Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

The digital world has a wealth of data, such as internet of things (IoT) data, business data,
health data, mobile data, urban data, security data, and many more, in the current age of the …

Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and
regression tasks in Big Data. Researchers should be familiar with the strengths and …

In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

This survey explores how Deep Learning has battled the COVID-19 pandemic and provides
directions for future research on COVID-19. We cover Deep Learning applications in Natural …

With the increase in popularity of Internet of Things (IoT) and the rise in interconnected
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …

Deep convolutional neural networks have performed remarkably well on many Computer
Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting …

Since canonical machine learning algorithms assume that the dataset has equal number of
samples in each class, binary classification became a very challenging task to discriminate …

The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …