchers ofthe related fields to practically implement the features and machine learning techniques. It is an effort to obtain knowledge of different types of sensor-based user monitoring technologies in-home environments. With the aim of adopting these technologies, research works, and their outcomes are reported. Besides, up to date references are included for the user monitoring technologies with the aim of facilitating independent living.Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. The target of this book is to contribute towards that direction.

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.

This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints. Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.

This edited book provides information on emerging fields of next-generation healthcare informatics with a special emphasis on emerging developments and applications of artificial intelligence, deep learning techniques, computational intelligence methods, Internet of medical things (IoMT), optimization techniques, decision making, nanomedicine, and cloud computing. The book provides a conceptual framework and roadmap for decision-makers for this transformation. The chapters involved in this book cover challenges and opportunities for diabetic retinopathy detection based on deep learning applications, deep learning accelerators in IoT and IoMT, health data analysis, deep reinforcement-based conversational AI agent in healthcare systems, examination of health data performance, multisource data in intelligent medicine, application of genetic algorithms in health care, mental disorder, digital healthcare system with big data analytics, encryption methods in healthcare data security, computation and cognitive bias in healthcare intelligence and pharmacogenomics, guided imagery therapy, cancer detection and prediction techniques, medical image processing for coronavirus, and imbalance learning in health care.

This book presents and discusses the numerous measures of test performance that can be derived from 2x2 tables. Worked examples based on pragmatic test accuracy study data are used in chapters to illustrate relevance to day-to-day clinical practice. Readers will gain a good understanding of sensitivity and specificity and predictive values along with many other parameters.The contents are highly structured and the use of worked examples facilitates understanding and interpretation. This book is a resource for clinicians in any discipline who are involved in the performance or assessment of test accuracy studies, and professionals in the disciplines of machine learning or informatics wishing to gain insight into clinical applications of 2x2 tables.

This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. In the past, it was a common requirement to have domain experts for developing models for biomedical or healthcare. However, recent advances in representation learning algorithms allow us to automatically learn the pattern and representation of the given data for the development of such models. Medical Image Mining, a novel research area (due to its large amount of medical images) are increasingly generated and stored digitally. These images are mainly in the form of: computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients'biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new and useful information that can be helpful for scientists and biomedical practitioners. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence..

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others.• Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications• Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches• Discusses various techniques of IoT systems for healthcare data analytics• Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics• Focuses more on the application of algorithms in various real life biomedical and engineering problems

One of the hallmarks of the 21st century medicine is the emergence of digital therapeutics (DTx)—evidence-based, clinically validated digital technologies to prevent, diagnose, treat, and manage various diseases and medical conditions. DTx solutions have been gaining interest from patients, investors, healthcare providers, health authorities, and other stakeholders because of the potential of DTx to deliver equitable, massively scalable, personalized and transformative treatments for different unmet medical needs.Digital Therapeutics: Scientific, Statistical, Clinical, and Regulatory Aspects is an unparalleled summary of the current scientific, statistical, developmental, and regulatory aspects of DTx which is poised to become the fastest growing area of the biopharmaceutical and digital medicine product development. This edited volume intends to provide a systematic exposition to digital therapeutics through 19 peer-reviewed chapters written by subject matter experts in this emerging field.This edited volume is an invaluable resource for business leaders and researchers working in public health, healthcare, digital health, information technology, and biopharmaceutical industries. It will be also useful for regulatory scientists involved in the review of DTx products, and for faculty and students involved in an interdisciplinary research on digital health and digital medicine.Key Features: Provides the taxonomy of the concepts and a navigation tool for the field of DTx. Covers important strategic aspects of the DTx industry, thereby helping investors, developers, and regulators gain a better appreciation of the potential value of DTx. Expounds on many existing and emerging state-of-the art scientific and technological tools, as well as data privacy, ethical and regulatory considerations for DTx product development. Presents several case studies of successful development of some of the most remarkable DTx products. Provides some perspectives and forward-looking statements on the future of digital medicine.

This book includes selected peer-reviewed papers presented at the International Conference on Trends in Electronics and Health Informatics (TEHI 2021), organized by Department of Electronics and Communication Engineering and Department of Computer Science and Engineering, Pranveer Singh Institute of Technology Kanpur, India, during 16–17 December 2021. The book is broadly divided into five sections—artificial intelligence and soft computing, healthcare informatics, Internet of things and data analytics, electronics, and communications.

This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare.

This book provides an insightful review and methodological contribution about future healthcare system. It also provides a space for creating and designing techniques for effective sensing, processing, and analysis of patient health situations based on bio-signal processing. Additionally, it discusses novel methods and algorithms which are needed to overcome limitations in current rhythmic bio-signals models. It also discusses solutions and systems needed to efficiently evaluate and process real-time data. The book is useful for wide range of users, including students, research scientists, teachers, and practitioners working in the field of heath informatics, neuroscience, biomedical engineering, and medical image processing and diagnosis.

The book focuses on mathematical modelling of COVID-19 pandemic using the Susceptible, Infectious, and Recovered (SIR) model. The predictive modelling of the disease, with the exact facts and figures, provides a ground to reason about growing trends and its future trajectory. The book emphasizes on how the pandemic actually spreads out, lockdown impact analysis, and future course of actions based on mathematical calculations. Moreover, since COVID-19 spread outburst has been twice, the intensity studies and comparative analysis of the two waves of COVID-19 are another interesting feature of the book content. The book is a knowledge base for various researchers and academicians to dive into the detailing of the COVID spread (mathematical) model and understand how it could be explored to draw outcomes. To represent the factual information and analytical results effectively, graphical and diagrammatic representations have been appended at appropriate places. To keep the explanation simple and yet concrete, mathematical concepts have also been introduced; to carry out analysis to generate results for understanding the viral dynamics.

The book discusses Society 5.0 which fills the gap between cyber and physical space by providing a balanced environment between economic and social needs. The book is divided into two parts; part A focuses on various concepts related to Society 5.0 such as cyber space, physical space, information management and digital transformation. Part B discusses various integrated fields in Society 5.0, such as super-smart healthcare system, super-smart hospitality system, smart building, and transport management system. It also illustrates the concepts of big data, real-time analytics for smart Society 5.0 with an insight of real-time case studies.

This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. It details the techniques used in collection, storage and analysis of data and their usage in different healthcare solutions. It also discusses the techniques of predictive analysis in early diagnosis of critical diseases. The edited book is divided into four parts – part A discusses introduction to artificial intelligence and machine learning in healthcare; part B highlights different analytical techniques used in healthcare; part C provides various security and privacy mechanisms used in healthcare; and finally, part D exemplifies different tools used in visualization and data analytics.

This book provides a practically applicable guide to the methodologies and technologies for the application of interactive process mining paradigm. Case studies are presented where this paradigm has been successfully applied in emergency medicine, surgery processes, human behavior modelling, strokes and outpatients'services, enabling the reader to develop a deep understanding of how to apply process mining technologies in healthcare to support them in inferring new knowledge from past actions, and providing accurate and personalized knowledge to improve their future clinical decision-making. Interactive Process Mining in Healthcare comprehensively covers how machine learning algorithms can be utilized to create real scientific evidence to improve daily healthcare protocols, and is a valuable resource for a variety of health professionals seeking to develop new methods to improve their clinical decision-making.

This book focuses on various advanced technologies which integrate with machine learning to assist one of the most leading industries, healthcare. It presents recent research works based on machine learning approaches supported by medical and information communication technologies with the use of data and image analysis. The book presents insight about techniques which broadly deals in delivery of quality, accurate and affordable healthcare solutions by predictive, proactive and preventative methods. The book also explores the possible use of machine learning in enterprises, such as enhanced medical imaging/diagnostics, understanding medical data, drug discovery and development, robotic surgery and automation, radiation treatments, creating electronic smart records and outbreak prediction.

This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.

This book offers a basic understanding of the Internet of Things (IoT), its design issues and challenges for healthcare applications. It also provides details of the challenges of healthcare big data, role of big data in healthcare and techniques, and tools for IoT in healthcare. This book offers a strong foundation to a beginner. All technical details that include healthcare data collection unit, technologies and tools used for the big data analytics implementation are explained in a clear and organized format.

This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.

The purpose of this book is to review the recent advances in E-health technologies and applications. In particular, the book investigates the recent advancements in physical design of medical devices, signal processing and emergent wireless technologies for E-health. In a second part, novel security and privacy solutions for IoT-based E-health applications are presented. The last part of the book is focused on applications, data mining and data analytics for E-health using artificial intelligence and cloud infrastructure. E-health has been an evolving concept since its inception, due to the numerous technologies that can be adapted to offer new innovative and efficient E-health applications. Recently, with the tremendous advancement of wireless technologies, sensors and wearable devices and software technologies, new opportunities have arisen and transformed the E-health field. Moreover, with the expansion of the Internet of Things, and the huge amount of data that connected E-health devices and applications are generating, it is also mandatory to address new challenges related to the data management, applications management and their security. Through this book, readers will be introduced to all these concepts. This book is intended for all practitioners (industrial and academic) interested in widening their knowledge in wireless communications and embedded technologies applied to E-health, cloud computing, artificial intelligence and big data for E-health applications and security issues in E-health.