Machine Learning and Knowledge Extraction, Volume 7, Issue 1

Equatorial plasma bubbles (EPBs) are regions of depleted electron density that form in the Earth’s ionosphere due to Rayleigh–Taylor instability. These bubbles can cause signal scintillation, leading to signal loss and errors in position...

Non-negative Matrix Factorization (NMF) has gained popularity due to its effectiveness in clustering and feature selection tasks. It is particularly valuable for managing high-dimensional data by reducing dimensionality and providing meaningful seman...

The electrical storm optimization (ESO) algorithm, inspired by the dynamic nature of electrical storms, is a novel population-based metaheuristic that employs three dynamically adjusted parameters: field resistance, field intensity, and field conduct...

This study investigates the integration of diverse data modalities within deep learning ensembles for Android malware classification. Android applications can be represented as binary images and function call graphs, each offering complementary persp...

We introduce a weakly supervised segmentation approach that leverages class activation maps and the Segment Anything Model to generate high-quality masks using only classification data. A pre-trained classifier produces class activation maps that, on...

The growing sophistication of cyber threats necessitates robust and interpretable intrusion detection systems (IDS) to safeguard network security. While machine learning models such as Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (K-NN...

This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the...

Explainability is essential for AI models, especially in clinical settings where understanding the model’s decisions is crucial. Despite their impressive performance, black-box AI models are unsuitable for clinical use if their operations canno...

The weighted kappa coefficient is commonly used for assessing agreement between two raters on an ordinal scale. This study is the first to assess the impact of missing data on the value of weighted kappa. We compared three methods for handling missin...

Artificial neural networks (ANNs) have been used for classification tasks involving functional magnetic resonance imaging (fMRI), though typically focusing only on fractions of the brain in the analysis. Recent work combined shallow neural networks (...