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Získávání znalostí z objektově relačních databází / Knowledge Discovery in Object Relational DatabasesChytka, Karel Unknown Date (has links)
The goal of this master's thesis is to acquaint with a problem of a knowledge discovery and objectrelational data classification. It summarizes problems which are connected with mining spatiotemporal data. There is described data mining kernel algorithm SVM. The second part solves classification method implementation. This method solves data mining in a Caretaker trajectory database. This thesis contains application's implementation for spatio-temporal data preprocessing, their organization in database and presentation too.
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Vytvoření nových predikčních modulů v systému pro dolování z dat na platformě NetBeans / Creation of New Prediction Units in Data Mining System on NetBeans PlatformHavlíček, David January 2009 (has links)
The issue of this master's thesis is a creation of new prediction unit for existing system of knowledge discovery in database. The first part of project deal with general problems of knowledge discovery in database and predictive analysis. The second part of the project deal with system developed on FIT, for which is module implemented, used technologies, concept and implementation of mining module for this system. The solution is implemented in Java language and is a built on the NetBeans platform.
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Development of Adaptive Computational Algorithms for Manned and Unmanned Flight SafetyElkin, Colin P. January 2018 (has links)
No description available.
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Data Driven Energy Efficiency of ShipsTaspinar, Tarik January 2022 (has links)
Decreasing the fuel consumption and thus greenhouse gas emissions of vessels has emerged as a critical topic for both ship operators and policy makers in recent years. The speed of vessels has long been recognized to have highest impact on fuel consumption. The solution suggestions like "speed optimization" and "speed reduction" are ongoing discussion topics for International Maritime Organization. The aim of this study are to develop a speed optimization model using time-constrained genetic algorithms (GA). Subsequent to this, this paper also presents the application of machine learning (ML) regression methods in setting up a model with the aim of predicting the fuel consumption of vessels. Local outlier factor algorithm is used to eliminate outlier in prediction features. In boosting and tree-based regression prediction methods, the overfitting problem is observed after hyperparameter tuning. Early stopping technique is applied for overfitted models.In this study, speed is also found as the most important feature for fuel consumption prediction models. On the other hand, GA evaluation results showed that random modifications in default speed profile can increase GA performance and thus fuel savings more than constant speed limits during voyages. The results of GA also indicate that using high crossover rates and low mutations rates can increase fuel saving.Further research is recommended to include fuel and bunker prices to determine more accurate fuel efficiency.
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Computational Analysis of Flow Cytometry DataIrvine, Allison W. 12 July 2013 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / The objective of this thesis is to compare automated methods for performing analysis of flow cytometry data. Flow cytometry is an important and efficient tool for analyzing the characteristics of cells. It is used in several fields, including immunology, pathology, marine biology, and molecular biology. Flow cytometry measures light scatter from cells and fluorescent emission from dyes which are attached to cells. There are two main tasks that must be performed. The first is the adjustment of measured fluorescence from the cells to correct for the overlap of the spectra of the fluorescent markers used to characterize a cell’s chemical characteristics. The second is to use the amount of markers present in each cell to identify its phenotype. Several methods are compared to perform these tasks. The Unconstrained Least Squares, Orthogonal Subspace Projection, Fully Constrained Least Squares and Fully Constrained One Norm methods are used to perform compensation and compared. The fully constrained least squares method of compensation gives the overall best results in terms of accuracy and running time. Spectral Clustering, Gaussian Mixture Modeling, Naive Bayes classification, Support Vector Machine and Expectation Maximization using a gaussian mixture model are used to classify cells based on the amounts of dyes present in each cell. The generative models created by the Naive Bayes and Gaussian mixture modeling methods performed classification of cells most accurately. These supervised methods may be the most useful when online classification is necessary, such as in cell sorting applications of flow cytometers. Unsupervised methods may be used to completely replace manual analysis when no training data is given. Expectation Maximization combined with a cluster merging post-processing step gives the best results of the unsupervised methods considered.
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Spectroscopy and Machine Learning: Development of Methods for Cancer Detection Using Mid-Infrared WavelengthsBradley, Rebecca C. January 2021 (has links)
No description available.
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Application of machine learning in 5G to extract prior knowledge of the underlying structure in the interference channel matrices / Applikation av maskininlärning inom 5G för att extrahera information av den underliggande strukturen i interferenskanalmatrisernaPeng, Danilo January 2019 (has links)
The data traffic has been growing drastic over the past few years due to digitization and new technologies that are introduced to the market, such as autonomous cars. In order to meet this demand, the MIMO-OFDM system is used in the fifth generation wireless network, 5G. Designing the optimal wireless network is currently the main research within the area of telecommunication. In order to achieve such a system, multiple factors has to be taken into account, such as the suppression of interference from other users. A traditional method called linear minimum mean square error filter is currently used to suppress the interferences. To derive such a filter, a selection of parameters has to be estimated. One of these parameters is the ideal interference plus noise covariance matrix. By gathering prior knowledge of the underlying structure of the interference channel matrices in terms of the number of interferers and their corresponding bandwidths, the estimation of the ideal covariance matrix could be facilitated. As for this thesis, machine learning algorithms were used to extract these prior knowledge. More specifically, a two or three hidden layer feedforward neural network and a support vector machine with a linear kernel was used. The empirical findings implies promising results with accuracies above 95% for each model. / Under de senaste åren har dataanvändningen ökat drastiskt på grund av digitaliseringen och allteftersom nya teknologier introduceras på marknaden, exempelvis självkörande bilar. För att bemöta denna efterfrågan används ett s.k. MIMO-OFDM system i den femte generationens trådlösa nätverk, 5G. Att designa det optimala trådlösa nätverket är för närvarande huvudforskningen inom telekommunikation och för att uppnå ett sådant system måste flera faktorer beaktas, bland annat störningar från andra användare. En traditionell metod som används för att dämpa störningarna kallas för linjära minsta medelkvadratfelsfilter. För att hitta ett sådant filter måste flera olika parametrar estimeras, en av dessa är den ideala störning samt bruskovariansmatrisen. Genom att ta reda på den underliggande strukturen i störningsmatriserna i termer av antal störningar samt deras motsvarande bandbredd, är något som underlättar uppskattningen av den ideala kovariansmatrisen. I följande avhandling har olika maskininlärningsalgoritmer applicerats för att extrahera dessa informationer. Mer specifikt, ett neuralt nätverk med två eller tre gömda lager samt stödvektormaskin med en linjär kärna har använts. De slutliga resultaten är lovande med en noggrannhet på minst 95% för respektive modell.
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GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input / GNSS Positionsfelestimering genom Maskinlärningstekniker med Indata om Kringliggande MiljöKuratomi, Alejandro January 2019 (has links)
In Intelligent Transport Systems (ITS), specifically in autonomous driving operations, accurate vehicle localization is essential for safe operations. The localization accuracy depends on both position and positioning error estimates. Technologies aiming to improve positioning error estimation are required and are currently being researched. This project has investigated machine learning algorithms applied to positioning error estimation by assessing relevant information obtained from a GNSS receiver and adding environmental information coming from a camera mounted on a radio controlled vehicle testing platform. The research was done in two stages. The first stage consists of the machine learning algorithms training and testing on existing GNSS data coming from Waysure´s data base from tests ran in 2016, which did not consider the environment surrounding the GNSS receiver used during the tests. The second stage consists of the machine learning algorithms training and testing on GNSS data coming from new test runs carried on May 2019, which include the environment surrounding the GNSS receiver used. The results of both stages are compared. The relevant features are obtained as a result of the machine learning decision trees algorithm and are presented. This report concludes that there is no statistical evidence indicating that the tested environmental input from the camera could improve positioning error estimation accuracy with the built machine learning models. / Inom Intelligenta transportsystem (ITS), specifikt för självkörande fordon, så är en exakt fordonspositionering en nödvändighet för ökad trafiksäkerhet. Positionsnoggrannheten beror på estimering av både positionen samt positionsfelet. Olika tekniker och tillämpningar som siktar på att förbättra positionsfeluppskattningen behövs, vilket det nu forskas kring. Denna uppsats undersöker olika maskininlärningsalgoritmer inriktade på estimering av positionsfel. Algoritmerna utvärderar relevant information från en GNSS-mottagare, samt information från en kamera om den kringliggande miljön. En GNSS-mottagare och kamera monterades på en radiostyrd mobil testplattform för insamling av data. Examensarbetet består av två delar. Första delen innehåller träning och testning av valda maskininlärningsalgoritmer med GNSS-data tillhandahållen av Waysure från tester gjorda under 2016. Denna data inkluderar ingen information från den omkringliggande miljön runt GNSS-mottagaren. Andra delen består av träning och testning av valda maskininlärningsalgoritmer på GNSS-data som kommer från nya tester gjorda under maj 2019, vilka inkluderar miljöinformation runt GNSS-mottagaren. Resultaten från båda delar analyseras. De viktigaste egenskaper som erhålls från en trädbaserad modell, algoritmens beslutsträd, presenteras. Slutsatsen från denna rapport är att det inte går att statistiskt säkerställa att inkludering av information från den omkringliggande miljön från en kamera förbättrar noggrannheten vid estimering av positionsfelet med de valda maskininlärningsmodellerna.
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Knowledge Discovery and Data Mining Using Demographic and Clinical Data to Diagnose Heart Disease. / Knowledge Discovery och Data mining med hjälp av demografiska och kliniska data för att diagnostisera hjärtsjukdomar.Fernandez Sanchez, Javier January 2018 (has links)
Cardiovascular disease (CVD) is the leading cause of morbidity, mortality, premature death and reduced quality of life for the citizens of the EU. It has been reported that CVD represents a major economic load on health care sys- tems in terms of hospitalizations, rehabilitation services, physician visits and medication. Data Mining techniques with clinical data has become an interesting tool to prevent, diagnose or treat CVD. In this thesis, Knowledge Dis- covery and Data Mining (KDD) was employed to analyse clinical and demographic data, which could be used to diagnose coronary artery disease (CAD). The exploratory data analysis (EDA) showed that female patients at an el- derly age with a higher level of cholesterol, maximum achieved heart rate and ST-depression are more prone to be diagnosed with heart disease. Furthermore, patients with atypical angina are more likely to be at an elderly age with a slightly higher level of cholesterol and maximum achieved heart rate than asymptotic chest pain patients. More- over, patients with exercise induced angina contained lower values of maximum achieved heart rate than those who do not experience it. We could verify that patients who experience exercise induced angina and asymptomatic chest pain are more likely to be diagnosed with heart disease. On the other hand, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Tree, Bagging and Boosting methods were evaluated by adopting a stratified 10 fold cross-validation approach. The learning models provided an average of 78-83% F-score and a mean AUC of 85-88%. Among all the models, the highest score is given by Radial Basis Function Kernel Support Vector Machines (RBF-SVM), achieving 82.5% ± 4.7% of F-score and an AUC of 87.6% ± 5.8%. Our research con- firmed that data mining techniques can support physicians in their interpretations of heart disease diagnosis in addition to clinical and demographic characteristics of patients.
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Machine Learning based Predictive Data Analytics for Embedded Test SystemsAl Hanash, Fayad January 2023 (has links)
Organizations gather enormous amounts of data and analyze these data to extract insights that can be useful for them and help them to make better decisions. Predictive data analytics is a crucial subfield within data analytics that make accurate predictions. Predictive data analytics extracts insights from data by using machine learning algorithms. This thesis presents the supervised learning algorithm to perform predicative data analytics in Embedded Test System at the Nordic Engineering Partner company. Predictive Maintenance is a concept that is often used in manufacturing industries which refers to predicting asset failures before they occur. The machine learning algorithms used in this thesis are support vector machines, multi-layer perceptrons, random forests, and gradient boosting. Both binary and multi-class classifier have been provided to fit the models, and cross-validation, sampling techniques, and a confusion matrix have been provided to accurately measure their performance. In addition to accuracy, recall, precision, f1, kappa, mcc, and roc auc measurements are used as well. The prediction models that are fitted achieve high accuracy.
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