0000000000845141
AUTHOR
José D. Martín
showing 23 related works from this author
Hemoglobin Level Analysis in Hemodialysis Patients Treated With Erythropoiesis Stimulating Agents
2010
In this chapter authors try to develop an expert system with the help of neural network method like Organizing Maps (SOMs) for hemodialysis patient.  Neural network models play a very important role for data analysis of hemodialysis patients with end-stage renal disease.  There are two main goals: firstly, the knowledge extraction from a database using Self-Organizing Maps (SOMs); and secondly, to provide an accurate prediction of Hb levels next month.
Qualitative Analysis of Feed Management Practice on Goat Herds by Self Organizing Maps in Murcia Region of Spain
2007
Abstract Fernandez, C., Soria, E., Magdalena, R., Martin, J.D. and Mata, C. 2007. Qualitative analysis of feed management practice on goat herds by self organizing maps in Murcia region of Spain. J. Appl. Anim. Res., 32: 41–47. Self organizing maps (SOM) were used to analyze data from ninety four herds. Data were obtained from surveys and management practices were evaluated. The 18% of farms were dairy goat farms with milking machines, with a herd size of 100 to 200 goats and most of these bought compound feed. 12% has the same characteristics but farmers prepared their own diet. 16% were similar to previous prototypes, but farmers in addition to dairy goat production kept sheep as well. 23…
Semi-Supervised Classification Method for Hyperspectral Remote Sensing Images
2004
A new approach to the classification of hyperspectral images is proposed. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning met…
Robust adaptive algorithm with low computational cost
2006
An adaptive algorithm, which is robust to impulsive noise, is proposed. The cost function underlying this algorithm contains a parameter that controls the immunity to impulsive noise and can be easily adapted. Moreover, weight updating involves a nonlinear function, which recently has been shown to have an efficient hardware implementation. The proposed adaptive algorithm has been successfully tested in terms of accuracy and convergence on a system-identification simulation.
Neural Models for Rainfall Forecasting
2010
This chapter is focused on obtaining an optimal forecast of one month lagged rainfall in Spain. It is assessed by analyzing 22 years of both satellite observations of vegetation activity (e.g. NDVI) and climatic data (precipitation, temperature). The specific influence of non-spatial climatic indices such as NAO and SOI is also addressed. The approaches considered for rainfall forecasting include classical Auto-Regressive Moving-Average with Exogenous Inputs (ARMAX) models and Artificial Neural Networks (ANN), the so-called Multilayer Perceptron (MLP), in particular. The use of neural models is proven to be an adequate mathematical prediction tool in this problem due the non-linearity of th…
A Matlab based interface for infrared thermographic diagnosis of pediatric musculoskeletal injuries
2016
Abstract Background and objective One of the main causes of emergency medical consultations done by children are musculoskeletal injures. In such cases, radiological tests are a common practice to diagnose the gravity of the trauma and determine the likely existence of a fracture. In order to avoid, or at least to reduce, the use of ionizing radiations with children, the infrared thermographic technique was studied as an alternative solution, since it is a non-harmful, non-invasive and non-contact technique, without excessive technical complications and moderate cost when compared to other types of imaging tools. Methodology When an infrared thermographic diagnostic test is performed, and o…
Towards interpretable classifiers with blind signal separation
2012
Blind signal separation (BSS) is a powerful tool to open-up complex signals into component sources that are often interpretable. However, BSS methods are generally unsupervised, therefore the assignment of class membership from the elements of the mixing matrix may be sub-optimal. This paper proposes a three-stage approach using Fisher information metric to define a natural metric for the data, from which a Euclidean approximation can then be used to drive BSS. Results with synthetic data models of real-world high-dimensional data show that the classification accuracy of the method is good for challenging problems, while retaining interpretability.
Thermographic imaging tool for children fracture detection
2016
The feasibility of thermographic imaging to diagnose pediatric fractures has been studied in the last years. The advantages lie in being a cost-effective, non-harmful, non-contact and non-invasive technique, since ionizing radiations are not used to get the image. Once the pictures of the affected limb are taken, the processing becomes necessary. In order to reduce the waiting time, a software tool has been developed to provide a diagnosis shortly after taking the image. Sin financiación No data (2016) UEV
Feature selection using ROC curves on classification problems
2010
Feature Selection (FS) is one of the key stages in classification problems. This paper proposes the use of the area under Receiver Operator Characteristic curves to measure the individual importance of every input as well as a method to discover the variables that yield a statistically significant improvement in the discrimination power of the classification model.
Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients
2016
International audience; Anemia management, based on erythropoiesis stimulating agents (ESA) and iron supplementation, has become an increasingly challenging problem in hemodialysis patients. Maintaining hemodialysis patients within narrow hemoglobin targets, preventing cycling outside target, and reducing ESA dosing to prevent adverse outcomes requires considerable attention from caregivers. Anticipation of the long-term response (i.e. at 3 months) to the ESA/iron therapy would be of fundamental importance for planning a successful treatment strategy. To this end, we developed a predictive model designed to support decision-making regarding anemia management in hemodialysis (HD) patients tr…
Qualitative analysis of goat and sheep production data using self-organizing maps
2009
The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self-organizing map. This tool allows high-dimensional input spaces to be mapped into much lower-dimensional spaces, thus making it much more straightforward to understand any set of data. These representations enable the visual extraction of qualitative relationships among variables (visual data mining), converting the data to maps. The data used in this study were obtained from surveys completed by farmers who are principally dedicated to goat and sheep production. With the self-organizing map …
BELM: Bayesian Extreme Learning Machine
2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
Classical Training Methods
2006
This chapter reviews classical training methods for multilayer neural networks. These methods are widely used for classification and function modelling tasks. Nevertheless, they show a number of flaws or drawbacks that should be addressed in the development of such systems. They work by searching the minimum of an error function which defines the optimal behaviour of the neural network. Different standard problems are used to show the capabilities of these models; in particular, we have benchmarked the algorithms in a nonlinear classification problem and in three function modelling problems.
Adaptive algorithms robust to impulsive noise with low computational cost using order statistic
2009
Abstract In this paper a family of adaptive algorithms robust to impulsive noise and with low computational cost are presented. Unlike other approaches, no cost functions or filtering of the gradient are considered in order to update the filter coefficients. Its initial basis is the basic LMS algorithm and its sign-error variant. The proposed algorithms can be considered as some sign-error variants of the LMS algorithm. The algorithms are successfully tested in terms of accuracy and convergence in a standard system identification simulation in which an impulsive noise is present. Simulations show that they improve the performance of LMS variants that are robust to impulsive noise.
Neural networks as effective techniques in clinical management of patients: some case studies
2004
In this paper, we present four examples of effective implementation of neural systems in the daily clinical practice. There are two main goals in this work; the first one is to show that neural networks are especially well-suited tools for solving different kind of medical/pharmaceutical problems, given the complex input output relationships and the few a priori knowledge about data distribution and variable relations. The second goal is to develop specific software applications, which enclose complex mathematical models, to clinicians; thus, the use of such models as decision support systems is facilitated. Four important pharmaceutical problems are considered in this study: identificatio…
Survival prediction in patients undergoing ischemic cardiopathy
2009
The ischemic cardiopathy is the main cause of death in developed countries. New improved drugs and therapies have appeared last years. However, the interventionist strategy and the most powerful drugs may have complications, and hence, it is very important to know the risk of death associated with patients during their stay in the hospital, or in the next six months. Thus, it is possible to tune the best treatment for each individual patient. In this framework, the use of artificial neural networks is proposed with a double objective: survival prediction and the extraction of the parameters with best predictive capabilities. A cohort of 691 patients treated in the Hospital Clinic, in Barcel…
An integrated framework for risk profiling of breast cancer patients following surgery.
2006
Objective: An integrated decision support framework is proposed for clinical oncologists making prognostic assessments of patients with operable breast cancer. The framework may be delivered over a web interface. It comprises a triangulation of prognostic modelling, visualisation of historical patient data and an explanatory facility to interpret risk group assignments using empirically derived Boolean rules expressed directly in clinical terms. Methods and materials: The prognostic inferences in the interface are validated in a multicentre longitudinal cohort study by modelling retrospective data from 917 patients recruited at Christie Hospital, Wilmslow between 1983 and 1989 and predictin…
Cloud detection for CHRIS/Proba hyperspectral images
2005
Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant source of error in both sea and land cover biophysical parameter retrieval. Sensors with spectral channels beyond 1 um have demonstrated good capabilities to perform cloud masking. This spectral range can not be exploited by recently developed hyperspectral sensors that work in the spectral range between 400- 1000 nm. However, one can take advantage of their high number of channels and spectral resolution to increase the cloud detection accuracy, and to describe properly the detected c…
Neural networks for animal science applications: Two case studies
2006
Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…
Maximum Likelihood Estimation and non-linear least squares fitting with Levenberg-Marquardt Algorithm implementation in FPGA devices for high resolut…
2012
This work compares two possible solutions to achieve a higher resolution in a hodoscope based on Plastic Scintillating Fibers (PSF) by obtaining the point of maximum incidence of the radioactive beam. The two fitting algorithms proposed have been tested and implemented in Field Programmable Gate Array (FPGA) devices. On one hand, a probabilistic model based on the Maximum Likelihood Estimation (MLE) and on the other hand, non-linear least-squares fit with the Levenberg-Marquardt Algorithm (LMA).
A principled approach to network-based classification and data representation
2013
Measures of similarity are fundamental in pattern recognition and data mining. Typically the Euclidean metric is used in this context, weighting all variables equally and therefore assuming equal relevance, which is very rare in real applications. In contrast, given an estimate of a conditional density function, the Fisher information calculated in primary data space implicitly measures the relevance of variables in a principled way by reference to auxiliary data such as class labels. This paper proposes a framework that uses a distance metric based on Fisher information to construct similarity networks that achieve a more informative and principled representation of data. The framework ena…
Study and simulation of the read-out electronics design for a high-resolution plastic scintillating fiber based hodoscope
2015
Abstract This work presents the study and simulation of a high-resolution charged particle detection device for beam positioning, monitoring and calibration, together with its read-out proposal. To provide the precise positional information of the beam, the detection system has been based on Plastic Scintillating Fibers (PSF), while the read-out on a Silicon-PhotoDiode (Si-PD) array. To carry out the study, a PSF prototype with one detection plane has been experimentally tested with a β particle source. Besides, Monte Carlo simulations of the complete system have also been conducted. Both simulations and experimental tests give consistency to the results obtained. The work presented in this…
A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
2013
Background: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing \ud information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic \ud Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyses \ud single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single \ud voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of\ud tumor type classification from the spectroscopic signal.\ud Methodology/Princ…