Abstract
Autistic Ailment is a development disorder distinguished by various factors such as non-verbal communication, repetitive patterns in behavior, and so on. It generally occurs at childhood but it is a type of ailment which grows till lifetime if not treated properly. In recent years, Autism is increasing in India at a massive rate, and therefore, it requires proper and timely diagnosis. Although Autism can be detected by using various tools that are used for screening purpose like Autism Detection Observation Schedule (ADOS), such tools are very time-consuming and lacks accuracy. With the advancement in data analytics, many machine learning techniques like (support vector machine and random forest techniques) and image processing have been used to diagnose the traits of Autism. The main aim of this study is to propose deep learning architecture to detect Autism Syndrome which works on unstructured data and provides faster and timely diagnosis.
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Sharma, A., Tanwar, P. (2022). Deep Learning Techniques for Detection of Autism Spectrum Syndrome (ASS). In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 91. Springer, Singapore. https://doi.org/10.1007/978-981-16-6285-0_27
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DOI: https://doi.org/10.1007/978-981-16-6285-0_27
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