ASD Classification in Adolescent and Adult Utilizing Deep Neural Network

DOI

10.2991/ahis.k.210913.025How to use a DOI?

Keywords

ASD, Classification, i-DNNPC, Landmark Isomap, Performance parameters

Abstract

Autism Spectrum Disorder (ASD) is one of the neurological illnesses affecting the behaviour and communicative skills of an individual. It hampers the recognition capability of an individual. Hence it is the primary responsibility towards the affected individuals with ASD for early detection to minimize its effect. ASD clinical diagnosis procedure is lengthy and expensive. So, against the procedure, ASD datasets are stored in authenticated sites like Kaggle and UCI Machine Learning (ML) repository to carry out clinical research. The data from all the category of individuals including adult, adolescent, child and toddler got collected by a mobile based ASDTest app with certain screening questions. The proposed method covered the category of adolescent and adult datasets with implementation of Landmark Isomap for dimension reduction and then improved Deep Neural Network prediction with classification (i-DNNPC) architecture for detecting ASD class. The evaluation of performance parameters confirmed the accomplishment of i-DNNPC classifier model.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ashima Sindhu Mohanty
AU  - Priyadarsan Parida
AU  - Krishna Chandra Patra
PY  - 2021
DA  - 2021/09/13
TI  - ASD Classification in Adolescent and Adult Utilizing Deep Neural Network
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 202
EP  - 210
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.025
DO  - 10.2991/ahis.k.210913.025
ID  - Mohanty2021
ER  -