Record Details

Detecting Autism spectrum disorder with sailfish optimisation

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Detecting Autism spectrum disorder with sailfish optimisation
 
Creator Balakrishnan, K
Dhanalakshmi, R
Khaire, Utkarsh Mahadeo
 
Subject Autism
Random opposition-based learning
Sailfish optimization
 
Description 68-73
Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, has been a bottleneck to several clinical researchers
due to data modularization, subjective analysis, and shifts in the accurate prediction of the disorder amongst the sample
population. Subjective clinical research suffers from a lengthy procedure, which is a time-consuming process. In this paper,
Sailfish Optimization (SFO), a recently developed nature-inspired meta-heuristics optimization algorithm, is being utilized
to detect ASD. The hunting methodology of sailfish inspires SFO. Classical SFO has examined the search space in only one
direction that affects its converging ability. The Random Opposition Based Learning (ROBL) strategy enhances the
exploration capacity of SFO and successfully converges the predictive model to global optima. The proposed ROBL-based
SFO (ROBL-SFO) selects relevant features from autism spectrum disorder (child and adult) datasets. According to the
results obtained, the proposed model outperforms the convergence capability and reduces local-optimal stagnation compared
to conventional SFOs.
 
Date 2021-12-28T09:42:56Z
2021-12-28T09:42:56Z
2021-06
 
Type Article
 
Identifier 0975-105X (Online); 0367-8393 (Print)
http://nopr.niscair.res.in/handle/123456789/58759
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJRSP Vol.50(2) [June 2021]