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Towards Independent Students’ Activities, Online Environment and Learning Performance: An Investigation through Synthetic Data and Artificial Neural Networks

open access: yesInformatics, 2023
During the pandemic, universities were forced to convert their educational process online. Students had to adapt to new educational conditions and the proposed online environment.
Malinka Ivanova, Tsvetelina Petrova
doaj   +3 more sources

Explainable deep learning for healthcare workforce attrition: a methodological study on the Watson healthcare synthetic benchmark [PDF]

open access: yesFrontiers in Public Health
BackgroundRetaining qualified nurses and allied-health staff has become a central occupational-health concern for modern hospital systems, yet existing attrition-prediction models face a persistent trade-off between predictive accuracy and auditability ...
Dan Yin, Xinyu Lu, Tingyi Mei
doaj   +2 more sources

Digital twin framework for personalized behavioral health optimization with Q-learning and graph-based planning [PDF]

open access: yesFrontiers in Artificial Intelligence
Promoting healthy lifestyle behaviors, including physical activity, sleep, diet, stress management, and healthy habits, requires adaptive systems capable of responding to dynamic changes in human behavior.
Ayan Chatterjee, Nurilla Avazov
doaj   +2 more sources

A digital twin-based comparative reinforcement learning framework for personalized behavioral recommendation [PDF]

open access: yesFrontiers in Artificial Intelligence
Promoting healthy lifestyle behaviors such as physical activity, sleep, diet quality, stress management, hydration, and healthy habits requires adaptive systems capable of responding dynamically to changing behavioral and environmental conditions ...
Ayan Chatterjee, Nurilla Avazov
doaj   +2 more sources

Design features of the synthetic learning environment

open access: yesEducational Technology Quarterly, 2021
The article considers the features of the learning transformation in the transition from the usual material-object environment to learning in the digital synthetic environment.
Oleksandr Burov
doaj   +1 more source

A meta-analysis of the most influential factors of the virtual reality in education for the health and efficiency of students' activity

open access: yesEducational Technology Quarterly, 2023
Learning focused on assimilation of facts, availability of information, free access to knowledge bases and convenient navigation in local and global networks is not a sufficient condition for the formation of an educated personality, active cognitive ...
Oleksandr Yu. Burov, Olha P. Pinchuk
doaj   +1 more source

Extended reality in digital learning: influence, opportunities and risks’ mitigation

open access: yesОсвітній вимір, 2021
The paper discusses AR/VR/MR/XR technologies in learning namely their influence/ opportunity and risks’ mitigation. Main aspects are as follows: methodology (factors influencing a student’s cybersickness in AR/VR/MR/XR, the improved model of the ...
Oleksandr Burov, Olga Pinchuk
doaj   +1 more source

Denoising-Based Decoupling-Contrastive Learning for Ubiquitous Synthetic Face Images

open access: yesIEEE Access, 2023
With the improvement of generative models such as GPT-4, GANs, and diffusion models, synthetic face images are increasingly pervading the current digital environment.
Yupeng Zhu, Xinyi Shen, Peilun Du
doaj   +1 more source

Methodological Experience in the Teaching-Learning of the English Language for Students with Visual Impairment

open access: yesEducation Sciences, 2021
This document evidences an innovative methodological vision in the teaching-learning process of the English language focused on the inclusion of all its students in a heterogeneous learning environment. Learning a foreign language as a second language is
Jorge Cárdenas, Esteban Inga
doaj   +1 more source

Performance Analysis of Reinforcement Learning Techniques for Augmented Experience Training Using Generative Adversarial Networks

open access: yesApplied Sciences, 2022
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN).
Smita Mahajan   +7 more
doaj   +1 more source

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