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From Bioinspired Systems and Biomedical Applications to Machine Learning

physical systems. The second volume deals with bioinspired systems and biomedical applications to machine learning and contains papers related;

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Understanding the Brain Function and Emotions

physical systems. The second volume deals with bioinspired systems and biomedical applications to machine learning and contains papers related;

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Advances in Artificial Intelligence and Its Applications

systems; bioinspired algorithms; neural networks; evolutionary algorithms; fuzzy logic; machine learning and data mining; natural language;

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Advances in Artificial Intelligence and Soft Computing

-agent systems; bioinspired algorithms; neural networks; evolutionary algorithms; fuzzy logic; machine learning and data mining; natural;

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Knowledge Engineering, Machine Learning and Lattice Computing with Applications

selected from 130 submissions. The papers are organized in topical sections on bioinspired and machine learning methods, machine learning;

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Applications of Evolutionary Computation

of topics, ranging from applications of evolutionary computation; applications of deep bioinspired algorithms; soft computing applied to games;

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Bioinspired Materials Science and Engineering

An authoritative introduction to the science and engineering of bioinspired materials Bioinspired Materials Science and Engineering;

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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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Handbook of Artificial Intelligence in Biomedical Engineering

promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning;

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Applications of Evolutionary Computation

, soft-computing applied to games, applications of deep-bioinspired algorithms, parallel and distributed systems, and evolutionary machine;

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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their;

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Machine Learning and the Internet of Medical Things in Healthcare

introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and;

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Bacteriophage Tail Fibers as a Basis for Structured Assemblies

, analysis and operation of biomedical, biotechnological and nanotechnology systems and applications. Authors are encouraged to submit their work;

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Bacteriophage Tail Fibers as a Basis for Structured Assemblies

, analysis and operation of biomedical, biotechnological and nanotechnology systems and applications. Authors are encouraged to submit their work;

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction Explains how to;

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Applied Soft Computing

, fusion-based learning, intelligent healthcare systems, bioinformatics, data mining, functional approximation, genetic and evolutionary algorithms;

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Machine Learning Systems

of failure and changes in load. But machine learning systems are different than other applications when it comes to testing, building, deploying, and;

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Human Inspired Computing and its Applications

eight sections: genetic and evolutionary algorithms, neural networks, machine learning, machine learning applications to audio and text, data;

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Intelligent Information and Database Systems

Applications; Intelligent and Context Systems; Multiple Model Approach to Machine Learning; Applications of Data Science; Artificial Intelligence;

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

computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning;

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Intelligent Information and Database Systems

Applications; Intelligent and Context Systems; Multiple Model Approach to Machine Learning; Applications of Data Science; Artificial Intelligence;

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Biomedical Applications Based on Natural and Artificial Computing

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

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Signal Processing and Machine Learning for Biomedical Big Data

information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such;

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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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Imbalanced Learning

, finance, biomedical, and defense, to name a few. The first comprehensive look at this new branch of machine learning, this volume offers a;

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Emerging Trends in Terahertz Solid State Physics and Devices

imaging, pattern recognition and tomographic reconstruction for THz biomedical imaging by machine learning and artificial intelligence, THz;

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Handbook of Deep Learning in Biomedical Engineering

-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and;

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