Handbook of Deep Learning in Biomedical Engineering

Handbook of Deep Learning in Biomedical Engineering
Author: Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Publsiher: Academic Press
Total Pages: 320
Release: 2020-11-12
ISBN: 0128230479
Category: Science
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering Book Excerpt:

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. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
Author: E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publsiher: CRC Press
Total Pages: 344
Release: 2021-09-22
ISBN: 1000370453
Category: Medical
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book Excerpt:

This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
Author: Golden Julie,S. M. Jaisakthi,Y. Harold Robinson
Publsiher: Unknown
Total Pages: 135
Release: 2021
ISBN: 9781774638170
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book Excerpt:

"This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat the patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. The volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students"--

The Role of the Internet of Things IoT in Biomedical Engineering

The Role of the Internet of Things  IoT  in Biomedical Engineering
Author: Sushree Bibhuprada B. Priyadarshini,Devendra Kumar Sharma,Rohit Sharma,Korhan Cengiz
Publsiher: CRC Press
Total Pages: 274
Release: 2022-02-17
ISBN: 1000400646
Category: Computers
Language: EN, FR, DE, ES & NL

The Role of the Internet of Things IoT in Biomedical Engineering Book Excerpt:

This volume introduces the key evolving applications of IoT in the medical field for patient care delivery through the usage of smart devices. It shows how IoT opens the door to a wealth of relevant healthcare information through real-time data analysis as well as testing, providing reliable and pragmatic data that yields enhanced solutions and discovery of previously undiscovered issues. This new volume discusses IoT devices that are deployed for enabling patient health tracking, various emergency issues, smart administration of patients, etc. It looks at the problems of cardiac analysis in e-healthcare, explores the employment of smart devices aimed for different patient issues, and examines the usage of Arduino kits where the data can be transferred to the cloud for internet-based uses. The volume also considers the roles of IoT in electroencephalography (EEG) and magnetic resonance imaging (MRI), which play significant roles in biomedical applications. This book also incorporates the use of IoT applications for smart wheelchairs, telemedicine, GPS positioning of heart patients, smart administration with drug tracking, and more.

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare
Author: Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Publsiher: Academic Press
Total Pages: 396
Release: 2021-04-08
ISBN: 0128222611
Category: Science
Language: EN, FR, DE, ES & NL

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare Book Excerpt:

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. Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence Helps readers analyze and do advanced research in specialty healthcare applications Includes links to websites, videos, articles and other online content to expand and support primary learning objectives

Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare
Author: Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
Publsiher: Academic Press
Total Pages: 292
Release: 2020-10-18
ISBN: 0128193158
Category: Science
Language: EN, FR, DE, ES & NL

Data Analytics in Biomedical Engineering and Healthcare Book Excerpt:

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. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

High Performance Medical Image Processing

High Performance Medical Image Processing
Author: Sanjay Saxena,Sudip Paul
Publsiher: CRC Press
Total Pages: 328
Release: 2022-07-07
ISBN: 1000410358
Category: Computers
Language: EN, FR, DE, ES & NL

High Performance Medical Image Processing Book Excerpt:

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results. With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques. Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented. Key features: Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

Internet of Things and Its Applications

Internet of Things and Its Applications
Author: Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Suneeta Satpathy
Publsiher: Springer Nature
Total Pages: 573
Release: 2021-11-25
ISBN: 3030775283
Category: Psychology
Language: EN, FR, DE, ES & NL

Internet of Things and Its Applications Book Excerpt:

This book offers a holistic approach to the Internet of Things (IoT) model, covering both the technologies and their applications, focusing on uniquely identifiable objects and their virtual representations in an Internet-like structure. The authors add to the rapid growth in research on IoT communications and networks, confirming the scalability and broad reach of the core concepts. The book is filled with examples of innovative applications and real-world case studies. The authors also address the business, social, and legal aspects of the Internet of Things and explore the critical topics of security and privacy and their challenges for both individuals and organizations. The contributions are from international experts in academia, industry, and research.

Nature Inspired Intelligent Techniques for Solving Biomedical Engineering Problems

Nature Inspired Intelligent Techniques for Solving Biomedical Engineering Problems
Author: Kose, Utku,Guraksin, Gur Emre,Deperlioglu, Omer
Publsiher: IGI Global
Total Pages: 381
Release: 2018-03-31
ISBN: 1522547703
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Nature Inspired Intelligent Techniques for Solving Biomedical Engineering Problems Book Excerpt:

Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress and novel opportunities for biomedical engineering. Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems is a pivotal reference source for emerging scholarly research on trends and techniques in the utilization of nature-inspired approaches in biomedical engineering. Featuring extensive coverage on relevant areas such as artificial intelligence, clinical decision support systems, and swarm intelligence, this publication is an ideal resource for medical practitioners, professionals, students, engineers, and researchers interested in the latest developments in biomedical technologies.

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Author: Sisodia, Dilip Singh,Pachori, Ram Bilas,Garg, Lalit
Publsiher: IGI Global
Total Pages: 420
Release: 2020-02-28
ISBN: 1799821226
Category: Medical
Language: EN, FR, DE, ES & NL

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering Book Excerpt:

Artificial intelligence (AI) is revolutionizing every aspect of human life including human healthcare and wellbeing management. Various types of intelligent healthcare engineering applications have been created that help to address patient healthcare and outcomes such as identifying diseases and gathering patient information. Advancements in AI applications in healthcare continue to be sought to aid rapid disease detection, health monitoring, and prescription drug tracking. TheHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering is an essential scholarly publication that provides comprehensive research on the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of healthcare engineering solutions. Featuring a wide range of topics such as genetic algorithms, mobile robotics, and neuroinformatics, this book is ideal for engineers, technology developers, IT consultants, hospital administrators, academicians, healthcare professionals, practitioners, researchers, and students.

Handbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
Author: Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G. S. Mahalakshmi
Publsiher: Apple Academic Press
Total Pages: 622
Release: 2020-12-15
ISBN: 9781771889209
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Handbook of Artificial Intelligence in Biomedical Engineering Book Excerpt:

"Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts. Topics include: Security and privacy issues in biomedical AI systems and potential solutions Healthcare applications using biomedical AI systems Machine learning in biomedical engineering Live patient monitoring systems Semantic annotation of healthcare data This book presents a broad exploration of biomedical systems using artificial intelligence techniques with detailed coverage of the applications, techniques, algorithms, platforms, and tools in biomedical AI systems. This book will benefit researchers, medical and industry practitioners, academicians, and students"--

The Biomedical Engineering Handbook

The Biomedical Engineering Handbook
Author: Joseph D. Bronzino,Donald R. Peterson
Publsiher: CRC Press
Total Pages: 5430
Release: 2018-10-03
ISBN: 1439863113
Category: Medical
Language: EN, FR, DE, ES & NL

The Biomedical Engineering Handbook Book Excerpt:

The definitive "bible" for the field of biomedical engineering, this collection of volumes is a major reference for all practicing biomedical engineers and students. Now in its fourth edition, this work presents a substantial revision, with all sections updated to offer the latest research findings. New sections address drugs and devices, personali

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
Author: Abdulhamit Subasi
Publsiher: Academic Press
Total Pages: 456
Release: 2019-03-16
ISBN: 0128176733
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Book Excerpt:

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction Explains how to apply machine learning techniques to EEG, ECG and EMG signals Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series

Deep Learning Machine Learning and IoT in Biomedical and Health Informatics

Deep Learning  Machine Learning and IoT in Biomedical and Health Informatics
Author: Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi
Publsiher: CRC Press
Total Pages: 382
Release: 2022-02-11
ISBN: 1000534057
Category: Computers
Language: EN, FR, DE, ES & NL

Deep Learning Machine Learning and IoT in Biomedical and Health Informatics Book Excerpt:

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

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
Author: Rani, Geeta,Tiwari, Pradeep Kumar
Publsiher: IGI Global
Total Pages: 586
Release: 2020-10-16
ISBN: 1799827437
Category: Medical
Language: EN, FR, DE, ES & NL

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Book Excerpt:

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Handbook on Intelligent Healthcare Analytics

Handbook on Intelligent Healthcare Analytics
Author: A. Jaya,K. Kalaiselvi,Dinesh Goyal,Dhiya Al-Jumeily
Publsiher: John Wiley & Sons
Total Pages: 448
Release: 2022-06-01
ISBN: 1119791790
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Handbook on Intelligent Healthcare Analytics Book Excerpt:

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; An exploration of predictive analytics in healthcare; The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics. Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Biomedical Engineering Handbook 2

Biomedical Engineering Handbook 2
Author: Joseph D. Bronzino
Publsiher: Springer Science & Business Media
Total Pages: 135
Release: 2000-02-15
ISBN: 9783540668084
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Biomedical Engineering Handbook 2 Book Excerpt:

Handbook of Research on Biomedical Engineering Education and Advanced Bioengineering Learning Interdisciplinary Concepts

Handbook of Research on Biomedical Engineering Education and Advanced Bioengineering Learning  Interdisciplinary Concepts
Author: Abu-Faraj, Ziad O.
Publsiher: IGI Global
Total Pages: 1122
Release: 2012-02-29
ISBN: 146660123X
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Handbook of Research on Biomedical Engineering Education and Advanced Bioengineering Learning Interdisciplinary Concepts Book Excerpt:

Description based on: v. 2, copyrighted in 2012.

Handbook of Research on New Investigations in Artificial Life AI and Machine Learning

Handbook of Research on New Investigations in Artificial Life  AI  and Machine Learning
Author: Habib, Maki K.
Publsiher: IGI Global
Total Pages: 565
Release: 2022-02-25
ISBN: 1799886875
Category: Computers
Language: EN, FR, DE, ES & NL

Handbook of Research on New Investigations in Artificial Life AI and Machine Learning Book Excerpt:

As technology spreads globally, researchers and scientists continue to develop and study the strategy behind creating artificial life. This research field is ever expanding, and it is essential to stay current in the contemporary trends in artificial life, artificial intelligence, and machine learning. This an important topic for researchers and scientists in the field as well as industry leaders who may adapt this technology. The Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning provides concepts, theories, systems, technologies, and procedures that exhibit properties, phenomena, or abilities of any living system or human. This major reference work includes the most up-to-date research on techniques and technologies supporting AI and machine learning. Covering topics such as behavior classification, quality control, and smart medical devices, it serves as an essential resource for graduate students, academicians, stakeholders, practitioners, and researchers and scientists studying artificial life, cognition, AI, biological inspiration, machine learning, and more.

Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention
Author: S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger
Publsiher: Academic Press
Total Pages: 1072
Release: 2019-10-18
ISBN: 0128165863
Category: Computers
Language: EN, FR, DE, ES & NL

Handbook of Medical Image Computing and Computer Assisted Intervention Book Excerpt:

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention