
María Martínez Ballesteros
PERFIL INVESTIGADOR · MINERVA
María del Mar Martínez Ballesteros
María del Mar Martínez Ballesteros forma parte del equipo de Minerva, grupo de investigación en inteligencia artificial y ciencia de datos de la Universidad de Sevilla.
Investigación
Su producción científica incluye trabajos sobre aprendizaje automático, inteligencia artificial explicable, selección de características y predicción de series temporales.
Perfil de María del Mar Martínez Ballesteros en PRISMA · Proyectos y contratos del grupo · Equipo de Minerva
Publicaciones de María del Mar Martínez Ballesteros
Artículos, contribuciones a congresos y otras publicaciones vinculadas a este perfil. Puedes consultar también el catálogo completo de publicaciones de Minerva.
2026
Explainable Graph Neural Networks for Omics-Based Cancer Classification Book Section
En: Communications in Computer and Information Science, vol. 2806 CCIS, pp. 227–236, 2026.
A novel interpretable ozone forecasting approach based on deep learning with masked residual connections Artículo de revista
En: ENVIRONMENTAL MODELLING & SOFTWARE, vol. 198, pp. 106878 -, 2026.
An Explainable Framework for Football Match Outcome Analysis Using Event-Based Data Book Section
En: Communications in Computer and Information Science, vol. 3046 CCIS, pp. 183–195, 2026.
En: Communications in Computer and Information Science, vol. 3046 CCIS, pp. 298–309, 2026.
Explainable Analysis of Fake Instagram Account Detection Using Graph and Behavioral Features Book Section
En: Communications in Computer and Information Science, vol. 3046 CCIS, pp. 323–334, 2026.
Exploring Zero-Shot Learning for Time Series Forecasting Using Large Language Models Book Section
En: Communications in Computer and Information Science, vol. 2806 CCIS, pp. 217–226, 2026.
Deep Learning or Trees? A Trade-off Analysis for Multivariate Time Series Forecasting Proceedings Article
En: Lecture Notes in Computer Science, pp. 616–627, 2026.
Tree-based vs. deep learning in time series: A prescriptive framework for performance-driven model selection Artículo de revista
En: APPLIED SOFT COMPUTING, vol. 195, pp. 114983 -, 2026.
Triclustering-Based Analysis of Circadian Gene Expression Patterns Book Section
En: Lecture Notes in Computer Science, vol. 16148 LNBI, pp. 540–554, 2026.
Feature Importance in Association Rule-Based Explanations for Time Series Forecasting Proceedings Article
En: Lecture Notes in Computer Science, pp. 237–248, 2026.
2025
A new metric based on association rules to assess feature-attribution explainability techniques for time series forecasting Artículo de revista
En: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 47, no 5, pp. 4140–4155, 2025.
A novel approach based on clustering and optimized ensemble deep learning for energy consumption forecasting in Ethiopia Artículo de revista
En: NEUROCOMPUTING, vol. 637, 2025.
Comparison of Explainable Machine Learning Methods for Early Prediction of Student Performance in Programming Courses Book Section
En: Communications in Computer and Information Science, vol. 2591 CCIS, pp. 161–168, 2025.
Road-traffic emissions of ultrafine particles and elemental black carbon in six Northern European cities Artículo de revista
En: Environmental Advances, vol. 21, 2025.
Urban Pollution Impact Assessment in Six Lithuanian Cities With a Focus on Road Traffic Emissions - Integrated Framework for Environmental Health Studies Artículo de revista
En: JOURNAL OF HAZARDOUS MATERIALS, vol. 497, pp. 139725 -, 2025.
Bioinspired evolutionary metaheuristic based on COVID spread for discovering numerical association rules Proceedings Article
En: Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, pp. 138–144, 2025.
Utilización de Socrative en el seguimiento y evaluación de clases de ingeniería informática Book Section
En: Acciones educativas innovadoras en el ámbito universitario, pp. 1097–1122, 2025.
Nonlinear Ensemble Deep Learning Model for Energy Consumption Prediction with Bayesian Optimization Artículo de revista
En: Communications on Applied Nonlinear Analysis, vol. 32, no 1, pp. 155–172, 2025.
2024
Embedded feature selection for neural networks via learnable drop layer Artículo de revista
En: Logic Journal of the IGPL, vol. N/A, pp. N/A, 2024.
From simple to complex: a sequential method for enhancing time series forecasting with deep learning Artículo de revista
En: Logic Journal of the IGPL, vol. N/A, pp. N/A, 2024.
- mariamartinez@us.es
- +34 954 556 949
- Universidad de Sevilla. ETSII. F1.81
