Application of Multi-objective Genetic Algorithm and asymmetrical Support Vector Machine to improve the reliability of an iris recognition system

2007 ◽  
Author(s):  
Roy Kaushik ◽  
Prabir Bhattacharya
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Iris Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals Multi-Objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection using Medical Image Data

2021 ◽  
Author(s):  
Shrey Bansal ◽  
Mukul Singh ◽  
Rahul Dubey ◽  
Bijaya Ketan Panigrahi
Keyword(s):  
Genetic Algorithm ◽  
Transmission Rate ◽  
Binary Classification ◽  
Feature Learning ◽  
Image Data ◽  
Support Vector ◽  
Current Standard ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Novel Coronavirus

Abstract In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT Scans. The model proposed in this study uses a three-step architecture, consisting of a Convolutional Autoencoder based unsupervised feature extractor, a Multi-Objective Genetic Algorithm based feature selector, and a Bagging Ensemble of Support Vector Machines(SVMs) based classifier. The Autoencoder generates a diverse set of features from the images, and an optimal subset, free of redundant and irrelevant features, is selected by the evolutionary selector. The Ensemble of SVMs then performs the binary classification of the features. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID vs.nonCOVID). A Dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. The experimental results prove the superiority of the proposed methodology in comparison to existing methods.

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