Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …

The usage of imbalanced databases is a recurrent problem in real-world data such as
medical diagnostic, fraud detection, and pattern recognition. Nevertheless, in class …

Abstract Model overfitting and data imbalance are two main challenges in radiomics studies.
In this study, we develop a high-generalizable classifier MERGE (Multi-kErnel Regression …

Detecting fraud in the healthcare insurance dataset is challenging due to severe class
imbalance, where fraud cases are rare compared to non-fraud cases. Various techniques …

Dealing with imbalanced data is crucial and challenging when develo** effective machine-
learning models for data classification purposes. It significantly impacts the classification …

Decision Tree (DT) induction is one of the popular data modelling techniques that commonly
used in many real-world supervised learning problems. DT is a top-down recursive divide …

In today's world, classification learning is a vital task because of the advancement in
technology. However, during the classification process, we found the classifiers (the …

Simple Summary Prediction of high-grade meningioma on preoperative Magnetic
Resonance Imaging (MRI) is essential in therapeutic planning and evaluation of prognosis …

With the growing internet, web spam is also increasing, which majorly affect the user
experiences with search engines. Web spam methods target the search engine's internal …

The identification of daily activities is mainly limited by the imbalanced number of actions
and the diversity of categories, which can lead to unsatisfactory classification results and …