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Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education

Edited by
April 2023
406 pages
  • ISBN978-3-0365-7246-8 (Hardback)
  • ISBN978-3-0365-7247-5 (PDF)
https://doi.org/10.3390/books978-3-0365-7247-5
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This book is a reprint of the Special Issue Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
Public Health & Healthcare
Summary

The present reprint contains all of the articles accepted and published in the Special Issue " Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education" from the MDPI journal Mathematics. This Special Issue aims to develop more efficient and effective approaches to healthcare and education, leveraging the increasing availability of big data and advancements in artificial intelligence. By sharing new methods, applications, and case studies, this reprint is dedicated to the development of innovative solutions that improve healthcare and education for all. The topics addressed in this Special Issue cover a wide range of areas, including data mining, machine learning, learning analytics, prediction methods, pattern recognition, decision analysis, probabilistic reasoning, fuzzy systems, student or patient modelling, adaptive systems, collaborative systems, recommendation systems, experimental design, and empirical study cases. We hope that this reprint will enable the scientific community in both medicine and education to leverage the techniques from statistics and artificial intelligence to drive significant advances in their respective fields. These approaches hold promise for improving patient outcomes and enhancing the quality of education for students around the world.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
Alzheimer’s disease; dementia; functional data analysis; functional depth; statistical data depth; symmetry; elemental information matrix; gamma distribution; poisson distribution; D-optimization; misspecification; machine learning; modeling; programming; text analysis; remote rehabilitation; recommender system; stroke; fuzzy logic; telemedicine; collaborative learning; collaborative work; genetic algorithms; group formation; personality traits; computer-supported cooperative learning; non-parametric statistics; predictive methods; statistical data depth; supervised classification; random methods; after-school exercise; academic performance; structural relationship; quantile regression; instrumental variable quantile regression; vitamin D; machine learning; decision making; anthropometric parameters; optimal experimental design; bioimpedance; impedance spectroscopy; algorithm; competency-based model; didactic planning; ontology; natural language processing; Bloom’s taxonomy; retina; fundus image; retinal vasculature; retinal disorders; semantic segmentation; learning behavior; student performance prediction; deep neural network (DNN); recurrent neural network (RNN); educational data mining (EDM); probabilistic graphical models; bayesian networks; value-based potentials; approximate inference; medical applications; electrocardiogram signal; discriminative convolutional sparse coding; dictionary filter learning; linear SVM; student dropout; machine learning; Feature Selection; Artificial Neural Networks; Support Vector Machines; decision trees; logistic regression; machine learning; ECG; mental fatigue; signal analysis; classification; OpenMarkov; Bayesian Networks; d-separation; inference; Learning Bayesian Networks; continuous assessment; Bayesian networks; artificial neural networks; classification; influenza-like illness; COVID-19; Arabic sentiment analysis; disease classification; Facebook; Algerian dialect; mobile computing; dual tasking; cognitive decline; human motion tracking; gait analysis; n/a