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el blog de jesús soto curriculum mooc pimedios test libros contacto 0 air-pollution prediction in smart cities through machine learning methods escrito por jesus soto el 23 jun, 2018, en docencia 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 so 2 , no x , o 3 , nh 3 , co, pm 10 , 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). o 3 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 o 3 prediction models from 90% and a root mean square error less than 11 μ/m 3 for the cities of the region of murcia involved in the study. 0 an unsupervised technique to discretize numerical values by fuzzy partitions escrito por jesus soto el 18 jun, 2018, en docencia 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. 0 high-throughput infrastructure for advanced its services escrito por jesús soto el 11 abr, 2018, en docencia hoy nos han comunicado la publicación de un nuevo artículo, con el que llevábamos trabajando desde el año pasado. high-throughput infrastructure for advanced its services: a case study on air pollution monitoring. abstract : novel cooperative intelligent transportation systems (its) serve as the basis for the provision of a number of services for drivers, occupants, and third parties. the vast amount of information to be collected, especially in vehicle-to-infrastructure (v2i) communication services, requires new algorithms and hardware platforms to cope with real-time requirements; however, this combination is not properly addressed in the literature. in this paper, we introduce a high-throughput hardware-software infrastructure to gather information from vehicles and efficiently process it to provide novel its services. we propose a parallelization approach of a fuzzy clustering technique on heterogeneous servers based on cpu and several gpus, tailored to classification problems in v2i. the infrastructure is empirically tested to offer a geo-located pollution information service through the periodical collection of both vehicle’s position and status data. we offer a real service that correctly identifies highly polluting traffic areas and drivers. the results indicate a good performance of the system under high loads, and our scalability analysis reveals a good operation in real-ambitious deployments thanks to the use of the both cpu and multiple gpus, showing that our proposal can efficiently host cooperative services involving high processing in the its context. 0 a novel fuzzy clustering approach to regionalise watersheds escrito por jesús soto el 28 nov, 2017, en docencia otra artículo que nos publican: a novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters. una nueva aplicación de los algoritmos de clasificación difusa. abstract one of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. however, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. in this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. fuzzy c-means algorithm (fcm), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. fuzzy minimals algorithm (fm), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. regional homogeneity test based on l-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. the validation of the fm algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in alto genil (south spain). according to the results, fm algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis. 0 influence of multivariate modeling in the prediction of soil carbon by a portable infrared sensor escrito por jesús soto el 19 sep, 2017, en docencia contribución workshop proceedings of the 13th international conference on intelligent environments, seoul, korea, august 2017. abstract the determination of carbon is one of the most important in soil analysis. however traditional techniques are costly and time consuming. in this manuscript we propose an alternative predictive approach based on portable mid-infrared spectroscopy data modeled by machine learning techniques. we evaluate the performance of different machine learning models and sample size to predict soil carbon in 457 australian soils. the results show a good performance of the models. all models are validate by statistical tests. the best performing technique with a 99% of confidence level is the gaussian process providing a 98% of accuracy for the prediction of soil carbon. moreover, this technique is the most robust for the different sample sizes tested. when compared with the commonly used partial least squares regression technique, the machine learning approaches provide more successful and balanced results. 0 big data con matlab escrito por jesús soto el 27 jun, 2017, en docencia interesante webinars de cómo matlab trata grandes conjuntos de datos big data con matlab paz tárrega, mathworks descripción general según crecen nuestros datos en tamaño y complejidad, se hace más difícil trabajar con ellos, particularmente cuando los datos no caben en memoria. matlab ofrece un entorno único para trabajar con big data conviertiendo el análisis y el proceso de big data en fácil, coveniente y escalable. en este webinar aprenderá estrategias

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http://jesussoto.es/pimedios/
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http://jesussoto.es/2018/06/18/an-unsupervised-technique-to-discretize-numerical-values-by-fuzzy-partitions/
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http://jesussoto.es/2017/06/27/big-data-con-matlab/
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http://jesussoto.es/2017/06/20/fuzzy-clustering-as-rational-partition-method-for-qsar/
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http://jesussoto.es/2017/11/28/a-novel-fuzzy-clustering-approach-to-regionalise-watersheds/#respond
http://jesussoto.es/2018/06/23/air-pollution-prediction-in-smart-cities-through-machine-learning-methods/
http://jesussoto.es/2018/06/

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