Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 3 - dataset -Artificial Intelligence Models Applied to Biomedical Engineering

Published: 7 June 2022| Version 2 | DOI: 10.17632/t96hmx65pd.2
Contributor:
Jorge Garza-Ulloa

Description

“Artificial intelligence (AI) in Biomedical Engineering (BME)” is the use of smart device software to analyze complex data, classify, model, optimize, and predict many fields of medicine and healthcare. “AI Optimization in BME” refers to finding parameters for maximizing or minimizing objectives while satisfying the constraints of defined functions relative to some dataset of biomedical data. “AI Optimization in BME” results allow the comparison of different choices for determining which one might be the best. In the last decade the development in fields of medicine, healthcare, biomedicine, bioinformatics, etc. Please read the chapter 3 at Science Direct: https://www.sciencedirect.com/science/article/pii/B9780128207185000040 Section 3.3 Evolutionary algorithms for AI optimization in BME. 3.3.1 A typical evolutionary algorithm Section 3.3.2 Genetic algorithms for AI optimization in BME. 3.3.2.1 Research 3.1 Genetic algorithm basic seven steps for selection by priorities. Section 3.3.3 Genetic algorithm for AI optimization in BME under MATLAB. 3.3.3.1 Research 3.2 Implementing genetic algorithm for AI optimization in BME with MATLAB. 3.3.4 General analysis and optimization of 2D and 3D data in biomedical engineering. 3.3.4 General analysis and optimization of 2D and 3D data in biomedical engineering. 3.3.6 MATLAB analysis and optimization of 3D data in biomedical engineering. 3.4 IBM Watson Studio for artificial intelligence. 3.4.1 IBM SPSS Modeler Flow

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Institutions

University of Texas at El Paso

Categories

Behavior Genetics, Artificial Intelligence Applications

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