ARCHIVES

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data and derivatives, Frontiers in Neuroinformatics, 7, Available at: https://doi.org/10.3389/conf.fninf.2013.09.00041
2. Zhao L., Sun Y., Xue S., et al. (2022). Identifying boys with autism spectrum disorder based on Whole-Brain Resting-State interregional
functional connections using a Boruta-Based Support Vector Machine approach, Frontiers in Neuroinformatics, 16, Available at:
https://doi.org/10.3389/fninf.2022.761942
3. Brahim A. & Farrugia N. (2020). Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the
classification of neuroimaging, Artificial Intelligence in Medicine, 106, 101870, Available at: https://doi.org/10.1016/j.artmed.2020.101870
4. Reiter M. A., Jahedi A., Fredo A. R. J., et al. (2020), Performance of machine learning classification models of autism using resting-state
fMRI is contingent on sample heterogeneity, Neural Computing and Applications, 33(8), 3299–3310, Available at:
https://doi.org/10.1007/s00521-020-05193-y
5. Ingalhalikar M., Shinde S., Karmarkar A., et al. (2021), Functional Connectivity-Based Prediction of Autism on Site harmonized ABIDE
Dataset. IEEE Transactions on Biomedical Engineering, 68(12), 3628–3637, Available at: https://doi.org/10.1109/tbme.2021.3080259
6. Sharif H., & Khan R. A. (2021), A novel machine learning based framework for detection of Autism Spectrum Disorder (ASD), Applied
Artificial Intelligence, 36(1), Available at: https://doi.org/10.1080/08839514.2021.2004655
7. Kumar, B. S., & Jayaraj, D. (2023), Zealous Particle Swarm Optimization Based Reliable Multi-Layer Perceptron Neural Networks For
Autism Spectrum Disorder Classification, Journal of Theoretical and Applied Information Technology, 101 (1), Available at:
https://www.jatit.org/volumes/Vol101No1/26Vol101No1.pdf
8. Z. Sherkatghanad, M. Akhondzadeh, S. Salari, et al. (2020), Automated detection of autism spectrum disorder using a convolutional neural
network, Frontiers in Neuroscience, 13, Available at: https://doi.org/10.3389/fnins.2019.01325
9. L. Qiu, & J. Zhai, (2024), A hybrid CNN-SVM model for enhanced autism diagnosis, PLoS ONE, 19(5), e0302236, Available at:
https://doi.org/10.1371/journal.pone.0302236
10. S. Gupta, M.R.I Bhuiyan, S.S. Chowa, et al. (2024), Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs
and Federated Learning on ABIDE-1 Dataset, Mathematics, 12(18), 2886, Available at: https://doi.org/10.3390/math12182886
11. Y. Wang, J. Liu, Y. Xiang, et al. (2021), MAGE: Automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional
networks and ensemble learning, Neurocomputing, 469, 346–353, Available at: https://doi.org/10.1016/j.neucom.2020.06.152
12. S. Liu, S. Wang, C. Sun, et al. (2024), DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis, CAAI Transactions
on Intelligence Technology, 9(4), 879–893, Available at: https://doi.org/10.1049/cit2.12340
13. C. Wang, Z. Xiao, Y. Xu, et al. (2024), A novel approach for ASD recognition based on graph attention networks, Frontiers in Computational
Neuroscience, 18, Available at: https://doi.org/10.3389/fncom.2024.1388083
14. S. Liu, B. Liang, S. Wang, et al. (2023), NF-GAT: a node Feature-Based Graph attention Network for ASD classification, IEEE Open Journal
of Engineering in Medicine and Biology, 5, 428–433, https://doi.org/10.1109/ojemb.2023.3267612