Handbook of Data Science Approaches for Biomedical Engineering

Handbook of Data Science Approaches for Biomedical Engineering
Author: Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Publsiher: Academic Press
Total Pages: 318
Release: 2019-11-13
ISBN 10: 0128183195
ISBN 13: 9780128183199
Language: EN, FR, DE, ES & NL

Handbook of Data Science Approaches for Biomedical Engineering Book Review:

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

Data Deduplication Approaches

Data Deduplication Approaches
Author: Tin Thein Thwel,G. R. Sinha
Publsiher: Academic Press
Total Pages: 404
Release: 2020-11-25
ISBN 10: 0128236337
ISBN 13: 9780128236338
Language: EN, FR, DE, ES & NL

Data Deduplication Approaches Book Review:

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. Includes data deduplication methods for a wide variety of applications Includes concepts and implementation strategies that will help the reader to use the suggested methods Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable methods for their applications Focuses on reduced storage, backup, recovery, and reliability, which are the most important aspects of implementing data deduplication approaches Includes case studies

Internet of Things for Healthcare Technologies

Internet of Things for Healthcare Technologies
Author: Chinmay Chakraborty,Amit Banerjee,Maheshkumar H. Kolekar,Lalit Garg,Basabi Chakraborty
Publsiher: Springer Nature
Total Pages: 324
Release: 2020-06-08
ISBN 10: 9811541124
ISBN 13: 9789811541124
Language: EN, FR, DE, ES & NL

Internet of Things for Healthcare Technologies Book Review:

This book focuses on recent advances in the Internet of Things (IoT) in biomedical and healthcare technologies, presenting theoretical, methodological, well-established, and validated empirical work in these fields. Artificial intelligence and IoT are set to revolutionize all industries, but perhaps none so much as health care. Both biomedicine and machine learning applications are capable of analyzing data stored in national health databases in order to identify potential health problems, complications and effective protocols, and a range of wearable devices for biomedical and healthcare applications far beyond tracking individuals’ steps each day has emerged. These prosthetic technologies have made significant strides in recent decades with the advances in materials and development. As a result, more flexible, more mobile chip-enabled prosthetics or other robotic devices are on the horizon. For example, IoT-enabled wireless ECG sensors that reduce healthcare cost, and lead to better quality of life for cardiac patients. This book focuses on three current trends that are likely to have a significant impact on future healthcare: Advanced Medical Imaging and Signal Processing; Biomedical Sensors; and Biotechnological and Healthcare Advances. It also presents new methods of evaluating medical data, and diagnosing diseases in order to improve general quality of life.

Web Information Systems Engineering WISE 2020

Web Information Systems Engineering     WISE 2020
Author: Zhisheng Huang
Publsiher: Springer Nature
Total Pages: 329
Release:
ISBN 10: 3030620085
ISBN 13: 9783030620080
Language: EN, FR, DE, ES & NL

Web Information Systems Engineering WISE 2020 Book Review:

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: 2020-11-23
ISBN 10: 0128230479
ISBN 13: 9780128230473
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering Book Review:

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. DL 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 DL and its applications in the field of biomedical engineering. DL has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. DL 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 artificial intelligence techniques such as DL and convolutional neural networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use DL include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic particle imaging, electroencephalography/magnetoencephalography (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 DL 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, attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), tumor prediction, and 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 Research on Cloud and Fog Computing Infrastructures for Data Science

Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science
Author: Raj, Pethuru,Raman, Anupama
Publsiher: IGI Global
Total Pages: 440
Release: 2018-05-18
ISBN 10: 1522559736
ISBN 13: 9781522559733
Language: EN, FR, DE, ES & NL

Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science Book Review:

Fog computing is quickly increasing its applications and uses to the next level. As it continues to grow, different types of virtualization technologies can thrust this branch of computing further into mainstream use. The Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science is a key reference volume on the latest research on the role of next-generation systems and devices that are capable of self-learning and how those devices will impact society. Featuring wide-ranging coverage across a variety of relevant views and themes such as cognitive analytics, data mining algorithms, and the internet of things, this publication is ideally designed for programmers, IT professionals, students, researchers, and engineers looking for innovative research on software-defined cloud infrastructures and domain-specific analytics.

The Data Science Handbook

The Data Science Handbook
Author: Field Cady
Publsiher: John Wiley & Sons
Total Pages: 416
Release: 2017-02-28
ISBN 10: 1119092949
ISBN 13: 9781119092940
Language: EN, FR, DE, ES & NL

The Data Science Handbook Book Review:

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

Handbook of Neuroimaging Data Analysis

Handbook of Neuroimaging Data Analysis
Author: Hernando Ombao,Martin Lindquist,Wesley Thompson,John Aston
Publsiher: CRC Press
Total Pages: 662
Release: 2016-11-18
ISBN 10: 1482220989
ISBN 13: 9781482220988
Language: EN, FR, DE, ES & NL

Handbook of Neuroimaging Data Analysis Book Review:

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.

Biomedical Engineering Handbook 2

Biomedical Engineering Handbook 2
Author: Joseph D. Bronzino
Publsiher: Springer Science & Business Media
Total Pages: 329
Release: 2000-02-15
ISBN 10: 9783540668084
ISBN 13: 354066808X
Language: EN, FR, DE, ES & NL

Biomedical Engineering Handbook 2 Book Review:

Strategies in Biomedical Data Science

Strategies in Biomedical Data Science
Author: Jay A. Etchings
Publsiher: John Wiley & Sons
Total Pages: 464
Release: 2016-12-27
ISBN 10: 111925597X
ISBN 13: 9781119255970
Language: EN, FR, DE, ES & NL

Strategies in Biomedical Data Science Book Review:

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

Handbook of Big Data Analytics

Handbook of Big Data Analytics
Author: Wolfgang Karl Härdle,Henry Horng-Shing Lu,Xiaotong Shen
Publsiher: Springer
Total Pages: 538
Release: 2018-07-20
ISBN 10: 3319182846
ISBN 13: 9783319182841
Language: EN, FR, DE, ES & NL

Handbook of Big Data Analytics Book Review:

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

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: 2020-10-23
ISBN 10: 0128193158
ISBN 13: 9780128193150
Language: EN, FR, DE, ES & NL

Data Analytics in Biomedical Engineering and Healthcare Book Review:

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

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: Biomedical Engineering
Total Pages: 622
Release: 2020-12-15
ISBN 10: 9781771889209
ISBN 13: 1771889209
Language: EN, FR, DE, ES & NL

Handbook of Artificial Intelligence in Biomedical Engineering Book Review:

"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"--

Handbook of Research on Engineering Business and Healthcare Applications of Data Science and Analytics

Handbook of Research on Engineering  Business  and Healthcare Applications of Data Science and Analytics
Author: Patil, Bhushan,Vohra, Manisha
Publsiher: IGI Global
Total Pages: 583
Release: 2020-10-23
ISBN 10: 1799830543
ISBN 13: 9781799830542
Language: EN, FR, DE, ES & NL

Handbook of Research on Engineering Business and Healthcare Applications of Data Science and Analytics Book Review:

Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies.

Internet of Things in Biomedical Engineering

Internet of Things in Biomedical Engineering
Author: Valentina E. Balas,Le Hoang Son,Sudan Jha,Manju Khari,Raghvendra Kumar
Publsiher: Academic Press
Total Pages: 379
Release: 2019-06-14
ISBN 10: 0128173572
ISBN 13: 9780128173572
Language: EN, FR, DE, ES & NL

Internet of Things in Biomedical Engineering Book Review:

Internet of Things in Biomedical Engineering presents the most current research in Internet of Things (IoT) applications for clinical patient monitoring and treatment. The book takes a systems-level approach for both human-factors and the technical aspects of networking, databases and privacy. Sections delve into the latest advances and cutting-edge technologies, starting with an overview of the Internet of Things and biomedical engineering, as well as a focus on ‘daily life.’ Contributors from various experts then discuss ‘computer assisted anthropology,’ CLOUDFALL, and image guided surgery, as well as bio-informatics and data mining. This comprehensive coverage of the industry and technology is a perfect resource for students and researchers interested in the topic. Presents recent advances in IoT for biomedical engineering, covering biometrics, bioinformatics, artificial intelligence, computer vision and various network applications Discusses big data and data mining in healthcare and other IoT based biomedical data analysis Includes discussions on a variety of IoT applications and medical information systems Includes case studies and applications, as well as examples on how to automate data analysis with Perl R in IoT

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
Author: K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
Publsiher: CRC Press
Total Pages: 250
Release: 2020-10-08
ISBN 10: 1000179532
ISBN 13: 9781000179538
Language: EN, FR, DE, ES & NL

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches Book Review:

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Encyclopedic Handbook of Biomaterials and Bioengineering v 1 2 Materials

Encyclopedic Handbook of Biomaterials and Bioengineering  v  1 2  Materials
Author: Donald Lee Wise
Publsiher: CRC Press
Total Pages: 1795
Release: 1995
ISBN 10: 9780824795955
ISBN 13: 0824795954
Language: EN, FR, DE, ES & NL

Encyclopedic Handbook of Biomaterials and Bioengineering v 1 2 Materials Book Review:

Integrating basic science, engineering, and medical applications, this handbook provides a treatment of materials used in or on the human body - ranging from biopolymers for controlled release drug delivery systems to metal plates used in bone repair and absorbable devices such as sutures.

Handbook of Neuroprosthetic Methods

Handbook of Neuroprosthetic Methods
Author: Warren E. Finn,Peter G. LoPresti
Publsiher: CRC Press
Total Pages: 456
Release: 2002-12-16
ISBN 10: 1420040871
ISBN 13: 9781420040876
Language: EN, FR, DE, ES & NL

Handbook of Neuroprosthetic Methods Book Review:

Work in the field of neuroprosthetics requires multidisciplinary teams, but these collaborators must meet on common ground to develop an understanding of the capabilities and limitations of each part of a bioengineering project. The Handbook of Neuroprosthetic Methods provides a comprehensive resource for the techniques, methodologies, and options available to properly design and undertake experiments within the field of neuroprosthetics. It combines the most commonly employed concepts, applications, and knowledge from the many disciplines associated with neuroprosthetic research to foster more effective, profitable, and productive collaborations. From basic neurophysiology to emerging technologies, this book provides a clear introduction to the entire range of neuroprosthetic systems. Each chapter includes background information, methodology, illustrative figures that clarify experimental methods, and tables that outline and compare experimental choices. The last part of each chapter provides practical applications and examples that relate the topic to the actual design and implementation of a neuroprosthetic system or device. Through its exploration of a variety of developmental processes, the book provides guidance on issues that have yet to be solved, strategies for solving such problems, and the pitfalls often encountered when developing neural prostheses. Whether you are new to or a veteran of the field, whether you work directly or indirectly with neuroprosthesis projects, the Handbook of Neuroprosthetic Methods provides an accessible common ground for all involved in neuroprosthetic design and research.

A Handbook of Internet of Things in Biomedical and Cyber Physical System

A Handbook of Internet of Things in Biomedical and Cyber Physical System
Author: Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar,Md. Atiqur Rahman Ahad
Publsiher: Springer
Total Pages: 314
Release: 2019-07-16
ISBN 10: 3030239837
ISBN 13: 9783030239831
Language: EN, FR, DE, ES & NL

A Handbook of Internet of Things in Biomedical and Cyber Physical System Book Review:

This book presents a compilation of state-of-the-art work on biomedical and cyber-physical systems in connection with the Internet of Things, and successfully blends theory and practice. The book covers the studies belonging to Biomedical and Cyber-physical System, so it is a unique effort by the research experts, who are divulging in the domain deeply. The book is very easy for the audience, who are doing study in the Biomedical and Cyber-physical System; it helps to read some real-time scenarios from where the reader in general gets many sparking ideas to convert it into the research problems in their studies. This book is of use to solve down the problems of graduate, postgraduate, doctoral industry executives, who are involving in the cutting-edge work of Internet of Things with Biomedical or Cyber-physical System, with the help of real-time solutions, given in the formation of chapters by subject’s experts. The key uses of this book are in the area of Internet of Things in connection with Cyber-physical System as well as Biomedical domain.

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
Author: Exarchos, Themis P.,Papadopoulos, Athanasios,Fotiadis, Dimitrios I.
Publsiher: IGI Global
Total Pages: 598
Release: 2009-04-30
ISBN 10: 1605663158
ISBN 13: 9781605663159
Language: EN, FR, DE, ES & NL

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications Book Review:

"This book includes state-of-the-art methodologies that introduce biomedical imaging in decision support systems and their applications in clinical practice"--Provided by publisher.