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This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan&ndash, Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today&rsquo, s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.

Lung modules are an initial indicator as to whether or not a patient will develop lung carcinoma which has a significant mortality rate if it is not detected at an early stage. The detection of these lung nodules is a complex task and is time-consuming for the radiologist. For these reasons, CAD systems have been developed and employed for the detection of lung nodules. In this article, we present a CAD system that uses a hybrid strategy: techniques for the analysis of medical images and soft computing (fuzzy clustering, SVM and ANN) with a description of the main stages: preprocessing, identification of ROIs (region of interest), creation of VOIs (volume interests) and ROI classifications. Remarkable elements of the system are: detection automation, a new phase to reduce the ROIs false positives and a new algorithm to build the VOIs in order to improve the location and a better detection. For its development, helical CT studies proceeding from the LIDC public database have been used. The system achieves similar and even better results to other CADs for the same purpose with a sensitivity of 82% and a number of false positives of 7.3 per study.

0, info:eu-repo/semantics/published

Graphical abstractDisplay Omitted HighlightsSelf-Organizing Maps (SOMs) are powerful tools with many applications. Nevertheless, they cannot deal directly with categorical variables.In order to present categorical variables to SOMs, they are usually transformed by binarization. This increases dramatically the dataset dimensionality.NCSOM has been presented in order to cope with categorical or mixed data. However, it presents some drawbacks: categorical and numerical variables are not equally balanced and the method is not convergent.A novel SOM variant, called FMSOM, is presented which is able to deal with numerical and categorical variables, giving the same weight to them and ensuring convergence. A scalable implementation of the method is fully described.FMSOM is applied to a benchmark of well known datasets, composed of categorical and mixed data. The results show the potential of the method to analyze this kind of datasets. Even though Self-Organizing Maps (SOMs) constitute a powerful and essential tool for pattern recognition and data mining, the common SOM algorithm is not apt for processing categorical data, which is present in many real datasets. It is for this reason that the categorical values are commonly converted into a binary code, a solution that unfortunately distorts the network training and the posterior analysis. The present work proposes a SOM architecture that directly processes the categorical values, without the need of any previous transformation. This architecture is also capable of properly mixing numerical and categorical data, in such a manner that all the features adopt the same weight. The proposed implementation is scalable and the corresponding learning algorithm is described in detail. Finally, we demonstrate the effectiveness of the presented algorithm by applying it to several well-known datasets.

Geographic Information Systems (GIS) have gained popularity in recent years because they provide spatial data management and access through the Web. This article gives a detailed description of a tool that offers an integrated framework for the detection and localization of marine spills using remote sensing, GIS, and cloud computing. Advanced segmentation algorithms are presented in order to isolate dark areas in SAR images, including fuzzy clustering and wavelets. In addition, cloud computing is used for scaling up the algorithms and providing communication between users.

Synthetic aperture radar (SAR) images are a valuable source of information for the detection of marine oil spills. For their effective analysis, it is important to have segmentation algorithms that can delimit possible oil spill areas. This article addresses the application of clustering, logistic regression and convolutional neural network algorithms for the detection of oil spills in Envisat and Sentinel-1 satellite images. Large oil spills do not occur frequently so that the identification of a pixel as oil is relatively uncommon. Metrics based on Precision–Recall curves have been employed because they are useful for problems with an imbalance in the number of samples from the classes. Although logistic regression and clustering algorithms can be considered useful for oil spill segmentation, the combination of convolutional techniques and neural networks achieves the best results with low computing time. A convolutional neural network has been integrated into a decision support system in order to facilitate decision-making and data analysis of possible oil spill events.

Gaia is an ESA cornerstone astronomical mission that will observe with unprecedented precision positions, distances, space motions, and many physical properties of more than one billion objects in our Galaxy and beyond. It will observe all objects in the sky in the visible magnitude range from 6 to 20, up to approximately 10^9 sources. An international scientific consortium, the Gaia Data Processing and Analysis Consortium (Gaia DPAC), has organized itself in several coordination units, with the aim, among others, of addressing the work of classifying the observed astronomical sources, using both supervised and unsupervised classification algorithms. This work focuses on the analysis of classification outliers by means of unsupervised classification. We present a novel method to combine SOMs trained with independent features that are calculated from spectrophotometry. The method as described here can help to improve the models used for the supervised classification of astronomical sources. Furthermore, it allows for data exploration and knowledge discovery in huge astronomical databases such as the upcoming Gaia mission.

The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium).

This work presents a strategy for the classification of astronomical objects based on spectrophotometric data and the use of unsupervised neural networks and statistical classification algorithms. Our strategy constitutes an essential part of the preparation phase of the automatic classification and parameterization algorithms for the data that are to be collected by the Gaia satellite of the European Space Agency (ESA), whose launch is foreseen for the spring of 2012. The proposed algorithm is based on a hierarchical structure of neural networks composed of various tree-structured SOM networks. The classification of possible astronomical objects (stars, galaxies, quasars, multiple objects, etc.) basically consists in the iterative segmentation of the inputs space and the ensuing generation of initial classifications and increase in classification precision by means of a refining process. Apart from providing a classification, our technique also measures the quality and precision of the classifications and segments the objects for which it cannot determine whether or not they belong to a pre-established class of astronomical objects (outliers).

Gaia is the next astrometric ESA mission, conceived to extend the Hipparcos legacy by producing what has been called the first stereoscopic census of the Galaxy. The spacecraft will be launched by spring of 2012 and will measure astrometry with unprecedented accuracy for a significant 1% of the objects in the Milky Way. Additionally, two spectrophotometers will determine the spectral energy distributions (SEDs) of the objects in the region of 0.3-1 μm, and a radial velocity spectrograph (RVS) will measure the kinematics of the brightest objects (down to 17 mag). The Gaia RVS will provide spectra in the near-IR Ca II triplet region with an expected signal-to- noise ratio (S/N) between 100 and 20 for FGK stars with visual magnitude between 8 and 15. In order to deal with the enormous volume of data that the mission will generate, automated specialized analysis tools are being devel- oped by the mission scientific Data Analysis and Processing Consortium (Gaia DPAC). In particular, we have been testing several analysis techniques in order to be prepared to extract all possible astrophysical information from RVS stellar spectra. A combination of data processing in transformed domains (Fourier analysis and wavelet multilevel decomposition) and connectionist systems (artificial neural networks, ANNs) has proven to be a good approach to derive the fundamental stellar parameters, Teff, log g, ½Fe=H� , and ½α=Fe� , on the basis of RVS synthetic spectra blurred with noise at different S/N. Signal-processing techniques allowed us to estimate and categorize the S/N, which in turn is found to be essential since the optimal algorithm for parameterization is highly dependent on S/N. In the case of low S/N (5-25) spectra, it is found that the wavelet transform provides a competitive approach for parameterization. The derivation of the stellar parameters is performed by the use of ANNs trained with the error backpropagation algorithm. The accuracy of the parameters' derivation is presented for typical Galaxy populations.

One of the upcoming cornerstone missions of the European Space Agency (ESA) is Gaia, a spacecraft that will be launched in 2011 and will carry out a stereoscopic census of our Galaxy and its environment by measuring with unprecedented exactitude the astrometry (distance and movements), the photometric distribution from ultraviolet to the infrared of its components, and, in the case of the brightest objects (mainly stars), the spectrum with intermediate resolution in the region of the infrared CaII triplet, with a spectrograph known as Radial Velocity Spectrometer (RVS). Stars are the basic constituents of our Galaxy, and they can be characterized if we can estimate their principal atmospheric parameters: effective temperature, gravity, metal content (general abundance of elements other than H and He), and their abundance of alpha elements (elements with Z>22, [@a/Fe]), which provide information on the physical environment in which the star was born. This work presents our results for the parameterization of stellar spectra with simulated data (synthetic spectra) in the spectral region of the RVS and with the application of Artificial Intelligence Techniques based on ANNs. Our work has two main purposes: to determine the optimal domain for the ANNs performance, and to develop an adequate noise detection and filtering algorithm.

Astrophysics is evolving toward a more rational use of costly observational data by intelligently exploiting the large terrestrial and spatial astronomical databases. In this paper, we present a study showing the suitability of an expert system to perform the classification of stellar spectra in the Morgan and Keenan (MK) system. Using the formalism of artificial intelligence for the development of such a system, we propose a rules' base that contains classification criteria and confidence grades, all integrated in an inference engine that emulates human reasoning by means of a hierarchical decision rules tree that also considers the uncertainty factors associated with rules. Our main objective is to illustrate the formulation and development of such a system for an astrophysical classification problem. An extensive spectral database of MK standard spectra has been collected and used as a reference to determine the spectral indexes that are suitable for classification in the MK system. It is shown that by considering 30 spectral indexes and associating them with uncertainty factors, we can find an accurate diagnose in MK types of a particular spectrum. The system was evaluated against the NOAO-INDO-US spectral catalog.

Estimating the presence and size of fishing banks has significant ecological and economic advantages: it contributes to a rational exploitation of the available marine resources, to the reduction of fuel costs, and to a general decrease in required fishing time. This work presents the integration of remote sensing techniques and connectionist models for the prediction of fishing banks. We have developed and implemented an information system that stores and processes the data from various satellites and visualizes them by generating a geographic forecasting map.

Since its launch in December 2013, the Gaia space mission has collected and continues to collect tremendous amounts of information concerning the objects that populate our Galaxy and beyond. The international Gaia Data and Analysis Consortium (DPAC) is in charge of developing computer algorithms that extract and process astrophysical information from these objects. It organizes its work by means of work packages; one of these packages, Outlier Analysis, is dedicated to the exploration of vast amounts of outlier objects detected during the main classification of the observations. We present a method that is based on Self-Organizing Maps (SOM) and parallelized by means of the Hadoop framework so as to improve its performance. We also compare the execution times of both the sequential and the distributed versions of the algorithm.

This article presents the application of various Artificial Intelligence techniques to images that proceed from Remote Sensing and serve to predict Prionace Glauca captures (the Prionace Glauca is a type of shark). Our data proceed from remote sensors whose spectral signature allows us to calculate products that are useful for ecological modelling. After digitally processing the Remote Sensing images, we created a database from which to extract the necessary patterns for the training of the artificial neural networks (Backpropagation network, RBF, functional separability network) and the neuro-diffuse networks (ANFIS). These data are used for the training of our system with the aforementioned algorithms. The results show that for this type of problems the generalisation capacity of the functional networks is reduced, which is probably due to the absence of a subjacent mathematical model. Finally, the implementation was carried out with a multilayer perceptron that was trained with a Backpropagation algorithm (error backpropagation); a method that is less complicated than the ANFIS and RBF networks.

This paper presents the application of artificial intelligence techniques to optical spectroscopy, a specific field of Astrophysics. We propose the analysis, design and implementation of an intelligent system for the analysis and classification of the low-resolution optical spectra of supergiant, giant and dwarf stars, with luminosity levels I, III and V, respectively. The developed system automatically and objectively collects the most important spectral features, and determines the temperature and luminosity of the stars according to the current standard system. The system development combines signal processing, expert systems and fuzzy logic techniques, and integrates them through the use of a relational database, which allows us to structure the collected astronomical data and to contrast the results of the different classification methods. As an additional research, we have designed and implemented several models of artificial neural networks, including them as an alternative method for the classification of spectra.

In recent years, functional networks have emerged as an extension of artificial neural networks (ANNs). In this article, we apply both network techniques to predict the catches of the Prionace Glauca (a class of shark) and the Katsowonus Pelamis (a variety of tuna, more commonly known as the Skipjack). We have developed an application that will help reduce the search time for good fishing zones and thereby increase the fleet’s competitivity. Our results show that, thanks to their superior learning and generalisation capacities, functional networks are more efficient than ANNs. Our data proceeds from remote sensors. Their spectral signatures allow us to calculate products that are useful for ecological modelling. After an initial phase of digital image processing, we created a database that provides all the necessary patterns to train both network types.

This paper describes the design and implementation of an intelligent system by means of which multiple anaerobic systems for wastewater treatment are controlled individually by local controllers which are linked to a common remote central supervisor via wide area networks in a client/server network architecture. The local control systems have a hybrid structure, comprising both algorithmic routines for data acquisition, signal preprocessing, calculation of plant operation parameters, etc., and an expert system based on production rules embodying non-mathematical or semiquantitative knowledge of the unit’s operation. The system shows a modular structure, which allows for easy modification or updating with improved versions. The expert system starts up faster than a human operator and maintains the stability of the biological process by modifications in the operating conditions of the plants, reporting warnings and alarms to the central system. The central supervisor acquires, analyses, interprets and stores the data sent by the on-site bioreactor control systems in a relational database, allowing remote specification of plant operating setpoints and continual development of the system. The database is linked with the expert system in order to extend the working memory to the database, allowing its use as an information source. The client/server architecture has been analysed using two different aspects. Firstly, different operating systems were under consideration in the local equipment, in order to determine the networking, multitasking and temporal behaviour. Secondly, different implementations of the network communication were taken into account to make it more flexible, and to allow better adaptation to the communication infrastructure.

This paper describes the design and implementation of an intelligent real-time supervision system for the monitoring and control of wastewater treatment plants. The system presents a hybrid structure which integrates knowledge engineering techniques and methodologies (an expert system based on production rules embodying non-mathematical or semiquantitative knowledge of unit operation) and algorithmic process-related methods (data acquisition, signal preprocessing, calculation of plant operation parameters etc.). The fundamental design aspects consist of temporal information treatment, artifact detection and a client- server structure which allows the telesupervision of various plants by a central system.

The complexity of the monitored data available in modern intensive care units (ICUs) means that they are best processed, for presentation to medical staff, by expert system techniques. This article describes an expert system that has an appropriately designed inference engine, handling the temporal considerations inherent in monitoring and manages data acquisition via a Medical Information Bus. Also, we will describe how we extended our monitoring system in ICUs by writing an interface allowing communication in dynamic SQL with a relational database management system. The extended system facilitates both permanent filing of case data and the use of filed data by the rules of the expert system, and allows automatic intelligent screening of data prior to permanent filing so as to ensure data reliability.

Because of the complexity of monitored data in modern intensive care units (ICUs), and the risk of information being overlooked if medical staff have to pay attention to a multiplicity of monitoring apparatuses and alarm signals, the data for each patient may well be best presented on a single bedside screen after digestion by expert system techniques. Such central units should be able to deal with data from any monitoring apparatus, not just a predefined set. Furthermore, relay of the information from each bed to a central control station (one per ICU) is desirable for the purposes of permanent storage and for in-depth analysis. The paper describes a comprehensive system for ICU monitoring management and patient data analysis that integrates multiple expert systems and computers. The basic difficulties in applying expert system techniques to monitoring are overcome with the shell OPS/83, which allows calls to sequential C routines and allows time-driven reasoning through appropriate design of the inference engine and rules. Flexibility as regards connectable monitoring apparatus is afforded by basing data acquisition mainly, though not exclusively, on the IEEE Medical Information Bus.

The Gaia satellite will survey the entire celestial sphere down to 20th magnitude, obtaining astrometry, photometry, and low resolution spectrophotometry on one billion astronomical sources, plus radial velocities for over one hundred million stars. Its main objective is to take a census of the stellar content of our Galaxy, with the goal of revealing its formation and evolution. Gaia's unique feature is the measurement of parallaxes and proper motions with hitherto unparalleled accuracy for many objects. As a survey, the physical properties of most of these objects are unknown. Here we describe the data analysis system put together by the Gaia consortium to classify these objects and to infer their astrophysical properties using the satellite's data. This system covers single stars, (unresolved) binary stars, quasars, and galaxies, all covering a wide parameter space. Multiple methods are used for many types of stars, producing multiple results for the end user according to different models and assumptions. Prior to its application to real Gaia data the accuracy of these methods cannot be assessed definitively. But as an example of the current performance, we can attain internal accuracies (RMS residuals) on F,G,K,M dwarfs and giants at G=15 (V=15-17) for a wide range of metallicites and interstellar extinctions of around 100K in effective temperature (Teff), 0.1mag in extinction (A0), 0.2dex in metallicity ([Fe/H]), and 0.25dex in surface gravity (logg). The accuracy is a strong function of the parameters themselves, varying by a factor of more than two up or down over this parameter range. After its launch in November 2013, Gaia will nominally observe for five years, during which the system we describe will continue to evolve in light of experience with the real data., Comment: 21 pages, accepted to A&A

The use of medical imaging for the diagnosis and, to a lesser extent, the prognosis and treatment of disease, is a common practice in modern medicine. Consequently, the need has arisen to develop applications that combine the ability to visualize digital medical images with the features required by clinical personnel in order to manage them. A number of medical image viewers are currently available, but nearly all of them are oriented towards visualizing and managing a single study of a patient, which limits the analysis of the expert. This paper introduces a novel application that contains the basic functionality required for common medical image analysis and which may be extended by a plug-in system with new features that could be demanded in the future. The application also makes it possible to visualize and analyze several studies at the same time, completely independently, increasing the accuracy of the analysis and facilitating the work of experts.

This chapter focuses on how to monitor marine spills using powerful tools such as remote sensing and Geographic Information Systems (GIS). On the one hand, remote sensing has been widely used as one of the main ways to periodically monitor large areas, as it allows to obtain data under poor weather conditions and in spite of darkness. We particularly center upon a sensor called “Advanced Synthetic Aperture Radar” (ASAR), which is part of the Envisat payload. On the other hand, GIS have emerged in recent years as a set of standards for data organization and representation that allow themanagement of geographic data.We provide a detailed description of the design and implementation of a tool that provides an integrated framework for the detection and localization of marine spills using remote sensing, GIS, and cloud computing. Cloud computing is used because of the enormous amount of data to be processed and the need of communication between users.

Geographic Information Systems (GIS) have gained popularity in recent years because they provide spatial data management and access through the Web. This article gives a detailed description of a tool that offers an integrated framework for the detection and localization of marine spills using remote sensing, GIS, and cloud computing. Advanced segmentation algorithms are presented in order to isolate dark areas in SAR images, including fuzzy clustering and wavelets. In addition, cloud computing is used for scaling up the algorithms and providing communication between users.

This work presents an algorithm that was developed to select the most relevant areas of a stellar spectrum to extract its basic atmospheric parameters. We consider synthetic spectra obtained from models of stellar atmospheres in the spectral region of the radial velocity spectrograph instrument of the European Space Agency’s Gaia space mission. The algorithm that demarcates the areas of the spectra sensitive to each atmospheric parameter (effective temperature and gravity, metallicity, and abundance of alpha elements) is a genetic algorithm, and the parameterization takes place through the learning of artificial neural networks. Due to the high computational cost of processing, we present a distributed implementation in both multiprocessor and multicomputer environments.

Among the myriad of data collected by the ESA Gaia satellite, about 150 million spectra will be delivered by the Radial Velocity Spectrometer (RVS) for stars as faint as G_RVS~16. A specific stellar parametrization will be performed for most of these RVS spectra. Some individual chemical abundances will also be estimated for the brightest targets. We describe the different parametrization codes that have been specifically developed or adapted for RVS spectra within the GSP-spec working group of the analysis consortium. The tested codes are based on optimization (FERRE and GAUGUIN), projection (MATISSE) or pattern recognition methods (Artificial Neural Networks). We present and discuss their expected performances in the recovered stellar atmospheric parameters (Teff, log(g), [M/H]) for B- to K- type stars. The performances for the determinations of [alpha/Fe] ratios are also presented for cool stars. For all the considered stellar types, stars brighter than G_RVS~12.5 will be very efficiently parametrized by the GSP-spec pipeline, including solid estimations of [alpha/Fe]. Typical internal errors for FGK metal-rich and metal-intermediate stars are around 40K in Teff , 0.1dex in log(g), 0.04dex in [M/H], and 0.03dex in [alpha/Fe] at G_RVS=10.3. Similar accuracies in Teff and [M/H] are found for A-type stars, while the log(g) derivation is more accurate. For the faintest stars, with G_RVS>13-14, a spectrophotometric Teff input will allow the improvement of the final GSP-spec parametrization. The reported results show that the contribution of the RVS based stellar parameters will be unique in the brighter part of the Gaia survey allowing crucial age estimations, and accurate chemical abundances. This will constitute a unique and precious sample for which many pieces of the Milky Way history puzzle will be available, with unprecedented precision and statistical relevance., Accepted for publication in A&A

In this document we present the implementation of three fuzzy clustering algorithms using the Insight Toolkit ITK. Firstly, we developed the conventional Fuzzy C-Means that will serve as the basis for the rest of the proposed algorithms. The next algorithms are the FCM with spatial constraints based on kernel-induced distance and the Modified Spatial Kernelized Fuzzy C-Means. Both of these introduce a Kernel function, replacing the Euclidean distance of the FCM, and spatial information into the membership function.These algorithms have been implemented in a threaded version to take advantage of the multicore processors. Moreover, providing an useful implementation make it possible that classes work with 2D/3D images, different kernels and spatial shapes.We included the source code as well as different 2D/3D examples, using several input parameters for the algorithms and obtaining the results generated on 2D/3D CT lung studies.

The complexity of detecting pulmonary nodules has led to the development of Computer Aided Systems (CAD) that automate and reduce the cost of this task. The first phase of such systems usually consists in preprocessing the Computer Tomography (CT) scans, with the aim of segmenting the lungs and eliminating the elements that might interfere with the process. This paper presents an automatic method for the segmentation of lungs into three-dimensional pulmonary high resolution CT images. The proposed method has three main steps, that combine both 3D and 2D techniques. Firstly the trachea and the main airways are removed from the volume; then the lung region is segmented by grey-level thresholding, separating the right and left lungs if a junction is visible in the image, and the lung contour is smoothed; finally, a ”region growing” is applied using two seeds from each identified lung, avoiding as such the incorporation of other elements that do not belong to the lungs.

The detection of pulmonary nodules is one of the most studied areas and challenging task in the field of medical image analysis, due the current relevance of the lung carcinoma. The difficulty and complexity of this task has led to the development of CAD systems for the automated detection of lung nodules in CT scans, which provides valuable assistance for radiologists and could improve the detection rate. A common phase of these systems is the detection of regions of interest (ROIs) that could be marked as nodules, in order to reduce the searching space problem. In this paper, we evaluate and compare the combination of various approaches of supervised vector machines (SVMs) with different kinds of fuzzy clustering algorithms, so as to improve the detection and segmentation of ROIs that could represent lung nodules in high resolution CT scans. These images are provided by the LIDC database (Lung Internet Database Consortium).

Within the scope of computational astropysics, this work presents an experimental study on the application of genetic algorithms to the automated extraction of relevant information from stellar spectra. The input data are a dataset obtained through the collaboration of our research group with the Gaia project of the European Space Agency. The results show that predictions based on spectral indices, which in turn were extracted by means of genetic algorithms, have accuracy levels that are very similar to those obtained through wavelength information. Working with a reduced dataset also implies the reduction of complexity and increased performance.

The detection of pulmonary nodules in CT images has been extensively researched because it is a highly complicated and socially interesting matter. The classical approach consists in the development of a computer-aided diagnosis (CAD) system that indicates, in phases, the presence or absence of nodules. A common phase of these systems is the detection of regions of interest (ROIs), that may correspond to nodules, in order to reduce the searching space. This paper evaluates the use of various neural networks for the defuzzification of the output of fuzzy clustering algorithms, in order to improve the detection of true positives and the reduction of false positives. Also, they are compared to the results from a support vector machine (SVM).

The detection of pulmonary nodules in radiological images or Computed Tomography has been widely researched in the field of medical image analysis, because it is a highly complicated but socially interesting matter. The classical approach consists in the development of a CAD system that indicates in phases the presence or absence of nodules. One of these phases is the detection of regions of interest that may be nodules, with the aim of reducing the problem area. This article evaluates various fuzzy clustering algorithms that represent current tendencies in the field, and proposes a new algorithm. The algorithms were evaluated with high resolution CTs from the Lung Internet Database Consortium.

This article describes a systems and networks monitoring system that is based on the Nagios tool and can easily be supervised by its user. The system is Web-managed and equipped with advanced 3D visualization features in Flash format that improve its interaction and allow it to reproduce the real management environment in multiple browsers with acceptable load and response times. Also, anomalies in the system's functioning are more easily detected thanks to the initial processing of the data from the monitoring tools with AI techniques.

A stellar spectrum is the finger-print identification of a particular star, the result of the radiation transport through its atmosphere. The physical conditions in the stellar atmosphere, its effective temperature, surface gravity, and the presence and abundance of chemical elements explain the observed features in the stellar spectra, such as the shape of the overall continuum and the presence and strength of particular lines and bands. The derivation of the atmospheric stellar parameters from a representative sample of stellar spectra collected by ground-based and spatial telescopes is essential when a realistic view of the Galaxy and its components is to be obtained. In the last decade, extensive astronomical surveys recording information of large portions of the sky have become a reality since the development of robotic or semi-automated telescopes. The Gaia satellite is one of the key missions of the European Space Agency (ESA) and its launch is planned for 2011. Gaia will carry out the so-called Galaxy Census by extracting precise information on the nature of its main constituents, including the spectra of objects (Wilkinson, 2005). Traditional methods for the extraction of the fundamental atmospheric stellar parameters (effective temperature (Teff), gravity (log G), metallicity ([Fe/H]), and abundance of alpha elements [a/Fe], elements integer multiples of the mass of the helium nucleus) are time-consuming and unapproachable for a massive survey involving 1 billion objects (about 1% of the Galaxy constituents) such as Gaia. This work presents the results of the authors’ study and shows the feasibility of an automated extraction of the previously mentioned stellar atmospheric parameters from near infrared spectra in the wavelength region of the Gaia Radial Velocity Spectrograph (RVS). The authors’ approach is based on a technique that has already been applied to problems of the non-linear parameterization of signals: artificial neural networks. It breaks ground in the consideration of transformed domains (Fourier and Wavelet Transforms) during the preprocessing stage of the spectral signals in order to select the frequency resolution that is best suited for each atmospheric parameter. The authors have also progressed in estimating the noise (SNR) that blurs the signal on the basis of its power spectrum and the application of noise-dependant algorithms of parameterization. This study has provided additional information that allows them to progress in the development of hybrid systems devoted to the automated classification of stellar spectra.

The detection of pulmonary nodules in radiological or CT images has been widely investigated in the field of medical image analysis due to the high degree of difficulty it presents. The traditional approach is to develop a multistage CAD system that will reveal the presence or absence of nodules to the radiologist. One of the stages within this system is the detection of ROIs (regions of interest) that may possibly be nodules, in order to reduce the scope of the problem. In this article we evaluate clustering algorithms that use different classification strategies for this purpose. In order to evaluate these algorithms we used high resolution CT images from the LIDC (Lung Internet Database Consortium) database.

The Gaia Galactic Survey Satellite is among the key future missions of the European Space Agency, who plans its launch near the end of 2011. Gaia will carry out what is being called a "Galaxy Survey", gathering exact information on the nature of the galaxy's main constituents, including the spectra of objects. A first approach to the extraction of atmospheric parameters from spectra (Effective Temperature (Teff), Gravity (log G), Metallicity ([Fe/H]), and Alpha elements abundance ([i¾?/Fe])) was their indirect measurement by means of the Morgan-Keenan (MK) classification system. However, traditional methods based on MK are slow and subjective. This is why an automatic and robust method has become an essential requirement. This work presents the preliminary results of a study on the automatic extraction potential of the main atmospheric stellar parameters: Teff, log G, [Fe/H], and [i¾?/Fe] in the spectral region of the RVS, between 8470 and 8740 A. The study was carried out by means of an algorithm based on artificial neural networks which is a technique widely used in that kind of automatic spectral parameterisation.

The main purpose of the GAIA mission of the European Space Agency (ESA) is to carry out a stereoscopic census of our galaxy and its environment. This task is based on measurements that will provide with unprecedented exactitude information on the astrometry (distance, movements, and spectral energy distribution) of approximately 1% of the objects in the milky way (109 objects). In the case of the brightest objects, essentially stars, spectra with intermediate resolution in the region of the infrared CaII triplet will be measured with a dedicated spectrograph, RVS (Radial Velocity Spectrometer). Stars can be characterized on the basis of their principal atmospheric parameters: effective temperature, gravity, metal content (general abundance of elements other than H and He), and their abundance of alpha elements (elements with Z>22, α), which provide information on the physical environment in which the star was born. The goal of the present work is to study spectral parameterization by means of ANN: it determines the optimal domain for the ANNs performance, and proposes an adequate noise detection and filtering algorithm by considering simulated data (synthetic spectra) in the spectral region of the RVS.

Ever since Zadeh established the basis of fuzzy logic in his famous article Fuzzy Sets (Zadeh, 1965), an increasing number of research areas have used his technique to solve and model problems and apply it, mainly, to control systems. This proliferation is largely due to the good results in classifying the ambiguous information that is typical of complex systems. Success in this field has been so overwhelming that it can be found in many industrial developments of the last decade: control of the Sendai train (Yasunobu & Miyamoto, 1985), control of air-conditioning systems, washing machines, auto-focus in cameras, industrial robots, etc. (Shaw, 1998) Fuzzy logic has also been applied to computerized image analysis (Bezdek & Keller & Krishnapuram & Pal, 1999) because of its particular virtues: high noise insensitivity and the ability to easily handle multidimensional information (Sutton & Bezdek & Cahoon, 1999), features that are present in most digital images analyses. In fuzzy logic, the techniques that have been most often applied to image analysis have been fuzzy clustering algorithms, ever since Bezdek proposed them in the seventies (Bezdek, 1973). This technique has evolved continuously towards correcting the problems of the initial algorithms and obtaining a better classification: techniques for a better initialization of these algorithms, and algorithms that would allow the evaluation of the solution by means of validity functions. Also, the classification mechanism was improved by modifying the membership function of the algorithm, allowing it to present an adaptative behaviour; recently, kernel functions were applied to the calculation of memberships. (Zhong & Wei & Jian, 2003) At the present moment, applications of fuzzy logic are found in nearly all Computer Sciences fields, it constitutes one of the most promising branches of Artificial Intelligence both from a theoretic and commercial point of view. A proof of this evolution is the development of intelligent systems based on fuzzy logic. This article presents several fuzzy clustering algorithms applied to medical images analysis. We also include the results of a study that uses biomedical images to illustrate the mentioned concepts and techniques.

Most astronomic databases include a certain amount of exceptional values that are generally called outliers. Isolating and analysing these "outlying objects" is important to improve the quality of the original dataset, to reduce the impact of anomalous observations, and most importantly, to discover new types of objects that were hitherto unknown because of their low frequency or short lifespan. We propose an unsupervised technique, based on artificial neural networks and combined with a specific study of the trained network, to treat the problem of outliers management. This work is an integrating part of the GAIA mission of the European Space Agency.

This article presents a framework, developed in JAVA, that facilitates the design and implementation of artificial neural networks and provides automatic multilanguage code generation (ANSI C and JAVA). The framework consists of three systems: a library, in which we factorize network-related functionalities, and two tools, an interpreter and a Web application, that provide ANN-related features. The functioning of this tool was tested by applying it to two problems in two different fields.

A widely used practice in Burn Wounds Centres is the incorporation of a photograph of the burned area into the patient’s clinical history. This photograph is used as a reference for each revision of the diagnosis or the therapeutic plan. This article presents the results of the evaluation of various fuzzy clustering algorithms applied to the segmentation of burn wounds images. The study compares recent and classical algorithms in order to establish a better comparison between the benefits of more complex techniques for pixel classification. Our final purpose is to develop a module that provides the medical expert with information on the extension of the burned area.

This chapter describes a hybrid approach to the unattended classification of low-resolution optical spectra of stars. The classification of stars in the standard MK system constitutes an important problem in the astrophysics area, since it helps to carry out proper stellar evolution studies. Manual methods, based on the visual study of stellar spectra, have been frequently and successfully used by researchers for many years, but they are no longer viable because of the spectacular advances of the objects collection technologies, which gather a huge amount of spectral data in a relatively short time. Therefore, we propose a cooperative system that is capable of classifying stars automatically and efficiently, by applying to each spectrum the most appropriate method or combined methods, which guarantees a reliable, consistent, and adapted classification. Our final objective is the integration of several artificial intelligence techniques in a unique hybrid system.

This chapter explains the foundations of a new support system for fisheries, based on connectionist techniques, digital image treatment, and fuzzy logic. The purpose of our system is to increase the output of the pelagic fisheries without endangering the natural balance of the fishing resources. It uses data from various remote sensors and the logbook of a collaborating fishing boat to improve the catches of the Prionace Glauca, a pelagic shark species also known as the blue shark.

This work presents an important part of our telemedicine system for critical care units: a distributed module based on intelligent agents technology that is dedicated to the process management of a network for medical monitoring, including distribution and control of processing tasks and bandwidth management. The system provides the real-time acquisition and analysis of physiological data, the graphical visualisation of these data, and their transmission to a central system charged with the collection and control of all the information concerning the patient, including knowledge-based systems (KBS) for medical reasoning.

For some years a strong increase in medical equipment for monitoring and controlling the physiological state of patients has been observed in intensive care units (ICU). The nursing staff of these units has a growing work load due to the fact that besides their assistential tasks they periodically perform transcriptions of the values monitored for the physiological variables of the patient. It is necessary to decrease the time dedicated by the nursing staff to low level tasks in order to improve the assistential quality and reduce costs. For this reason in this work we present an architecture which: a) Facilitates the connectivity of medical equipment by means of the integration of MIB (Medical Information Bus) norms. b) Reduces the low level tasks of the nursing staff by means of distributed intelligence at bed level using knowledge based supervision systems. c) Facilitates the man-machine interaction using ergonomic criteria in the design of the input (keyboard). output (screen) and mixed (voice) interfxes. 9 Possibilitates a global monitoring in the ICU for following the state of all the patients.

This work presents a comparative study of two computational techniques - expert systems and artificial neural networks - applied to a specific field of astrophysics, the classification of the optical spectra of stars. We present a description of various expert systems and neural networks models, and the comparison of the results obtained by each technique individually and by a combination of both. We do not only intend to analyse the efficiency of these two approaches in the classification of stellar spectra; our final objective is the integration of several techniques in a unique hybrid system. This system will be capable of applying the most appropriate classification method to each spectrum, which widely opens the research in the field of automatic spectral classification.

The purpose of this work is to present a comparative analysis of knowledge-based systems, artificial neural networks and statistical clustering algorithms applied to the classification of low resolution stellar spectra. These techniques were used to classify a sample of approximately 258 optical spectra from public catalogues using the standard MK system. At present, we already dispose of a hybrid system that carries out this task, applying the most appropriate classification method to each spectrum with a success rate that is similar to that of human experts.