Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T17:23:07Z No. of bitstreams: 2 Dissertacao Thiago Tavares.pdf: 3582760 bytes, checksum: dfee6c424fc987631aeae3fbd4e4e524 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T13:23:44Z (GMT) No. of bitstreams: 2 Dissertacao Thiago Tavares.pdf: 3582760 bytes, checksum: dfee6c424fc987631aeae3fbd4e4e524 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-13T13:23:44Z (GMT). No. of bitstreams: 2 Dissertacao Thiago Tavares.pdf: 3582760 bytes, checksum: dfee6c424fc987631aeae3fbd4e4e524 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013 / As doenças cardiovasculares são as que mais matam no Brasil e no mundo. Dessas, a cardiopatia congênita, uma malformação cardíaca presente desde o nascimento, acomete 8 a 10 em cada 1000 nascidos vivos e aproximadamente 1/3 deles necessitam de tratamento já no primeiro ano de vida. Inúmeros trabalhos demonstram que quanto antes for estabelecido o diagnóstico maiores serão as chances de sucesso no tratamento. O atendimento de crianças com suspeita de cardiopatia gera uma grande quantidade de informação, porém a diferenciação entre sinais e sintomas normais ou patológicos logo no início, por exemplo, na marcação da consulta, pode ser aspecto fundamental para agilizar o atendimento. Há algum tempo a Inteligência Artificial, mais especificamente a subárea de Mineração de Dados, tem sido utilizada como ferramenta de suporte à decisão médica em diversas especialidades, inclusive na cardiologia. Apesar da maioria das aplicações nesse contexto utilizarem Árvore de Decisão para classificação devido ao seu poder de interpretação e extração de regras, Máquinas de Vetor de Suporte (Support Vector Machines - SVM) têm demonstrado, em várias aplicações, um maior poder de generalização apresentando melhores resultados. No entanto, esse tipo de algoritmo, caixa-preta, não produz um conhecimento explícito de modo que um médico, especialista no domínio, possa interpretá-lo. A proposta desse trabalho é o desenvolvimento de um sistema de apoio à decisão médica que auxilie na detecção de cardiopatias em crianças, a partir de dados iniciais, como gênero, peso, altura e presença de sopros, com o objetivo de priorizar o seu atendimento médico. Técnicas para lidar com bases de dados desbalanceadas, tais como SMOTE e SVM com pesos foram utilizadas a fim de melhorar os resultados com relação a classificadores convencionais. Além disso, foi possível realizar a extração de regras a partir dos resultados obtidos pela SVM. Segundo os especialistas, os resultados obtidos viabilizam a utilização do sistema de apoio à decisão que pode ser incorporado à prática clínica para melhorar a qualidade dos serviços prestados.

Text categorisation is an important feature for organising text data and making it easier to find information on the world wide web.  The categorisation of text data can be done through the use of machine learning classifiers. These classifiers need to be trained with data in order to predict a result for future input. The authors chose to investigate how accurate two classifiers are when classifying recent news articles on a classifier model that is trained with older news articles. To reach a result the authors chose the Naive Bayes and Support Vector Machine classifiers and conducted an experiment. The experiment involved training models of both classifiers with news articles from 2005 and testing the models with news articles from 2005 and 2017 to compare the results. The results showed that both classifiers did considerably worse when classifying the news articles from 2017 compared to classifying the news articles from the same year as the training data.

I detta arbete presenteras hatfulla kommentarer på internet som ett sam- hällsproblem som vi bör göra något åt. Webbplatsen Exponerat.net presenteras som en källa till hatfulla kommentarer. Med hjälp av ett förenklande antagande om att de kommentarer som finns på Exponerat kan utgöra en god representation för hatfulla kommentarer på internet konstruerar vi en klassificerare. Klassificeraren utvärderas i två steg; det ena med hjälp av tiofaldig korsvalidering och det andra manuellt. Klassificeraren uppvisar acceptabla precision/recall-värden i det första utvärderingssteget men faller kort i det manuella. Arbetet avslutas med en diskussion om rimligheten i det förenklande antagandet att använda en enda källa. / Hate speech on the internet is a serious issue. This study asks the question: "Is it possible to use machine learning to do something about it?". By using crawled comments from the blog Exponerat.net as a representation of “hate” and comments from the blog Feber.se as “not-hate” we try to construct a classifier. Evaluation in done in two steps; one using 10-fold cross validation and one using manual evaluation methods. The classifier produces an acceptable result in the first step but falls short in the second. The study ends with discussions about if it is even possible to train a classifier using only one source of data.

The use of patient data is essential in research but it is on the other hand confidential and can only be used after acquiring approval from an Ethical Board and informed consent from the individual patient. A large amount of patient data is therefore difficult to obtain if sensitive information, such as names, id numbers and contact details, are not removed from the data, by so called de-identification. Uppsala Monitoring Centre maintains the world's larges database of individual case reports of any suspected adverse drug reaction. There exists, of today, no method for efficiently de-identifying the narrative text included in these which causes countries like the United States of America reports to exclude the narratives in the reports. The aim of this thesis is to develop and evaluate a method for automatic de-identification of case narratives in reports from the WHO Global Individual Case Safety Report Database System, VigiBase. This report compares three different models, namely Regular Expressions, used for text pattern matching, and the statistical models Support Vector Machine (SVM) and Conditional Random Fields (CRF). Performance, advantages and disadvantages are discussed as well as how identified sensitive information is handled to maintain readability of the narrative text. The models developed in this thesis are also compared to existing solutions to the de-identification problem. The 400 reports extracted from VigiBase were already well de-identified in terms of names, ID numbers and contact details, making it difficult to train statistical models on these categories. The reports did however, contain plenty of dates and ages. For these categories Regular Expression would be sufficient to achieve a good performance. To identify entities in other categories more advanced methods such as the SVM and CRF are needed and will require more data. This was prominent when applying the models on the more information rich i2b2 de-identification challenge benchmark data set where the statistical models developed in this thesis performed at a competing level with existing models in the literature.

High Impedance Faults (HIFs) are undetectable by conventional protection technology under certain<p>conditions. These faults occur when an energized conductor makes undesired contact with a<p>quasi-insulating object, such as a tree or a road. This contact restricts the level of the fault current to a very low value, from a few mA up to 75A. In solidly grounded distribution networks where the value of the residual current under normal conditions is considerable, overcurrent devices do not protect against HIFs. However, such a protection is essential for guaranteeing public security, because of the possibility of reaching the fallen conductor and the risk of fire. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the given labels or label noise affect the classifier performance, classifier complexity, class proportions, etc. It may be that a relatively small, but important class needs to have all its examples identified. Typical solutions to the label noise problem involve creating classifiers that are robust or tolerant to errors in the labels, or removing the suspected examples using machine learning algorithms. Finding the label noise examples through a manual review process is largely unexplored due to the cost and time factors involved. Nevertheless, we believe it is the only way to create a label noise free dataset. This dissertation proposes a solution exploiting the characteristics of the Support Vector Machine (SVM) classifier and the sparsity of its solution representation to identify uniform random label noise examples in a dataset. Application of this method is illustrated with problems involving two real-world large scale datasets. This dissertation also presents results for datasets that contain adversarial label noise. A simple extension of this method to a semi-supervised learning approach is also presented. The results show that most mislabels are quickly and effectively identified by the approaches developed in this dissertation.

All dairy cows in Europe wear unique identification tags in their ears. These eartags are standardized and contains the cows identification numbers, today only used for visual identification by the farmer. The cow also needs to be identified by an automatic identification system connected to milk machines and other robotics used at the farm. Currently this is solved with a non-standardized radio transmitter which can be placed on different places on the cow and different receivers needs to be used on different farms. Other drawbacks with the currently used identification system are that it is expensive and unreliable. This thesis explores the possibility to replace this non standardized radio frequency based identification system with a standardized computer vision based system. The method proposed in this thesis uses a color threshold approach for detection, a flood fill approach followed by Hough transform and a projection method for segmentation and evaluates template matching, k-nearest neighbour and support vector machines as optical character recognition methods. The result from the thesis shows that the quality of the data used as input to the system is vital. By using good data, k-nearest neighbour, which showed the best results of the three OCR approaches, handles 98 % of the digits.

District heating systems could help manage the expected increase of volatility on the Nordic electricity market by starting a combined heat and power production plant (CHP) instead of a heat only production plant when electricity prices are expected to be high. Fortum Värme is interested in adjusting the production planning of their district heating system more towards high electricity prices and in their system there is a peak load CHP unit that could be utilised for this purpose. The economic potential of starting the CHP, instead of a heat only production unit, when profitable was approximated for 2013-2016. Three machine learning classification algorithms, Support vector machine (SVM), Naive Bayes and an ensemble of decision trees were implemented and compared with the purpose of predicting price spikes in price area SE3, where Fortum Värme operates, and to assist production planning. The results show that the SVM model achieved highest performance and could be useful in production planning towards high electricity prices. The results also show a potential profit of adjusting production planning. A potential that might increase if the electricity market becomes more volatile.

Fabrics are unique materials which consist of various properties affecting their performance and end-uses. A computerized fabric property evaluation and analysis method plays a crucial role not only in textile industry but also in scientific research. An accurate analysis and measurement of fabric property provides a powerful tool for gauging product quality, assuring regulatory compliance and assessing the performance of textile materials. This thesis investigated the solutions for applying computerized methods to evaluate and intelligently interpret the texture and material properties of fabric in an inexpensive and efficient way. Firstly, a method which allows automatic recognition of basic weave pattern and precisely measuring the yarn count is proposed. The yarn crossed-areas are segmented by a spatial domain integral projection approach. Combining fuzzy c-means (FCM) and principal component analysis (PCA) on grey level co-occurrence matrix (GLCM) feature vectors extracted from the segments enables to classify detected segments into two clusters. Based on the analysis on texture orientation features, the yarn crossed-area states are automatically determined. An autocorrelation method is used to find weave repeats and correct detection errors. The method was validated by using computer simulated woven samples and real woven fabric images. The test samples have various yarn counts, appearance, and weave types. All weave patterns of tested fabric samples are successfully recognized and computed yarn counts are consistent to the manual counts. Secondly, we present a methodology for using the high resolution 3D surface data of fabric samples to measure surface roughness in a nondestructive and accurate way. A parameter FDFFT, which is the fractal dimension estimation from 2DFFT of 3D surface scan, is proposed as the indicator of surface roughness. The robustness of FDFFT, which consists of the rotation-invariance and scale-invariance, is validated on a number of computer simulated fractal Brownian images. Secondly, in order to evaluate the usefulness of FDFFT, a novel method of calculating standard roughness parameters from 3D surface scan is introduced. According to the test results, FDFFT has been demonstrated as a fast and reliable parameter for measuring the fabric roughness from 3D surface data. We attempt a neural network model using back propagation algorithm and FDFFT for predicting the standard roughness parameters. The proposed neural network model shows good performance experimentally. Finally, an intelligent approach for the interpretation of fabric objective measurements is proposed using supported vector machine (SVM) techniques. The human expert assessments of fabric samples are used during the training phase in order to adjust the general system into an applicable model. Since the target output of the system is clear, the uncertainty which lies in current subjective fabric evaluation does not affect the performance of proposed model. The support vector machine is one of the best solutions for handling high dimensional data classification. The complexity problem of the fabric property has been optimally dealt with. The generalization ability shown in SVM allows the user to separately implement and design the components. Sufficient cross-validations are performed and demonstrate the performance test of the system.

Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate some form of decision-making process. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various field including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objectives of this research project were to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. The goal was not to find a new way to “fuse” the visual and thermal images together but rather establish a methodology to extract multispectral descriptors in order to improve a machine vision system’s ability to recognize specific classes of objects.A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM’s class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets. Commonly used performance metrics were applied to assess the sensitivity, specificity and accuracy of each classifier. The research demonstrated that the highest recognition rate was achieved by an expert system (multiple classifiers) that combined the expertise of the visual-only classifier, the thermal-only classifier and the combined visual-thermal classifier.