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 ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

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 …

Advances in computer science and data-analytic methods are driving a new era in mental
health research and application. Artificial intelligence (AI) technologies hold the potential to …

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 …

In many medical imaging and classical computer vision tasks, the Dice score and Jaccard
index are used to evaluate the segmentation performance. Despite the existence and great …

Abstract The Dice score and Jaccard index are commonly used metrics for the evaluation of
segmentation tasks in medical imaging. Convolutional neural networks trained for image …

Human activity recognition (HAR) based on sensor networks is an important research
direction in the fields of pervasive computing and body area network. Existing researches …

We developed DeepSolar, a deep learning framework analyzing satellite imagery to identify
the GPS locations and sizes of solar photovoltaic panels. Leveraging its high accuracy and …

This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …