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Detecting Autism spectrum disorder with sailfish optimisation

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Title Statement Detecting Autism spectrum disorder with sailfish optimisation
 
Added Entry - Uncontrolled Name Balakrishnan, K ; Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli 620 012, India
 
Uncontrolled Index Term Autism, Random opposition-based learning, Sailfish optimization
 
Summary, etc. Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, has been a bottleneck to several clinical researchersdue to data modularization, subjective analysis, and shifts in the accurate prediction of the disorder amongst the samplepopulation. 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 utilizedto detect ASD. The hunting methodology of sailfish inspires SFO. Classical SFO has examined the search space in only onedirection that affects its converging ability. The Random Opposition Based Learning (ROBL) strategy enhances theexploration capacity of SFO and successfully converges the predictive model to global optima. The proposed ROBL-basedSFO (ROBL-SFO) selects relevant features from autism spectrum disorder (child and adult) datasets. According to theresults obtained, the proposed model outperforms the convergence capability and reduces local-optimal stagnation comparedto conventional SFOs.
 
Publication, Distribution, Etc. Indian Journal of Radio & Space Physics (IJRSP)
2022-04-27 12:56:22
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/IJRSP/article/view/62098
 
Data Source Entry Indian Journal of Radio & Space Physics (IJRSP); ##issue.vol## 50, ##issue.no## 2 (2021): IJRSP-JUNE 2021
 
Language Note en
 
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