Work

Leveraging Support Vector Machines to Develop Technology-Driven Educational Tools for Children with ASD

Year: 2025

Type: article

Abstract: Autism Spectrum Disorder (ASD) presents unique challenges in learning, communication, and social interaction. The integration of machine learning techniques, such as Support Vector Machines (SVM), has the potential to revolutionize personalized educational interventions for autistic children. This research aims to utilize SVM algorithms to identify patterns in behavioural, cognitive, and learning preferences of children with ASD. By analysing large datasets from behavioral studies, educational performance, and interaction with technology, SVM can classify and predict individualized learning needs, thereby enabling the development of tailored educational tools.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15710588

Authors: Bubere, Naznin, Sable, Shraddha

Language: English

Topic: Autism Spectrum Disorder Research

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Quality education

Open Access status: green

OpenAlex ID: W6893381850

FWCI:

Cites:

Cited by:

Related to: