83 research outputs found
Training Set Expansion in Handwritten Character Recognition
No full text[EN] In this paper, a process of expansion of the training set by synthetic generation of handwritten uppercase letters via deformations of natural images is tested in combination with an approximate k-Nearest Neighbor (k-NN) classifier. It has been previously shown [11] [10] that approximate nearest neighbors search in large databases can be successfully used in an OCR task, and that significant performance improvements can be consistently obtained by simply increasing the size of the training set. In this work, extensive experiments adding distorted characters to the training set are performed, and the results are compared to directly adding new natural samples to the set of prototypes.Work partially supported by the Spanish CICYT under grant TIC2000-1703-CO3-01Cano Pérez, J.;Perez-Cortes, J.;Arlandis, J.;Llobet Azpitarte, R. (2002). Training Set Expansion in Handwritten Character Recognition. Lecture Notes in Computer Science. 2396:548-556. https://doi.org/10.1007/3-540-70659-3_57S548556239
Comparison of Feature Extraction Methods for Breast Cancer Detection
No full text[EN] Although screening mammography is widely used for the detection of breast tumors, it is difficult for a radiologist to interpret correctly a mammogram. It is possible to improve this task by using a computer aided diagnosis system (CAD) which highlights the areas most likely to contain cancer cells. In this paper, we present and compare five different feature extraction methods for breast cancer detection in digitized mammograms. All the methods are based on features extracted from a local window and on a k-nearest neighbor classifier with fast search.This work has been partially supported by the Spanish CICYT under grant TIC2003-08496-CO4 and by Agencia Valenciana de Ciencia y Tecnologıa under contract GRUPOS03/031.Llobet Azpitarte, R.;Paredes Palacios, R.;Perez-Cortes, J. (2005). Comparison of Feature Extraction Methods for Breast Cancer Detection. Lecture Notes in Computer Science. 3523:495-502. https://doi.org/10.1007/11492542_61S495502352
Fast and Accurate Handwritten Character Recognition Using Approximate Nearest Neighbours Search on Large Databases
No full text[EN] In this work, fast approximate nearest neighbours search algorithms are shown to provide high accuracies, similar to those of exact nearest neighbour search, at a fraction of the computational cost in an OCR task. Recent studies [26,15] have shown the power of k-nearest neighbour classifiers (k-nn) using large databases, for character recognition. In those works, the error rate is found to decrease consistently as the size of the database increases. Unfortunately, a large database implies large search times if an exhaustive search algorithm is used. This is often cited as a major problem that limits the practical value of the k- nearest neighbours classification method. The error rates and search times presented in this paper prove, however, that k-nn can be a practical technique for a character recognition task.Perez-Cortes, J.;Llobet Azpitarte, R.;Arlandis, J. (2000). Fast and Accurate Handwritten Character Recognition Using Approximate Nearest Neighbours Search on Large Databases. Lecture Notes in Computer Science. 1876:767-776. https://doi.org/10.1007/3-540-44522-6_79S767776187
Computer-Aided Prostate Cancer Detection in Ultrasonographic Images
No full text[EN] Prostate cancer is one of the most frequent cancer in men and a major cause of mortality in developed countries. Detection of the prostate carcinoma at an early stage is crucial for a succesfull treatment. In this paper, a method for analysis of transrectal ultrasonography images aimed at computer-aided diagnosis of prostate cancer is presented. Althogh the task is extremely difficult due to a problem of imperfect supervision, we have obtained promising results indicating that valid information for the diagnostic is present in the images. Two classifiers based on k-Nearest Neighbours and Hidden Markov Models are compared.This work has been partially supported by the Valencian OCYT under grant CTIDIA/2002/80 and by the Spanish CICYT under grant TIC2000-1703-CO3-01.Llobet Azpitarte, R.;Toselli, AH.;Perez-Cortes, J.;Juan, A. (2003). Computer-Aided Prostate Cancer Detection in Ultrasonographic Images. Lecture Notes in Computer Science. 2652:411-419. https://doi.org/10.1007/978-3-540-44871-6_48S411419265
Recensione a F. BALAGUER CALLEJÓN, M. AZPITARTE SÁNCHEZ, E. GUILLÉN LÓPEZ, J. F. SÁNCHEZ BARRILAO (a cura di), Los derechos fundamentales ante las crisis económicas y de seguridad en un marco constitucional fragmentado, Navarra, Thomson Reuters, 2020, pp. 549
No full textlo scopo del volume “Los Derechos fundamentales ante las crisis económicas y de seguridad en un marco constitucional fragmentado”, a cura di Francisco Balaguer Callejón, Miguel Azpitarte Sánchez, Enrique Guillén López e Juan Francisco Sánchez Barrilao, si pone l’obiettivo di riflettere sulle crisi che il costituzionalismo sta affrontando. Come evidenziato da Balaguer Callejón, nel suo contributo “El constitucionalismo en su hora crítica. Crisis clásicas y crisis inéditas”, non si tratta solo delle crisi classiche – ovvero la crisi dello Stato liberale e quella della democrazia rappresentativa –, ma anche di crisi inedite che hanno comportato la crisi dello Stato e della democrazia tout court.
Divengono, allora, concetti assolutamente cruciali nel presente e nel futuro del costituzionalismo, le nozioni di “economia”, e – forse soprattutto – di “sicurezza”: due grandezze capaci di determinare nuove dinamiche nel diritto costituzionale e, nello specifico, in uno dei suo aspetti chiave ovvero i diritti fondamental
Composition of Constraint, Hypothesis and Error Models to improve interaction in Human-Machine Interfaces
No full textWe use Weighted Finite-State Transducers (WFSTs) to represent the different sources of information available: the initial hypotheses, the possible errors, the constraints imposed by the task (interaction language) and the user input. The fusion of these models to find the most probable output string can be performed efficiently by using carefully selected transducer operations. The proposed system initially suggests an output based on the set of hypotheses, possible errors and Constraint Models. Then, if human intervention is needed, a multimodal approach, where the user input is combined with the aforementioned models, is applied to produce, with a minimum user effort, the desired output. This approach offers the practical advantages of a de-coupled model (e.g. input-system + parameterized rules + post-processor), keeping at the same time the error-recovery power of an integrated approach, where all the steps of the process are performed in the same formal machine (as in a typical HMM in speech recognition) to avoid that an error at a given step remains unrecoverable in the subsequent steps. After a presentation of the theoretical basis of the proposed multi-source information system, its application to two real world problems, as an example of the possibilities of this architecture, is addressed. The experimental results obtained demonstrate that a significant user effort can be saved when using the proposed procedure. A simple demonstration, to better understand and evaluate the proposed system, is available on the web https://demos.iti.upv.es/hi/. (C) 2015 Elsevier B.V. All rights reserved.Navarro Cerdan, JR.; Llobet Azpitarte, R.; Arlandis, J.; Perez-Cortes, J. (2016). Composition of Constraint, Hypothesis and Error Models to improve interaction in Human-Machine Interfaces. Information Fusion. 29:1-13. doi:10.1016/j.inffus.2015.09.001S1132
SEQENS: An ensemble method for relevant gene identification in microarray data
No full text[EN] This paper describes an ensemble feature identification algorithm called SEQENS, and measures its capability to identify the relevant variables in a case-control study using a genetic expression microarray dataset. SEQENS uses Sequential Feature Search on multiple sample splitting to select variables showing stronger relation with the target, and a variable relevance ranking is finally produced. Although designed for feature identification, SEQENS could also serve as a basis for feature selection (classifier optimisation). Cliff, a ranking evaluation metric is also presented and used to assess the feature identification algorithms when a groundtruth of relevant variables is available. To test performance, three types of synthetic groundtruths emulating fictitious diseases are generated from ten randomly chosen variables following different target pattern distributions using the E-MTAB-3732 dataset. Several sample-to-dimensionality ratios ranging from 300 to 3,000 observations and 854 to 54,675 variables are explored. SEQENS is compared with other feature selection or identification state-of-the-art methods. On average, the proposed algorithm identifies better the relevant genes and exhibits a stronger stability. The algorithm is available to the community.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.It was also funded by the Cervera Network of Excellence Project in Data-based Enabling Technologies (AI4ES) , co-funded by the Centre for Industrial and Technological Development, E.P.E. (CDTI) and by the European Union through the NextGenerationEU Fund, within the Cervera Aids program for Technological Centres, with the expedient number CER-20211030.Signol, F.; Arnal-Benedicto, L.; Navarro Cerdan, JR.; Llobet Azpitarte, R.; Arlandis, J.; Perez-Cortes, J. (2023). SEQENS: An ensemble method for relevant gene identification in microarray data. Computers in Biology and Medicine. 152. https://doi.org/10.1016/j.compbiomed.2022.106413S10641315
Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients
No full text[EN] Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.This work was supported by Generalitat Valenciana through the "Instituto Valenciano de Competitividad Empresarial-IVACE'' under Grant IMDEEA/2020/69.Omar del Tejo Catalá; Salvador Igual, I.; Perez-Benito, FJ.; Millan-Escriva, D.; Ortiz, V.; Llobet Azpitarte, R.; Perez-Cortes, J. (2021). Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients. IEEE Access. 9:42370-42383. https://doi.org/10.1109/ACCESS.2021.3065456S4237042383
Terapia farmacológica después del infarto agudo de miocardio
No full textThe treatment after myocardial infarction depends on the patient risk. Daily aspirine
is advised for patients at low risk. There is also a growing tendency to prescribe an
"statine" in order to mantain the cholesterol level below 210 mg/dL. Estrogen therapy
can be considered in post-menopausal women. Beta-blocker agents have a proved benefIt
for patients at moderate risk because they reduce sudden death and reinfarction. Verapamil
is an option when the beta-blocker can not be tolerated. Treatment with ACE inhibitors
benefIt patients with left ventricular systolic dysfunction. Other pharmacologic agents are
of unproved benefIt - eg, nitrates- or have harrnful effects --eg, nifedipine, diltiazem
in patients with heart failure and cIass 1 antiarrhythmic drugs-. Only amiodarone seems
to be useful for patients with severe ventricular arryhthmias.El tratamiento después del infarto depende del riesgo del paciente. Se aconseja la aspirina
para los enfennos de bajo riesgo. Hay también una tendencia creciente a prescribir una
"estatina" con el fIn de mantener el nivel de colesterol por debajo de los 210 mg/dL. La
terapia estrogénica puede ser útil en las mujeres post-menopáusicas. Los betabloqueantes,
en los pacientes de moderado riesgo, son benefIciosos puesto que reducen la muerte súbita
y el reinfarto. El verapamilo puede ser una alternativa cuando el betabloqueante no es
bien tolerado. Los inhibidores ECA son benefIciosos en los pacientes de alto riesgo con
disfunción sistólica del ventrículo izquierdo. Otros fánnacos no son de benefIcio probado
-p. ej ., los nitratos- o tienen efectos perjudiciales -p. ej ., el nifedipino, el diltiazem
en los pacientes con insufIciencia cardíaca y los antiarrítmicos de clase 1- . La amiodarona
es el único fármaco que puede ser útil en los pacientes con arritmias ventriculares severa
Mefistófeles : año I, domingo 16 de junio de 1889, núm. 13
No full textBHR/B-022-142 (14) enc. junto con otras obras, formando un vol. facticio2 ej. de la misma obraBHR/B-022-142 (14) Enc. Hol.Zorrilla está entre nosotros. Al eminente poeta Don José Zorrilla/ F. Gálvez Durán.|tZorrilla en Granada/ Francisco de Paula Valladar. La Llegada del poeta/ Cayetano del Castillo.Don José Zorrilla/ J. Azpitarte. Á Zorrilla/ Adolfo Sánchez Ortega. Historia de la Corona/ Ignacio Legaza Herrera. Á Zorrilla/ F. Jiménez Campaña. El Taller del poeta/ S. Madrazo y Villar. Al ilustre poeta D. José Zorrilla en su coronación/ Nicolás Calleja ... [et al.
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