Gualberto Asencio Cortés

Citations and socials

Prof. Gualberto Asencio Cortés, Ph.D. is a Computer Science Engineer (University of Seville, 2008), Master in Software Engineering and Technology (University of Seville, 2010), Ph.D. (University of Pablo de Olavide, 2013) and he has an Executive Master in Innovation (EOI, Spain, 2016). He is Associate Professor of Computer Science (Profesor Titular de Universidad), in the area of Languages and Information Systems at the University of Pablo de Olavide. He is the author of more than 28 publications in impact journals according to JCR (20 of them between Q1 and Q2) and author of more than 30 articles in international and national conferences, most of them published in LNCS and LNBI. He has participated in three projects of the National Plan and three more of the Andalusian Research Plan. He is an editor of PLOS ONE (IF: 2.806, Q1), a regular reviewer of journals indexed in JCR (PLOS ONE, Bioinformatics, Neurocomputing, Computer and Geosciences, etc.) and member of the program committee in numerous international conferences. He has participated in more than 12 technology transfer contracts between the university and the company, including ISOTROL, Red Eléctrica Española and DETEA. He has 5 months of international research stays and 3 national months.

The research lines of Prof. Gualberto Asencio Cortés, Ph.D. are focused on data mining, machine learning, prediction of time series and bioinformatics, with different fields of application: prediction of natural series (seismic, air quality, meteorological, agronomic, …), prediction of electricity consumption and market prices, prediction of urban traffic, as well as bioinformatics in prediction of biological structures. He has also been data scientist and member of the steering committee responsible for artificial intelligence and data science technologies at the private company easytosee AgTech SL for more than 2 years (2015-2017).

Publications

57 entries « ‹ 1 of 4 › »

2023

M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés

PHILNet: A Novel Efficient Approach for Time Series Forecasting using Deep Learning Journal Article

Information Sciences, in press , 2023.

Abstract | BibTeX

M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoco and G. Asencio-Cortés

From Simple to Complex: A Sequential Method for Enhancing Time Series Forecasting with Deep Learning Journal Article

Logic Journal of the IGPL, in press , 2023.

Abstract | BibTeX

M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Sousa Brito and F. Martínez-Álvarez and G. Asencio-Cortés

A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal Conference

8th ACM/SIGAPP Symposium on Applied Computing (SAC ’23), ACM 2023.

BibTeX

M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés

Embedded temporal feature selection for time series forecasting using deep learning Conference

2023.

BibTeX

2022

M. Á. Molina and M. J. Jiménez-Navarro and R. Arjona and F. Mártinez-Álvarez and G. Asencio-Cortés

DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting Journal Article

Knowledge-Based Systems, 254 , pp. 109644, 2022.

Abstract | Links | BibTeX

@article{MOLINA22,
title = {DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting},
author = {M. Á. Molina and M. J. Jiménez-Navarro and R. Arjona and F. Mártinez-Álvarez and G. Asencio-Cortés},
url = {https://www.sciencedirect.com/science/article/pii/S0950705122008322},
doi = {https://doi.org/10.1016/j.knosys.2022.109644},
year = {2022},
date = {2022-10-22},
journal = {Knowledge-Based Systems},
volume = {254},
pages = {109644},
abstract = {The agricultural sector has been, and still is, the most important economic sector in many countries. Due to advances in technology, the amount and variety of available data have been increasing over the years. However, compared to other economic sectors, there is not always enough quality data for one particular domain (crops, plantations, plots) to obtain acceptable forecasting results with machine learning algorithms. In this context, transfer learning can help extract knowledge from different but related domains with enough data to transfer it to a target domain with scarce data. This process can overcome forecasting accuracy compared to training models uniquely with data from the target domain. In this work, a novel instance weighting-based transfer learning algorithm is proposed and applied to the phenology forecasting problem. A new metric named DIAFAN is proposed to weight samples from different source domains according to their relationship with the target domain, promoting the diversity of the information and avoiding inconsistent samples. Additionally, a set of validation schemes is specifically designed to ensure fair comparisons in terms of data volume with other benchmark transfer learning algorithms. The proposed algorithm, DIAFAN-TL, is tested with a proposed dataset of 16 plots of olive groves from different places, including information fusion from satellite images, meteorological stations and human field sampling of crop phenology. DIAFAN-TL achieves a remarkable improvement with respect to 15 other well-known transfer learning algorithms and three nontransfer learning scenarios. Finally, several performance analyses according to the different phenological states, prediction horizons and source domains are also performed.},
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A. M. Chacón-Maldonado and M. A. Molina and A. Troncoso and F. Martínez-Álvarez and G. Asencio-Cortés

Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning Conference

13469 , Lecture Notes in Computer Science 2022.

Links | BibTeX

A. Gómez-Losada and G. Asencio-Cortés and N. Duch-Brown

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm Journal Article

Information, 13 (44), pp. 1–16, 2022.

Abstract | Links | BibTeX

J. Roiz-Pagador and A. Chacon-Maldonado and R. Ruiz and G. Asencio-Cortes

Earthquake Prediction in California using Feature Selection techniques Conference

16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021), Advances in Intelligent Systems and Computing 2022.

Links | BibTeX

M.A. Castán-Lascorz and P. Jiménez-Herrera and A. Troncoso and G. Asencio-Cortés

A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting Journal Article

Information Sciences, 586 , pp. 611–627, 2022.

Abstract | Links | BibTeX

@article{castan2022,
title = {A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting},
author = {M.A. Castán-Lascorz and P. Jiménez-Herrera and A. Troncoso and G. Asencio-Cortés},
url = {https://www.sciencedirect.com/science/article/pii/S0020025521012226?via%3Dihub},
doi = {10.1016/j.ins.2021.12.001},
year = {2022},
date = {2022-01-01},
journal = {Information Sciences},
volume = {586},
pages = {611--627},
abstract = {Time series forecasting has become indispensable for multiple applications and industrial processes. Currently, a large number of algorithms have been developed to forecast time series, all of which are suitable depending on the characteristics and patterns to be inferred in each case. In this work, a new algorithm is proposed to predict both univariate and multivariate time series based on a combination of clustering, classification and forecasting techniques. The main goal of the proposed algorithm is first to group windows of time series values with similar patterns by applying a clustering process. Then, a specific forecasting model for each pattern is built and training is only conducted with the time windows corresponding to that pattern. The new algorithm has been designed using a flexible framework that allows the model to be generated using any combination of approaches within multiple machine learning techniques. To evaluate the model, several experiments are carried out using different configurations of the clustering, classification and forecasting methods that the model consists of. The results are analyzed and compared to classical prediction models, such as autoregressive, integrated, moving average and Holt-Winters models, to very recent forecasting methods, including deep, long short-term memory neural networks, and to well-known methods in the literature, such as k nearest neighbors, classification and regression trees, as well as random forest.},
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P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso

Streaming big time series forecasting based on nearest similar patterns with application to energy consumption Journal Article

Logic Journal of the IGPL, 31 (2), pp. 255-270, 2022.

Abstract | Links | BibTeX

M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Sousa Brito and F. Martínez-Álvarez and G. Asencio-Cortés

Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection Conference

SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, 531 , Lecture Notes in Networks Systems 2022.

Links | BibTeX

2021

M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés

HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting Conference

SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021.

Links | BibTeX

M. A. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés

A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery Conference

HAIS, 12886 , Lecture Notes in Computer Science 2021.

Links | BibTeX

A. J. Pérez-Pulido and G. Asencio-Cortés and A. M. Brokate-Llanos and G. Brea-Calvo and M. R. Rodríguez-Griñolo and A. Garzón and M. J. Muñoz

Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators Journal Article

Briefings in Bioinformatics, 22 (2), pp. 1038–1052, 2021.

Abstract | Links | BibTeX

@article{pulido2021,
title = {Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators},
author = {A. J. Pérez-Pulido and G. Asencio-Cortés and A. M. Brokate-Llanos and G. Brea-Calvo and M. R. Rodríguez-Griñolo and A. Garzón and M. J. Muñoz},
url = {https://academic.oup.com/bib/article/22/2/1038/6103172},
doi = {10.1093/bib/bbaa419},
year = {2021},
date = {2021-01-01},
journal = {Briefings in Bioinformatics},
volume = {22},
number = {2},
pages = {1038--1052},
abstract = {The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data allow the design of secondary analyses that take advantage of this information to create new knowledge. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types. Based on this idea, we have developed Automatic and Serial Analysis of CO-expression, which performs expression profiles for a given gene along hundreds of heterogeneous and normalized transcriptomics experiments and discover other genes that show either a similar or an inverse behavior. It might help to discover co-regulated genes, and common transcriptional regulators in any biological model. The present severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is an opportunity to test this novel approach due to the wealth of data that are being generated, which could be used for validating results. Thus, we have identified 35 host factors in the literature putatively involved in the infectious cycle of SARS-CoV viruses and searched for genes tightly co-expressed with them. We have found 1899 co-expressed genes whose assigned functions are strongly related to viral cycles. Moreover, this set of genes heavily overlaps with those identified by former laboratory.},
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2020

P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso

A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series Conference

HAIS 15th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2020.

Links | BibTeX

57 entries « ‹ 1 of 4 › »

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2023

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2020