Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area.

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques. Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis.

Structural Biomaterials: Properties, Characteristics, and Selection serves as a single point of reference to digest current research and develop a deeper understanding in the field of biomaterials engineering. This book uses a materials-focused approach, allowing the reader to quickly access specific, detailed information on biomaterials characterization and selection. Relevant to a range of readers, this book provides holistic coverage of the broad categories of structural biomaterials currently available and used in medical applications, highlighting the property requirements for structural biomaterials, their biocompatibility performance and their safety regulation in key categories such as metals, ceramics and polymers. The materials science perspective of this text ensures the content is accessible even to those without an extensive background in applied medicine, positioning this text not just for students, but as an overview and reference for researchers, scientists and engineers entering the field from related materials science disciplines.

Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks.

Web Semantics strengthen the description of web resources to exploit them better and make them more meaningful for both humans and machines, thereby contributing to the development of a knowledgeintensive data web. The world is experiencing the movement of concept from data to knowledge and the movement of web from document model to data model. The underlying idea is making the data machine understandable and processable. In the light of these trends, conciliation of Semantic and the Web is of paramount importance for further progress in the area. Web Semantics: Cutting Edge and Future Directions in Healthcare describes the three major components of the study of Semantic Web, namely Representation, Reasoning, and Security with a special focus on the healthcare domain. This book summarizes the trends and current research advances in web semantics, emphasizing the existing tools and techniques, methodologies, and research solutions. It provides easily comprehensible information on Web Semantics including semantics for data and semantics for services.

Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big Data and suggests ways to overcome them, with a primary emphasis on data repositories, challenges, and concepts for data scientists, engineers and clinicians. The applications of Big Data have grown tremendously within the past few years and its growth can not only be attributed to its competence to handle large data streams but also to its abilities to find insights from complex, noisy, heterogeneous, longitudinal and voluminous data. The main objectives of Big Data in the healthcare sector is to come up with ways to provide personalized healthcare to patients by taking into account the enormous amounts of already existing data.

Theory and Applications of Nonparenteral Nanomedicines presents thoroughly analysed data and results regarding the potential of nanomedicines conceived by diverse non-parenteral routes. In the context of nanotechnology-based approaches, various routes such as oral, pulmonary, transdermal, delivery and local administration of nanomedicine have been utilized for the delivery of nanomedicine. This book discusses the non-parenteral application of nanomedicine, its regulatory implications, application of mucus penetrating nanocarrier, and detailed chapters on development of nanomedicines developed for drug delivery by various route. Beginning with a brief introduction to the non-parenteral delivery of nanomedicine and the safety and regulatory implications of the nanoformulations, further chapters discuss the physiology of the biological barriers, the specificity of the nanocarriers as well as their multiple applications. Theory and Applications of Nonparenteral Nanomedicines helps clinical researchers, researchers working in pharmaceutical industries, graduate students, and anyone working in the development of non-parenteral nanomedicines to understand the recent progress in the design and development of nanoformulations compatible with non-parenteral applications.

Digital Health: Mobile and Wearable Devices for Participatory Health Applications is a key reference for engineering and clinical professionals considering the development or implementation of mobile and wearable solutions in the healthcare domain. The book presents a comprehensive overview of devices and appropriateness for the respective applications. It also explores the ethical, privacy, and cybersecurity aspects inherent in networked and mobile technologies. It offers expert perspectives on various approaches to the implementation and integration of these devices and applications across all areas of healthcare. The book is designed with a multidisciplinary audience in mind; from software developers and biomedical engineers who are designing these devices to clinical professionals working with patients and engineers on device testing, human factors design, and user engagement/compliance.

Second-Order Consensus of Continuous-Time Multi-Agent Systems focuses on the characteristics and features of second-order agents, communication networks, and control protocols/algorithms in continuous consensus of multi-agent systems. The book provides readers with background on consensus control of multi-agent systems and introduces the intrinsic characteristics of second-order agents' behavior, including the development of continuous control protocols/algorithms over various types of underlying communication networks, as well as the implementation of computation- and communication-efficient strategies in the execution of protocols/algorithms. The book's authors also provide coverage of the frameworks of stability analysis, algebraic criteria and performance evaluation. On this basis, the book provides an in-depth study of intrinsic nonlinear dynamics from agents' perspective, coverage of unbalanced directed topology, random switching topology, event-triggered communication, and random link failure, from a communication networks' perspective, as well as leader-following control, finite-time control, and global consensus control, from a protocols/algorithms' perspective. Finally, simulation results including practical application examples are presented to illustrate the effectiveness and the practicability of the control protocols and algorithms proposed in this book.

Cell Instructive Materials to Control and Guide Cell Function: Programmable Bioactive Interfaces looks at the key determinants of the dynamic interface between cell and materials and how this can be applied in developing new, bioactive biomaterials surfaces. The interface between cell and synthetic materials has attracted considerable scientific and technological interest, leading to the awareness that functional interfaces can actively guide and control specific adhesion and recognitions events.

Metallic Biomaterials Processing and Medical Device Manufacturing details the principles and practices of the technologies used in biomaterials processing and medical device manufacturing. The book reviews the main categories of metallic biomaterials and the essential considerations in design and manufacturing of medical devices. It bridges the gap between the designing of biomaterials and manufacturing of medical devices including requirements and standards. Main themes of the book include, manufacturing, coatings and surface modifications of medical devices, metallic biomaterials and their mechanical behaviour, degradation, testing and characterization, and quality controls, standards and FDA regulations of medical devices. The leading experts in the filed discuss the requirements, challenges, recent progresses and future research directions in the processing of materials and manufacturing of medical devices. Metallic Biomaterials Processing and Medical Device Manufacturing is ideal for those working in the disciplines of materials science, manufacturing, biomedical engineering, and mechanical engineering.

Biomaterials for 3D Tumor Modeling reviews the fundamentals and most relevant areas of the latest advances of research of 3D cancer models, focusing on biomaterials science, tissue engineering, drug delivery and screening aspects. The book reviews advanced fundamental topics, including the causes of cancer, existing cancer models, angiogenesis and inflammation during cancer progression, and metastasis in 3D biomaterials. Then, the most relevant biomaterials are reviewed, including methods for engineering and fabrication of biomaterials. 3D models for key biological systems and types of cancer are also discussed, including lung, liver, oral, prostate, pancreatic, ovarian, bone and pediatric cancer. This book is suitable for those working in the disciplines of materials science, biochemistry, genetics, molecular biology, drug delivery and regenerative medicine.

Osseoconductive Surface Engineering for Orthopedic Implants provides a comprehensive overview of the state of the art of osseointegration based on surface-mediated engineering. It offers a practical approach to the design and development of implant surface engineering, by reviewing and discussing the usability and efficacy of each processing technique. The reader can learn about the variety, characteristics, advantages, challenges, and optimum parameters for each process-enabling targeted selection of coatings and technologies to enhance long-term implant-bone integration.

An Introduction to Hypergeometric, Supertigonometric, and Superhyperbolic Functions gives a basic introduction to the newly established hypergeometric, supertrigonometric, and superhyperbolic functions from the special functions viewpoint. The special functions, such as the Euler Gamma function, the Euler Beta function, the Clausen hypergeometric series, and the Gauss hypergeometric have been successfully applied to describe the real-world phenomena that involve complex behaviors arising in mathematics, physics, chemistry, and engineering.

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.

In general, every problem of science and engineering is governed by mathematical models. There is often a need to model, solve and interpret the problems one encounters in the world of practical problems. Models of practical application problems usually need to be handled by efficient computational models. New Paradigms in Computational Modeling and Its Applications deals with recent developments in mathematical methods, including theoretical models as well as applied science and engineering. The book focuses on subjects that can benefit from mathematical methods with concepts of simulation, waves, dynamics, uncertainty, machine intelligence, and applied mathematics. The authors bring together leading-edge research on mathematics combining various fields of science and engineering. This perspective acknowledges the inherent characteristic of current research on mathematics operating in parallel over different subject fields. New Paradigms in Computational Modeling and Its Applications meets the present and future needs for the interaction between various science and technology/engineering areas on the one hand and different branches of mathematics on the other. As such, the book contains 13 chapters covering various aspects of computational modeling from theoretical to application problems. The first six chapters address various problems of structural and fluid dynamics. The next four chapters include solving problems where the governing parameters are uncertain regarding fuzzy, interval, and affine. The final three chapters will be devoted to the use of machine intelligence in artificial neural networks.

Health Care Paradigms in the Internet of Things Ecosystem brings all IoT-enabled health care related technologies into a single platform so that undergraduate and postgraduate students, researchers, academicians and industry leaders can easily understand IoT-based healthcare systems. The book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable IoT-enabled health care ecosystem and to implement cyber-physical pervasive infrastructure solutions. It takes the reader on a journey that begins with understanding the healthcare monitoring paradigm in IoT-enabled technologies and how it can be applied in various aspects. In addition, the book walks readers through real-time challenges and presents a guide on how to build a safe infrastructure for IoT-based health care. It also helps researchers and practitioners understand the e-health care architecture through IoT and the state-of-the-art in IoT countermeasures. Readers will find this to be a comprehensive discussion on functional frameworks for IoT-based healthcare systems, intelligent medicine, RFID technology, HMI, Cognitive Interpretation, Brain-Computer Interface, Remote Health Monitoring systems, wearable sensors, WBAN, and security and privacy issues in IoT-based health care monitoring systems.

Foundational Handbook of Artificial Intelligence in Healthcare and Bioscience: A User Friendly Guide for IT Professionals, Healthcare Providers, Researchers, and Clinicians uses color-coded illustrations to explain AI from its basics to modern technologies. Other sections cover extensive, current literature research and citations regarding AI's role in the business and clinical aspects of health care. The book provides readers with a unique opportunity to appreciate AI technology in practical terms, understand its applications, and realize its profound influence on the clinical and business aspects of health care. Artificial Intelligence is a disruptive technology that is having a profound and growing influence on the business of health care as well as medical diagnosis, treatment, research and clinical delivery. The AI relationships in health care are complex, but understandable, especially when discussed and developed from their foundational elements through to their practical applications in health care.

Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis.

In the age of data science, the rapidly increasing amount of data is a major concern in numerous applications of computing operations and data storage. Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strategies, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated segments or chunks of data within the associated files. Due to ever-increasing data duplication, its deduplication has become an especially useful field of research for storage environments, in particular persistent data storage. Data Deduplication Approaches provides readers with an overview of the concepts and background of data deduplication approaches, then proceeds to demonstrate in technical detail the strategies and challenges of real-time implementations of handling big data, data science, data backup, and recovery. The book also includes future research directions, case studies, and real-world applications of data deduplication, focusing on reduced storage, backup, recovery, and reliability.

Nanoemulsions are produced by mixing an oil phase with an aqueous phase under shear pressure. This procedure yields uniform populations of oil droplets ranging in diameter from 200 to 8 nm that are kinetically stable colloidal substances with enhanced properties compared to the conventional emulsion substances. Nanoemulsions have broad potential applications in agriculture, food, health, and biomedical sciences. Nanoemulsion Applications in Agriculture, Food, Health, and Biomedical Sciences focuses on the aspects of nanoemulsion-like synthesis, characterization, and more and examines recent trends in their applications within a variety of relevant fields. Nanoemulsions have broad application in many different fields; without emulsification, process product development would not be possible. Covering topics such as cancer treatment, healthcare applications, and food manufacturing, this book is essential for scientists, doctors, researchers, post-graduate students, medical students, government officials, hospital directors, professors, and academicians.

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.

Wearable Sensors: Fundamentals, Implementation and Applications has been written by a collection of experts in their field, who each provide you with an understanding of how to design and work with wearable sensors. Together these insights provide the first single source of information on wearable sensors that would be a fantastic addition to the library of any engineers working in this field. Wearable Sensors covers a wide variety of topics associated with development and applications of wearable sensors. It also provides an overview and a coherent summary of many aspects of wearable sensor technology. Both professionals in industries and academic researchers need this package of information in order to learn the overview and each specific technology at the same time. This book includes the most current knowledge on the advancement of light-weight hardware, energy harvesting, signal processing, and wireless communications and networks. Practical problems with smart fabrics, biomonitoring and health informatics are all addressed, plus end user centric design, ethical and safety issues. The new edition is completely reviewed by key figures in the field, who offer authoritative and comprehensive information on the various topics. A new feature for the second edition is the incorporation of key background information on topics to allow the less advanced user access to the field and to make the title more of an auto-didactic book for undergraduates.