Biblioteca Digital

O objetivo deste estudo foi buscar evidências sobre o uso de sistemas de classificação de enfermagem na assistência, por meio de revisão integrativa da literatura. Com a busca nas bases LILACS e PubMed, com as palavras-chave classificação, enfermagem, padronizado, sistema, linguagem, selecionaram-se 38 artigos. Encontraram-se cinco sistemas de classificação principais implementados nos serviços: de diagnósticos de enfermagem (da North American Nursing Diagnosis Association International), intervenções de enfermagem (Nursing Interventions Classification), resultados de enfermagem (Nursing Outcomes Classification), a Classificação Internacional para a Prática de Enfermagem e a Classificação Internacional das Práticas de Enfermagem em Saúde Coletiva. Os artigos abordaram aspectos relacionados à implementação, avaliação, educação continuada e validação de termos relacionados aos sistemas de classificação. Há benefícios para a assistência com a implementação desses sistemas, com melhora da assistência, da qualidade das informações e da organização do serviço.; Este estudio buscó evidencias sobre el uso de sistemas de clasificación de enfermería en la atención, a través de revisión integradora de la literatura. Fueron utilizadas las bases de datos LILACS y PubMed...

Background: Traditional data classification techniques usually divide the data space into sub-spaces, each representing a class. Such a division is carried out considering only physical attributes of the training data (e.g., distance, similarity, or distribution). This approach is called low-level classification. On the other hand, network or graph-based approach is able to capture spacial, functional, and topological relations among data, providing a so-called high-level classification. Usually, network-based algorithms consist of two steps: network construction and classification. Despite that complex network measures are employed in the classification to capture patterns of the input data, the network formation step is critical and is not well explored. Some of them, such as K-nearest neighbors algorithm (KNN) and -radius, consider strict local information of the data and, moreover, depend on some parameters, which are not easy to be set. Methods: We propose a network-based classification technique, named high-level classification on K-associated optimal graph (HL-KAOG), combining the K-associated optimal graph and high-level prediction. In this way, the network construction algorithm is non-parametric, and it considers both local and global information of the training data. In addition...

Em Aprendizado de Máquina e Mineração de Dados, muitos dos trabalhos de classificação reportados na literatura envolvem classificação plana (flat classification), em que cada exemplo é associado a uma dentre um conjunto finito (e normalmente pequeno) de classes, todas em um mesmo nível. Entretanto, existem problemas de classificação mais complexos em que as classes a serem preditas podem ser dispostas em uma estrutura hierárquica. Para esses problemas, a utilização de técnicas e conceitos de classificação hierárquica tem se mostrado útil. Uma das linhas de pesquisa com grande potencial para a utilização de tais técnicas é a Bioinformática. Dessa forma, esta dissertação apresenta um estudo envolvendo técnicas de classificação hierárquica aplicadas à predição de classes funcionais de proteínas. No total foram investigados doze algoritmos hierárquicos diferentes, sendo onze deles representantes da abordagem Top-Down, que foi o enfoque da investigação realizada. O outro algoritmo investigado foi o HC4.5, um algoritmo baseado na abordagem Big- Bang. Parte dos algoritmos estudados foram desenvolvidos com base em uma variação da abordagem Top-Down, denominada de Top-Down Ensemble, que foi proposta neste estudo. Alguns do algoritmos baseados nessa nova abordagem apresentaram resultados promissores...

Classificação é, provavelmente, a tarefa mais estudada na área de Aprendizado de Máquina, possuindo aplicação em uma grande quantidade de problemas reais, como categorização de textos, diagnóstico médico, problemas de bioinformática, além de aplicações comerciais e industriais. De um modo geral, os problemas de classificação podem ser categorizados quanto ao número de rótulos de classe que podem ser associados à cada exemplo de entrada. A abordagem mais investigada pela comunidade de Aprendizado de Máquina é a de classes mutuamente exclusivas. Entretanto, existe uma grande variedade de problemas importantes em que cada exemplo de entrada pode ser associado a mais de um rótulo ou classe. Esses problemas são denominados problemas de classificação multirrótulo. Os Learning Classifier Systems(LCS) constituem uma técnica de Indução de Regras de Classificação que tem como principal mecanismo de busca um Algoritmo Genético. Essa técnica busca encontrar um conjunto de regras que tenha alta precisão de classificação, que seja compreensível e que possua regras consideradas interessantes sob o ponto de vista de classificação. Apesar de existirem na literatura diversos trabalhos sobre os LCS para problemas de classificação com classes mutuamente exclusivas...

O processo automático de classificação de dados em geral, e em particular de classificação de imagens, é uma tarefa computacionalmente intensiva e variável em termos de precisão, sendo consideravelmente dependente da configuração do classificador e da representação dos dados utilizada. Muitos dos fatores que afetam uma adequada aplicação dos métodos de classificação ou categorização para imagens apontam para a necessidade de uma maior interferência do usuário no processo. Para isso são necessárias mais ferramentas de apoio às várias etapas do processo de classificação, tais como, mas não limitadas, a extração de características, a parametrização dos algoritmos de classificação e a escolha de instâncias de treinamento adequadas. Este doutorado apresenta uma metodologia para Classificação Visual de Imagens, baseada na inserção do usuário no processo de classificação automática através do uso de técnicas de visualização. A ideia é permitir que o usuário participe de todos os passos da classificação de determinada coleção, realizando ajustes e consequentemente melhorando os resultados de acordo com suas necessidades. Um estudo de diversas técnicas de visualização candidatas para a tarefa é apresentado...

Em problemas convencionais de classificação, cada exemplo de um conjunto de dados é associado a apenas uma dentre duas ou mais classes. No entanto, existem problemas de classificação mais complexos, nos quais as classes envolvidas no problema são estruturadas hierarquicamente, possuindo subclasses e superclasses. Nesses problemas, exemplos podem ser atribuídos simultaneamente a classes pertencentes a dois ou mais caminhos de uma hierarquia, ou seja, exemplos podem ser classificados em várias classes localizadas em um mesmo nível hierárquico. Tal hierarquia pode ser estruturada como uma árvore ou como um grafo acíclico direcionado. Esses problemas são chamados de problemas de classificação hierárquica multirrótulo, sendo mais difíceis devido à alta complexidade, diversidade de soluções, difícil modelagem e desbalanceamento dos dados. Duas abordagens são utilizadas para tratar esses problemas, chamadas global e local. Na abordagem global, um único classificador é induzido para lidar com todas as classes do problema simultaneamente, e a classificação de novos exemplos é realizada em apenas um passo. Já na abordagem local, um conjunto de classificadores é induzido, sendo cada classificador responsável pela predição de uma classe ou de um conjunto de classes...

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups...

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies; Wetland is one of the most valuable ecological systems in nature. Wetland habitat is a set of comprehensive information of wetland distribution, wetland habitat types are essential to wetland management programs. Maps of wetland should provide sufficient detail, retain an appropriate scale and be useful for further mapping and inventory work (Queensland wetland framework). Remotely sensed image classification techniques are useful to detect vegetation patterns and species combination in the inaccessible regions. Automated classification procedures are conducted to save the time of the research. The purpose of the research was to develop a hierarchical classification approach that effectively integrate ancillary information into the classification process and combines ISODATA (iterative self-organizing data analysis techniques algorithm) clustering, Maximum likelihood and rule-based classifier. The main goal was to find out the best possible combination or sequence of classifiers for typically classifying wetland habitat types yields higher accuracy than the existing classified wetland map from Landsat ETM data. Three classification schemes were introduced to delineate the wetland habitat types in the idea of comparison among the methods. The results showed the low accuracy of different classification schemes revealing the fact that image classification is still on the way toward a fine proper procedure to get high accuracy result with limited effort to make the investigation on sites. Even though the motivation of the research was to apply an appropriate procedure with acceptable accuracy of classified map image...

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.; This study investigates the potential of using very high resolution (VHR) QuickBird data to conduct vegetation classification of the Sundarban mangrove forest in Bangladesh and compares the results with Landsat TM data. Previous studies of vegetation classification in Sundarban involved Landsat images using pixel-based methods. In this study, both pixelbased and object-based methods were used and results were compared to suggest the preferred method that may be used in Sundarban. A hybrid object-based classification method was also developed to simplify the computationally demanding object-based classification, and to provide a greater flexibility during the classification process in absence of extensive ground validation data. The relation between NDVI (Normalized Difference Vegetation Index) and canopy cover was tested in the study area to develop a method to classify canopy cover type using NDVI value. The classification process was also designed with three levels of thematic details to see how different thematic scales affect the analysis results using data of different spatial resolutions. The results show that the classification accuracy using QuickBird data stays higher than that of Landsat TM data. The difference of classification accuracy between QuickBird and Landsat TM remains low when thematic details are low...

AIM: We evaluated five stream classification systems observing: 1) differences in richness, abundance and macroinvertebrates communities among stream classes within classification systems; and 2) whether classification systems present better performance using macroinvertebrates. Additionally, we evaluated the effects of taxonomic resolution and data type (abundance and presence) on results. METHODS: Five stream classification systems were used, two based on hydroregions, one based on ecoregions by FEOW, a fourth one based on stream orders and the last one based on clusters of environment variables sampled in 37 streams at Rio Grande do Sul state, Brazil. We used a randomization test to evaluate differences of richness and abundance, a db-MANOVA to evaluate the differences of species assemblages and Classification Strength (CS) to evaluate the classifications performance. RESULTS: There were differences of richness and abundance among stream classes within each stream classification. The same result was found for community data, except for stream order classifications in family level. We observed that stream classes obtained for each stream classification differed in terms of environment variables (db-MANOVA). The classification based on environment variables showed higher CS values than other classification systems. The taxonomic resolution was important to the observed results. Data on genera level presented CS values 12% higher than family level for cluster classification...

This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system...

This book examines the nature of folk classification, including biological, food, totemic and linguistic systems of plant and animal taxonomy, by the Anindiyakwa people of Groote Eyelandt. It discusses the suggestion that all folk classification is complexive, based on associations and not hierarchical. It seeks to show that complexive classification most commonly appears in the realm of symbolic and not biological classification. Compares and contrasts the nature of classification systems on a case by case basis. Considers data in the light of historical records, and discusses theoretical and practical implications of the research undertaken.

In this thesis we present a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. For this purpose, we put a camera on a mobile robot and use it to capture images which are then analyzed to recognize the terrains present in these images. There are two sets of approaches that we use to classify terrains. The first is based on greyscale images and the second one is based on color images. For greyscale images, we use two different robot platforms for two different scenarios. The first robot platform is a wheeled outdoor robot. The second platform is a flying robot. For terrain classification, we modify and test three approaches called SURF, Daisy and Contrast Context Histogram, which are traditionally not used for texture classification. We compare these with more traditional texture classification approaches, such as Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and a newer extension Local Adaptive Ternary Patterns (LATP). The image is divided into a grid and local features are calculated on the cells of this grid. These features are then used to train a classifier that can differentiate between different terrain classes. Images of different terrain types are captured using a single camera mounted on a mobile outdoor robot. We drove our robot under different weather and ground conditions and captured data of different terrain types for our experiments. We did not filter out blurred images which occur due to robot motion and other artifacts caused by rain...

The idea of organizing knowledge and the determinism in classifícation structures implicitly involve certain limits which are translated into a General Theory on the Classifícation of Knowledge, given that classifícation responds to specific parameters and structures more than to a theoretical concept. The classifícation of things is a refiection of their classifícation by man, and this is what determines classifícation structures. The classifícation and organization of knowledge are presented to us as an artificial construct or as a useful fiction elaborated by man. Positivist knowledge reached its peak in the 20* century when science classifications and implemented classifícation systems based on the latter were to be gestated and Consolidated. Pragmatism was to serve as the epistemological and theoretical basis for science and its classifícation. If the classifícation of the sciences has given rise to clastification systems, the organisation and representation of knowledge has to currendy give rise to the context of the globalisation of electronic information in the hypertextual organisational form of electronic information where, if in information the médium ivas the message, in organisation the médium is the structure. The virtual reality of electronic information delves even deeper into it; the process is completed as the subject attempts to look for information. This information market needs standards of an international nature for documents and data. This body of information organization will be characterized by its dynamic nature. If formal and material structures change our concept of knowledge and the way it is structured...

Diese Dissertation befasst sich mit den Mechanismen, die Menschen verwenden, um Merkmale aus visuellen Reizen zu erzeugen und anschliessend zu klassifizieren. Es wird eine experimentelle Methode entwickelt, die menschliche Psychophysik mit maschinellem Lernen verbindet. Im Mittelpunkt der Arbeit steht ein Geschlechtsklassifikationsexperiment, das mit Hilfe der Kopfdatenbank des Max Planck Instituts durchgeführt wird. Hierzu werden verschiedene niedrig-dimensionale Merkmale aus den Gesichtsbildern extrahiert. Das Klassifikationsverfahren auf diesen Merkmalen ist durch eine Trennebene zwischen den beiden Klassen modelliert. Die Antworten der Versuchspersonen werden verglichen und korreliert mit der Distanz der Merkmale zur Trennebene. In dieser Arbeit wird bewiesen, dass maschinelles Lernen ein neues und wirksames algorithmisches Verfahren ist, um Einblicke in menschliche kognitive Prozesse zu erhalten. In einem ersten psychophysischen Klassifikationsexperiment wird gezeigt, dass eine hohe Fehlerrate und ein niedriges Vertrauen der Versuchspersonen einer längeren Verarbeitung der Information im Gehirn entsprechen. Ein zweites Klassifikationsexperiment auf den selben Reizen aber in unterschiedlicher Reihenfolge, bestätigt die Konsistenz der Antworten der Versuchspersonen und die Reproduzierbarkeit der folgenden Resultate. Es wird gezeigt...

v. 25, n. 4, p. 406-417, out./dez., 2013; Aim: We evaluated five stream classification systems observing: 1) differences in richness, abundance and macroinvertebrates communities among stream classes within classification systems; and 2) whether classification systems present better performance using macroinvertebrates. Additionally, we evaluated the effects of taxonomic resolution and data type (abundance and presence) on results. Methods: Five stream classification systems were used, two based on hydroregions, one based on ecoregions by FEOW, a fourth one based on stream orders and the last one based on clusters of environment variables sampled in 37 streams at Rio Grande do Sul state, Brazil. We used a randomization test to evaluate differences of richness and abundance, a db-MANOVA to evaluate the differences of species assemblages and Classification Strength (CS) to evaluate the classifications performance. Results: There were differences of richness and abundance among stream classes within each stream classification. The same result was found for community data, except for stream order classifications in family level. We observed that stream classes obtained for each stream classification differed in terms of environment variables (db-MANOVA). The classification based on environment variables showed higher CS values than other classification systems. The taxonomic resolution was important to the observed results. Data on genera level presented CS values 12% higher than family level for cluster classification...

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases...

Les employés d’un organisme utilisent souvent un schéma de classification personnel pour organiser les documents électroniques qui sont sous leur contrôle direct, ce qui suggère la difficulté pour d’autres employés de repérer ces documents et la perte possible de documentation pour l’organisme. Aucune étude empirique n’a été menée à ce jour afin de vérifier dans quelle mesure les schémas de classification personnels permettent, ou même facilitent, le repérage des documents électroniques par des tiers, dans le cadre d’un travail collaboratif par exemple, ou lorsqu’il s’agit de reconstituer un dossier. Le premier objectif de notre recherche était de décrire les caractéristiques de schémas de classification personnels utilisés pour organiser et classer des documents administratifs électroniques. Le deuxième objectif consistait à vérifier, dans un environnement contrôlé, les différences sur le plan de l’efficacité du repérage de documents électroniques qui sont fonction du schéma de classification utilisé. Nous voulions vérifier s’il était possible de repérer un document avec la même efficacité, quel que soit le schéma de classification utilisé pour ce faire. Une collecte de données en deux étapes fut réalisée pour atteindre ces objectifs. Nous avons d’abord identifié les caractéristiques structurelles...

Research that incorporates GIS and remotely sensed imagery has become increasingly popular and important for large-scale environmental applications, such as generating land use and land cover maps. One of the critical aspects of land cover analyses is assigning a land use and land cover classification scheme. This research evaluated two classification schemes, the 2002 Natural Heritage Classification and the 1976 James Anderson System in a land cover analysis of the Canandaigua Lake Watershed using Landsat imagery. It was hypothesized that the Landsat imagery could be used to identify unique ecological communities such as those delineated by the Natural Heritage Classification. A composite image, created from an August 15, 2003 Landsat image using bands 1, 3 and 5, was used for the fine cluster analysis, which produced 38 unique clusters. Using the Canandaigua Lake Watershed Council's land use and land cover map as a truth image (26 single NHC classes and 14 mixed NHC classes), the clustered Landsat image was used in an unsupervised classification analysis that resulted in generalized land use and land cover maps using the Natural Heritage and James Anderson Classification schemes (5 and 6 dominant land covers respectively). Because many clusters were associated with several land cover classes...

Carcass classification is an essential part of efficient animal production, price fixing and meeting consumer demands. Carcass classification (or grading) is based on the description of carcasses by means of clearly defined characteristics that are of prime importance to the meat industry, retailers and consumers. Significant variation exists in carcass composition and quality due to the effects of species, age, maturity type, sex and interaction effects with animal production systems. A number of extrinsic and intrinsic factors affects carcass and meat quality and the purpose of carcass classification in South Africa is to classify carcasses to ensure more consistent meat quality, composition and consumer satisfaction. Although carcass inspection is compulsory in South Africa, carcass classification is not a requisite at all South African abattoirs. South Africa employed a carcass grading system from 1944 to 1992, which was replaced by a carcass classification system in 1992. Carcass classification differs fundamentally from carcass grading. In carcass classification there is a shift of emphasis to classifying carcasses in order to provide the meat industry and consumers with a choice of different types of carcasses in terms of carcass composition and physical attributes...