Correction: FastKAN-DDD: A novel fast Kolmogorov-Arnold network-based approach for driver drowsiness detection optimized for TinyML deployment
Siham Essahraui, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui, Mouncef Filali Bouami, Ibrahim Ouahbi, Hela Elmannai, Ahmed A. Abd El-Latif
Abstract
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Taxonomy
TopicsSleep and Work-Related Fatigue · Advanced Data and IoT Technologies · Intravenous Infusion Technology and Safety
There are errors in the Acknowledgement statement. The correct Acknowledgement statement is as follows: Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R747), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
There is an error in affiliation 3 for author Hela Elmannai. The correct affiliation 3 is: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1Essahraui S, Lamaakal I, Maleh Y, El Makkaoui K, Bouami MF, Ouahbi I, et al. Fast KAN-DDD: A novel fast Kolmogorov-Arnold network-based approach for driver drowsiness detection optimized for Tiny ML deployment. P Lo S One. 2025;20(11):e 0332577. doi: 10.1371/journal.pone.0332577 41191636 PMC 12588462 · doi ↗ · pubmed ↗