Abstract
Neurodevelopmental disorders affect the nervous system and functioning of the brain, which affects expressing emotions, learning ability, and memorising things. ASD—Autism Spectrum Disorder is a type of by-birth neurodevelopmental disorder and is difficult to diagnose because there are no clinical tests such as blood test to diagnose ASD. Manual detection model is time-consuming process and requires a lot of specialists to analyse individual condition. In recent years, most of the research is carried out on automated ASD detection by employing advanced technologies. The proposed model automates ASD detection and overcomes limitations of existing methods. In this model both convolutional neural networks and supervised system studying methods are used jointly to improve accuracy in ASD detection compared to other models. The proposed ASD detection model has two phases, in the first phase, features are extracted utilising the convolutional neural network model and in the second phase extracted features are given as input to the detection process employed with supervised machine learning algorithms. Findings of the suggested paradigm are contrasted with different supervised machine learning algorithms.
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Acknowledgements
Fundamental information was gathered by F. F. Thabtah, he developed an upwardly mobile product for identifying ASD. F. F. Thabtah sited dataset in UCI ML depository along with open gain access and benefitted to research scholars those are working on ASD. Thankful to F. F. Thabtah for encouraging investigation upon ASD.
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Lakshmi Praveena, T., Muthu Lakshmi, N.V. (2021). An Enhanced Autism Spectrum Disorder Detection Model Using Convolutional Neural Networks and Machine Learning Algorithms. In: Jyothi, S., Mamatha, D.M., Zhang, YD., Raju, K.S. (eds) Proceedings of the 2nd International Conference on Computational and Bio Engineering . Lecture Notes in Networks and Systems, vol 215. Springer, Singapore. https://doi.org/10.1007/978-981-16-1941-0_63
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DOI: https://doi.org/10.1007/978-981-16-1941-0_63
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