No description available.

The thesis investigates a publicly available dataset which covers morethan three million events in football matches. The aim of the study isto train machine learning models capable of modeling the relationshipbetween a shot event and its outcome. That is, to predict if a footballshot will result in a goal or not. By representing the shot indifferent ways, the aim is to draw conclusion regarding what elementsof a shot allows for a good prediction of its outcome. The shotrepresentation was varied both by including different numbers of eventspreceding the shot and by varying the set of features describing eachevent.The study shows that the performance of the machine learning modelsbenefit from including events preceding the shot. The highestpredictive performance was achieved by a long short-term memory neuralnetwork trained on the shot event and six events preceding the shot.The features which were found to have the largest positive impact onthe shot events were the precision of the event, the position on thefield and how the player was in contact with the ball. The size of thedataset was also evaluated and the results suggest that it issufficiently large for the size of the networks evaluated.

This thesis explores the potential of using machine learning to superviseand diagnose a computer system by comparing how Multilayer Perceptron(MLP) and Random Forest (RF) perform at this task in a controlledenvironment. The base of comparison is primarily how accurate theyare in their predictions, but some thought is given to how cost effectivethey are regarding time. The specific system used is a content management system (CMS)called Polopoly. The thesis details how training samples were collectedby inserting Java proxys into the Polopoly system in order to time theinter-server method calls. Errors in the system were simulated by limitingindividual server’s bandwith, and a normal use case was simulatedthrough the use of a tool called Grinder. The thesis then delves into the setup of the two algorithms andhow the parameters were decided upon, before comparing their finalimplementations based on their accuracy. The accuracy is noted to bepoor, with both being correct roughly 20% of the time, but discussesif there could still be a use case for the algorithms with this level ofaccuracy. Finally, the thesis concludes that there is no significant difference(p 0.05) in the MLP and RF accuracies, and in the end suggeststhat future work should focus either on comparing the algorithms or ontrying to improve the diagnosing of errors in Polopoly. / Denna uppsats utforskar potentialen i att använda maskininlärning föratt övervaka och diagnostisera ett datorsystem genom att jämföra hureffektivt Multilayer Perceptron (MLP) respektive Random Forest (RF)gör detta i en kontrollerad miljö. Grunden för jämförelsen är främst hurträffsäkra MLP och RF är i sina klassifieringar, men viss tanke ges ocksååt hur kostnadseffektiva de är med hänseende till tid. Systemet som används är ett “content management system” (CMS)vid namn Polopoly. Uppsatsen beskriver hur träningsdatan samlades invia Java proxys, som injicerades i Polopoly systemet för att mäta hurlång tid metodanrop mellan servrarna tar. Fel i systemet simulerades genomatt begränsa enskilda servrars bandbredd, och normalt användandesimulerades med verktyget Grinder. Uppsatsen går sedan in på hur de två algoritmerna användes ochhur deras parametrar sattes, innan den fortsätter med att jämföra detvå slutgiltiga implementationerna baserat på deras träffsäkerhet. Detnoteras att träffsäkerheten är undermålig; både MLP:n och RF:n gissarrätt i ca 20% av fallen. En diskussion förs om det ändå finns en användningför algoritmerna med denna nivå av träffsäkerhet. Slutsatsen drasatt det inte finns någon signifikant skillnad (p 0.05) mellan MLP:nsoch RF:ns träffsäkerhet, och avslutningsvis så föreslås det att framtidaarbete borde fokusera antingen på att jämföra de två algoritmerna ellerpå att försöka förbättra feldiagnosiseringen i Polopoly.

With an increasing demand for rapid high-capacity internet, the telecommunication industry is constantly driven to explore and develop new technologies to ensure stable and reliable networks. To provide a competitive internet service in this growing market, proactive detection and prevention of disturbances are key elements for an operator. Therefore, analyzing network traffic for forecasting disturbances is a well-researched area. This study explores the advantages and drawbacks of implementing a long short-term memory model for forecasting codeword errors in a hybrid fiber-coaxial network. Also, the impact of using multivariate and univariate data for training the model is explored. The performance of the long short-term memory model is compared with a multilayer perceptron model. Analysis of the results shows that the long short-term model, in the vast majority of the tests, performs better than the multilayer perceptron model. This result aligns with the hypothesis, that the long short-term memory model’s ability to handle sequential data would be superior to the multilayer perceptron. However, the difference in performance between the models varies significantly based on the characteristics of the used data set. On the set with heavy fluctuations in the sequential data, the long short-term memory model performs on average 44% better. When training the models on data sets with longer sequences of similar values and with less volatile fluctuations, the results are much more alike. The long short-term model still achieves a lower error on most tests, but the difference is never larger than 7%. If a low error is the sole criterion, the long short-term model is the overall superior model. However, in a production environment, factors such as data storage capacity and model complexity should be taken into consideration. When training the models on multivariate and univariate datasets, the results are unambiguous. When training on all three features, ratios of uncorrectable and correctable codewords, and signal-to-noise ratio, the models always perform better. That is, compared to using uncorrectable codewords as the only training data. This aligns with the hypothesis, which is based on the know-how of hybrid fiber-coaxial experts, that correctable codewords and signal-to-noise ratio have an impact on the occurrence of uncorrectable codewords. / På grund av den ökade efterfrågan av högkvalitativt internet, så drivs telekomindustrin till att konsekvent utforska och utveckla nya teknologier som kan säkerställa stabila och pålitliga nätverk. För att kunna erbjuda konkurrenskraftiga internettjänster, måste operatörerna kunna förutse och förhindra störningar i nätverken. Därför är forskningen kring hur man analyserar och förutser störningar i ett nätverk ett väl exploaterat område. Denna studie undersökte för- och nackdelar med att använda en long short-term memory (LSTM) för att förutse kodordsfel i ett hybridfiber-koaxialt nätverk. Utöver detta undersöktes även hur multidimensionell träningsdata påverkade prestandan. I jämförelsesyfte användes en multilayer perceptron (MLP) och dess resultat. Analysen av resultaten visade att LSTM-modellen presterade bättre än MLP-modellen i majoriteten av de utförda testerna. Men skillnaden i prestanda varierade kraftigt, beroende på vilken datauppsättning som användes vid träning och testning av modellerna. Slutsatsen av detta är att i denna studie så är LSTM den bästa modellen, men att det inte går att säga att LSTM presterar bättre på en godtycklig datauppsättning. Båda modellerna presterade bättre när de tränades på multidimensionell data. Vidare forskning krävs för att kunna determinera om LSTM är den mest självklara modellen för att förutse kodordsfel i ett hybridfiber-koaxialt nätverk.

Primary health care is facing extensive changes due to digitalization, while the field of application for machine learning is expanding. The merging of these two fields could result in a range of outcomes, one of them being an improved and more rigorous adoption of clinical decision support systems. Clinical decision support systems have been around for a long time but are still not fully adopted in primary health care due to insufficient performance and interpretation. Clinical decision support systems have a range of supportive functions to assist the clinician during decision making, where one of the most researched topics is diagnostic support. This thesis investigates how the use of self-described anamnesis in the form of free text and multiple-choice questions performs in prediction of diagnostic outcome. The chosen approach is to compare text to different subsets of multiple-choice questions for diagnostic prediction on a range of classification methods. The results indicate that text data holds a substantial amount of information, and that the multiple-choice questions used in this study are of varying quality, yet suboptimal compared to text data. The over-all tendency is that Support Vector Machines perform well on text classification and that Random Forests and Naive Bayes have equal performance to Support Vector Machines on multiple-choice questions. / Primärvården förväntas genomgå en utbredd digitalisering under de kommande åren, samtidigt som maskininlärning får utökade tillämpningsområden. Sammanslagningen av dessa två fält möjliggör en mängd förbättrade tekniker, varav en vore ett förbättrat och mer rigoröst anammande av kliniska beslutsstödsystem. Det har länge funnits varianter av kliniska beslutsstödsystem, men de har ännu inte lyckats blivit fullständigt inkorporerade i primärvården, framför allt på̊ grund av bristfällig prestanda och förmåga till tolkning. Kliniskt beslutstöd erbjuder en mängd funktioner för läkare vid beslutsfattning, där ett av de mest uppmärksammade fälten inom forskningen är support vid diagnosticering. Denna uppsats ämnar att undersöka hur självbeskriven anamnes i form av fritext och flervalsfrågor presterar för förutsägning av diagnos. Det valda tillvägagångssättet har varit att jämföra text med olika delmängder av flervalsfrågor med hjälp av en mängd metoder för klassificering. Resultaten indikerar att textdatan innehåller en avsevärt större mängd information än flervalsfrågorna, samt att flervalsfrågorna som har använts i denna studie är av varierande kvalité, men generellt sett suboptimala vad gäller prestanda i jämförelse med textdatan. Den generella tendensen är att Support Vector Machines presterar bra för klassificering med text data medan Random Forests och Naive Bayes är likvärdiga alternativ till Support Vector Machines för predicering vid användning av flervalsfrågor.

Chronic cardiovascular disease (CVD) is increasingly becoming a burden for global healthcare systems. This burden can be attributed in part to traditional methods of managing CVD in an aging population that involves periodic meetings between the patient and their healthcare provider. There is growing interest in developing continuous monitoring systems to assist in the management of CVD. Monitoring systems can utilize advances in wearable devices and health records, which provides minimally invasive methods to monitor a patient’s health. Despite these advances, the algorithms deployed to automatically analyze the wearable sensor and health data is considered too computationally expensive to run on the mobile device. Instead, current mobile devices continuously transmit the collected data to a server for analysis at great computational and data transmission expense. In this thesis a novel mobile system designed for monitoring CVD is presented. Unlike existing systems, the proposed system allows for the continuous monitoring of physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each algorithm for monitoring CVD is also discussed. Both models were able to classify CVD risk with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, unlike current systems the resource requirements for each component in the system was evaluated. The MLP was found to be more efficient when running on the mobile device compared to the SVM. The results of thesis also show that the MLAs complexity was not a barrier to deployment on a mobile device. / Thesis / Master of Applied Science (MASc) / In this thesis, a novel mobile system for monitoring cardiovascular (CVD) disease is presented. The system allows for the continuous monitoring of both physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a remote server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each MLA for monitoring CVD is also discussed. Both models were able to classify CVD severity with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, the resource requirements for each component in the system were evaluated. The results show that the MLAs complexity was not a barrier to deployment on a mobile device.

No description available.

O objetivo deste trabalho foi desenvolver um modelo para reconhecer e reproduzir padrões de viagens encadeadas a pé. O processo de modelagem foi conduzido através da aplicação das técnicas das Redes Neurais Artificiais (RNAs), utilizando-se de uma rede estática MLP e de rede dinâmica Elman. A análise do desempenho do modelo foi baseada nos dados de uma pesquisa de Origem-Destino realizada, em 1987, pelo METRÔ-SP na Região Metropolitana de São Paulo. Na modelagem foi fixado o modo de viagem a pé, e, na abordagem seqüencial, padrões de viagens individuais foram representados em termos de dois componentes: duração da viagem e tipo de atividades. A análise foi realizada partindo da classificação geral e específica para cada segmento do encadeamento de viagens, o que permitiu a comparação dos resultados entre padrões de viagens observados e os reproduzidos pelas redes. Na classificação geral, cinco dos padrões previstos com maior freqüência pelas RNAs representaram em média 58,9% dos indivíduos no conjunto de dados usado para testar o desempenho do modelo. Para o vetor de duas e quatro viagens, as redes neurais reproduziram 50% das durações de viagem e 90% das atividades, tais como Trabalho e Escola. Embora esses resultados não pareçam muito robustos, não significa que eles estejam errados. As porcentagens acima representam a probabilidade de uma pessoa realizar viagens com aquelas durações ou tipo de atividades. / The main objective of this work was to develop a model for recognizing and reproduzing trip-chaining patterns by walk. The process of modeling was conducted applying the techniques of Artificial Neural Networks (ANNs), by using one of the static networks MLP and the Elman dynamic network. The analysis of the performance of the model was based on the origin-destination home-interview survey carried out by METRÔ-SP in São Paulo Metropolitan Area in 1987. The mode of trip by walk was fixed in the model, and, in the sequential approach, individual travel patterns were represented in terms of two components: trip duration and activity type. The analysis was accomplished starting from the general and specific classifications for each segment of the chained trips, which allowed the comparison of the results between the observed travel patterns and reproduced ones through ANNs. In general classification, 5 of the patterns most frequently predicted by the ANNs represented 58.9% of the individuals in the dataset used for testing the model performance. For the vectors of two and four trips, the neural networks reproduced 50% of trip durations and 90% of the activities, such as work and school. Although those results seem not so robust, it does not mean that they are wrong. The percentages above represent the probability of a person making trips with those durations or type of activities.

O objetivo deste trabalho foi desenvolver um modelo para reconhecer e reproduzir padrões de viagens encadeadas a pé. O processo de modelagem foi conduzido através da aplicação das técnicas das Redes Neurais Artificiais (RNAs), utilizando-se de uma rede estática MLP e de rede dinâmica Elman. A análise do desempenho do modelo foi baseada nos dados de uma pesquisa de Origem-Destino realizada, em 1987, pelo METRÔ-SP na Região Metropolitana de São Paulo. Na modelagem foi fixado o modo de viagem a pé, e, na abordagem seqüencial, padrões de viagens individuais foram representados em termos de dois componentes: duração da viagem e tipo de atividades. A análise foi realizada partindo da classificação geral e específica para cada segmento do encadeamento de viagens, o que permitiu a comparação dos resultados entre padrões de viagens observados e os reproduzidos pelas redes. Na classificação geral, cinco dos padrões previstos com maior freqüência pelas RNAs representaram em média 58,9% dos indivíduos no conjunto de dados usado para testar o desempenho do modelo. Para o vetor de duas e quatro viagens, as redes neurais reproduziram 50% das durações de viagem e 90% das atividades, tais como Trabalho e Escola. Embora esses resultados não pareçam muito robustos, não significa que eles estejam errados. As porcentagens acima representam a probabilidade de uma pessoa realizar viagens com aquelas durações ou tipo de atividades. / The main objective of this work was to develop a model for recognizing and reproduzing trip-chaining patterns by walk. The process of modeling was conducted applying the techniques of Artificial Neural Networks (ANNs), by using one of the static networks MLP and the Elman dynamic network. The analysis of the performance of the model was based on the origin-destination home-interview survey carried out by METRÔ-SP in São Paulo Metropolitan Area in 1987. The mode of trip by walk was fixed in the model, and, in the sequential approach, individual travel patterns were represented in terms of two components: trip duration and activity type. The analysis was accomplished starting from the general and specific classifications for each segment of the chained trips, which allowed the comparison of the results between the observed travel patterns and reproduced ones through ANNs. In general classification, 5 of the patterns most frequently predicted by the ANNs represented 58.9% of the individuals in the dataset used for testing the model performance. For the vectors of two and four trips, the neural networks reproduced 50% of trip durations and 90% of the activities, such as work and school. Although those results seem not so robust, it does not mean that they are wrong. The percentages above represent the probability of a person making trips with those durations or type of activities.

[pt] As descargas atmosféricas são de grande importância para o setor elétrico, sendo frequentemente responsáveis por desligamentos de linhas de transmissão, que podem desencadear uma sequência de eventos que levem o sistema elétrico interligado ao colapso. As longas extensões de linhas de transmissão, expostas a intemperes climáticas, determinam uma probabilidade significativa de incidência direta de descargas atmosféricas nestes equipamentos. Devido ao caráter estratégico das linhas para o fornecimento de energia e a constatação de que descargas atmosféricas estão entre as principais causas de desligamentos, torna-se importante o estudo do comportamento das descargas atmosféricas, antes do instante da ocorrência do desligamento das linhas de transmissão, para compreender os padrões característicos potenciais causadores destes desligamentos. Os estudos encontrados atualmente estão orientados na eficiência das redes de detecção de descargas atmosféricas e na identificação de condições climáticas que indiquem a ocorrência de raios de forma preditiva, sem correlação a ocorrências em linhas de transmissão. Assim, essa dissertação consiste na determinação do risco de desligamentos de linhas de transmissão por descargas atmosféricas, visando fornecer informações antecipadas e possibilitar ações operativas para manter a segurança do sistema elétrico. O modelo desenvolvido nesse estudo, denominado Risco de Desligamentos de Linhas de Transmissão por Raios (RDLR), é composto de dois módulos principais, sendo o primeiro o agrupamento do conjunto amostral de descargas atmosféricas, realizado através de um método baseado em densidade. Nesse módulo, os ruídos são eliminados de forma eficiente e são formados grupos representativos de descargas atmosféricas. O segundo módulo consiste em uma etapa classificatória, baseado em redes neurais artificiais para identificar padrões de grupos de descargas que representem riscos de desligamentos de linhas de transmissão. Visando a otimização do modelo, foi aplicado um método de seleção das variáveis, através de componentes principais, para determinar aquelas que mais contribuem na caracterização desses eventos. O modelo RDLR foi testado com dados reais dos registros de desligamentos de linhas de transmissão, associado a outro banco com dados reais contendo milhões de registros de descargas atmosféricas oriundos das redes de detecção de raios, sendo obtidos excelentes resultados na determinação dos riscos de desligamentos de linhas de transmissão por descargas atmosféricas. / [en] Atmospheric discharges are of great importance to power systems, and are often responsible for outages of transmission lines, which can trigger a sequence of events that leads to a system collapse. The long extensions of transmission lines, exposed to climatic conditions, create significant probability of direct incidence of atmospheric discharges in these equipments. Due to the strategic nature of power supply lines and the fact that atmospheric discharges are among the main causes of outages, it is important to study atmospheric discharges characteristics before failure of transmission lines and understand patterns that are responsible for interruptions. Current studies focus on efficiency of lightning detection networks and on identification of climatic conditions that indicate lightning occurrence in a predictive approach, without any correlation with transmission lines outages. Therefore, this thesis consists on real-time risk determination of transmission lines outage by lightning, providing early information to enabling operational procedures for power system safety. The proposed model, named Transmission Lines Outage Risk by Lightning (TLORL) is composed of two main modules: Atmospheric Discharge Data Clustering and Classification. In the atmospheric discharges data-clustering module, performed by a density-based method, the outages are efficiently eliminated and representative groups of atmospheric discharges are formed. The second module consists of a classification step, based on artificial neural networks, to identify patterns of discharges groups that represent risks to cause transmission lines outages. Aiming at improving the proposed model, principal components analysis (PCA) was applied to determine the input variables that most contribute to the events characterization. The TLORL model was tested with real data transmission line outages, associated to another database with millions lightning records from the detection networks, producing excellent results of transmission lines outages caused by atmospheric discharges.