Digitalisation and artificial intelligence have widened the gap between competencies certified by university curricula and skills demanded by employers. MOOCs could close this gap, but existing recommenders rarely connect what a programme teaches, what the market requires, and what online courses develop within a s...
S. Serikbayeva, M. Sambetbayeva, V. Ramazanova et al.· Frontiers in Artificial Inte...· 0 citations
Introduction Matching short, specialized skill expressions across English, Russian, and Kazakh is challenging because general-domain multilingual encoders underperform on terse, code-mixed, domain-specific phrases, particularly in the low-resource Kazakh setting. Methods We fine-tuned a multilingual Sentence Transforme...
S. Serikbayeva, M. Sambetbayeva, V. Ramazanova et al.· Frontiers in Big Data· 0 citations
An attention-based CNN–LSTM–Attention architecture for smartphone-based HAR using accelerometer signals is proposed, confirming that convolutional feature extraction, recurrent temporal modeling, and attention-based weighting improve the reliability of accelerometer-based HAR while maintaining practical computational e...
M. Turdalyuly, Nazerke Baisholan, Sergiy Gnatyuk et al.· 1 citation
An integration model based on knowledge graphs and machine learning methods for analyzing relationships between educational programs, labor market requirements, and online courses (MOOCs) and has the capability to identify missing competencies and automatically recommend relevant courses for their acquisition.
K. S. Serikbayeva, V. Ramazanova, M. Sambetbayeva et al.· Herald of Kazakh-British tec...· 0 citations
It is demonstrated that pretrained multilingual models effectively address TTG transformation for low-resource agglutinative languages through transfer learning, with practical applications for Kazakh Sign Language assistive systems and education.
N. Amangeldy, A. Yerimbetova, N. Tursynova et al.· Frontiers in Artificial Inte...· 0 citations
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