Abstract Machine Learning (ML) algorithms have been increasingly replacing people in
several application domains—in which the majority suffer from data imbalance. In order to …

This study considers the task of applying artificial intelligence to recognize bank fraud. In
recent years, due to the COVID-19 pandemic, bank fraud has become even more common …

The ability to conduct accurate regional seismic risk assessments is key to informing a risk-
reduction policy and fostering community resilience. This paper presents a machine learning …

Significance: Early detection of oral cancer is vital for high-risk patients, and machine
learning-based automatic classification is ideal for disease screening. However, current …

Compulsory school-dropout is a serious problem affecting not only the education systems,
but also the developmental progress of any country as a whole. Identifying the risk of …

There are growing security risks tied to the recent proliferation of Internet of Things (IoT)
devices. Due to this fact, datasets such as Bot-IoT were designed to train machine learning …

Landslide susceptibility assessment (LSA) based on machine learning methods has been
widely used in landslide geological hazard management and research. However, the …

This study investigates the application of Artificial Neural Networks (ANN) supplemented
with optimization algorithms for modeling and mapping groundwater quality in an extensive …

With the rapid population growth, increasing agricultural productivity is an extreme
requirement to meet demands. Early identification of crop diseases is essential to prevent …

The rapid spread of mobile banking and e-commerce has coincided with a dramatic
increase in fraudulent online payments in recent years. Although machine learning and …