Citations and socials
Federico Divina obtained his Ph.D. in Artificial Intelligence from the Vrije Universiteit of Amsterdam, and after that he worked as a postdoc at the University of Tilburg, within the European project NEWTIES. In 2006 he moved to the Pablo de Olavide University, where he is actually an Associate Professor.
He has been working on knowledge extraction since his Ph.D. thesis at the Vrije Universiteit of Amsterdam. He has extensive experience in the application of Machine Learning, especially techniques based on Soft Computing, for the extraction of knowledge from massive data.
His main research interests are:
- Bioinformatics
- Evolutionary Computation
- Machine Learning
- Big Data
Projects
Federico Divina has participated in various research project projects, for instance:
- Differential: this project aims to develop new tools and methods to manage and analyse information coming from several sources with the final goal of better understanding how and when energy is consumed in distributed facilities. This project was developed as a coordinated project with three complementary research groups from three different universities (Universidad de Granada, Universidad Pablo de Olavide and Universidad de Castilla La Mancha).
- GALICIAME: project that aimed at applying machine learning tools in order to extract knowledge from genetic data related to spinal muscular atrophy (SMA), in collaboration with the “Centro Andaluz de Biología del Desarrollo” (CABD).
- NEWTIES: EU project that aimed at developing an artificial society. This project involved the Vrije Universiteit van Amsterm, the University of Tilburg, the Napier University, University of Surrey, Napier University and Eötvös Loránd University.
Publications
For a complete list of my publications, please visit my Google Scholar Profile or my ORCID.
2025 |
A. Lopez-Fernandez and F. Divina and F. A. Gomez-Vela and M. Garcia-Torres Data mining for enhancing learning and assessment to a microcompetence-based methodology in higher education Journal Article In: IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 2025. @article{lopez2025data,This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education. url = https://ieeexplore.ieee.org/abstract/document/10849581 |
2024 |
F. Morales-Mareco and M. García-Torres and F. Divina and D. H Stalder and C. Sauer Machine learning for electric energy consumption forecasting: Application to the Paraguayan system Journal Article In: Logic Journal of the IGPL, pp. jzae035, 2024. @article{morales2024machine,In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions. |
F. Divina and M. García-Torres and F. Gómez-Vela and D. S. Rodriguez-Baena A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach Journal Article In: AIMS Mathematics, vol. 9, no. 5, pp. 13358–13384, 2024. @article{divina2024stacking,Automatic determination of abnormal animal activities can be helpful for the timely detection of signs of health and welfare problems. Usually, this problem is addressed as a classification problem, which typically requires manual annotation of behaviors. This manual annotation can introduce noise into the data and may not always be possible. This motivated us to address the problem as a time-series forecasting problem in which the activity of an animal can be predicted. In this work, different machine learning techniques were tested to obtain activity patterns for Iberian pigs. In particular, we propose a novel stacking ensemble learning approach that combines base learners with meta-learners to obtain the final predictive model. Results confirm the superior performance of the proposed method relative to the other tested strategies. We also explored the possibility of using predictive models trained on an animal to predict the activity of different animals on the same farm. As expected, the predictive performance degrades in this case, but it remains acceptable. The proposed method could be integrated into a monitoring system that may have the potential to transform the way farm animals are monitored, improving their health and welfare conditions, for example, by allowing the early detection of a possible health problem. |
2023 |
A. Vellinger and J. F. Torres and F. Divina and W. Vanhoof Neuroevolutionary Transfer Learning for Time Series Forecasting Conference SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, vol. 749, Lecture Notes in Networks and Systems 2023. @conference{VELLINGER23, |
O. Cardozo and V. Ojeda and R. Parra and J. C. Mello-Román and J. L. Noguera Vázquez and M. García-Torres and F. Divina and S. Grillo and C. Villalba and J. Facon Dataset of fundus images for the diagnosis of ocular toxoplasmosis Journal Article In: Data in Brief, pp. 109056, 2023. @article{cardozo2023dataset,Toxoplasmosis chorioretinitis is commonly diagnosed by an ophthalmologist through the evaluation of the fundus images of a patient. Early detection of these lesions may help to prevent blindness. In this article we present a data set of fundus images labeled into three categories: healthy eye, inactive and active chorioretinitis. The dataset was developed by three ophthalmologists with expertise in toxoplasmosis detection using fundus images. The dataset will be of great use to researchers working on ophthalmic image analysis using artificial intelligence techniques for the automatic detection of toxoplasmosis chorioretinitis. |
M. García-Torres and R. Ruiz and F. Divina Evolutionary feature selection on high dimensional data using a search space reduction approach Journal Article In: Engineering Applications of Artificial Intelligence, vol. 117, pp. 105556, 2023. @article{garcia2023evolutionary,Feature selection is becoming more and more a challenging task due to the increase of the dimensionality of the data. The complexity of the interactions among features and the size of the search space make it unfeasible to find the optimal subset of features. In order to reduce the search space, feature grouping has arisen as an approach that allows to cluster feature according to the shared information about the class. On the other hand, metaheuristic algorithms have proven to achieve sub-optimal solutions within a reasonable time. In this work we propose a Scatter Search (SS) strategy that uses feature grouping to generate an initial population comprised of diverse and high quality solutions. Solutions are then evolved by applying random mechanisms in combination with the feature group structure, with the objective of maintaining during the search a population of good and, at the same time, as diverse as possible solutions. Not only does the proposed strategy provide the best subset of features found but it also reduces the redundancy structure of the data. We test the strategy on high dimensional data from biomedical and text-mining domains. The results are compared with those obtained by other adaptations of SS and other popular strategies. Results show that the proposed strategy can find, on average, the smallest subsets of features without degrading the performance of the classifier. |
2022 |
G. Velázquez and F. Morales and M. García-Torres and F. Gómez-Vela and F. Divina and J.L. Vázquez Noguera and F. Daumas-Ladouce and C. Ayala and D. Pinto-Roaand P. Gardel-Sotomayor Distribution level Electric current consumption and meteorological data set of the East region of Paraguay Journal Article In: Data in Brief, vol. 40, pp. 107699, 2022. @article{velazquez2022distribution,This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.445 records of temperature, relative humidity, wind speed and atmospheric pressure. On the other hand, the electrical energy consumption data set contains a total of 1.848.947 records, all of them coming from the one hundred and fifteen feeders located throughout the Alto Paraná region of Paraguay. Electrical energy consumption data was provided by Administración Nacional de Electricidad (ANDE). The analysis of this data can yield insights regarding the energy consumption in the area. |
F. Delgado-Chaves and P. M. Martínez-García and A. Herrero-Ruiz and F. Gómez-Vela and F. Divina and S. Jimeno-González and F. Cortés-Ledesma Data of transcriptional effects of the merbarone-mediated inhibition of TOP2 Journal Article In: Data in Brief, vol. 44, pp. 108499, 2022. @article{delgado2022data,Type II DNA topoisomerases relax topological stress by transiently gating DNA passage in a controlled cut-and-reseal mechanism that affects both DNA strands. Therefore, they are essential to overcome topological problems associated with DNA metabolism. Their aberrant activity results in the generation of DNA double-strand breaks, which can seriously compromise cell survival and genome integrity. Here, we profile the transcriptome of human-telomerase-immortalized retinal pigment epithelial 1 (RPE-1) cells when treated with merbarone, a drug that catalytically inhibits type II DNA topoisomerases. We performed RNA-Seq after 4 and 8 h of merbarone treatment and compared transcriptional profiles versus untreated samples. We report raw sequencing data together with lists of gene counts and differentially expressed genes. |
F. Morales and M. García-Torres and G. Velázquez and F. Daumas-Ladouce and P. Gardel-Sotomayor and F. Gómez-Vela and F. Divina and J. L. Vázquez Noguera and C. Sauer Ayala and D. Pinto-Roa Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study Journal Article In: Electronics, vol. 11, no. 2, pp. 267, 2022. @article{morales2022analysisb,Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters. |
J. A. Gallardo-Gómez and F. Divina and A. Troncoso and F. Martínez-Álvarez Explainable Artificial Intelligence for the Electric Vehicle Load Demand Forecasting Problem Conference SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2022. @conference{gallardo2022explainable, |
2021 |
M. García-Torres and F. Gómez-Vela and F. Divina and D.P. Pinto-Roa and J.L. Vázquez Noguera and J.C. Román Scatter search for high-dimensional feature selection using feature grouping Conference GECCO Genetic and Evolutionary Computation Conference, 2021. @conference{garcia2021scatter, |
R. Parra and V. Ojeda and J.L. Vázquez Noguera and M. García-Torres and J.C. Mello-Román and C. Villalba and J. Facon and F. Divina and O. Cardozo and V. Castillo A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images Journal Article In: Diagnostics, vol. 11, no. 11, pp. 1951, 2021. @article{parra2021trust, |
P.M. Martínez-García and M. García-Torres and F. Divina and J. Terrón-Bautista and I. Delgado-Sainz and F. Gómez-Vela and F. Cortés-Ledesma Genome-wide prediction of topoisomerase II $beta$ binding by architectural factors and chromatin accessibility Journal Article In: PLoS computational biology, vol. 17, no. 1, pp. e1007814, 2021. @article{martinez2021genome, |
S.A. Grillo and J.C. Román and J.D. Mello-Román and J.L. Vázquez Noguera and M. García-Torres and F. Divina and P.E. Sotomayor Adjacent Inputs With Different Labels and Hardness in Supervised Learning Journal Article In: IEEE Access, pp. 162487–162498, 2021. @article{grillo2021adjacent, |
J. Ayala and M. García-Torres and J.L. Vázquez Noguera and F. Gómez-Vela and F. Divina Technical analysis strategy optimization using a machine learning approach in stock market indices Journal Article In: Knowledge-Based Systems, pp. 107119, 2021. @article{ayala2021technical, |