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Autism Spectrum disorder Detection in Toddlers and Adults Using Deep Learning

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25 gru 2024

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Ahmed, I.A., Senan, E.M., Rassem, T.H., Ali, M.A., Shatnawi, H.S.A., Alwazer, S.M. and Alshahrani, M. (2022). Eye tracking-based diagnosis and early detection of autism spectrum disorder using machine learning and deep learning techniques, Electronics 11(4): 530.Search in Google Scholar

Al Duhayyim, M., Abbas, S., Al Hejaili, A., Kryvinska, N., Almadhor, A. and Mohammad, U.G. (2023). An ensemble machine learning technique for stroke prognosis, Computer Systems Science & Engineering 47(1): 413–429.Search in Google Scholar

Alqaysi, M., Albahri, A. and Hamid, R.A. (2022). Hybrid diagnosis models for autism patients based on medical and sociodemographic features using machine learning and multicriteria decision-making (MCDM) techniques: An evaluation and benchmarking framework, Computational and Mathematical Methods in Medicine 2022(1): 9410222.Search in Google Scholar

Alsuliman, M. and Al-Baity, H.H. (2022). Efficient diagnosis of autism with optimized machine learning models: An experimental analysis on genetic and personal characteristic datasets, Applied Sciences 12(8): 3812.Search in Google Scholar

Amin, J., Sharif, M., Haldorai, A., Yasmin, M. and Nayak, R.S. (2021). Brain tumor detection and classification using machine learning: A comprehensive survey, Complex & Intelligent Systems 8(4): 3161–3183.Search in Google Scholar

Ashok, K. and Gopikrishnan, S. (2023). Improving security performance of healthcare data in the Internet of medical things using a hybrid metaheuristic model, International Journal of Applied Mathematics and Computer Science 33(4): 623–636, DOI: 10.34768/amcs-2023-0044.Search in Google Scholar

Atlam, E.-S., Masud, M., Rokaya, M., Meshref, H., Gad, I. and Almars, A.M. (2024). EASDM: Explainable autism spectrum disorder model based on deep learning, Journal of Disability Research 3(1): 20240003.Search in Google Scholar

Baizer, J.S. (2024). Neuroanatomy of autism: What is the role of the cerebellum?, Cerebral Cortex 34(13): 94–103.Search in Google Scholar

Bala, M., Ali, M.H., Satu, M.S., Hasan, K.F. and Moni, M.A. (2022). Efficient machine learning models for early stage detection of autism spectrum disorder, Algorithms 15(5): 166.Search in Google Scholar

Barik, K., Watanabe, K., Bhattacharya, J. and Saha, G. (2023). A fusion-based machine learning approach for autism detection in young children using magnetoencephalography signals, Journal of Autism and Developmental Disorders 53(12): 4830–4848.Search in Google Scholar

Beary, M., Hadsell, A., Messersmith, R. and Hosseini, M.-P. (2020). Diagnosis of autism in children using facial analysis and deep learning, arXiv: 2008.02890.Search in Google Scholar

Casalino, G., Castellano, G., Hryniewicz, O., Leite, D., Opara, K., Radziszewska, W. and Kaczmarek-Majer, K. (2023). Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder, International Journal of Applied Mathematics and Computer Science 33(3): 419–428, DOI: 10.34768/amcs-2023-0030.Search in Google Scholar

Chaste, P. and Leboyer, M. (2012). Autism risk factors: Genes, environment, and gene-environment interactions, Dialogues in Clinical Neuroscience 14(3): 281–292.Search in Google Scholar

Chen, G. (2016). A gentle tutorial of recurrent neural network with error backpropagation, arXiv: 1610.02583.Search in Google Scholar

Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp. 785–794.Search in Google Scholar

Deng, X., Zhang, J., Liu, R. and Liu, K. (2022). Classifying ASD based on time-series FMRI using spatial-temporal transformer, Computers in Biology and Medicine 151: 106320.Search in Google Scholar

Farooq, M.S., Tehseen, R., Sabir, M. and Atal, Z. (2023). Detection of autism spectrum disorder (ASD) in children and adults using machine learning, Scientific Reports 13(1): 9605.Search in Google Scholar

Francese, R. and Yang, X. (2022). Supporting autism spectrum disorder screening and intervention with machine learning and wearables: A systematic literature review, Complex & Intelligent Systems 8(5): 3659–3674.Search in Google Scholar

Garg, A., Parashar, A., Barman, D., Jain, S., Singhal, D., Masud, M. and Abouhawwash, M. (2022). Autism spectrum disorder prediction by an explainable deep learning approach, Computers, Materials & Continua 71(1): 1459–1471.Search in Google Scholar

Hosmer Jr, D.W., Lemeshow, S. and Sturdivant, R.X. (2013). Applied Logistic Regression, Wiley, Hoboken.Search in Google Scholar

Hsu, C.-W. (2003). A Practical Guide to Support Vector Classification, National Taiwan University, Taipei.Search in Google Scholar

Islam, M.Z., Islam, M.M. and Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images, Informatics in Medicine Unlocked 20: 100412.Search in Google Scholar

Kanhirakadavath, M.R. and Chandran, M.S.M. (2022). Investigation of eye-tracking scan path as a biomarker for autism screening using machine learning algorithms, Diagnostics 12(2): 518.Search in Google Scholar

Lu, A. and Perkowski, M. (2021). Deep learning approach for screening autism spectrum disorder in children with facial images and analysis of ethnoracial factors in model development and application, Brain Sciences 11(11): 1446.Search in Google Scholar

Mohammad, U.G., Imtiaz, S., Shakya, M., Almadhor, A. and Anwar, F. (2022). An optimized feature selection method using ensemble classifiers in software defect prediction for healthcare systems, Wireless Communications and Mobile Computing 2022(1): 1028175.Search in Google Scholar

Mohanty, A.S., Parida, P. and Patra, K. (2021). Identification of autism spectrum disorder using deep neural network, Journal of Physics: Conference Series, 1921: 012006.Search in Google Scholar

Omar, K.S., Mondal, P., Khan, N.S., Rizvi, M.R.K. and Islam, M.N. (2019). A machine learning approach to predict autism spectrum disorder, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, Bangladesh, pp. 1–6.Search in Google Scholar

Raj, S. and Masood, S. (2020). Analysis and detection of autism spectrum disorder using machine learning techniques, Procedia Computer Science 167: 994–1004.Search in Google Scholar

Rasul, R.A., Saha, P., Bala, D., Karim, S.R.U., Abdullah, M.I. and Saha, B. (2024). An evaluation of machine learning approaches for early diagnosis of autism spectrum disorder, Healthcare Analytics 5: 100293.Search in Google Scholar

Reddy, P. (2024). Diagnosis of autism in children using deep learning techniques by analyzing facial features, Engineering Proceedings 59(1): 198.Search in Google Scholar

Shahamiri, S.R. and Thabtah, F. (2020). Autism AI: A new autism screening system based on artificial intelligence, Cognitive Computation 12(4): 766–777.Search in Google Scholar

Shahamiri, S.R., Thabtah, F. and Abdelhamid, N. (2022). A new classification system for autism based on machine learning of artificial intelligence, Technology and Health Care 30(3): 605–622.Search in Google Scholar

Sharma, N., Bhandari, H.V., Yadav, N.S. and Shroff, H. (2020). Optimization of IDS using filter-based feature selection and machine learning algorithms, International Journal of Innovative Technology and Exploring Engineering 10(2): 96–102.Search in Google Scholar

Shrivastava, T., Singh, V. and Agrawal, A. (2024). Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening, Health Information Science and Systems 12(1): 18.Search in Google Scholar

Simeoli, R., Rega, A., Cerasuolo, M., Nappo, R. and Marocco, D. (2024). Using machine learning for motion analysis to early detect autism spectrum disorder: A systematic review, Review Journal of Autism and Developmental Disorders pp. 1–20.Search in Google Scholar

Song, Y.-Y. and Ying, L. (2015). Decision tree methods: Applications for classification and prediction, Shanghai Archives of Psychiatry 27(2): 130.Search in Google Scholar

Wang, H., Li, L., Chi, L. and Zhao, Z. (2019). Autism screening using deep embedding representation, Computational Science—ICCS 2019: 19th International Conference, Faro, Portugal, pp. 160–173.Search in Google Scholar

Zhang, M.-L. and Zhou, Z.-H. (2005). A k-nearest neighbor based algorithm for multi-label classification, 2005 IEEE International Conference on Granular Computing, Beijing, China, Vol. 2, pp. 718–721.Search in Google Scholar

Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Matematyka, Matematyka stosowana