Indian Journal of Radio & Space Physics

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Detecting Autism spectrum disorder with sailfish optimisation


Affiliations
1 Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli 620 012
2 Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli 620 012, India
3 Department of Data Science and Intelligent Systems, Indian Institute of Information Technology Dharwad, Karnataka 580 009, India
 

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.
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  • Detecting Autism spectrum disorder with sailfish optimisation

Abstract Views: 101  |  PDF Views: 71

Authors

K Balakrishnan
Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli 620 012
R Dhanalakshmi
Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli 620 012, India
Utkarsh Mahadeo Khaire
Department of Data Science and Intelligent Systems, Indian Institute of Information Technology Dharwad, Karnataka 580 009, India

Abstract


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.