Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

The development of a classification scheme for brake linings. Volume I. Summary report.

National Highway Traffic Safety Administration, Washington, D.C.

The development of a classification scheme for brake linings. Volume II. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Standards and tests for the measurement of the efficiency of schools and school systems,

cont. VI. The application of standard measurements to school administration. [By] D.C. Bliss. VII. A half-year's progress in the achievement of one school system. A. The progress as measured by the Thorndike visual vocabulary test. B. The progress as measured by the Courtis tests, series B. [By] H.G. Childs. VIII. Courtis tests in arithmetic: value to superintendents and teacher. [By] S.A. Courtis. IX. Use of standard tests at Salt Lake City, Utah. [By] E. P. Cubberley. X. Reading. [By] C.H. Judd. XI. Studies by the Bureau of research and efficiency of Kansas City, Mo. [By] George Melcher. XII. The effects of efficiency tests in reading on a city school system. [By] E.E. Oberholtzer. XIII. Investigation of spelling in the schools of Oakland, Cal. [By] J.B. Sears. XIV. Standard tests as aids in the classification and promotion of pupils. [By] Daniel Starch. XV. The use of mental tests in the school. [By] G.M. Whipple.

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature. (C) 2004 Elsevier Inc. All rights reserved.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.

Mechatronic system for performing blood pre-transfusion tests

The blood types determination is essential to perform safe blood transfusions. In emergency situations isadministrated the “universal donor” blood type. However, sometimes, this blood type can cause incom-patibilities in the transfusion receptor. A mechatronic prototype was developed to solve this problem.The prototype was built to meet specific goals, incorporating all the necessary components. The obtainedsolution is close to the final system that will be produced later, at industrial scale, as a medical device.The prototype is a portable and low cost device, and can be used in remote locations. A computer appli-cation, previously developed is used to operate with the developed mechatronic prototype, and obtainautomatically test results. It allows image acquisition, processing and analysis, based on Computer Visionalgorithms, Machine Learning algorithms and deterministic algorithms. The Machine Learning algorithmsenable the classification of occurrence, or alack of agglutination in the mixture (blood/reagents), and amore reliable and a safer methodology as test data are stored in a database. The work developed allowsthe administration of a compatible blood type in emergency situations, avoiding the discontinuity of the“universal donor” blood type stocks, and reducing the occurrence of human errors in the transfusion practice.

Probabilistic Models in Cryptography, Coding Theory and Tests for PRNG

2000 Mathematics Subject Classification: 94A29, 94B70

Associação entre comprometimento motor, mobilidade e qualidade de vida em crianças com paralisia cerebral espástica, nível I, II e III do Gross Motor Function Classification System

The objective of this study was to verify the association between some mobility items of the International Classification Functionality (ICF), with the evaluations Gross Motor Function Measure (GMFM-88), 1-minute walk test (1MWT) and if the motor impairment influences the quality of life in children with Cerebral Palsy (PC), by using the Paediatric Quality of Life Inventory (PedsQL 4.0 versions for children and parents). The study included 22 children with cerebral palsy spastic, classified in levels I, II, and III on the Gross Motor Function Classification System (GMFCS), with age group of 9.9 years old. Among those who have participated, seven of them were level I, eight of them were level II and seven of them were level III. All of the children and teenagers were rated by using check list ICF (mobility item), GMFM-88, 1-minute walk test and PedsQL 4.0 questionnaires for children and parents. It was observed a strong correlation between GMFM-88 with check list ICF (mobility item), but moderate correlation between GMFM-88 and 1-minute walk test (1MWT). It was also moderate the correlation between the walking test and the check list ICF (mobility item). The correlation between PedsQl 4.0 questionnaires for children and parents was weak, as well as the correlation of both with GMFM, ICF (mobility item) and the walking test. The lack of interrelation between physical function tests and quality of life, indicates that, regardless of the severity of the motor impairment and the difficulty with mobility, children and teenagers suffering of PC spastic, functional level I, II and III GMFCS and their parents have a varied opinion regarding the perception of well being and life satisfaction.

Collocations used in DELE B1 reading tests

This paper aims to investigate the use of collocations in DELE B1. We select the reading texts from DELE B1 (2010 to 2014) as research data. The investigation includes: First of all, we will study the theory of collocation and the classification as well as its application to the foreign language learning and teaching. Second, we will analyze the types of collocation annotated by Corpus Tool. Third, we tend to calculate the frequency use of each type of collocations written in Spanish reading texts. Next, we will discuss the interrelationship between collocations and text themes. Finally, we would like to compare the differences of results of collocation use between these two corpus tools: Corpus Tool and Corpus del Español in order to understand the native speakers’ preference of use collocations as well as to provide supplementay materials for teaching of Spanish reading. We hope that the expected results of our research will offer useful references for improving students' Spanish reading comprehension to pass DELE B1 examinations.

Classification multi-échelle d'images à très haute résolution spatiale basée sur une nouvelle approche texturale

Résumé : Face à l’accroissement de la résolution spatiale des capteurs optiques satellitaires, de nouvelles stratégies doivent être développées pour classifier les images de télédétection. En effet, l’abondance de détails dans ces images diminue fortement l’efficacité des classifications spectrales; de nombreuses méthodes de classification texturale, notamment les approches statistiques, ne sont plus adaptées. À l’inverse, les approches structurelles offrent une ouverture intéressante : ces approches orientées objet consistent à étudier la structure de l’image pour en interpréter le sens. Un algorithme de ce type est proposé dans la première partie de cette thèse. Reposant sur la détection et l’analyse de points-clés (KPC : KeyPoint-based Classification), il offre une solution efficace au problème de la classification d’images à très haute résolution spatiale. Les classifications effectuées sur les données montrent en particulier sa capacité à différencier des textures visuellement similaires. Par ailleurs, il a été montré dans la littérature que la fusion évidentielle, reposant sur la théorie de Dempster-Shafer, est tout à fait adaptée aux images de télédétection en raison de son aptitude à intégrer des concepts tels que l’ambiguïté et l’incertitude. Peu d’études ont en revanche été menées sur l’application de cette théorie à des données texturales complexes telles que celles issues de classifications structurelles. La seconde partie de cette thèse vise à combler ce manque, en s’intéressant à la fusion de classifications KPC multi-échelle par la théorie de Dempster-Shafer. Les tests menés montrent que cette approche multi-échelle permet d’améliorer la classification finale dans le cas où l’image initiale est de faible qualité. De plus, l’étude effectuée met en évidence le potentiel d’amélioration apporté par l’estimation de la fiabilité des classifications intermédiaires, et fournit des pistes pour mener ces estimations.