Handbook of Data Science Approaches for Biomedical Engineering

Download or Read online Handbook of Data Science Approaches for Biomedical Engineering full in PDF, ePub and kindle. This book written by Valentina E Balas and published by Academic Press which was released on 15 November 2019 with total pages 300. We cannot guarantee that Handbook of Data Science Approaches for Biomedical Engineering book is available in the library, click Get Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like.

Handbook of Data Science Approaches for Biomedical Engineering
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Publisher : Academic Press
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ISBN : 0128183187
Pages : 300 pages
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Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress in alignment with the latest technologies of Big Data and the Internet of Things. The book includes the most current research developments in the field of biomedical engineering applications based on IoT and Big data, including various real-time and offline medical applications that directly or indirectly rely on medical and information technology. The book also includes case studies in the field of medical science, biomedical engineering, computer science, information security, interdisciplinary tools, along with the latest tools and technologies used. Ninety percent of data currently available in the world has been generated in past couple of years, and the volume of data is rapidly increasing day by day. The reason for this growth is the increase in communication via electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, and other aspects of the Internet of Things. This data may come in a continuous flow, may be from various sources, and likely impacts technical systems in real time, which may lead to a problem of managing and processing data using traditional methods. This wide variety of data flows, sources, and types may also overwhelm your systems. Handbook of Data Science Approaches for Biomedical Engineering showcases the most cutting-edge information management tools and technologies to deal with the large volume of real-time data. 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 andforecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, big data in IoT, and data analytics with machine learning tools

Handbook of Data Science Approaches for Biomedical Engineering

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress in alignment with the latest technologies of Big Data and the Internet of Things. The book includes the most current research developments in the field of biomedical engineering applications based on IoT

GET BOOK!
Handbook of Data Science Approaches for Biomedical Engineering

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

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Handbook of Deep Learning in Biomedical Engineering

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

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Data Deduplication Approaches

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

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Our goal is to develop automated methods for the segmentation of thr- dimensional biomedical images. Here, we describe the segmentation of c- focal microscopy images of bee brains (20 individuals) by registration to one or several atlas images. Registration is performed by a highly parallel imp- mentation of an entropy-based nonrigid

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The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift

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Internet of Things in Biomedical Engineering

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,

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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

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Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to

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