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 …

Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …

Federated learning (FL) is a promising approach for training decentralized data located on
local client devices while improving efficiency and privacy. However, the distribution and …

Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …

Binary classifiers are routinely evaluated with performance measures such as sensitivity and
specificity, and performance is frequently illustrated with Receiver Operating Characteristics …

Nowadays, healthcare is the prime need of every human being in the world, and clinical
datasets play an important role in developing an intelligent healthcare system for monitoring …

Purpose The South London and Maudsley National Health Service (NHS) Foundation Trust
Biomedical Research Centre (SLaM BRC) Case Register and its Clinical Record Interactive …

With the continuous expansion of data availability in many large-scale, complex, and
networked systems, such as surveillance, security, Internet, and finance, it becomes critical …

In this paper we have focused a variety of techniques, approaches and different areas of the
research which are helpful and marked as the important field of data mining Technologies …

The imbalanced data classification problem widely exists in many real-world applications.
Data resampling is a promising technique to deal with imbalanced data through either …