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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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Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning;

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

of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining;

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Smart Computational Intelligence in Biomedical and Health Informatics

Covers evolutionary approaches to solve optimization problems in biomedical engineering. Discusses IoT, Cloud computing, data analytics;

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Deep Learning for Data Analytics

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety;

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Data Science and Predictive Analytics

, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and;

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Data Analytics, Computational Statistics, and Operations Research for Engineers

With the rapidly advancing fields of Data Analytics and Computational Statistics, it's important to keep up with current trends;

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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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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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Advances on Smart and Soft Computing

, big data analytics, smart computing, smart cities, Internet of things (IoT), data communication, cloud computing, machine learning algorithms;

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Deep Learning Techniques and Optimization Strategies in Big Data Analytics

industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear;

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Handbook of Artificial Intelligence in Biomedical Engineering

acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical;

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Handbook of Deep Learning in Biomedical Engineering

Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that;

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Translational Bioinformatics Applications in Healthcare

Translational bioinformatics (TBI) involves development of storage, analytics, and advanced computational methods to harvest knowledge from;

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Data Analytics Approaches in Educational Games and Gamification Systems

can enhance learning outcomes. This book focuses on the application of data analytics approaches and research on human behaviour analysis;

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Data Analytics Approaches in Educational Games and Gamification Systems

can enhance learning outcomes. This book focuses on the application of data analytics approaches and research on human behaviour analysis;

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Data Analytics in Bioinformatics

learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are;

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Application of Omics, AI and Blockchain in Bioinformatics Research

With the increasing availability of omics data and mounting evidence of the usefulness of computational approaches to tackle multi-level;

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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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Analytics and Data Science

and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements;

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Machine Learning and Big Data Analytics Paradigms

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered;

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Recent Developments in Machine Learning and Data Analytics

This book presents high-quality papers from an international forum for research on computational approaches to learning. It includes;

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Bioinformatics and Biomedical Engineering: 8th International Work-Conference, Iwbbio 2020, Granada, Spain, May 6-8, 2020, Proceedings

sections as follows: Biomarker Identification; Biomedical Engineering; Biomedical Signal Analysis; Bio-Nanotechnology; Computational Approaches for;

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Computational Intelligence Applications in Business Intelligence and Big Data Analytics

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather;

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Machine Intelligence and Signal Processing

in the fields of machine learning, big data analytics, signal processing, computational learning theory, and their real-time applications. 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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Probability, Random Variables, and Data Analytics with Engineering Applications

on data sets relevant to machine vision, machine learning and medical diagnostics; Intersperses analytical approaches with;

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