2025 March - 26 articles
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 (...
This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. For this, we introduce methods to optimize M...
Generative large language models (LLMs) have revolutionized the development of knowledge-based systems, enabling new possibilities in applications like ChatGPT, Bing, and Gemini. Two key strategies for domain adaptation in these systems are Domain-Sp...
Adversarial training, a widely used technique for fortifying the robustness of machine learning models, has seen its effectiveness further bolstered by modifying loss functions or incorporating additional terms into the training objective. While thes...
The integration of machine learning (ML) with big data has revolutionized industries by enabling the extraction of valuable insights from vast and complex datasets. This convergence has fueled advancements in various fields, leading to the developmen...
This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluat...
Basketball players are traditionally classified into five positions. This study examines the correlation between player performance, game statistics, and designated positions. It also explores how statistical contributions have evolved over time. Mac...
Transformer models produce advanced text representations that have been used to break through the hard challenge of natural language understanding. Using the Transformer’s attention mechanism, which acts as a language learning memory, trained o...
This study investigates the relationship between consumer personality traits, specifically openness, and responses to product designs. Consumers are categorized based on their levels of openness, and their affective responses to nine vase designs, va...
Representing and interpreting human opinions within an unstructured framework is inherently complex. Hesitant fuzzy linguistic term sets offer a comprehensive context that facilitates a nuanced understanding of diverse perspectives. This study introd...
Interdisciplinary research (IDR) is essential for addressing complex global challenges that surpass the capabilities of any single discipline. However, measuring interdisciplinarity remains challenging due to conceptual ambiguities and inconsistent m...
Artificial intelligence (AI) is increasingly applied in a wide range of healthcare and Intensive Care Unit (ICU) areas to serve—among others—as a tool for disease detection and prediction, as well as for healthcare resources’ manage...
Balancing the accuracy and the complexity of models is a well established and ongoing challenge. Models can be misleading if they are not accurate, but models may be incomprehensible if their accuracy depends upon their being complex. In this paper,...
Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, and infrastructural factors. This study introduces an advanced hybrid model combining a gradient boostin...
The now-globally recognized concerns of AI’s environmental implications resulted in a growing awareness of the need to reduce AI carbon footprints, as well as to carry out AI processes responsibly and in an environmentally friendly manner. Benc...
Data drift can significantly impact the outcome of a model. Early detection of data drift is crucial for ensuring user confidence in predictions. It allows the user to check if a particular model needs retraining using updated data to adapt to the ev...
Deep learning models are widely used for medical image analysis and require large datasets, while sufficient high-quality medical data for training are scarce. Data augmentation has been used to improve the performance of these models. The lack of tr...
