UNIVERSIDAD PABLO DE OLAVIDE
Publications
agricultural association rules astrostatistics big data bioinformatics deep learning education energy feature selection IoT natural disasters pattern recognition time series transfer learning XAI
2023 |
O. S. Mazari and A. Sebaa and J. L. Amaro-Mellado and F. Martínez-Álvarez Creating a homogenized earthquake catalog for Algeria and mapping the main seismic parameters using a geographic information system (Journal Article) Journal of African Earth Sciences, 201 , pp. 104859, 2023. (Abstract | Links | BibTeX | Tags: natural disasters) @article{MAZARI23, title = {Creating a homogenized earthquake catalog for Algeria and mapping the main seismic parameters using a geographic information system}, author = {O. S. Mazari and A. Sebaa and J. L. Amaro-Mellado and F. Martínez-Álvarez}, url = {https://www.sciencedirect.com/science/article/pii/S1464343X23000687}, doi = {https://doi.org/10.1016/j.jafrearsci.2023.104895}, year = {2023}, date = {2023-03-03}, journal = {Journal of African Earth Sciences}, volume = {201}, pages = {104859}, abstract = {A homogeneous earthquake catalog is an essential instrument to study earthquake occurrence patterns, employing diverse engineering applications. In this paper, we describe a series of compilation and processing steps to compile an updated earthquake catalog for Algeria, a North African country with relatively high seismic activity. The procedure consisted of several steps. First, a range of reliable catalogs were considered; second, the data was integrated and refined; third, magnitudes are homogenized from different kinds of magnitudes into moment magnitude (M_w); declustering is then performed; and, finally, the magnitude-year completeness was estimated. The resulting Algeria catalog is bounded by the geographical limits (19° - 38.5° N and 9.5° W - 12.5° E), and covers the 1960-2020 period. It includes 4021 seismic events, reported up to M_w 7.1. We also calculate a set of seismic parameters, namely M_max and b-value, and mapped them using a geographic information system. Thus, the territory is divided into cells based on different grids to conduct the analysis. The results of the seismic parameters mapping are discussed, highlighting significant details. Several cells presented a M_max between 6.0 and 7.1. Regarding the b-value, two regions (Oran and Constantine) presented a high b-value, implying low-stress areas, and three regions (Algiers, Batna, and Chlef) a low b-value (0.65- 0.85), suggesting high-stress areas. Finally, we suggest some recommendations for future seismic hazard assessment studies.}, keywords = {natural disasters}, pubstate = {published}, tppubtype = {article} } A homogeneous earthquake catalog is an essential instrument to study earthquake occurrence patterns, employing diverse engineering applications. In this paper, we describe a series of compilation and processing steps to compile an updated earthquake catalog for Algeria, a North African country with relatively high seismic activity. The procedure consisted of several steps. First, a range of reliable catalogs were considered; second, the data was integrated and refined; third, magnitudes are homogenized from different kinds of magnitudes into moment magnitude (M_w); declustering is then performed; and, finally, the magnitude-year completeness was estimated. The resulting Algeria catalog is bounded by the geographical limits (19° - 38.5° N and 9.5° W - 12.5° E), and covers the 1960-2020 period. It includes 4021 seismic events, reported up to M_w 7.1. We also calculate a set of seismic parameters, namely M_max and b-value, and mapped them using a geographic information system. Thus, the territory is divided into cells based on different grids to conduct the analysis. The results of the seismic parameters mapping are discussed, highlighting significant details. Several cells presented a M_max between 6.0 and 7.1. Regarding the b-value, two regions (Oran and Constantine) presented a high b-value, implying low-stress areas, and three regions (Algiers, Batna, and Chlef) a low b-value (0.65- 0.85), suggesting high-stress areas. Finally, we suggest some recommendations for future seismic hazard assessment studies. |
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 | Tags: deep learning, time series) @article{JIMENEZ-NAVARRO23b, title = {PHILNet: A Novel Efficient Approach for Time Series Forecasting using Deep Learning}, author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés}, year = {2023}, date = {2023-03-03}, journal = {Information Sciences}, volume = {in press}, abstract = {Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series.}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {article} } Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series. |
D. Azzouguer and A. Sebaa and D. Hadjout and F. Martínez-Álvarez Fraud Detection of Electricity Consumption using Robust Exponential and Holt-Winters Smoothing method (Conference) IEEE International Conference on Advanced Systems and Emergent Technologies, 2023. (Abstract | BibTeX | Tags: energy, time series) @conference{AZZOUGUER23, title = {Fraud Detection of Electricity Consumption using Robust Exponential and Holt-Winters Smoothing method}, author = {D. Azzouguer and A. Sebaa and D. Hadjout and F. Martínez-Álvarez}, year = {2023}, date = {2023-02-20}, booktitle = {IEEE International Conference on Advanced Systems and Emergent Technologies}, abstract = {Non-technical losses (NTL), especially fraud detection, is very important for electricity distribution enterprises. Fraud detection allows for maximizing the effective economic return for such enterprises. In this paper, we provide an electricity consumption fraud detection approach based on robust exponential and Holt-Winters Smoothing methods. The proposed approach aims to effectively discover the fraudulent behaviors of electricity consumers. To validate the proposed method, we compared our results to two enterprise classifications: Activity Sector and Maximum Power Demand. Experimental validation is presented using a large dataset of real users from the Algerian economic sector with almost 2000 clients and 14 years of monthly electricity consumption. The results of the proposed method show highly efficient and realistic countermeasures to fraud detection and can enhance company profit.}, keywords = {energy, time series}, pubstate = {published}, tppubtype = {conference} } Non-technical losses (NTL), especially fraud detection, is very important for electricity distribution enterprises. Fraud detection allows for maximizing the effective economic return for such enterprises. In this paper, we provide an electricity consumption fraud detection approach based on robust exponential and Holt-Winters Smoothing methods. The proposed approach aims to effectively discover the fraudulent behaviors of electricity consumers. To validate the proposed method, we compared our results to two enterprise classifications: Activity Sector and Maximum Power Demand. Experimental validation is presented using a large dataset of real users from the Algerian economic sector with almost 2000 clients and 14 years of monthly electricity consumption. The results of the proposed method show highly efficient and realistic countermeasures to fraud detection and can enhance company profit. |
E. T. Habtemariam and K. Kekeba and M. Martínez-Ballesteros and F. Mártinez-Álvarez A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia (Journal Article) Energies, 16 , pp. 2317, 2023. (Abstract | Links | BibTeX | Tags: deep learning, time series) @article{EJIGU23, title = {A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia}, author = {E. T. Habtemariam and K. Kekeba and M. Martínez-Ballesteros and F. Mártinez-Álvarez}, url = {https://www.mdpi.com/1996-1073/16/5/2317}, doi = {https://doi.org/10.3390/en16052317}, year = {2023}, date = {2023-02-19}, journal = {Energies}, volume = {16}, pages = {2317}, abstract = {Renewable energies such as solar and wind power have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve current energy crises. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized Long-Short Term Memory network for forecasting wind power generation in the day ahead in the context of Ethiopia's renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performance of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics and outperformed both the baseline models and the optimized Gated Recurrent Unit architecture.}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {article} } Renewable energies such as solar and wind power have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve current energy crises. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized Long-Short Term Memory network for forecasting wind power generation in the day ahead in the context of Ethiopia's renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performance of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics and outperformed both the baseline models and the optimized Gated Recurrent Unit architecture. |
A. M.Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Mártinez-Álvarez A new Apache Spark-based framework for big data streaming forecasting in IoT networks (Journal Article) Journal of Supercomputing, pp. 1-23, 2023. (Abstract | Links | BibTeX | Tags: big data, IoT) @article{FERNANDEZ23, title = {A new Apache Spark-based framework for big data streaming forecasting in IoT networks}, author = {A. M.Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Mártinez-Álvarez}, url = {https://link.springer.com/article/10.1007/s11227-023-05100-x}, doi = {https://doi.org/10.1007/s11227-023-05100-x}, year = {2023}, date = {2023-02-02}, journal = {Journal of Supercomputing}, pages = {1-23}, abstract = {Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules.}, keywords = {big data, IoT}, pubstate = {published}, tppubtype = {article} } Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules. |
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso A new approach based on association rules to add explainability to time series forecasting models (Journal Article) Information Fusion, 94 , pp. 169-180, 2023. (Abstract | Links | BibTeX | Tags: association rules, time series, XAI) @article{TRONCOSO-GARCIA23, title = {A new approach based on association rules to add explainability to time series forecasting models}, author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S1566253523000295}, doi = {10.1016/j.inffus.2023.01.021}, year = {2023}, date = {2023-01-22}, journal = {Information Fusion}, volume = {94}, pages = {169-180}, abstract = {Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.}, keywords = {association rules, time series, XAI}, pubstate = {published}, tppubtype = {article} } Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal. |
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 | Tags: deep learning, time series) @article{JIMENEZ-NAVARRO23a, title = {From Simple to Complex: A Sequential Method for Enhancing Time Series Forecasting with Deep Learning}, author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoco and G. Asencio-Cortés}, year = {2023}, date = {2023-01-20}, journal = {Logic Journal of the IGPL}, volume = {in press}, abstract = {Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished.Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer (TFT), NBEATS and Echo State Network (ESN).}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {article} } Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished.Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer (TFT), NBEATS and Echo State Network (ESN). |
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) Data in Brief, pp. 109056, 2023. (Abstract | Links | BibTeX | Tags: bioinformatics) @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 = {bioinformatics}, pubstate = {published}, tppubtype = {article} } 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) Engineering Applications of Artificial Intelligence, 117 , pp. 105556, 2023. (Abstract | Links | BibTeX | Tags: big data, feature selection) @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 = {big data, feature selection}, pubstate = {published}, tppubtype = {article} } 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. |
Gaia Collaboration Gaia Data Release 3: Mapping the asymmetric disc of the Milky Way (Journal Article) Astronomy and Astrophysics, 2023. (Abstract | Links | BibTeX | Tags: astrostatistics) @article{collaboration2022gaia, title = {Gaia Data Release 3: Mapping the asymmetric disc of the Milky Way}, author = {Gaia Collaboration}, url = {https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202243797}, doi = {10.1051/0004-6361/202243797}, year = {2023}, date = {2023-01-01}, journal = {Astronomy and Astrophysics}, abstract = {With the most recent Gaia data release the number of sources with complete 6D phase space information (position and velocity) has increased to well over 33 million stars, while stellar astrophysical parameters are provided for more than 470 million sources, in addition to the identification of over 11 million variable stars. Using the astrophysical parameters and variability classifications provided in Gaia DR3, we select various stellar populations to explore and identify non-axisymmetric features in the disc of the Milky Way in both configuration and velocity space. Using more about 580 thousand sources identified as hot OB stars, together with 988 known open clusters younger than 100 million years, we map the spiral structure associated with star formation 4-5 kpc from the Sun. We select over 2800 Classical Cepheids younger than 200 million years, which show spiral features extending as far as 10 kpc from the Sun in the outer disc. We also identify more than 8.7 million sources on the red giant branch (RGB), of which 5.7 million have line-of-sight velocities, allowing the velocity field of the Milky Way to be mapped as far as 8 kpc from the Sun, including the inner disc. The spiral structure revealed by the young populations is consistent with recent results using Gaia EDR3 astrometry and source lists based on near infrared photometry, showing the Local (Orion) arm to be at least 8 kpc long, and an outer arm consistent with what is seen in HI surveys, which seems to be a continuation of the Perseus arm into the third quadrant. Meanwhile, the subset of RGB stars with velocities clearly reveals the large scale kinematic signature of the bar in the inner disc, as well as evidence of streaming motions in the outer disc that might be associated with spiral arms or bar resonances. (abridged)}, keywords = {astrostatistics}, pubstate = {accepted}, tppubtype = {article} } With the most recent Gaia data release the number of sources with complete 6D phase space information (position and velocity) has increased to well over 33 million stars, while stellar astrophysical parameters are provided for more than 470 million sources, in addition to the identification of over 11 million variable stars. Using the astrophysical parameters and variability classifications provided in Gaia DR3, we select various stellar populations to explore and identify non-axisymmetric features in the disc of the Milky Way in both configuration and velocity space. Using more about 580 thousand sources identified as hot OB stars, together with 988 known open clusters younger than 100 million years, we map the spiral structure associated with star formation 4-5 kpc from the Sun. We select over 2800 Classical Cepheids younger than 200 million years, which show spiral features extending as far as 10 kpc from the Sun in the outer disc. We also identify more than 8.7 million sources on the red giant branch (RGB), of which 5.7 million have line-of-sight velocities, allowing the velocity field of the Milky Way to be mapped as far as 8 kpc from the Sun, including the inner disc. The spiral structure revealed by the young populations is consistent with recent results using Gaia EDR3 astrometry and source lists based on near infrared photometry, showing the Local (Orion) arm to be at least 8 kpc long, and an outer arm consistent with what is seen in HI surveys, which seems to be a continuation of the Perseus arm into the third quadrant. Meanwhile, the subset of RGB stars with velocities clearly reveals the large scale kinematic signature of the bar in the inner disc, as well as evidence of streaming motions in the outer disc that might be associated with spiral arms or bar resonances. (abridged) |
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A.Troncoso Identifying novelties and anomalies for incremental learning in streaming time series forecasting (Journal Article) Engineering Applications of Artificial Intelligence, 123 , pp. 106326, 2023. (Links | BibTeX | Tags: energy, IoT, time series) @article{Melgar2023b, title = {Identifying novelties and anomalies for incremental learning in streaming time series forecasting}, author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A.Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S0952197623005109}, doi = {https://doi.org/10.1016/j.engappai.2023.106326}, year = {2023}, date = {2023-01-01}, journal = {Engineering Applications of Artificial Intelligence}, volume = {123}, pages = {106326}, keywords = {energy, IoT, time series}, pubstate = {published}, tppubtype = {article} } |
L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso A novel distributed forecasting method based on information fusion and incremental learning for streaming time series (Journal Article) Information Fusion, 95 , pp. 163-173, 2023. (Links | BibTeX | Tags: energy, IoT, time series) @article{Melgar2023a, title = {A novel distributed forecasting method based on information fusion and incremental learning for streaming time series}, author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S1566253523000635}, doi = {https://doi.org/10.1016/j.inffus.2023.02.023}, year = {2023}, date = {2023-01-01}, journal = {Information Fusion}, volume = {95}, pages = {163-173}, keywords = {energy, IoT, time series}, pubstate = {published}, tppubtype = {article} } |
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 | Tags: deep learning, feature selection, time series) @conference{TSFADL23, title = {Embedded temporal feature selection for time series forecasting using deep learning}, author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Martínez-Álvarez and G. Asencio-Cortés}, year = {2023}, date = {2023-01-01}, keywords = {deep learning, feature selection, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: agricultural, deep learning, time series) @conference{EVAPOCVOA23, title = {A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal}, author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Sousa Brito and F. Martínez-Álvarez and G. Asencio-Cortés}, year = {2023}, date = {2023-01-01}, booktitle = {8th ACM/SIGAPP Symposium on Applied Computing (SAC ’23)}, series = {ACM}, keywords = {agricultural, deep learning, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: time series, transfer learning) @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.}, keywords = {time series, transfer learning}, pubstate = {published}, tppubtype = {article} } 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. |
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 | Tags: deep learning, pattern recognition, time series) @conference{HAIS22_Andres, title = {Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning}, author = {A. M. Chacón-Maldonado and M. A. Molina and A. Troncoso and F. Martínez-Álvarez and G. Asencio-Cortés}, url = {https://link.springer.com/chapter/10.1007/978-3-031-15471-3_24}, year = {2022}, date = {2022-09-12}, journal = {HAIS 17th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2022}, volume = {13469}, pages = {274-285}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, pattern recognition, time series}, pubstate = {published}, tppubtype = {conference} } |
A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso Explainable machine learning for sleep apnea prediction (Conference) KES International Conference on Knowledge Based and Intelligent information and Engineering Systems, 2022. (Abstract | Links | BibTeX | Tags: association rules, deep learning, time series, XAI) @conference{TRONCOSO-GARCIA22, title = {Explainable machine learning for sleep apnea prediction}, author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso}, url = {https://www.sciencedirect.com/science/article/pii/S1877050922012406}, doi = {https://doi.org/10.1016/j.procs.2022.09.351}, year = {2022}, date = {2022-09-10}, booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems}, pages = {2930-2939}, abstract = {Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep.}, keywords = {association rules, deep learning, time series, XAI}, pubstate = {published}, tppubtype = {conference} } Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep. |
D. Hadjout and J. F. Torres and A. Troncoso and A. Sebaa and F. Martínez-Álvarez Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market (Journal Article) Energy, 243 , pp. 123060, 2022. (Abstract | Links | BibTeX | Tags: deep learning, energy, time series) @article{HADJOUT22, title = {Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market}, author = {D. Hadjout and J. F. Torres and A. Troncoso and A. Sebaa and F. Martínez-Álvarez}, url = {https://www.sciencedirect.com/science/article/pii/S0360544221033090}, doi = {https://doi.org/10.1016/j.energy.2021.123060}, year = {2022}, date = {2022-03-15}, journal = {Energy}, volume = {243}, pages = {123060}, abstract = {The economic sector is one of the most important pillars of countries. Economic activities of industry are intimately linked with the ability to meet their needs for electricity. Therefore, electricity forecasting is a very important task. It allows for better planning and management of energy resources. Several methods have been proposed to forecast energy consumption. In this work, to predict monthly electricity consumption for the economic sector, we develop a novel approach based on ensemble learning. Our approach combines three models that proved successful in the field, namely: Long Short Term Memory and Gated Recurrent Unit neural networks, and Temporal Convolutional Networks. The experiments have been conducted with almost 2000 clients and 14 years of monthly electricity consumption from Bejaia, Algeria. The results show that the proposed ensemble models achieve better performance than both the company's requirements and the prediction of the traditional individual models. Finally, statistical tests have been carried out to prove that significance of the ensemble models developed.}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {article} } The economic sector is one of the most important pillars of countries. Economic activities of industry are intimately linked with the ability to meet their needs for electricity. Therefore, electricity forecasting is a very important task. It allows for better planning and management of energy resources. Several methods have been proposed to forecast energy consumption. In this work, to predict monthly electricity consumption for the economic sector, we develop a novel approach based on ensemble learning. Our approach combines three models that proved successful in the field, namely: Long Short Term Memory and Gated Recurrent Unit neural networks, and Temporal Convolutional Networks. The experiments have been conducted with almost 2000 clients and 14 years of monthly electricity consumption from Bejaia, Algeria. The results show that the proposed ensemble models achieve better performance than both the company's requirements and the prediction of the traditional individual models. Finally, statistical tests have been carried out to prove that significance of the ensemble models developed. |
F. Martínez-Álvarez and A. Troncoso and H. Quintián and E. Corchado Special issue SOCO-CISIS 2019-IGPL (Journal Article) Logig Journal of the IGPL, 30 (2), pp. 211-213, 2022. @article{MARTINEZ22b, title = {Special issue SOCO-CISIS 2019-IGPL}, author = {F. Martínez-Álvarez and A. Troncoso and H. Quintián and E. Corchado}, url = {https://doi.org/10.1093/jigpal/jzaa066}, doi = {https://doi.org/10.1093/jigpal/jzaa066}, year = {2022}, date = {2022-03-10}, journal = {Logig Journal of the IGPL}, volume = {30}, number = {2}, pages = {211-213}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
J. F. Torres and F. Martínez-Álvarez and A. Troncoso A deep LSTM network for the Spanish electricity consumption forecasting (Journal Article) Neural Computing and Applications, 34 , pp. 10533-10545, 2022. (Abstract | Links | BibTeX | Tags: deep learning, energy) @article{TORRES22b, title = {A deep LSTM network for the Spanish electricity consumption forecasting}, author = {J. F. Torres and F. Martínez-Álvarez and A. Troncoso}, url = {https://link.springer.com/article/10.1007/s00521-021-06773-2}, doi = {https://doi.org/10.1007/s00521-021-06773-2}, year = {2022}, date = {2022-02-05}, journal = {Neural Computing and Applications}, volume = {34}, pages = {10533-10545}, abstract = {Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.}, keywords = {deep learning, energy}, pubstate = {published}, tppubtype = {article} } Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%. |
F. Martínez-Álvarez and A. Troncoso and H. Quintián and E. Corchado Special Issue SOCO 2019: New trends in soft computing and its application in industrial and environmental problems (Journal Article) Neurocomputing, 470 , pp. 278-279, 2022. @article{MARTINEZ22, title = {Special Issue SOCO 2019: New trends in soft computing and its application in industrial and environmental problems}, author = {F. Martínez-Álvarez and A. Troncoso and H. Quintián and E. Corchado}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0925231221001399}, doi = {https://doi.org/10.1016/j.neucom.2021.01.071}, year = {2022}, date = {2022-01-22}, journal = {Neurocomputing}, volume = {470}, pages = {278-279}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
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 | Tags: feature selection, time series) @article{losada2022, title = {Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm}, author = {A. Gómez-Losada and G. Asencio-Cortés and N. Duch-Brown}, url = {https://www.mdpi.com/2078-2489/13/2/44}, doi = {10.3390/info13020044}, year = {2022}, date = {2022-01-01}, journal = {Information}, volume = {13}, number = {44}, pages = {1--16}, abstract = {Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.}, keywords = {feature selection, time series}, pubstate = {published}, tppubtype = {article} } Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box. |
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 | Tags: energy, pattern recognition, time series) @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.}, keywords = {energy, pattern recognition, time series}, pubstate = {published}, tppubtype = {article} } 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. |
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 | Tags: energy, IoT, time series) @article{jimenez2022, title = {Streaming big time series forecasting based on nearest similar patterns with application to energy consumption}, author = {P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso}, url = {https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzac017/6534493?redirectedFrom=fulltext}, doi = {https://doi.org/10.1093/jigpal/jzac017}, year = {2022}, date = {2022-01-01}, journal = {Logic Journal of the IGPL}, volume = {31}, number = {2}, pages = {255-270}, abstract = {This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbors algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbors algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and a forecasting model based on the nearest neighbors is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbors model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of 10 minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy.}, keywords = {energy, IoT, time series}, pubstate = {published}, tppubtype = {article} } This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbors algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbors algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and a forecasting model based on the nearest neighbors is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbors model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of 10 minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy. |
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 | Tags: feature selection, natural disasters, time series) @conference{roiz2022, title = {Earthquake Prediction in California using Feature Selection techniques}, author = {J. Roiz-Pagador and A. Chacon-Maldonado and R. Ruiz and G. Asencio-Cortes}, url = {https://link.springer.com/chapter/10.1007/978-3-030-87869-6_69}, year = {2022}, date = {2022-01-01}, booktitle = {16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021)}, series = {Advances in Intelligent Systems and Computing}, keywords = {feature selection, natural disasters, time series}, pubstate = {published}, tppubtype = {conference} } |
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) Data in Brief, 40 , pp. 107699, 2022. (Abstract | Links | BibTeX | Tags: energy, time series) @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 = {energy, time series}, pubstate = {published}, tppubtype = {article} } 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. |
S. Gómez-Guerrero and I. Ortiz and G. and Sosa-Cabrera and M. García-Torres and C.E. Schaerer Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty (Journal Article) Entropy, 24 (1), pp. 64, 2022. (Abstract | Links | BibTeX | Tags: feature selection) @article{gomez2022measuring, title = {Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty}, author = {S. Gómez-Guerrero and I. Ortiz and G. and Sosa-Cabrera and M. García-Torres and C.E. Schaerer}, url = {https://www.mdpi.com/1099-4300/24/1/64}, doi = {10.3390/e24010064}, year = {2022}, date = {2022-01-01}, journal = {Entropy}, volume = {24}, number = {1}, pages = {64}, publisher = {Multidisciplinary Digital Publishing Institute}, abstract = {Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for n nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables. Two series of experiments are presented. In the first series, we observe that datasets where some record types or combinations of categories are absent, forming patterns of records, which often display interactions among their attributes. In the second series, the interaction/non-interaction behavior of a regression model (entirely built on continuous variables) gets successfully replicated under a discretized version of the dataset. It is shown that there is an interaction-wise correspondence between the continuous and the discretized versions of the dataset. Hence, we demonstrate that the proposed definition of interaction enabled by the MSU is a valuable tool for detecting and measuring interactions within linear and non-linear models.}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for n nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables. Two series of experiments are presented. In the first series, we observe that datasets where some record types or combinations of categories are absent, forming patterns of records, which often display interactions among their attributes. In the second series, the interaction/non-interaction behavior of a regression model (entirely built on continuous variables) gets successfully replicated under a discretized version of the dataset. It is shown that there is an interaction-wise correspondence between the continuous and the discretized versions of the dataset. Hence, we demonstrate that the proposed definition of interaction enabled by the MSU is a valuable tool for detecting and measuring interactions within linear and non-linear models. |
C. Segarra-Martín and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm (Conference) SAC 37th Symposium On Applied Computing, 2022. (Abstract | BibTeX | Tags: association rules) @conference{SAC2022, title = {A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm }, author = {C. Segarra-Martín and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez}, year = {2022}, date = {2022-01-01}, booktitle = {SAC 37th Symposium On Applied Computing}, abstract = {The disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in 2019. This pandemic inspired the development of the Coronavirus Optimization Algorithm (CVOA), a bio-inspired metaheuristic that was originally used to adjust deep learning models for time series forecasting, by means of a binary codification. In this paper, a integer codification for the CVOA individual is introduced and used for optimizing a novel approach for numerical association rules mining. In addition, the CVOA setting parameters have been updated and a vaccination rate based on real data has been incorporated, to make it more efficient. As an application case, the prediction of earthquakes of large magnitude has been addressed. This kind of events are rare and, therefore, they can be characterized by rules with very high interest or lift and low support. Thus, the algorithm has been applied to the extraction of rules meeting specific criteria in an earthquake data set, provided by the National Geographic Institute of Spain. The results show CVOA as a promising tool for numerical association rules mining, obtaining rules with useful and meaningful information for predicting the occurrence of large earthquakes.}, keywords = {association rules}, pubstate = {published}, tppubtype = {conference} } The disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in 2019. This pandemic inspired the development of the Coronavirus Optimization Algorithm (CVOA), a bio-inspired metaheuristic that was originally used to adjust deep learning models for time series forecasting, by means of a binary codification. In this paper, a integer codification for the CVOA individual is introduced and used for optimizing a novel approach for numerical association rules mining. In addition, the CVOA setting parameters have been updated and a vaccination rate based on real data has been incorporated, to make it more efficient. As an application case, the prediction of earthquakes of large magnitude has been addressed. This kind of events are rare and, therefore, they can be characterized by rules with very high interest or lift and low support. Thus, the algorithm has been applied to the extraction of rules meeting specific criteria in an earthquake data set, provided by the National Geographic Institute of Spain. The results show CVOA as a promising tool for numerical association rules mining, obtaining rules with useful and meaningful information for predicting the occurrence of large earthquakes. |
L. Melgar-García and D. Gutiérrez-Avilés and M. T. Godinho and R. Espada and I. S. Brito and F. Martínez-Álvarez and A. Troncoso and C. Rubio-Escudero A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture (Journal Article) Neurocomputing, 500 , pp. 268-278, 2022. (Abstract | Links | BibTeX | Tags: big data, pattern recognition) @article{MELGAR21_NEUCOMb, title = {A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture}, author = {L. Melgar-García and D. Gutiérrez-Avilés and M. T. Godinho and R. Espada and I. S. Brito and F. Martínez-Álvarez and A. Troncoso and C. Rubio-Escudero}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0925231222006415}, doi = {https://doi.org/10.1016/j.neucom.2021.06.101}, year = {2022}, date = {2022-01-01}, journal = {Neurocomputing}, volume = {500}, pages = {268-278}, abstract = {Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional patterns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets.}, keywords = {big data, pattern recognition}, pubstate = {published}, tppubtype = {article} } Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional patterns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets. |
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, Lecture Notes in Computer Science 2022. (Links | BibTeX | Tags: big data, energy, time series) @conference{gallardo2022explainable, title = {Explainable Artificial Intelligence for the Electric Vehicle Load Demand Forecasting Problem}, author = {J. A. Gallardo-Gómez and F. Divina and A. Troncoso and F. Martínez-Álvarez }, url = {https://link.springer.com/chapter/10.1007/978-3-030-87869-6_65}, year = {2022}, date = {2022-01-01}, booktitle = {SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications}, pages = {413-422}, series = {Lecture Notes in Computer Science}, keywords = {big data, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
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) Data in Brief, 44 , pp. 108499, 2022. (Abstract | Links | BibTeX | Tags: bioinformatics) @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 = {bioinformatics}, pubstate = {published}, tppubtype = {article} } 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) Electronics, 11 (2), pp. 267, 2022. (Abstract | Links | BibTeX | Tags: big data, energy, time series) @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 = {big data, energy, time series}, pubstate = {published}, tppubtype = {article} } 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 and M. García-Torres and F. Gómez-Vela and F. Morales and F. Divina and D. Becerra-Alonso and G. Velázquez and F. Daumas-Ladouce and J. L. Vázquez Noguera and C. Ayala Sauer Forecasting Electricity Consumption Data from Paraguay Using a Machine Learning Approach (Conference) SOCO 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2022. (Links | BibTeX | Tags: big data, energy, time series) @conference{gallardo2022forecasting, title = {Forecasting Electricity Consumption Data from Paraguay Using a Machine Learning Approach}, author = {J. A. Gallardo and M. García-Torres and F. Gómez-Vela and F. Morales and F. Divina and D. Becerra-Alonso and G. Velázquez and F. Daumas-Ladouce and J. L. Vázquez Noguera and C. Ayala Sauer}, url = {https://link.springer.com/chapter/10.1007/978-3-030-87869-6_65}, year = {2022}, date = {2022-01-01}, booktitle = {SOCO 16th International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {685-694}, series = {Advances in Intelligent Systems and Computing}, keywords = {big data, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
P. Mugariri and H. Abdullah and M. García-Torres and B.D. Parameshchari and K.N. Abdul-Sattar Promoting Information Privacy Protection Awareness for Internet of Things (IoT) (Journal Article) Mobile Information Systems, 2022 , pp. 1–11, 2022. (Abstract | Links | BibTeX | Tags: IoT) @article{mugariri2022promoting, title = {Promoting Information Privacy Protection Awareness for Internet of Things (IoT)}, author = {P. Mugariri and H. Abdullah and M. García-Torres and B.D. Parameshchari and K.N. Abdul-Sattar}, url = {https://www.hindawi.com/journals/misy/2022/4247651/}, doi = {10.1155/2022/4247651}, year = {2022}, date = {2022-01-01}, journal = {Mobile Information Systems}, volume = {2022}, pages = {1--11}, abstract = {The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.}, keywords = {IoT}, pubstate = {published}, tppubtype = {article} } The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet. |
D. Aquino-Brítez and J.A. Gómez and J.L. Vázquez Noguera and M. García-Torres and J.C. Mello Román and P.E. Gardel-Sotomayor and V.E. Castillo Benitez and I. Castro Matto and D.P. Pinto-Roa and J. Facon and S.A. Grillo Automatic Diagnosis of Diabetic Retinopathy from Fundus Images Using Neuro-Evolutionary Algorithms (Journal Article) Studies in Health Technology and Informatics, 290 , pp. 689–693, 2022. (Abstract | Links | BibTeX | Tags: bioinformatics) @article{aquino2022automatic, title = {Automatic Diagnosis of Diabetic Retinopathy from Fundus Images Using Neuro-Evolutionary Algorithms}, author = {D. Aquino-Brítez and J.A. Gómez and J.L. Vázquez Noguera and M. García-Torres and J.C. Mello Román and P.E. Gardel-Sotomayor and V.E. Castillo Benitez and I. Castro Matto and D.P. Pinto-Roa and J. Facon and S.A. Grillo}, doi = {10.3233/SHTI220166}, year = {2022}, date = {2022-01-01}, journal = {Studies in Health Technology and Informatics}, volume = {290}, pages = {689--693}, abstract = {Due to the presence of high glucose levels, diabetes mellitus (DM) is a widespread disease that can damage blood vessels in the retina and lead to loss of the visual system. To combat this disease, called Diabetic Retinopathy (DR), retinography, using images of the fundus of the retina, is the most used method for the diagnosis of Diabetic Retinopathy. The Deep Learning (DL) area achieved high performance for the classification of retinal images and even achieved almost the same human performance in diagnostic tasks. However, the performance of DL architectures is highly dependent on the optimal configuration of the hyperparameters. In this article, we propose the use of Neuroevolutionary Algorithms to optimize the hyperparameters corresponding to the DL model for the diagnosis of DR. The results obtained prove that the proposed method outperforms the results obtained by the classical approach.}, keywords = {bioinformatics}, pubstate = {published}, tppubtype = {article} } Due to the presence of high glucose levels, diabetes mellitus (DM) is a widespread disease that can damage blood vessels in the retina and lead to loss of the visual system. To combat this disease, called Diabetic Retinopathy (DR), retinography, using images of the fundus of the retina, is the most used method for the diagnosis of Diabetic Retinopathy. The Deep Learning (DL) area achieved high performance for the classification of retinal images and even achieved almost the same human performance in diagnostic tasks. However, the performance of DL architectures is highly dependent on the optimal configuration of the hyperparameters. In this article, we propose the use of Neuroevolutionary Algorithms to optimize the hyperparameters corresponding to the DL model for the diagnosis of DR. The results obtained prove that the proposed method outperforms the results obtained by the classical approach. |
Gaia collaboration Gaia Early Data Release 3-The celestial reference frame (Gaia-CRF3) (Journal Article) Astronomy & Astrophysics, 667 (A148), pp. A148, 2022. (Abstract | Links | BibTeX | Tags: astrostatistics) @article{klioner2022gaia, title = {Gaia Early Data Release 3-The celestial reference frame (Gaia-CRF3)}, author = {Gaia collaboration}, url = {https://www.aanda.org/articles/aa/abs/2022/11/aa43483-22/aa43483-22.html}, doi = {10.1051/0004-6361/202243483}, year = {2022}, date = {2022-01-01}, journal = {Astronomy & Astrophysics}, volume = {667}, number = {A148}, pages = {A148}, abstract = {Gaia-CRF3 is the celestial reference frame for positions and proper motions in the third release of data from the Gaia mission, Gaia DR3 (and for the early third release, Gaia EDR3, which contains identical astrometric results). The reference frame is defined by the positions and proper motions at epoch 2016.0 for a specific set of extragalactic sources in the (E)DR3 catalogue. Aims. We describe the construction of Gaia-CRF3 and its properties in terms of the distributions in magnitude, colour, and astrometric quality. Methods. Compact extragalactic sources in Gaia DR3 were identified by positional cross-matching with 17 external catalogues of quasi-stellar objects (QSO) and active galactic nuclei (AGN), followed by astrometric filtering designed to remove stellar contaminants. Selecting a clean sample was favoured over including a higher number of extragalactic sources. For the final sample, the random and systematic errors in the proper motions are analysed, as well as the radio-optical offsets in position for sources in the third realisation of the International Celestial Reference Frame (ICRF3). Results. Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2 million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million have six-parameter solutions. The sources span the magnitude range G = 13–21 with a peak density at 20.6 mag, at which the typical positional uncertainty is about 1 mas. The proper motions show systematic errors on the level of 12 µas yr–1 on angular scales greater than 15 deg. For the 3142 optical counterparts of ICRF3 sources in the S/X frequency bands, the median offset from the radio positions is about 0.5 mas, but it exceeds 4 mas in either coordinate for 127 sources. We outline the future of Gaia-CRF in the next Gaia data releases. Appendices give further details on the external catalogues used, how to extract information about the Gaia-CRF3 sources, potential (Galactic) confusion sources, and the estimation of the spin and orientation of an astrometric solution.}, keywords = {astrostatistics}, pubstate = {published}, tppubtype = {article} } Gaia-CRF3 is the celestial reference frame for positions and proper motions in the third release of data from the Gaia mission, Gaia DR3 (and for the early third release, Gaia EDR3, which contains identical astrometric results). The reference frame is defined by the positions and proper motions at epoch 2016.0 for a specific set of extragalactic sources in the (E)DR3 catalogue. Aims. We describe the construction of Gaia-CRF3 and its properties in terms of the distributions in magnitude, colour, and astrometric quality. Methods. Compact extragalactic sources in Gaia DR3 were identified by positional cross-matching with 17 external catalogues of quasi-stellar objects (QSO) and active galactic nuclei (AGN), followed by astrometric filtering designed to remove stellar contaminants. Selecting a clean sample was favoured over including a higher number of extragalactic sources. For the final sample, the random and systematic errors in the proper motions are analysed, as well as the radio-optical offsets in position for sources in the third realisation of the International Celestial Reference Frame (ICRF3). Results. Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2 million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million have six-parameter solutions. The sources span the magnitude range G = 13–21 with a peak density at 20.6 mag, at which the typical positional uncertainty is about 1 mas. The proper motions show systematic errors on the level of 12 µas yr–1 on angular scales greater than 15 deg. For the 3142 optical counterparts of ICRF3 sources in the S/X frequency bands, the median offset from the radio positions is about 0.5 mas, but it exceeds 4 mas in either coordinate for 127 sources. We outline the future of Gaia-CRF in the next Gaia data releases. Appendices give further details on the external catalogues used, how to extract information about the Gaia-CRF3 sources, potential (Galactic) confusion sources, and the estimation of the spin and orientation of an astrometric solution. |
Gaia Collaboration Gaia Data Release 3. Summary of the content and survey properties (Journal Article) Astronomy & Astrophysics, 2022. (Abstract | Links | BibTeX | Tags: astrostatistics) @article{vallenari2022gaia, title = {Gaia Data Release 3. Summary of the content and survey properties}, author = {Gaia Collaboration}, url = {https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202243940}, doi = {10.1051/0004-6361/202243940}, year = {2022}, date = {2022-01-01}, journal = {Astronomy & Astrophysics}, abstract = {We present the third data release of the European Space Agency’s Gaia mission, Gaia DR3. This release includes a large variety of new data products, notably a much expanded radial velocity survey and a very extensive astrophysical characterisation of Gaia sources. Aims. We outline the content and the properties of Gaia DR3, providing an overview of the main improvements in the data processing in compar- ison with previous data releases (where applicable) and a brief discussion of the limitations of the data in this release. Methods. The Gaia DR3 catalogue is the outcome of the processing of raw data collected with the Gaia instruments during the first 34 months of the mission by the Gaia Data Processing and Analysis Consortium. Results. The Gaia DR3 catalogue contains the same source list, celestial positions, proper motions, parallaxes, and broad band photometry in the G, GBP, and GRP pass-bands already present in the Early Third Data Release, Gaia EDR3. Gaia DR3 introduces an impressive wealth of new data products. More than 33 million objects in the ranges GRVS < 14 and 3100 < Teff < 14500, have new determinations of their mean radial velocities based on data collected by Gaia. We provide GRVS magnitudes for most sources with radial velocities, and a line broadening parameter is listed for a subset of these. Mean Gaia spectra are made available to the community. The Gaia DR3 catalogue includes about 1 million mean spectra from the radial velocity spectrometer, and about 220 million low-resolution blue and red prism photometer BP/RP mean spectra. The results of the analysis of epoch photometry are provided for some 10 million sources across 24 variability types. Gaia DR3 includes astrophysical parameters and source class probabilities for about 470 million and 1500 million sources, respectively, including stars, galaxies, and quasars. Orbital elements and trend parameters are provided for some 800 000 astrometric, spectroscopic and eclipsing binaries. More than 150 000 Solar System objects, including new discoveries, with preliminary orbital solutions and individual epoch observations are part of this release. Reflectance spectra derived from the epoch BP/RP spectral data are published for about 60 000 asteroids. Finally, an additional data set is provided, namely the Gaia Andromeda Photometric Survey, consisting of the photometric time series for all sources located in a 5.5 degree radius field centred on the Andromeda galaxy. Conclusions. This data release represents a major advance with respect to Gaia DR2 and Gaia EDR3 because of the unprecedented quantity, quality, and variety of source astrophysical data. To date this is the largest collection of all-sky spectrophotometry, radial velocities, variables, and astrophysical parameters derived from both low- and high-resolution spectra and includes a spectrophotometric and dynamical survey of SSOs of the highest accuracy. The non-single star content surpasses the existing data by orders of magnitude. The quasar host and galaxy light profile collection is the first such survey that is all sky and space based. The astrophysical information provided in Gaia DR3 will unleash the full potential of Gaia’s exquisite astrometric, photometric, and radial velocity surveys.}, keywords = {astrostatistics}, pubstate = {published}, tppubtype = {article} } We present the third data release of the European Space Agency’s Gaia mission, Gaia DR3. This release includes a large variety of new data products, notably a much expanded radial velocity survey and a very extensive astrophysical characterisation of Gaia sources. Aims. We outline the content and the properties of Gaia DR3, providing an overview of the main improvements in the data processing in compar- ison with previous data releases (where applicable) and a brief discussion of the limitations of the data in this release. Methods. The Gaia DR3 catalogue is the outcome of the processing of raw data collected with the Gaia instruments during the first 34 months of the mission by the Gaia Data Processing and Analysis Consortium. Results. The Gaia DR3 catalogue contains the same source list, celestial positions, proper motions, parallaxes, and broad band photometry in the G, GBP, and GRP pass-bands already present in the Early Third Data Release, Gaia EDR3. Gaia DR3 introduces an impressive wealth of new data products. More than 33 million objects in the ranges GRVS < 14 and 3100 < Teff < 14500, have new determinations of their mean radial velocities based on data collected by Gaia. We provide GRVS magnitudes for most sources with radial velocities, and a line broadening parameter is listed for a subset of these. Mean Gaia spectra are made available to the community. The Gaia DR3 catalogue includes about 1 million mean spectra from the radial velocity spectrometer, and about 220 million low-resolution blue and red prism photometer BP/RP mean spectra. The results of the analysis of epoch photometry are provided for some 10 million sources across 24 variability types. Gaia DR3 includes astrophysical parameters and source class probabilities for about 470 million and 1500 million sources, respectively, including stars, galaxies, and quasars. Orbital elements and trend parameters are provided for some 800 000 astrometric, spectroscopic and eclipsing binaries. More than 150 000 Solar System objects, including new discoveries, with preliminary orbital solutions and individual epoch observations are part of this release. Reflectance spectra derived from the epoch BP/RP spectral data are published for about 60 000 asteroids. Finally, an additional data set is provided, namely the Gaia Andromeda Photometric Survey, consisting of the photometric time series for all sources located in a 5.5 degree radius field centred on the Andromeda galaxy. Conclusions. This data release represents a major advance with respect to Gaia DR2 and Gaia EDR3 because of the unprecedented quantity, quality, and variety of source astrophysical data. To date this is the largest collection of all-sky spectrophotometry, radial velocities, variables, and astrophysical parameters derived from both low- and high-resolution spectra and includes a spectrophotometric and dynamical survey of SSOs of the highest accuracy. The non-single star content surpasses the existing data by orders of magnitude. The quasar host and galaxy light profile collection is the first such survey that is all sky and space based. The astrophysical information provided in Gaia DR3 will unleash the full potential of Gaia’s exquisite astrometric, photometric, and radial velocity surveys. |
E. T. Habtermariam and K. Kekeba and A. Troncoso and F. Martínez-Álvarez A Cluster-Based Deep Learning Model for Energy Consumption Forecasting in Ethiopia (Conference) 531 , 2022. (Links | BibTeX | Tags: deep learning, energy, pattern recognition, time series) @conference{SOCO22_Ejigu, title = {A Cluster-Based Deep Learning Model for Energy Consumption Forecasting in Ethiopia}, author = {E. T. Habtermariam and K. Kekeba and A. Troncoso and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-031-18050-7_41}, year = {2022}, date = {2022-01-01}, journal = {SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, Lecture Notes in Networks Systems, Vol. 531.}, volume = {531}, pages = {423-432}, keywords = {deep learning, energy, pattern recognition, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: deep learning, feature selection) @conference{FADL23, title = {Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection}, author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and I. S. Sousa Brito and F. Martínez-Álvarez and G. Asencio-Cortés}, url = {https://link.springer.com/chapter/10.1007/978-3-031-18050-7_54}, year = {2022}, date = {2022-01-01}, booktitle = {SOCO 17th International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {531}, pages = {557-566}, series = {Lecture Notes in Networks Systems}, keywords = {deep learning, feature selection}, pubstate = {published}, tppubtype = {conference} } |
2021 |
K.-T. T. Bui and J. F. Torres and D. Gutiérrez-Avilés and V. H. Nhu and F. Martínez-Álvarez and D. T. Bui Deformation forecasting of a hydropower dam by hybridizing a Long Short-Term Memory deep learning network with the Coronavirus Optimization Algorithm (Journal Article) Computer-Aided Civil and Infrastructure Engineering, 37 , pp. 1368-1386, 2021. (Abstract | Links | BibTeX | Tags: deep learning, time series) @article{BUI22b, title = {Deformation forecasting of a hydropower dam by hybridizing a Long Short-Term Memory deep learning network with the Coronavirus Optimization Algorithm}, author = {K.-T. T. Bui and J. F. Torres and D. Gutiérrez-Avilés and V. H. Nhu and F. Martínez-Álvarez and D. T. Bui}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12810}, doi = {https://doi.org/10.1111/mice.12810}, year = {2021}, date = {2021-11-24}, journal = {Computer-Aided Civil and Infrastructure Engineering}, volume = {37}, pages = {1368-1386}, abstract = {The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people's safety in many countries; therefore, modeling and forecasting the hydropower dam's deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the deformations of the hydropower dam. The second-largest hydropower dam of Vietnam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to optimize the three parameters of the LSTM, the number of hidden layers, the learning rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art benchmarks, sequential minimal optimization for support vector regression, Gaussian process, M5' model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural network. The result shows that the proposed CVOA-LSTM model has high forecasting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam's deformations.}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {article} } The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people's safety in many countries; therefore, modeling and forecasting the hydropower dam's deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the deformations of the hydropower dam. The second-largest hydropower dam of Vietnam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to optimize the three parameters of the LSTM, the number of hidden layers, the learning rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art benchmarks, sequential minimal optimization for support vector regression, Gaussian process, M5' model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural network. The result shows that the proposed CVOA-LSTM model has high forecasting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam's deformations. |
R. Scitovski and K. Sabo and F. Martínez-Álvarez and S. Ungar Cluster analysis and applications (Book) Springer, 2021, ISBN: 978-3-030-74551-6. (Abstract | Links | BibTeX | Tags: pattern recognition) @book{SCITOVSKI21, title = {Cluster analysis and applications}, author = {R. Scitovski and K. Sabo and F. Martínez-Álvarez and S. Ungar}, url = {https://www.springer.com/gp/book/9783030745516}, doi = {10.1007/978-3-030-74552-3 tppubtype = book}, isbn = {978-3-030-74551-6}, year = {2021}, date = {2021-09-26}, publisher = {Springer}, abstract = {With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods. With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science.}, keywords = {pattern recognition}, pubstate = {published}, tppubtype = {book} } With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods. With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science. |
J. F. Torres and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso Electricity consumption time series forecasting using Temporal Convolutional Networks (Conference) Conference of the Spanish Association for Artificial Intelligence (CAEPIA'21), Lecture Notes in Artificial Intelligence 2021. (BibTeX | Tags: deep learning, time series) @conference{TORRES21b, title = {Electricity consumption time series forecasting using Temporal Convolutional Networks}, author = {J. F. Torres and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso}, year = {2021}, date = {2021-09-01}, booktitle = {Conference of the Spanish Association for Artificial Intelligence (CAEPIA'21)}, series = {Lecture Notes in Artificial Intelligence}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {conference} } |
A. Melara and J. F. Torres and A. Troncoso and F. Martínez-Álvarez Electricity Generation Forecasting in Concentrating Solar-Thermal Power Plants with Ensemble Learning (Conference) SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021. (Links | BibTeX | Tags: deep learning, energy, time series) @conference{MELARA21, title = {Electricity Generation Forecasting in Concentrating Solar-Thermal Power Plants with Ensemble Learning}, author = {A. Melara and J. F. Torres and A. Troncoso and F. Martínez-Álvarez}, doi = {https://doi.org/10.1007/978-3-030-87869-6_63}, year = {2021}, date = {2021-09-01}, booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {665-674}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
D. Hadjout and J. F. Torres and A. Sebaa and F. Martínez-Álvarez SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021. (Links | BibTeX | Tags: deep learning, energy, time series) @conference{HADJOUT21, title = {Medium-Term Electricity Consumption Forecasting in Algeria Based on Clustering, Deep Learning and Bayesian Optimization Methods}, author = {D. Hadjout and J. F. Torres and A. Sebaa and F. Martínez-Álvarez}, doi = {https://doi.org/10.1007/978-3-030-87869-6_70}, year = {2021}, date = {2021-09-01}, booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {739-748}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, energy, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: deep learning, time series) @conference{JIMENEZ-NAVARRO21, title = {HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting}, author = {M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés}, doi = {https://doi.org/10.1007/978-3-030-87869-6_68}, year = {2021}, date = {2021-09-01}, booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {717-727}, series = {Advances in Intelligent Systems and Computing}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {conference} } |
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 | Tags: deep learning, time series) @conference{MOLINA21, title = {A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery}, author = {M. A. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés}, url = {https://link.springer.com/chapter/10.1007/978-3-030-86271-8_43}, doi = {https://doi.org/10.1007/978-3-030-86271-8_43}, year = {2021}, date = {2021-09-01}, booktitle = {HAIS}, volume = {12886}, pages = {511-523}, series = {Lecture Notes in Computer Science}, keywords = {deep learning, time series}, pubstate = {published}, tppubtype = {conference} } |
A. Morales-Esteban and F. Martínez-Álvarez and S. Scitovski and R. Scitovski Mahalanobis clustering for the determination of incidence-magnitude seismic parameters for the Iberian Peninsula and the Republic of Croatia (Journal Article) Computers and Geosciences, 156 , pp. 104873, 2021. (Abstract | Links | BibTeX | Tags: natural disasters, pattern recognition) @article{MORALES21, title = {Mahalanobis clustering for the determination of incidence-magnitude seismic parameters for the Iberian Peninsula and the Republic of Croatia}, author = {A. Morales-Esteban and F. Martínez-Álvarez and S. Scitovski and R. Scitovski}, url = {https://www.sciencedirect.com/science/article/pii/S0098300421001667}, doi = {https://doi.org/10.1016/j.cageo.2021.104873}, year = {2021}, date = {2021-07-09}, journal = {Computers and Geosciences}, volume = {156}, pages = {104873}, abstract = {The aim of this paper is to analyse the seismic activity of the Iberian Peninsula and a wide area of the Republicof Croatia. To do so, two incidence-magnitude seismic parameters have been defined. First, the areas have beendivided into several ellipsoidal clusters using Mahalanobis clustering. Four generalised indexes (Mahalanobis Calinski Harabasz, Mahalanobis Davies–Bouldin, Mahalanobis Simplified Silhouette Width Criterion and Mahalanobis Area) have been used to determine the most appropriate number of ellipsoidal clusters, on the basis of which a partition with four and a partition with eleven clusters have been considered. For the widearea of the Republic of Croatia there are fourteen clusters and the five areas that just affect Croatia have been analysed in detail. Then, to analyse the seismic activity of the areas, two incidence-magnitude seismic parameters have been defined and calculated: a) 𝛥(4), that represents the minimal number of successive years in which at least one earthquake of magnitude between 4 and 5 has been registered; b) 𝛥(5), that shows the number of years in which at least one earthquake of magnitude larger than 5 occurred. The calculation of 𝛥(4) for the South-west and the South-east of the Iberian Peninsula has provided two years for both. Regarding 𝛥(5), 10 and 12 years have been obtained for the South-west and the South-east of the Iberian Peninsula,respectively. The analysis of Croatia has shown that the Ston–Metković area has the highest seismic activity. The following results have been determined: 5 years for 𝛥(4) and 22 for 𝛥(5). It should be mentioned that the seresults cannot be used for predicting earthquakes. However, data about the incidences of earthquake events and their magnitudes can certainly serve as useful information in civil engineering.}, keywords = {natural disasters, pattern recognition}, pubstate = {published}, tppubtype = {article} } The aim of this paper is to analyse the seismic activity of the Iberian Peninsula and a wide area of the Republicof Croatia. To do so, two incidence-magnitude seismic parameters have been defined. First, the areas have beendivided into several ellipsoidal clusters using Mahalanobis clustering. Four generalised indexes (Mahalanobis Calinski Harabasz, Mahalanobis Davies–Bouldin, Mahalanobis Simplified Silhouette Width Criterion and Mahalanobis Area) have been used to determine the most appropriate number of ellipsoidal clusters, on the basis of which a partition with four and a partition with eleven clusters have been considered. For the widearea of the Republic of Croatia there are fourteen clusters and the five areas that just affect Croatia have been analysed in detail. Then, to analyse the seismic activity of the areas, two incidence-magnitude seismic parameters have been defined and calculated: a) 𝛥(4), that represents the minimal number of successive years in which at least one earthquake of magnitude between 4 and 5 has been registered; b) 𝛥(5), that shows the number of years in which at least one earthquake of magnitude larger than 5 occurred. The calculation of 𝛥(4) for the South-west and the South-east of the Iberian Peninsula has provided two years for both. Regarding 𝛥(5), 10 and 12 years have been obtained for the South-west and the South-east of the Iberian Peninsula,respectively. The analysis of Croatia has shown that the Ston–Metković area has the highest seismic activity. The following results have been determined: 5 years for 𝛥(4) and 22 for 𝛥(5). It should be mentioned that the seresults cannot be used for predicting earthquakes. However, data about the incidences of earthquake events and their magnitudes can certainly serve as useful information in civil engineering. |
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) Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021. (Links | BibTeX | Tags: big data, feature selection, pattern recognition) @conference{garcia2021scatter, title = {Scatter search for high-dimensional feature selection using feature grouping}, author = {M. García-Torres and F. Gómez-Vela and F. Divina and D.P. Pinto-Roa and J.L. Vázquez Noguera and J.C. Román}, doi = {10.1145/3449726.3459481 pages=149--150}, year = {2021}, date = {2021-07-01}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}, keywords = {big data, feature selection, pattern recognition}, pubstate = {published}, tppubtype = {conference} } |
J. F. Torres and D. Hadjout and A. Sebaa and F. Martínez-Álvarez and A. Troncoso Deep Learning for Time Series Forecasting: A Survey (Journal Article) Big Data, 9 (1), pp. 3-21, 2021. (Abstract | Links | BibTeX | Tags: big data, deep learning, time series) @article{TORRES21, title = {Deep Learning for Time Series Forecasting: A Survey}, author = {J. F. Torres and D. Hadjout and A. Sebaa and F. Martínez-Álvarez and A. Troncoso}, url = {https://www.liebertpub.com/doi/10.1089/big.2020.0159}, doi = {10.1089/big.2020.0159}, year = {2021}, date = {2021-02-05}, journal = {Big Data}, volume = {9}, number = {1}, pages = {3-21}, abstract = {Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech recognition, and text understanding. It is powerful engines capable of learning arbitrary mapping functions, not require a scaled or stationary time series as input, support multivariate inputs, and support multi-step outputs. All of these features together make deep learning useful tools when dealing with more complex time series prediction problems involving large amounts of data, and multiple variables with complex relationships. This paper provides an overview of the most common Deep Learning types for time series forecasting, Explain the relationships between deep learning models and classical approaches to time series forecasting. A brief background of the particular challenges presents in time-series data and the most common deep learning techniques that are often used for time series forecasting is provided. Previous studies that applied deep learning to time series are reviewed.}, keywords = {big data, deep learning, time series}, pubstate = {published}, tppubtype = {article} } Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech recognition, and text understanding. It is powerful engines capable of learning arbitrary mapping functions, not require a scaled or stationary time series as input, support multivariate inputs, and support multi-step outputs. All of these features together make deep learning useful tools when dealing with more complex time series prediction problems involving large amounts of data, and multiple variables with complex relationships. This paper provides an overview of the most common Deep Learning types for time series forecasting, Explain the relationships between deep learning models and classical approaches to time series forecasting. A brief background of the particular challenges presents in time-series data and the most common deep learning techniques that are often used for time series forecasting is provided. Previous studies that applied deep learning to time series are reviewed. |
R. Mortazavi and S. Mortazavi and A. Troncoso Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer (Journal Article) Engineering with Computers, 2021. (Abstract | Links | BibTeX | Tags: feature selection) @article{Mortazavi21, title = {Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer}, author = {R. Mortazavi and S. Mortazavi and A. Troncoso}, url = {https://link.springer.com/article/10.1007/s00366-020-01226-1}, doi = {10.1007/s00366-020-01226-1}, year = {2021}, date = {2021-01-01}, journal = {Engineering with Computers}, abstract = {This paper introduces different types of regression trees for viscosity property forecasting in polymer solutions. Although regression trees have been extensively used in other fields, they do not have been explored to predict the viscosity. One key issue in the context of materials science is to determine a priori which characteristics must be included to describe the prediction model due to a large number of molecular descriptors is obtained. To deal with this, we propose a wrapper method to select the features based on regression trees. Thus, we use regression trees to evaluate different subsets of attributes and build a model from the subset of features that achieved the minimum error. In particular, the performance of eight regression tree algorithms, including both linear and non-linear models, is evaluated and compared to other forecasting approaches using a dataset composed of 64 polymers and 2962 molecular descriptors. The results show that regression trees with nearest neighbors based local models in leaves predict with high accuracy. Moreover, results have been compared to other forecasting approaches such as multivariate linear regression, neural networks and support vector machines showing remarkable improvements in terms of accuracy.}, keywords = {feature selection}, pubstate = {published}, tppubtype = {article} } This paper introduces different types of regression trees for viscosity property forecasting in polymer solutions. Although regression trees have been extensively used in other fields, they do not have been explored to predict the viscosity. One key issue in the context of materials science is to determine a priori which characteristics must be included to describe the prediction model due to a large number of molecular descriptors is obtained. To deal with this, we propose a wrapper method to select the features based on regression trees. Thus, we use regression trees to evaluate different subsets of attributes and build a model from the subset of features that achieved the minimum error. In particular, the performance of eight regression tree algorithms, including both linear and non-linear models, is evaluated and compared to other forecasting approaches using a dataset composed of 64 polymers and 2962 molecular descriptors. The results show that regression trees with nearest neighbors based local models in leaves predict with high accuracy. Moreover, results have been compared to other forecasting approaches such as multivariate linear regression, neural networks and support vector machines showing remarkable improvements in terms of accuracy. |