Predicting autism spectrum disorder through sentiment analysis with attention mechanisms: a deep learning approach
Year: 2024
Type: article
Abstract: Autism spectrum disorder (ASD) is considered a spectrum disorder. The availability of technology to identify the characteristics of ASD will have major implications for clinicians. In this article, we present a new autism diagnosis method based on attention mechanisms for behavior modeling-based feature embedding along with aspect-based analysis for a better classification of ASD. The hybrid model comprises a convolutional neural network (CNN) architecture that integrates two bidirectional long short-term memory (BiLSTM) blocks, together with additional propagation techniques, for the purpose of classification the origins of Autism Tweet dataset; the proposed work takes Autism Tweet dataset and preprocesses them to employ n-gram to extract features of which the features of the ASD behavior are fed to generate the significant behavior for classification. The model takes into account both behavior-guided features across every aspect of the Class/ASD to provide higher accuracy using Adam optimizer. The experimental values inferred that the n-BiLSTM technique reaches maximum accuracy with 98%.
Authors: Murali Anand Mareeswaran, Kanchana Selvarajan
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403941792