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Deep Learning for Data Analytics

of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications;

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Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based;

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Data Analytics in Biomedical Engineering and Healthcare

medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data;

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Handbook of Deep Learning in Biomedical Engineering

composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the;

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Deep Learning for Biomedical Applications

This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the;

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Deep Learning for Biomedical Data Analysis

This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches;

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Statistical Modeling in Biomedical Research

and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their;

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Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis;

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Statistical Modeling in Biomedical Research: Contemporary Topics and Voices in the Field

and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their applications;

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Deep Learning Techniques for Biomedical and Health Informatics

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim;

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Deep Learning Techniques for Biomedical and Health Informatics

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim;

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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex;

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Trends in Deep Learning Methodologies

predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive;

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Computer Analysis of Images and Patterns

, and the First Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019. The 12 papers presented in this;

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Neural Information Processing Part I

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part II

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part III

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part IV

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Deep Learning and Data Labeling for Medical Applications

Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data;

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Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced;

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Signal Processing and Machine Learning for Biomedical Big Data

alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the;

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Biomedical Applications Based on Natural and Artificial Computing

as biomedical applications; mobile brain computer interaction; human robot interaction; deep learning; machine learning applied to big data;

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Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition;

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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization;

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Natural and Artificial Computation for Biomedicine and Neuroscience

; physiological computing in affective smart environments; emotions; as well as signal processing and machine learning applied to biomedical and;

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

and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural;

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Quantitative Medical Data Analysis Using Mathematical Tools And Statistical Techniques

Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics;

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