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Keyword: Generalized Linear Model
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11 Pages, 1,302 KB Download PDF

Generalized Linear Model for Predicting the Credit Card Default Payment Risk

Advances in Science, Technology and Engineering Systems Journal, Volume 7, Issue 3, Page # 51–61, 2022; DOI: 10.25046/aj070306
Abstract:

Predicting the credit card default is important to the bank and other lenders. The credit default risk directly affects the interest charged to the borrower and the business decision of the lenders. However, very little research about this problem used the Generalized Linear Model (GLM). In this paper, we apply the GLM to predict the…

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(This article belongs to the SP13 (Special Issue on Innovation in Computing, Engineering Science & Technology 2022) & Section Information Systems in Computer Science (CIS))
Open AccessArticle
7 Pages, 790 KB Download PDF

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

Advances in Science, Technology and Engineering Systems Journal, Volume 5, Issue 4, Page # 344–350, 2020; DOI: 10.25046/aj050440
Abstract:

Dengue is a viral infection disease transmitted to people through the bite of specific mosquito species living in a tropical zone. According to the World Health Organization, dengue has been listed among the top-ten diseases for 2019 as it makes 3.9 billion people in 128 countries be at risk of infection. One major cause of…

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(This article belongs to the SP9 (Special Issue on Multidisciplinary Innovation in Engineering Science & Technology 2020) & Section Artificial Intelligence in Computer Science (CAI))
Open AccessArticle
11 Pages, 517 KB Download PDF

Use of machine learning techniques in the prediction of credit recovery

Advances in Science, Technology and Engineering Systems Journal, Volume 2, Issue 3, Page # 1432–1442, 2017; DOI: 10.25046/aj0203179
Abstract:

This paper is an extended version of the paper originally presented at the International Conference on Machine Learning and Applications (ICMLA 2016), which proposes the construction of classifiers, based on the application of machine learning techniques, to identify defaulting clients with credit recovery potential. The study was carried out in 3 segments of a Bank’s…

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(This article belongs to the SP3 (Special issue on Recent Advances in Engineering Systems 2017) & Section Interdisciplinary Applications of Computer Science (CSI))

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