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

Lacave, Carmen ; Molina, Ana Isabel

Basel: MDPI - Multidisciplinary Digital Publishing Institute, 2023

Online E-Book - 406

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View record at Directory of Open Access Books (Volltext)

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.

Titel:	Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education
Autor/in / Beteiligte Person:	Lacave, Carmen ; Molina, Ana Isabel
Link:	View record at Directory of Open Access Books (Volltext)
Veröffentlichung:	Basel: MDPI - Multidisciplinary Digital Publishing Institute, 2023
Medientyp:	E-Book
Umfang:	406
ISBN:	978-3-0365-7246-8 (print) ; 978-3-0365-7247-5 (print)
DOI:	10.3390/books978-3-0365-7247-5
Schlagwort:	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 supervised classification random methods after-school exercise academic performance structural relationship quantile regression instrumental variable quantile regression vitamin D 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 Feature Selection Artificial Neural Networks Support Vector Machines decision trees logistic regression ECG mental fatigue signal analysis classification OpenMarkov Bayesian Networks d-separation inference Learning Bayesian Networks continuous assessment Bayesian networks artificial neural networks influenza-like illness COVID-19 Arabic sentiment analysis disease classification Facebook Algerian dialect mobile computing dual tasking cognitive decline human motion tracking gait analysis thema EDItEUR:G Reference, Information and Interdisciplinary subjects:GP Research and information: general thema EDItEUR:P Mathematics and Science
Sonstiges:	Nachgewiesen in: Directory of Open Access Books Sprachen: English Document Type: eBook File Description: image/jpeg Language: English Rights: Attribution 4.0 International ; URL: https://creativecommons.org/licenses/by/4.0/ Notes: ONIX_20230511_9783036572468_63

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