showing 35 related works from this author
Machine Learning Methods for One-Session Ahead Prediction of Accesses to Page Categories
2004
This paper presents a comparison among several well-known machine learning techniques when they are used to carry out a one-session ahead prediction of page categories. We use records belonging to 18 different categories accessed by users on the citizen web portal Infoville XXI. Our first approach is focused on predicting the frequency of accesses (normalized to the unity) corresponding to the user’s next session. We have utilized Associative Memories (AMs), Classification and Regression Trees (CARTs), Multilayer Perceptrons (MLPs), and Support Vector Machines (SVMs). The Success Ratio (SR) averaged over all services is higher than 80% using any of these techniques. Nevertheless, given the …
Automatic mass spectra recognition for Ultra High Vacuum systems using multilabel classification
2021
Abstract In Ultra High-Vacuum (UHV) systems it is common to find a mixture of many gases originating from surface outgassing, leaks and permeation that contaminate vacuum chambers and cause issues to reach ultimate pressures. The identification of these contaminants is, in general, done manually by trained technicians from the analysis of mass spectra. This task is time consuming and can lead to misinterpretation or partial understanding of issues. The challenge resides in the rapid identification of these contaminants by using some automatic gas identification technique. This paper explores the automatic and simultaneous identification of 80 molecules, including some of the most commonly p…
Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms
2006
This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …
Decay Detection in Citrus Fruits Using Hyperspectral Computer Vision
2012
The citrus industry is nowadays an important part of the Spanish agricultural sector. One of the main problems present in the citrus industry is decay caused by Penicillium digitatum and Penicillium italicum fungi. Early detection of decay produced by fungi in citrus is especially important for the citrus industry of distribution. This chapter presents a hyperspectral computer vision system and a set of machine learning techniques in order to detect decay caused by Penicillium digitatum and Penicillium italicum fungi that produce more economic losses to the sector. More specifically, the authors employ a hyperspectral system and artificial neural networks. Nowadays, inspection and removal o…
In-line Sorting of Processed Fruit Using Computer Vision
2013
Nowadays, there is a growing demand for quality fruits and vegetables that are simple to prepare and consume, like minimally processed fruits. These products have to accomplish some particular characteristics to make them more attractive to the consumers, like a similar appearance and the total absence of external defects. Although recent advances in machine vision have allowed for the automatic inspection of fresh fruit and vegetables, there are no commercially available equipments for sorting of minority processed fruits, like arils of pomegranate (Punica granatum L) or segments of Satsuma mandarin (Citrus unshiu) ready to eat. This work describes a complete solution based on machine visi…
Least-squares temporal difference learning based on an extreme learning machine
2014
Abstract Reinforcement learning (RL) is a general class of algorithms for solving decision-making problems, which are usually modeled using the Markov decision process (MDP) framework. RL can find exact solutions only when the MDP state space is discrete and small enough. Due to the fact that many real-world problems are described by continuous variables, approximation is essential in practical applications of RL. This paper is focused on learning the value function of a fixed policy in continuous MPDs. This is an important subproblem of several RL algorithms. We propose a least-squares temporal difference (LSTD) algorithm based on the extreme learning machine. LSTD is typically combined wi…
Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics
2021
[EN] The main cause of flesh browning in 'Rojo Brillante' persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450-1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through…
Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model
2015
Abstract The early detection of decay caused by fungi in citrus fruit is a primary concern in the post-harvest phase, the automation of this task still being a challenge. This work reports new progress in the automatic detection of early symptoms of decay in citrus fruit after infection with the pathogen Penicillium digitatum using laser-light backscattering imaging. Backscattering images of sound and decaying parts of the surface of oranges cv. ‘Valencia late’ were obtained using laser diode modules emitting at five wavelengths in the visible and near-infrared regions. The images of backscattered light captured by a camera had radial symmetry with respect to the incident point of the laser…
Educational Software Based on Matlab GUIs for Neural Networks Courses
2016
Neural Networks (NN) are one of the most used machine learning techniques in different areas of knowledge. This has led to the emergence of a large number of courses of Neural Networks around the world and in areas where the users of this technique do not have a lot of programming skills. Current software that implements these elements, such as Matlab®, has a number of important limitations in teaching field. In some cases, the implementation of a MLP requires a thorough knowledge of the software and of the instructions that train and validate these systems. In other cases, the architecture of the model is fixed and they do not allow an automatic sweep of the parameters that determine the a…
Analysis of computer user behavior, security incidents and fraud using Self-Organizing Maps
2019
Abstract This paper addresses several topics of great interest in computer security in recent years: computer users’ behavior, security incidents and fraud exposure on the Internet, due to their high economic and social cost. Traditional research has been based mainly on gathering information about security incidents and fraud through surveys. The novelty of the present study is given by the use of Self-Organizing Maps (SOMs), a visual data mining technique. SOMs are applied to two data sets acquired using two different methodologies for collecting data about computer security. First, a traditional online survey about fraud exposure, security and user behavior was used. Second, in addition …
Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images
2021
Abstract Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 arc…
Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most rele…
2013
[EN] Green mold (Penicillium digitatum) and blue mold (Penicillium italicum) are important sources of postharvest decay affecting the commercialization of mandarins. These fungi infections produce enormous economic losses in mandarin production if early detection is not carried out. Nowadays, this detection is performed manually in dark chambers, where the fruit is illuminated by ultraviolet light to produce fluorescence, which is potentially dangerous for humans. This paper documents a new methodology based on hyperspectral imaging and advanced machine-learning techniques (artificial neural networks and classification and regression trees) for the segmentation and classification of images …
Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors.
2013
Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single…
Early decay detection in citrus fruit using laser-light backscattering imaging
2013
Early detection of fungal infections in citrus fruit still remains one of the major problems in postharvest technology. The potential of laser-light backscattering imaging was evaluated for detecting decay in citrus fruit after infection with the pathogen Penicillium digitatum, before the appearance of fruiting structures (green mould). Backscattering images of oranges cv. Navelate with and without decay were obtained using diode lasers emitting at five different wavelengths in the visible and near infrared range for addressing the absorption of fruit carotenoids, chlorophylls and water/carbohydrates. The apparent region of backscattered photons captured by a camera had radial symmetry with…
Use of Reinforcement Learning in Two Real Applications
2008
In this paper, we present two sucessful applications of Reinforcement Learning (RL) in real life. First, the optimization of anemia management in patients undergoing Chronic Renal Failure is presented. The aim is to individualize the treatment (Erythropoietin dosages) in order to stabilize patients within a targeted range of Hemoglobin (Hb). Results show that the use of RL increases the ratio of patients within the desired range of Hb. Thus, patients' quality of life is increased, and additionally, Health Care System reduces its expenses in anemia management. Second, RL is applied to modify a marketing campaign in order to maximize long-term profits. RL obtains an individualized policy depe…
Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins
2008
The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrususing visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic …
Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques
2021
Abstract Loquat (Eriobotrya japonica L.) is an important fruit for the economy of some regions of Spain that is very susceptible to mechanical damage and physiological disorders. These problems depreciate its value and prevent it from being exported. Visible (VIS) and near infrared (NIR) hyperspectral imaging was used to discriminate between external and internal common defects of loquat cv. ‘Algerie’. Two classifiers, random forest (RF) and extreme gradient boost (XGBoost), and different spectral pre-processing techniques were evaluated in terms of their capacity to distinguish between sound and defective features according to three approaches. In the first approach the fruit pixels were c…
Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques
2012
Penicillium fungi are among the main defects that may affect the commercialization of citrus fruits. Economic losses in fruit production may become enormous if an early detection of that kind of fungi is not carried out. That early detection is usually based either on UltraViolet light carried out manually. This work presents a new approach based on hyperspectral imagery for defect segmentation. Both the physical device and the data processing (geometric corrections and band selection) are presented. Achieved results using classifiers based on Artificial Neural Networks and Decision Trees show an accuracy around 98%; it shows up the suitability of the proposed approach.
Application of Machine Learning Techniques in the Study of the Relevance of Environmental Factors in Prediction of Tropospheric Ozone
2010
This work presents a new approach for one of the main problems in the analysis of atmospheric phenomena, the prediction of atmospheric concentrations of different elements. The proposed methodology is more efficient than other classical approaches and is used in this work to predict tropospheric ozone concentration. The relevance of this problem stems from the fact that excessive ozone concentrations may cause several problems related to public health. Previous research by the authors of this work has shown that the classical approach to this problem (linear models) does not achieve satisfactory results in tropospheric ozone concentration prediction. The authors’ approach is based on Machin…
Self-Organising Maps: A new way to screen the level of satisfaction of dialysis patients
2012
Highlights? FME as dialysis services global provider monitors patient satisfaction in its network. ? A specific questionnaire was developed and administered to the hemodialysis patients. ? To detect residual area of low satisfaction the Self-Organising Map was implemented. ? This method allows identifying niches of dissatisfaction for specific patient groups. Evaluation of patient satisfaction has become an important indicator for assessing health care quality. Fresenius Medical Care (FME) as a global provider of dialysis services through its NephroCare network has a strong interest in monitoring patient satisfaction.The aim of the paper is to test and validate a methodology for detecting a…
Sectors on sectors (SonS): A new hierarchical clustering visualization tool
2011
Clustering techniques have been widely applied to extract information from high-dimensional data structures in the last few years. Graphs are especially relevant for clustering, but many graphs associated with hierarchical clustering do not give any information about the values of the centroids' attributes and the relationships among them. In this paper, we propose a new visualization approach for hierarchical cluster analysis in which the above-mentioned information is available. The method is based on pie charts. The pie charts are divided into several pie segments or sectors corresponding to each cluster. The radius of each pie segment is proportional to the number of patterns included i…
Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features
2009
The computer vision systems currently used for the automatic inspection of citrus fruits are normally based on supervised methods that are capable of detecting defects on the surface of the fruit but are unable to discriminate between different types of defects. identifying the type of the defect affecting each fruit is very important in order to optimise the marketing profit and to be able to take measures to prevent such defects from occurring in the future. In this paper, we present a computer vision system that was developed for the recognition and classification of the most common external defects in citrus. in order to discriminate between 11 types of defects, images of the defects we…
Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning
2014
Mango fruit are sensitive and can easily develop brown spots after suffering mechanical stress during postharvest handling, transport and marketing. The manual inspection of this fruit used today cannot detect the damage in very early stages of maturity and to date no automatic tool capable of such detection has been developed, since current systems based on machine vision only detect very visible damage. The application of hyperspectral imaging to the postharvest quality inspection of fruit is relatively recent and research is still underway to find a method of estimating internal properties or detecting invisible damage. This work describes a new system to evaluate mechanically induced da…
Regularized extreme learning machine for regression problems
2011
Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…
Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration
2006
Abstract This paper deals with tropospheric ozone modelling by using Artificial Neural Networks (ANNs). In this study, ambient ozone concentrations are estimated using surface meteorological variables and vehicle emission variables as predictors. The work is especially focused on analysing the importance of the input variables used by these models. This analysis is carried out in different time windows: all the time of study (April of 1997, 1999 and 2000), one month (April 1999), and finally, an hourly analysis. All the information extracted from these analyses can determine the most important factors in tropospheric ozone formation, thus achieving a qualitative model from the quantitative …
A Survey of Bayesian Techniques in Computer Vision
2010
The Bayesian approach to classification is intended to solve questions concerning how to assign a class to an observed pattern using probability estimations. Red, green and blue (RGB) or hue, saturation and lightness (HSL) values of pixels in digital colour images can be considered as feature vectors to be classified, thus leading to Bayesian colour image segmentation. Bayesian classifiers are also used to sort objects but, in this case, reduction of the dimensionality of the feature vector is often required prior to the analysis. This chapter shows some applications of Bayesian learning techniques in computer vision in the agriculture and agri-food sectors. Inspection and classification of…
Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit
2015
Abstract The development of systems for automatically detecting decay in citrus fruit during quality control is still a challenge for the citrus industry. The feasibility of reflectance spectroscopy in the visible and near infrared (NIR) regions was evaluated for the automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Reflectance spectra of sound and decaying surface parts of mandarins cv. ‘Clemenvilla’ were acquired in two different spectral regions, from 650 nm to 1050 nm (visible–NIR) and from 1000 nm to 1700 nm (NIR), pointing to significant differences in spectra between sound and decaying skin for both spectral ranges. Three diffe…
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…
Optimization of anemia treatment in hemodialysis patients via reinforcement learning
2013
Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. Methods: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDP…
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.
Modelling net radiation at surface using “in situ” netpyrradiometer measurements with artificial neural networks
2011
The knowledge of net radiation at the surface is of fundamental importance because it defines the total amount of energy available for the physical and biological processes such as evapotranspiration, air and soil warming. It is measured with net radiometers, but, the radiometers are expensive sensors, difficult to handle, that require constant care and also involve periodic calibration. This paper presents a methodology based on neural networks in order to replace the use of net radiometers (expensive tools) by modeling the relationships between the net radiation and meteorological variables measured in meteorological stations. Two different data sets (acquired at different locations) have…
Analysis of Hyperspectral Images of Citrus Fruits
2010
Publisher Summary Some of the most important aspects that need to be taken into consideration when developing a hyperspectral inspection system for citrus include the geometry of the fruit, the emission spectrum of the lighting source, and their interaction. Because many citrus fruits are almost spherical, each point of their surface reflects the electromagnetic radiation differently toward the camera. This causes a gradual darkening of the image especially the further pixels from the light source, which is a phenomenon that must be artificially corrected. In addition, the variation of the efficiency of the filters with the wavelength should be also taken into consideration to enable the ap…
Discrimination of astringent and deastringed hard ‘Rojo Brillante’ persimmon fruit using a sensory threshold by means of hyperspectral imaging
2019
[EN] Persimmon fruit cv. 'Rojo Brillante' is an astringent cultivar due to its content of soluble tannins, which are insolubilised during the ripening of the fruit. Traditionally, the consumption of this cultivar has only been possible when the fruit is overripe and the texture is soft. Postharvest treatments based on exposing fruits to high CO2 concentrations allow astringency removal while preserving high flesh firmness. However, the effectiveness of this treatment is controlled by means of slow destructive methods. The aim of this work is to study the application of hyperspectral imaging in the spectral range 450-1040 nm to discriminate astringent (A) and deastringed (DA) fruits non-dest…
Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins
2008
Abstract Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils, present in the skin of citrus and which are released by the action of fungi, thus increasing the contrast between sound and rotten skin. This work analyses a set of techniques aimed at detecting rotten citrus without the use of UV lighting. The techniques used include hyperspectral image acquisition, pre-processing and calibration, feature selection and segmentation using linear and non-linear methods for classification of fruits. Different methods such as correlation analysis…
Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits
2008
This study proposes a method for correcting the adverse effects produced by the curvature of spherical objects in acquiring images with a computer vision system. Its suitability has been illustrated in a specific case of citrus fruits. The images of this kind of fruit are darker in areas nearer the edge than in the centre, and this makes them more difficult to analyse. This methodology considers the fruit as being a Lambertian ellipsoidal surface and produces a 3D model of the fruit. By doing it becomes possible to calculate the part of the radiation that should really reach the camera and to make the intensity of the radiation uniform over the whole of the fruit surface captured by the cam…