Authors :
Opeoluwa Omotayo Ajilore; Adekunle Eludire; Dr. Mosud Yinusa Olumoye; Olajide Adegunwa
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/27kp3j7w
Scribd :
https://tinyurl.com/3epjua6b
DOI :
https://doi.org/10.5281/zenodo.10212138
Abstract :
In the pursuit of inclusive education, early
detection of learning disabilities plays a pivotal role.
Traditional methods of identification often fall short due
to their subjective nature and limited ability to capture
nuanced behavioral and cognitive patterns. This study
delves into the practical implementation of image
classification algorithms, specifically Convolutional
Neural Networks (CNNs), for early detection. Building
on existing research, the study investigates the
adaptability of CNNs to diverse cultural contexts and
emphasizing the importance of transparent algorithms.
By analyzing behavioral patterns, emotional cues, and
handwriting traits, the study aims to identify early signs
of learning disabilities. The research not only contributes
to the growing body of knowledge in educational
technology but also offers actionable insights for
educators, policymakers, and technologists, fostering an
inclusive learning environment where every student's
needs are proactively met.
Keywords :
Visual data, Image classification, Convolutional Neural Networks.
In the pursuit of inclusive education, early
detection of learning disabilities plays a pivotal role.
Traditional methods of identification often fall short due
to their subjective nature and limited ability to capture
nuanced behavioral and cognitive patterns. This study
delves into the practical implementation of image
classification algorithms, specifically Convolutional
Neural Networks (CNNs), for early detection. Building
on existing research, the study investigates the
adaptability of CNNs to diverse cultural contexts and
emphasizing the importance of transparent algorithms.
By analyzing behavioral patterns, emotional cues, and
handwriting traits, the study aims to identify early signs
of learning disabilities. The research not only contributes
to the growing body of knowledge in educational
technology but also offers actionable insights for
educators, policymakers, and technologists, fostering an
inclusive learning environment where every student's
needs are proactively met.
Keywords :
Visual data, Image classification, Convolutional Neural Networks.
