@article{morales2024machine,

title = {Machine learning for electric energy consumption forecasting: Application to the Paraguayan system},

author = { F. Morales-Mareco and M. García-Torres and F. Divina and D. H Stalder and C. Sauer},

url = {https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzae035/7639120},

doi = {10.1093/jigpal/jzae035},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Logic Journal of the IGPL},

pages = {jzae035},

publisher = {Oxford University Press},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{divina2024stacking,

title = {A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach},

author = { F. Divina and M. García-Torres and F. Gómez-Vela and D. S. Rodriguez-Baena},

url = {https://www.aimspress.com/article/doi/10.3934/math.2024652},

doi = {10.3934/math.2024652},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {AIMS Mathematics},

volume = {9},

number = {5},

pages = {13358–13384},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cardozo2023dataset,

title = {Dataset of fundus images for the diagnosis of ocular toxoplasmosis},

author = {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},

url = {https://www.sciencedirect.com/science/article/pii/S2352340923001749},

doi = {10.1016/j.dib.2023.109056},

year  = {2023},

date = {2023-01-01},

journal = {Data in Brief},

pages = {109056},

publisher = {Elsevier},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{garcia2023evolutionary,

title = {Evolutionary feature selection on high dimensional data using a search space reduction approach},

author = {M. García-Torres and R. Ruiz and F. Divina},

url = {https://www.sciencedirect.com/science/article/pii/S0952197622005462},

doi = {10.1016/j.engappai.2022.105556},

year  = {2023},

date = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {117},

pages = {105556},

publisher = {Elsevier},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{velazquez2022distribution,

title = {Distribution level Electric current consumption and meteorological data set of the East region of Paraguay},

author = {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},

url = {https://www.sciencedirect.com/science/article/pii/S2352340921009744},

doi = {10.1016/j.dib.2021.107699},

year  = {2022},

date = {2022-01-01},

journal = {Data in Brief},

volume = {40},

pages = {107699},

publisher = {Elsevier pubstate = published},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{delgado2022data,

title = {Data of transcriptional effects of the merbarone-mediated inhibition of TOP2},

author = {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},

url = {https://www.sciencedirect.com/science/article/pii/S235234092200693X},

doi = {10.1016/j.dib.2022.108499},

year  = {2022},

date = {2022-01-01},

journal = {Data in Brief},

volume = {44},

pages = {108499},

publisher = {Elsevier},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{morales2022analysisb,

title = {Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study},

author = {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},

url = {https://www.mdpi.com/2079-9292/11/2/267},

doi = {10.3390/electronics11020267},

year  = {2022},

date = {2022-01-01},

journal = {Electronics},

volume = {11},

number = {2},

pages = {267},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{parra2021trust,

title = {A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images},

author = {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},

doi = {10.3390/diagnostics11111951},

year  = {2021},

date = {2021-01-01},

journal = {Diagnostics},

volume = {11},

number = {11},

pages = {1951},

publisher = {Multidisciplinary Digital Publishing Institute pubstate = published},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{martinez2021genome,

title = {Genome-wide prediction of topoisomerase II $beta$ binding by architectural factors and chromatin accessibility},

author = {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},

doi = {10.1371/journal.pcbi.1007814},

year  = {2021},

date = {2021-01-01},

journal = {PLoS computational biology},

volume = {17},

number = {1},

pages = {e1007814},

publisher = {Public Library of Science San Francisco, CA USA pubstate = published},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{grillo2021adjacent,

title = {Adjacent Inputs With Different Labels and Hardness in Supervised Learning},

author = {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},

doi = {10.1109/ACCESS.2021.3131150 volume=9},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Access},

pages = {162487--162498},

publisher = {IEEE pubstate = published},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ayala2021technical,

title = {Technical analysis strategy optimization using a machine learning approach in stock market indices},

author = {J. Ayala and M. García-Torres and J.L. Vázquez Noguera and F. Gómez-Vela and F. Divina},

doi = {10.1016/j.knosys.2021.107119 volume=225},

year  = {2021},

date = {2021-01-01},

journal = {Knowledge-Based Systems},

pages = {107119},

publisher = {Elsevier pubstate = published},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{divina2021advanced,

title = {Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast},

author = {F. Divina and F. Gómez-Vela and M. García-Torres},

url = {https://www.mdpi.com/2076-3417/11/3/1261/htm},

doi = {10.3390/app11031261},

year  = {2021},

date = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

number = {3},

pages = {1261},

publisher = {Multidisciplinary Digital Publishing Institute},

keywords = {},

pubstate = {published},

tppubtype = {article}

}