Evaluation and benchmarking of hybrid machine learning models for autism spectrum disorder diagnosis using a 2-tuple linguistic neutrosophic fuzzy sets-based decision-making model-Reference-Cited by-同舟云学术

Evaluation and benchmarking of hybrid machine learning models for autism spectrum disorder diagnosis using a 2-tuple linguistic neutrosophic fuzzy sets-based decision-making model

Published:2024-07-20 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Alqaysi M. E.,Albahri A. S.^ORCID,Hamid Rula A.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00521-024-09905-6.pdf

Reference87 articles.

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2. Joudar SS, Albahri AS, Hamid RA, Zahid IA, Alqaysi ME, Albahri OS, Alamoodi AH (2023) Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues. Artif Intell Rev 56(S1):53–117. https://doi.org/10.1007/s10462-023-10536-x

3. Satu MS, Farida Sathi F, Arifen MS, Hanif Ali M, Moni A (2019) Early detection of autism by extracting features: A case study in Bangladesh In: 1st International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2019, , pp. 400–405. https://doi.org/10.1109/ICREST.2019.8644357

4. Yu W, Lei B, Ng MK, Cheung AC, Shen Y, Wang S (2022) Tensorizing GAN with high-order pooling for alzheimer’s disease assessment. IEEE Trans Neural Networks Learn Syst 33(9):4945–4959. https://doi.org/10.1109/TNNLS.2021.3063516

5. Islam S, Akter T, Zakir S, Sabreen S, Hossain MI (2020). Autism Spectrum Disorder Detection in Toddlers for Early Diagnosis Using Machine Learning In: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, , pp. 1–6. https://doi.org/10.1109/CSDE50874.2020.9411531

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