Evaluation of Clustering Algorithms on HPC Platforms

Cebrian JM, Imbernón B, Soto J, Cecilia JM. Evaluation of Clustering Algorithms on HPC Platforms. Mathematics [Internet] 2021;9:2156. Available from: http://dx.doi.org/10.3390/math9172156

Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson–Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform. 

Antibióticos como contaminantes emergentes

Martínez-Alcalá, I., Soto, J., & Lahora, A. (2020). Antibióticos como contaminantes emergentes. Riesgo ecotoxicológico y control en aguas residuales y depuradas. Ecosistemas29(3), 2070. https://doi.org/10.7818/ECOS.2070

La presencia de contaminantes emergentes en aguas es cada vez mayor. Especialmente preocupan los antibióticos, debido a que pueden dar lugar a la aparición de bacterias resistentes, pero también a que dichos antibióticos pueden afectar negativamente a los ecosistemas y a los organismos que los habitan. Los antibióticos empleados para el consumo humano terminan llegando a las estaciones depuradoras de aguas residuales (EDAR) donde se ha visto que se eliminan solo en parte…

Entrevista en Onda Regional

Murycia
MURyCÍA. Muret, novela histórica del profesor Jesús Soto vía @ormurcia

El pasado 14/01/2020 me entrevistaron en el programa MURyCÍA, de Onda Regional.

Novela histórica Muret

Muret, la batalla que acabó con la Gran Corona de Aragón
Muret, la batalla que decidió la Gran Corona de Aragón

Ya está a la venta mi novela MURET.

En ella relato la historia de Adán de Alascún, un joven aragonés cuya vida da un vuelco la noche que se celebra la victoria en la batalla de las Navas de Tolosa. Resignado por el peso de la culpa, descubre que su pasado ha sido un engaño. Un engaño que comenzó unos años atrás, cuando iba a casarse con su amada. Pronto un oscuro secreto se cobijará en su interior, que lo conducirá hacia el único propósito que lo mantiene vivo: la venganza.

La novela nos relata una historia de amor, traición y poder en el devenir del reino de Aragón. A través de sus páginas cabalgaremos parejos a la historia de Aragón en Occitania. Estaremos presentes en momentos decisivos que explican la cruzada cátara; la situación feudal de Occitania en el siglo XIII; la injerencia de los reinos colindantes por el dominio de una tierra rica y la influencia de la Corona de Aragón.

De la mano del joven Alascún viviremos el amor, el odio y la sinrazón que llevaba a los caballeros medievales a morir en las batallas. Adán nos guiará por un entramado de caminos de orgullo, ambición y felonía que confluirá en la villa de Muret. El castillo de Muret será testigo mudo de reyes vasallos, condes más poderosos que sus señores y cruzados que solo se arrodillan ante la cruz. Unas murallas frente a las que todos empeñarán sus destinos a un Juicio de Dios.

Developing an intelligent system for the prediction of soil properties with a portable mid-infrared instrument

biosystemsYa está disponible nuestro nueva publicación.

Developing an intelligent system for the prediction of soil properties with a portable mid-infrared instrument.

Highlights

•Different machine learning techniques have been tested to predict soil properties.
•The predicted soil properties are TC, TN, CEC, clay, silt and Na+.
•The best predictive machine learning technique has been the Gaussian Process.
•The Gaussian process is better compared to the traditional PLSR technique.
•The Gaussian Process is the candidate for the development of intelligent system.

Air-Pollution Prediction in Smart Cities through Machine Learning Methods

jucs
Estamos de suerte, al final nos han aceptado el artículo que llevamos meses en revisión.

Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain

Abstract:Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five. Smart cities are called to play a decisive role to improve such pollution by first collecting, in real-time, different parameters such as SO2, NOx, O3, NH3, CO, PM10, just to mention a few, and then performing the subsequent data analysis and prediction. However, some machine learning techniques may be more well-suited than others to predict pollution-like variables. In this paper several machine learning methods are analyzed to predict the ozone level (O3) in the Region of Murcia (Spain). O3 is one of the main hazards to health when it reaches certain levels. Indeed, having accurate air-quality prediction models is a previous step to take mitigation activities that may benefit people with respiratory disease like Asthma, Bronchitis or Pneumonia in intelligent cities. Moreover, here it is identified the most-significant variables to monitor the air-quality in cities. Our results indicate an adjustment for the proposed O3 prediction models from 90% and a root mean square error less than 11 μ/m3 for the cities of the Region of Murcia involved in the study.

An unsupervised technique to discretize numerical values by fuzzy partitions

articulo_jaise

Una nueva publicación con técnicas aplicadas al análisis de datos.

An unsupervised technique to discretize numerical values by fuzzy partitions.

Abstract:

The numerical value discretization is a process that is performed in the data preprocessing phase of intelligent data analysis. Preprocessing phase is very relevant because the quality of the models obtained in data mining step depends on this phase. Value discretization is an important task in data preprocessing because not all data mining techniques can handle continuous values. In this paper an unsupervised technique to discretize continuous data values using fuzzy partitions is proposed. Specifically a clustering technique that gets fuzzy partitions is presented. In addition, to evaluate the behavior of the proposed technique a series of experiments have been proposed using a Extreme Learning Machine classifier and a committee of Extreme Learning Machine. Beside comparing with the K-means discretization technique. These experiments have been validated statistically obtaining the best results the approach proposed.