The basic objective of this study is to develop a model that analyzes and predicts the
occurrence of flight arrival delays in the United States. Macroscopic and microscopic delay …

Today, data is increasingly easily accessible, with corporations storing information with high
volume, variety, speed, and value. This data is derived from several sources, including …

Confidence calibration in classification models, a technique to achieve accurate posterior
probability estimation for classification results, is crucial for assessing the likelihood of …

This study investigates credit card fraud detection using various machine learning
techniques. It emphasizes handling the imbalanced nature of fraud data through a …

Federated Learning (FL) has gained prominence in fields where safeguarding data privacy
is of utmost importance, presenting a valuable approach for the detection of credit card or …

The present study aimed to address the issue of imbalanced data in classification tasks and
evaluated the suitability of SMOTE, ADASYN, and GAN techniques in generating synthetic …

Logistic regression (LR) is one of the most popular classifiers. However, LR cannot perform
effectively on imbalanced data. There are two approaches to imbalanced data for LR …

Class imbalance has been widely accepted as a significant factor that negatively impacts a
machine learning classifier's performance. One of the techniques to avoid this problem is to …

Credit fraud modeling is a crucial area of research that is highly relevant to the credit loan
industry. Effective risk management is a key factor in providing quality credit services and …

Mental disorders present one of the leading causes of worldwide disability and have
become a major social concern, as the symptoms behind mental disorders are almost …