2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 15 , Issue 1 , PP: 120-127, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Monalin Pal 1 * , Rubini P. 2
Doi: https://doi.org/10.54216/FPA.150110
Autism spectrum disorder (ASD) is a neurological and developmental condition impacting individuals' interactions with others, communication, learning, and behavior. While autism can be identified at any point in life, it is characterized as a "developmental disorder" due to the typical onset of symptoms within the initial two years of life. As individuals with ASD transition from childhood to adolescence and young adulthood, they might face challenges in establishing and having friendships, communicating with both peers and adults, and understanding the expected behaviors in education or work. The current study introduces a novel approach for suggesting the right behavioral strategy to assist Autistic Spectrum Disorder with the help of supervised BERT (Bidirectional Encoder Representations from Transformers). Our model achieved an accuracy of 88% with the help of BERT to predict the right behavioral trait. This research demonstrates cost-effectiveness and efficiency in offering recommendations for ASD, making it suitable for applications requiring near real-time outcomes.
Deep Learning , Machine Learning , Autism Spectrum Disorder , BERT (Bidirectional Encoder Representations from Transformers) , Fusion Processing , Information Fusion , Neural networks , Social Media , Applied Behavioral Analysis
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