Recommendation learning management system for autism using deep convolutional neural networks and gene expression programming

Tholkapiyan. M (), D. Krishna Madhuri (), R. Sundar (), Gayatri Parasa (), Vivek Duraivelu () and N. Krishnaveni ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 2, 910-935

Abstract: Autism is a complex condition that affects children at an early stage and interferes with their daily activities in life. People affected by autism have problems with social interaction, communication, and exhibit repetitive behaviors. A personalized recommendation learning system modifies an academic subject, educational program, and the environment in which the student learns according to the individual student’s learning style and interests. This paper creates a personalized recommendation learning system tailored for autistic students affected by Rett and Asperger’s syndromes to meet their needs using Gene Expression Programming and Deep Learning via Convolutional Neural Networks. Gene Expression Programming creates a recommendation-based learning content based on the autistic student’s profile. Deep Convolutional Neural Networks (DCNN) identify the student’s facial emotions and detect disorientation towards the course. If any disorientation is identified, the course is terminated immediately, and an alternate learning style that reduces the disorientation is provided. To evaluate the efficiency of the proposed approach, extensive experiments are conducted. DCNN's ability to predict the student's emotions to avoid challenging courses is 98% effective.

Keywords: Asperger syndrome; Autism spectrum disorder (ASD); Deep learning; Gene expression programming rett syndrome. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/4625/1766 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:2:p:910-935:id:4625

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().