idUS - Perfil del autor: Martínez Ballesteros, María del Mar

Nombre	Martínez Ballesteros, María del Mar
Departamento	Lenguajes y Sistemas Informáticos
Área de conocimiento	Lenguajes y Sistemas Informáticos
Categoría profesional	Profesora Titular de Universidad
Correo electrónico	Solicitar

Nº publicaciones

53
Nº visitas

3767
Nº descargas

7643

Tipología

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Publicaciones
Capítulo de Libro Uso de herramientas software colaborativas para el seguimiento, estudio y evaluación de clases de enseñanzas prácticas y desarrollo Martínez Ballesteros, María del Mar; Jiménez Navarro, Manuel Jesús; Carranza García, Manuel; Gutiérrez Avilés, David; Martínez Ballesteros, María del Mar (Dykinson, 2024) Para ayudar a la integración de todos los desarrollos individuales existen herramientas de control de versiones como ...
Artículo Explaining deep learning models for ozone pollution prediction via embedded feature selection Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (ScienceDirect, 2024) Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is ...
Ponencia Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (Springer Link, 2023) Traditional time series forecasting models often use all available variables, including potentially irrelevant or noisy ...
Ponencia Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals Troncoso García, Ángela del Robledo; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia (Springer Link, 2023) This paper explores the use of deep learning techniques for detecting sleep apnea. Sleep apnea is a common sleep disorder ...
Ponencia Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation Jiménez Navarro, Manuel Jesús; Vega Márquez, Belén; Luna Romera, José María; Carranza García, Manuel; Martínez Ballesteros, María del Mar (Springer Link, 2023) The quality of university teaching is essential for the success of students and the academic excellence of an educational ...
Ponencia A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia Tefera Habtemariam, Ejigu; Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia (Springer Link, 2023) Renewable energies are currently experiencing promising growth as an alternative solution to minimize the emission of ...
Ponencia A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma Linares Barrera, María Lourdes; Martínez Ballesteros, María del Mar; García Heredia, José Manuel; Riquelme Santos, José Cristóbal (SpringerLink, 2023) Sarcomas are rare mesodermal tumors of heterogeneous nature and have a higher incidence in children. The relative 5-year ...
Ponencia Explaining Learned Patterns in Deep Learning by Association Rules Mining Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (SpringerLink, 2023) This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the ...
Ponencia Evolutionary computation to explain deep learning models for time series forecasting Troncoso García, Ángela del Robledo; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia (Association for Computing Machinery, 2023) Deep learning has become one of the most useful tools in the last years to mine information from large datasets. Despite ...
Ponencia A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Sofia Brito, Isabel (Association for Computing Machinery, 2023) The year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially ...
Tesis Doctoral Novel efficient deep learning architectures for time series forecasting Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Asencio Cortés, Gualberto (2023) This thesis focuses on the study of time series prediction using the technique known as deep learning or neural networks. ...
Artículo A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia Tefera Habtemariam, Ejigu; Kekeba, Kula; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco (MDPI, 2023) Renewable energies, such as solar and wind power, have become promising sources of energy to address the increase in ...
Artículo A new treatment for sarcoma extracted from combination of miRNA deregulation and gene association rules García Heredia, José Manuel; Perez, Marco; Verdugo Sivianes, Eva María; Martínez Ballesteros, María del Mar; Ortega Campos, Sara M.; Carnero, Amancio (Springer Nature, 2023)
Artículo A new approach based on association rules to add explainability to time series forecasting models Troncoso García, Ángela del Robledo; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia (ScienceDirect, 2023) Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information ...
Artículo PHILNet: A novel efficient approach for time series forecasting using deep learning Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (ScienceDirect, 2023) Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior ...
Trabajo Fin de Grado Curvas de Edwards para firma digital: EdDSA Sendín Martín, José Cristóbal; Soto Prieto, Manuel Jesús; Martínez Ballesteros, María del Mar (2023) EdDSA is the digital signature used in the Signal protocol. The development of this protocol was a milestone for instant ...
Ponencia A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm Segarra Martín, C.; Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia; Martínez Álvarez, Francisco (Association for Computing Machinery, 2022) The disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in ...
Ponencia Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Sousa Brito, Isabel Sofía; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto (SpringerLink, 2022) Neural networks have proven to be a good alternative in application fields such as healthcare, time-series forecasting and ...
Artículo Explainable machine learning for sleep apnea prediction Troncoso García, Ángela del Robledo; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia (ScienceDirect, 2022) Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool ...
Artículo Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance Macías García, Laura; Martínez Ballesteros, María del Mar; Luna Romera, José María; García Heredia, José Manuel; García Gutiérrez, Jorge; Riquelme Santos, José Cristóbal (Elsevier, 2020) Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the ...
Tesis Doctoral New internal and external validation indices for clustering in Big Data Luna Romera, José María; García Gutiérrez, Jorge; Martínez Ballesteros, María del Mar (2019) Esta tesis, presentada como un compendio de artículos de investigación, analiza el concepto de índices de validación de ...
Artículo External clustering validity index based on chi-squared statistical test Luna Romera, José María; Martínez Ballesteros, María del Mar; García Gutiérrez, Jorge; Riquelme Santos, José Cristóbal (Elsevier, 2019) Clustering is one of the most commonly used techniques in data mining. Its main goal is to group objects into clusters so ...
Artículo Analysis of the evolution of the Spanish labour market through unsupervised learning Luna Romera, José María; Núñez Hernández, Fernando; Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal; Ibáñez, Carlos Usabiaga (Institute of Electrical and Electronics Engineers (IEEE), 2019) Unemployment in Spain is one of the biggest concerns of its inhabitants. Its unemployment rate is the second highest in ...
Artículo MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems Martín, D.; Martínez Ballesteros, María del Mar; García Gil, D.; Alcalá Fernández, J.; Herrera, F.; Riquelme Santos, José Cristóbal (Elsevier, 2018) Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. ...
Artículo An approach to validity indices for clustering techniques in Big Data Luna Romera, José María; García Gutiérrez, Jorge; Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal (Springer, 2018) Clustering analysis is one of the most used Machine Learning techniques to discover groups among data objects. Some ...
Ponencia Aproximación al índice externo de validación de clustering basado en chi cuadrado Luna Romera, José María; García Gutiérrez, Jorge; Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal (Asociación Española para la Inteligencia Artificial (AEPIA), 2018) El clustering es una de las técnicas más utilizadas en minería de datos. Tiene como objetivo principal agrupar datos en ...
Artículo A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation Macías García, Laura; Luna Romera, José María; García Gutiérrez, Jorge; Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal; González Cámpora, Ricardo (Elsevier, 2017) Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes ...
Artículo Applications of Computational Intelligence in Time Series Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Reyes, Jorge; Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal (Hindawi, 2017)
Artículo Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources Martínez Ballesteros, María del Mar; García Heredia, José Manuel; Nepomuceno Chamorro, Isabel de los Ángeles; Riquelme Santos, José Cristóbal (Elsevier, 2017) Alzheimer’s disease is a complex progressive neurodegenerative brain disorder, being its prevalence ex pected to rise over ...
Ponencia Predicción de módulos defectuosos como un problema de optimización multiobjetivo Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal; Ruiz, R.; Rodríguez, D. (Asociación de Ingeniería del Software y Tecnologías de Desarrollo de Software (SISTEDES), 2017) La dificultad de aplicar técnicas de análisis de datos al problema de la calidad del software radica principalmente en dos ...
Artículo Obtaining optimal quality measures for quantitative association rules Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia; Martínez Álvarez, Francisco; Riquelme Santos, José Cristóbal (Elsevier, 2016) There exist several works in the literature in which fitness functions based on a combination of weighted measures for the ...
Ponencia Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules Sánchez Medina, Alejandro; Gil Pichardo, Alberto; García Heredia, José Manuel; Martínez Ballesteros, María del Mar (Springer, 2016) This work proposes a methodology to identify genes highly related with cancer. In particular, a multi-objective evolutionary ...
Ponencia A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting Talavera Llames, Ricardo L.; Pérez Chacón, Rubén; Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia; Martínez Álvarez, Francisco (Springer, 2016) A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted ...
Ponencia An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark Luna Romera, José María; Martínez Ballesteros, María del Mar; García Gutiérrez, Jorge; Riquelme Santos, José Cristóbal (Springer, 2016) K-Means and Bisecting K-Means clustering algorithms need the optimal number into which the dataset may be divided. Spark ...
Ponencia MOPNAR-BigData: un diseño MapReduce para la extracción de reglas de asociación cuantitativas en problemas de big data Martín, D.; Martínez Ballesteros, María del Mar; Río, S.; Alcalá Fernández, J.; Riquelme Santos, José Cristóbal; Herrrera, F. (Asociación Española de Inteligencia Artificial, 2015) El término big data se ha extendido rápidamente en el área de la minera de datos debido a que las grandes cantidades de ...
Artículo Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets Martínez Ballesteros, María del Mar; Bacardit, Jaume; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal (iOS Press, 2015) Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes ...
Artículo Improving a multi-objective evolutionary algorithm to discover quantitative association rules Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia; Martínez Álvarez, Francisco; Riquelme Santos, José Cristóbal (Springer, 2015) This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association ...
Artículo Selecting the best measures to discover quantitative association rules Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal (Elsevier, 2014) The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate ...
Artículo Discovering gene association networks by multi-objective evolutionary quantitative association rules Martínez Ballesteros, María del Mar; Nepomuceno Chamorro, Isabel de los Ángeles; Riquelme Santos, José Cristóbal (Elsevier, 2014) In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the ...
Capítulo de Libro A Sensitivity Analysis for Quality Measures of Quantitative Association Rules Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal (Springer, 2013) There exist several fitness function proposals based on a combination of weighted objectives to optimize the discovery of ...
Ponencia On the use of algorithms to discover motifs in DNA sequences Rubio Escudero, Cristina; Martínez Álvarez, Francisco; Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal (IEEE, 2011) Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging ...
Artículo Extensiones para el Ciclo de Mejora Continua en la enseñanza e investigación de Ingeniería Informática Álvarez de la Concepción, Miguel Ángel; Jiménez Ramírez, Andrés; Martínez Ballesteros, María del Mar; Martínez Gasca, Rafael; Parody Núñez, María Luisa; Soria Morillo, Luis Miguel (2011) Este trabajo expone cómo añadiendo aspectos relacionados con la vigilancia tecnológica, las técnicas creativas aplicadas ...
Artículo Evolutionary association rules for total ozone content modeling from satellite observations Martínez Ballesteros, María del Mar; Salcedo Sanz, S.; Riquelme Santos, José Cristóbal; Casanova Mateo, C.; Camacho, J. L. (Elsevier, 2011) In this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quan titative Association ...
Capítulo de Libro Inferring Gene-Gene Associations from Quantitative Association Rules Martínez Ballesteros, María del Mar; Nepomuceno Chamorro, Isabel de los Ángeles; Riquelme Santos, José Cristóbal (IEEE, 2011) The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The ...
Ponencia Mining Quantitative Association Rules in Microarray Data Using Evolutive Algorithms Martínez Ballesteros, María del Mar; Rubio Escudero, Cristina; Riquelme Santos, José Cristóbal; Martínez Álvarez, Francisco (SciTePress, 2011) The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The ...
Capítulo de Libro Analysis of Measures of Quantitative Association Rules Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal (Springer, 2011) This paper presents the analysis of relationships among different interestingness measures of quality of association rules ...
Artículo An evolutionary algorithm to discover quantitative association rules in multidimensional time series Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal (Springer, 2011) An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is ...
Tesis Doctoral Evolutionary Algorithms to Discover Quantitative Association Rules Martínez Ballesteros, María del Mar; Riquelme Santos, José Cristóbal; Troncoso Lora, Alicia (2011)
Ponencia EVFUZZYSYSTEM: evolución de sistemas difusos para problemas de regresión multi-dimensionales Martínez Ballesteros, María del Mar; Rivas, Víctor M. (Universidad de Huelva, 2010) Este trabajo presenta EvFuzzySystem, un método evolutivo que permite el diseño com pleto de sistemas de lógica difusa, ...
Ponencia Cis-cop: Multiobjective identification of cis-regulatory modules based on constrains Romero Zaliz, Rocío; Martínez Ballesteros, María del Mar; Zwir, Igor; Val, Coral del (Universidad de Huelva, 2010) Gene expression regulation is an intricate, dynamic phenomenon essential for all biolog ical functions. The necessary ...
Artículo Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia; Martínez Álvarez, Francisco; Riquelme Santos, José Cristóbal (IOS Press, 2010) This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, ...
Capítulo de Libro Quantitative Association Rules Applied to Climatological Time Series Forecasting Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal (2009) This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships ...
Ponencia Descubriendo Reglas de Asociación Numéricas entre Series Temporales Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal (Universidad de Sevilla, 2009) Este trabajo presenta el descubrimiento de reglas de asociación basadas en técnicas evolutivas para obtener relaciones ...

Perfil del autor: Martínez Ballesteros, María del Mar

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