The Design of effective deep-learning methods for medical image analysis represents a
great challenge given the scarcity of balanced datasets leading to biased results and …

This investigation underscores the paramount imperative of discerning network intrusions as
a pivotal measure to fortify digital systems and shield sensitive data from unauthorized …

A model framework for the prediction of defects in strip steel is proposed with the objective of
enhancing the accuracy of defect detection. Initially, the data are balanced through the …

One of the major problems in classifying network attack tactics is the imbalanced nature of
data. Typical network datasets have an extremely high percentage of normal or benign traffic …

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

Missing data and class imbalance represent a hindrance to accurate prediction of rare
events such as mastitis (udder inflammation). Various methods are susceptible to handle the …

This research aimed to create effective fraud detection models using machine learning (ML)
approaches. The research was conducted on two distinct datasets, both of which showed a …