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Learning Disabilities

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection.

Nermine Mahmoud¹, Mohamed Abd Elaziz², Abdelghani Dahou³

¹Faculty of Social and Human Sciences, Galala University, Suez, Egypt.

Frontiers in Artificial Intelligence

|February 16, 2026

Summary

This summary is machine-generated.

This study introduces an all-optical Particle Swarm Optimization (PSO) that improves search capabilities. This novel optical PSO enhances dyslexia detection and benchmark function optimization.

Keywords:

dyslexia global optimization metaheuristic optical computer particle swarm optimization (PSO)

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Area of Science:

Computational intelligence
Optical computing
Machine learning

Background:

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm.
Traditional PSO relies on digital computation, which can be a bottleneck for complex problems.
Coherent optical systems offer unique advantages for computation, including speed and parallelism.

Purpose of the Study:

To develop a modified Particle Swarm Optimization (PSO) algorithm utilizing an all-optical computational update mechanism.
To leverage the properties of coherent optical systems for enhanced search space exploration and exploitation.
To evaluate the performance of the proposed optical PSO (OPSO) in optimization tasks and a real-world application.

Main Methods:

The study presents a modified PSO with an all-optical computational update mechanism.
The performance was assessed by comparing OPSO with traditional PSO on CEC benchmark functions.
OPSO was applied to enhance dyslexia detection using the eye-tracking dataset (ETDD).

Main Results:

The all-optical PSO demonstrated superior performance compared to traditional PSO in solving benchmark functions.
OPSO significantly enhanced the detection of dyslexia when compared to conventional methods.
The optical approach proved effective for both abstract optimization problems and the specific task of dyslexia detection.

Conclusions:

The integration of an all-optical computational update mechanism in PSO enhances its performance.
OPSO shows significant potential for improving complex optimization tasks and aiding in medical diagnoses like dyslexia detection.
This research highlights the benefits of optical computing for advancing artificial intelligence and machine learning applications.