Publications

@article{Melgar21_IS,
title = {Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach},
author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso},
year = {2021},
date = {2021-01-01},
journal = {Information Sciences},
volume = {in press},
abstract = {Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper, a new triclustering approach for data streams is introduced. It follows a streaming scheme of learning in two steps: offline and online phases. First, the offline phase provides a summary model with the components of the triclusters. Then, the second stage is the online phase to deal with data in streaming. This online phase consists in using the summary model obtained in the offline stage to update the triclusters as fast as possible with genetic operators. Results using three types of synthetic datasets and a real-world environmental sensor dataset are reported. The performance of the proposed triclustering streaming algorithm is compared to a batch triclustering algorithm, showing an accurate performance both in terms of quality and running times.
},
keywords = {big data, IoT, pattern recognition},
pubstate = {published},
tppubtype = {article}
}

@conference{SOCO20,
title = {Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering},
author = {L. Melgar-García and M. T. Godinho and R. Espada and D. Gutiérrez-Avilés and I. S. Brito and F. Martínez-Álvarez and A. Troncoso and C. Rubio-Escudero},
url = {https://link.springer.com/chapter/10.1007/978-3-030-57802-2_22},
year = {2020},
date = {2020-08-29},
booktitle = {SOCO 15th International Conference on Soft Computing Models in Industrial and Environmental Applications},
pages = {226-236},
series = {Advances in Intelligent Systems and Computing },
keywords = {IoT, pattern recognition},
pubstate = {published},
tppubtype = {conference}
}

@article{IGPL20b,
title = {Special issue: HAIS16-IGPL},
author = {F. Martínez-Álvarez and A. Troncoso and H. Quintián and
E. Corchado},
url = {https://doi.org/10.1093/jigpal/jzz066},
doi = {10.1093/jigpal/jzz066},
year = {2020},
date = {2020-02-01},
journal = {Logic Journal of the IGPL},
volume = {28},
number = {1},
pages = {1-3},
abstract = {Following, Fournier-Viger et al. propose to integrate
the concept of correlation in high-utility itemset mining to find
profitable itemsets that are highly correlated, using the all-confidence
and bond measures. An efficient algorithm named FCHM (fast correlated
high-utility itemset miner) is proposed to efficiently discover
correlated high-utility itemsets. Two versions of the algorithm are
proposed, named FCHMall-confidence and FCHMbond based on the
all-confidence and bond measures, respectively. An experimental
evaluation was done using four real-life benchmark data sets from the
high-utility itemset mining literature: mushroom, retail, kosarak and
foodmart. Results show that FCHM is efficient and can prune a huge
amount of weakly correlated high-utility itemsets.},
keywords = {big data, deep learning, pattern recognition},
pubstate = {published},
tppubtype = {article}
}

@article{Lezcano20,
title = {A multi-objective approach for designing optimized operation sequence on binary image processing},
author = {C. Lezcano and J. L. Vázquez-Noguera and D. P. Pinto-Roa and M. García-Torres and C. Gaona and P. E. Gardel-Sotomayor},
year = {2020},
date = {2020-01-01},
journal = {Heliyon},
volume = {6},
number = {4},
pages = {e03670},
abstract = {In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.},
keywords = {pattern recognition},
pubstate = {published},
tppubtype = {article}
}

@article{Daumas-Ladouce20,
title = {Multi-Objective Pareto Histogram Equalization},
author = {F. Daumas-Ladouce and
M. García-Torres and
J. Luis Vázquez-Noguera and
D. P. Pinto-Roa and
H. Legal-Alaya},
year = {2020},
date = {2020-01-01},
journal = {Electronic Notes in Theoretical Computer Science},
volume = {349},
pages = {3-23},
abstract = {Several histogram equalization methods focus on enhancing the contrast as one of their main objectives, but usually without considering the details of the input image. Other methods seek to keep the brightness while improving the contrast, causing distortion. Among the multi-objective algorithms, the classical optimization (a priori) techniques are commonly used given their simplicity. One of the most representative method is the weighted sum of metrics used to enhance the contrast of an image. These type of techniques, beside just returning a single image, have problems related to the weight assignment for each selected metric. To avoid the pitfalls of the algorithms just mentioned, we propose a new method called MOPHE (MultiObjective Pareto Histogram Equalization) which is based on Multi-objective Particle Swarm Optimization (MOPSO) approach combining different metrics in a posteriori selection criteria context. The goal of this method is three-fold: (1) improve the contrast (2) without losing important details, (3) avoiding an excessive distortion. MOPHE, is a pure multi-objective optimization algorithm, consequently a set of tradeoff optimal solutions are generated, thus providing alternative solutions to the decision-maker, allowing the selection of one or more resulting images, depending on the application needs. Experimental results indicate that MOPHE is a promising approach, as it calculates a set of trade-off optimal solutions that are better than the results obtained from representative algorithms from the state-of-the-art regarding visual quality and metrics measurement.},
keywords = {pattern recognition},
pubstate = {published},
tppubtype = {article}
}

@article{Rodriguez-Baena20,
title = {Identifying livestock behavior patterns based on accelerometer dataset},
author = {D. S. Rodríguez-Baena and
F. Gómez-Vela and
M. García-Torres and
F. Divina and
C. D. Barranco and
N- Díaz-Díaz and
M. Jimenez and
G. Montalvo},
url = {https://doi.org/10.1016/j.jocs.2020.101076},
doi = {10.1016/j.jocs.2020.101076},
year = {2020},
date = {2020-01-01},
journal = {Journal of Computational Science},
volume = {41},
pages = {101076},
abstract = {In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However, in large breeding farms this may not represent the best approach, since, in this way, not all the animals can be effectively monitored all the time. Moreover, the traditional approach heavily rely on human experience, which cannot be always taken for granted. To this aim, in this paper, we propose a new method for automatically detecting activity and inactivity time periods of animals, as a behavior indicator of livestock. In order to do this, we collected data with sensors located in the body of the animals to be analyzed. In particular, the reliability of the method was tested with data collected on Iberian pigs and calves. Results confirm that the proposed method can help breeders in detecting activity and inactivity periods for large livestock farming.},
keywords = {pattern recognition},
pubstate = {published},
tppubtype = {article}
}