Artificial Bee Colony for Logic Mining in Credit Scoring

During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided the proper credit to the right person. The existing methods employed by financial entities were prone to overfitting and did not provide any information regarding the behavior of the creditor. However, the outcome did not consider the attribute of the creditor that led to the default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented to optimize the learning phase of the Hopfield Neural Network with 2 Satisfiability-based Reverse Analysis Methods. The proposed hybrid model will be used to extract logical information in the credit data with more than 80% accuracy compared to the existing method. The effectiveness of the proposed hybrid model was evaluated and showed superior results compared to other models.

Artificial Bee Colony for Logic Mining in Credit Scoring

During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided the proper credit to the right person. The existing methods employed by financial entities were prone to overfitting and did not provide any information regarding the behavior of the creditor. However, the outcome did not consider the attribute of the creditor that led to the default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented to optimize the learning phase of the Hopfield Neural Network with 2 Satisfiability-based Reverse Analysis Methods. The proposed hybrid model will be used to extract logical information in the credit data with more than 80% accuracy compared to the existing method. The effectiveness of the proposed hybrid model was evaluated and showed superior results compared to other models. © 2022 Malaysian Journal of Fundamental and Applied Sciences.

A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network.

BACKGROUND: Widely spread of the COVID-19 virus has put the whole world in jeopardy. At this moment, using new techniques to detect and treat this novel disease is of significance or maybe the first priority of many scientists and researchers throughout the world. PURPOSE: To present a new algorithm for detecting the novel coronavirus 2019 using chest CT images with high accuracy. MATERIALS AND METHODS: In this study, we looked at the newly-presented data and detection methods of this disease using chest CT; then, a new neural network algorithm was presented to recognize the COVID-19 symptoms. A mathematical model is used to enhance the accuracy of masking, and a high accuracy Hopfield Neural Network (HNN) is used for finding symptoms. A dataset of CT scans, including 12 pattern images, was trained by this neural network, and 295CT images from three different datasets were tested via the model. RESULTS: The sensitivity and specificity of the model for detecting COVID-19 in test data were 97.4% (149 of 153) and 98.6% (140 of 142) respectively. Also, the sensitivity and specificity of the model for detecting CAP (community-acquired pneumonia) in test data were 97.3% (106 of 109) and 99.5% (185 of 186) respectively, and, the sensitivity and specificity of the model for detecting non-pneumonia patients were 100% (33 of 33) and 98.5% (258 of 262) respectively. CONCLUSION: This new algorithm can potentially help detect the novel Coronavirus patients using CT images.