Global ETD Search

81	Aplikace statistické analýzy řeči pacientů s Parkinsonovou nemocí / Application of statistical analysis of speech in patients with Parkinson's disease Bijota, Jan January 2016 (has links) This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
82	Mapping Landcover/Landuse and Coastline Change in the Eastern Mekong Delta (Viet Nam) from 1989 to 2002 using Remote Sensing SOHAIL, ARFAN January 2012 (has links) There has been rapid change in the landcover/landuse in the Mekong delta, Viet Nam. The landcover/landuse has changed very fast due to intense population pressure, agriculture/aquaculture farming and timber collection in the coastal areas of the delta. The changing landuse pattern in the coastal areas of the delta is threatened to be flooded by sea level rise; sea level is expected to rise 33 cm until 2050; 45 cm until 2070 and 1 m until 2100. The coastline along the eastern Mekong delta has never been static, but the loss of mangrove forests along the coast has intensified coastline change. The objective of the present study is to map the changes in landcover/landuse along the eastern coast of the Mekong delta; and to detect the changes in position of the eastern coastline over the time period from 1989 to 2002.To detect changes in landuse, two satellite images of the same season, acquired by the TM sensor of Landsat 5 and the ETM+ sensor of Landsat 7 were used. The TM image was acquired on January 16, 1989 and ETM+ image was acquired on February 13, 2002. The landcover/landuse classes selected for the study are water, forest, open vegetation, soil and shrimp farms. Image differencing and post classification comparison are used to detect the changes between two time periods. Image to image correction technique is used to align satellite images. Maximum likelihood supervised classification technique is used to classify images. The result of the classification consists of five classes for 1989 and 2002, respectively. Overall accuracies of 87.5% and 86.8%, with kappa values of 0.85 and 0.84 are obtained for landuse 1989 and landuse 2002, respectively. The overall accuracy for the change map is 82% with kappa value 0.80. Post classification comparison is carried out in this study based on the supervised classification results. According to the results obtained from the post classification comparison, a significant decrease of 48% in forest and a significant increase of 74% in open vegetation and 21% in shrimp farms area observed over the entire study area. The coastline obtained by the combination of histogram thresholding and band ratio showed an overall advancement towards the South China Sea. The results showed that new land patches emerged along the eastern coast. The amount of new land patches appeared along the coast of the Mekong delta is approximately 2% of the entire study area. REMOTE SENSING GIS LANDCOVER/LANDUSE CHANGE ACCURACY ASSESSMENT CHANGE DETECTION COASTLINE CHANGE HISTOGRAM IMAGE THRESHOLD BAND RATIO MEKONG RIVER DELTA VIETNAM MAPPING IMAGE DIFFERENCING CLASSIFICATION MAXIMUM LIKELIHOOD POST CLASSIFICATION COMPARISON SEA LEVEL RISE OVERALL ACCURACY LANDSAT TM ETM+ PRE-PROCESSING RADIOMETRIC NORMALIZATION NDVI NDSI NDWI IMAGE MASK Engineering and Technology Teknik och teknologier
83	Machine Learning Based Prediction and Classification for Uplift Modeling / Maskininlärningsbaserad prediktion och klassificering för inkrementell responsanalys Börthas, Lovisa, Krange Sjölander, Jessica January 2020 (has links) The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investigated. The statistical machine learning methods applied are Random Forests and Neural Networks along with the standard method Logistic Regression. The data is collected from a well established retail company and the purpose of the project is thus to investigate which uplift modeling approach and statistical machine learning method that yields in the best performance given the data used in this project. The variable selection step was shown to be a crucial component in the modeling processes as so was the amount of control data in each data set. For the uplift to be successful, the method of choice should be either the Modeling Uplift Directly using Random Forests, or the Class Variable Transformation using Logistic Regression. Neural network - based approaches are sensitive to uneven class distributions and is hence not able to obtain stable models given the data used in this project. Furthermore, the Subtraction of Two Models did not perform well due to the fact that each model tended to focus too much on modeling the class in both data sets separately instead of modeling the difference between the class probabilities. The conclusion is hence to use an approach that models the uplift directly, and also to use a great amount of control data in each data set. / Behovet av att kunna modellera den verkliga vinsten av riktad marknadsföring har lett till den idag vanligt förekommande metoden inkrementell responsanalys. För att kunna utföra denna typ av metod krävs förekomsten av en existerande testgrupp samt kontrollgrupp och målet är således att beräkna differensen mellan de positiva utfallen i de två grupperna. Sannolikheten för de positiva utfallen för de två grupperna kan effektivt estimeras med statistiska maskininlärningsmetoder. De inkrementella responsanalysmetoderna som undersöks i detta projekt är subtraktion av två modeller, att modellera den inkrementella responsen direkt samt en klassvariabeltransformation. De statistiska maskininlärningsmetoderna som tillämpas är random forests och neurala nätverk samt standardmetoden logistisk regression. Datan är samlad från ett väletablerat detaljhandelsföretag och målet är därmed att undersöka vilken inkrementell responsanalysmetod och maskininlärningsmetod som presterar bäst givet datan i detta projekt. De mest avgörande aspekterna för att få ett bra resultat visade sig vara variabelselektionen och mängden kontrolldata i varje dataset. För att få ett lyckat resultat bör valet av maskininlärningsmetod vara random forests vilken används för att modellera den inkrementella responsen direkt, eller logistisk regression tillsammans med en klassvariabeltransformation. Neurala nätverksmetoder är känsliga för ojämna klassfördelningar och klarar därmed inte av att erhålla stabila modeller med den givna datan. Vidare presterade subtraktion av två modeller dåligt på grund av att var modell tenderade att fokusera för mycket på att modellera klassen i båda dataseten separat, istället för att modellera differensen mellan dem. Slutsatsen är således att en metod som modellerar den inkrementella responsen direkt samt en relativt stor kontrollgrupp är att föredra för att få ett stabilt resultat. Uplift Modeling Data Pre-Processing Predictive Modeling Incremental Modeling Random Forests Logistic Regression Neural Networks Ensemble Methods Machine Learning Multi-Layer Perceptron Inkrementell responsanalys databehandling prediktiv modellering random forests logistisk regression neurala nätverk mulit-layer perceptron ensemble metoder maskininlärning Mathematics Matematik
84	Tuning of machine learning algorithms for automatic bug assignment Artchounin, Daniel January 2017 (has links) In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection technique and to tune several classifiers. The aforementioned method has been applied on three software projects: 66 066 bug reports of a proprietary project, 24 450 bug reports of Eclipse JDT and 30 358 bug reports of Mozilla Firefox. 619 configurations have been applied and compared on each of these three projects. In production, using the approach introduced in this work on the bug reports of the proprietary project would have increased the accuracy by up to 16.64 percentage points. bug triage bug assignment bug mining bug report activity-based approach issue tracking bug repository bug tracker pre-processing feature extraction feature selection tuning model selection hyper-parameter optimization text mining text classification classifier supervised learning machine learning information retrieval bugzilla eclipse jdt mozilla firefox open source software proprietary project accuracy mean reciprocal rank software development software maintenance software engineering Computer and Information Sciences Data- och informationsvetenskap
85	Méthodologie d’analyse des signaux et caractérisation hydrogéologique : application aux chroniques de données obtenues aux laboratoires souterrains du Mont Terri, Tournemire et Meuse/Haute-Marne / Signal analyzis methodology and hydrogeologic characterization : application to time series collected at the underground research laboratories of Mont Terri, Tournemire, and Meuse/Haute-Marne Fatmi, Hassane 29 May 2009 (has links) Ce rapport présente des méthodes de prétraitement, d'analyse statistique et d'interprétation de chroniques hydrogéologiques de massifs peu perméables (argilites) dans le cadre d'études sur le stockage profond de déchets radioactifs. Les séries temporelles analysées sont la pression interstitielle et la pression atmosphérique, en relation avec différents phénomènes (marées terrestres, effet barométrique, évolution de l'excavation des galeries). Les pré-traitements permettent de reconstituer et homogénéiser les chroniques de données en présence de lacunes, aberrations, et pas de temps variables. Les signaux prétraités sont ensuite analysés en vue de caractériser les propriétés hydrauliques du massif peu perméable (emmagasinement spécifique ; porosité effective). Pour cela, on a développé et mis en oeuvre les méthodes d'analyses suivantes (implémentées en Matlab): analyses corrélatoires et spectrales (Fourier) ; analyses ondelettes multirésolution ; enveloppes de signaux aléatoires. Cette méthodologie est appliquée aux données acquises au Laboratoire Souterrain du Consortium International du Mont Terri (Jura Suisse), ainsi qu'à certaines données des Laboratoires Souterrains de Tournemire (Aveyron) et de Meuse / Haute-Marne (ANDRA) / This report presents a set of statistical methods for pre-processing and analyzing multivariate hydrogeologic time series, such as pore pressure and its relation to atmospheric pressure. The goal is to study the hydrogeologic characteristics of low permeability geologic formations (argilite) in the context of deep disposal of radioactive waste. The pressure time series are analyzed in relation with different phenomena, such as earth tides, barometric effects, and the evolution of excavated galleries. The pre-processing is necessary for reconstituting and homogenizing the time series in the presence of data gaps, outliers, and variable time steps. The preprocessed signals are then analyzed with a view to characterizing the hydraulic properties of this type of low permeability formation (specific storativity; effective porosity). For this sake, we have developed and used the following methods (implemented in Matlab): temporal correlation analyses; spectral/Fourier analyses; multiresolution wavelet analyses envelopes of random processes. This methodology is applied to data collected at the URL (Underground Research Laboratory) of the Mont Terri International Consortium (Swiss Jura), as well as some other data collected at the URL of IRSN at Tournemire (Aveyron) and at the URL of ANDRA (Meuse / Haute-Marne) Mont Terri Tournemire Bure Argilite Argiles à Opalines Callovo-Oxfordien Pression interstitielle Chroniques Séries temporelles Traitement des signaux Processus aléatoires Prétraitements Reconstitution Analyse corrélatoire Analyse spectrale Ondelettes multirésolution Marées terrestres Effet barométrique Compressibilité Porosité Emmagasinement spécifique Porosité effective EDZ (Excavation Damaged Zone) Perméabilité Pore pressure Argilite Signal pre-processing Random processes Earth tides Barometric effect Specific storativity Effective porosity Correlation analyses Spectral Fourier analyses Multiresolution wavelet analyses EDZ (Excavation Damaged Zone) Hydrogeology
86	Atrial Fibrillation Detection Algorithm Evaluation and Implementation in Java / Utvärdering av algoritmer för detektion av förmaksflimmer samt implementation i Java Dizon, Lucas, Johansson, Martin January 2014 (has links) Atrial fibrillation is a common heart arrhythmia which is characterized by a missing or irregular contraction of the atria. The disease is a risk factor for other more serious diseases and the total medical costs in society are extensive. Therefore it would be beneficial to improve and optimize the prevention and detection of the disease. Pulse palpation and heart auscultation can facilitate the detection of atrial fibrillation clinically, but the diagnosis is generally confirmed by an ECG examination. Today there are several algorithms that detect atrial fibrillation by analysing an ECG. A common method is to study the heart rate variability (HRV) and by different types of statistical calculations find episodes of atrial fibrillation which deviates from normal sinus rhythm. Two algorithms for detection of atrial fibrillation have been evaluated in Matlab. One is based on the coefficient of variation and the other uses a logistic regression model. Training and testing of the algorithms were done with data from the Physionet MIT database. Several steps of signal processing were used to remove different types of noise and artefacts before the data could be used. When testing the algorithms, the CV algorithm performed with a sensitivity of 91,38%, a specificity of 93,93% and accuracy of 92,92%, and the results of the logistic regression algorithm was a sensitivity of 97,23%, specificity of 93,79% and accuracy of 95,39%. The logistic regression algorithm performed better and was chosen for implementation in Java, where it achieved a sensitivity of 97,31%, specificity of 93,47% and accuracy of 95,25%. / Förmaksflimmer är en vanlig hjärtrytmrubbning som kännetecknas av en avsaknad eller oregelbunden kontraktion av förmaken. Sjukdomen är en riskfaktor för andra allvarligare sjukdomar och de totala kostnaderna för samhället är betydande. Det skulle därför vara fördelaktigt att effektivisera och förbättra prevention samt diagnostisering av förmaksflimmer. Kliniskt diagnostiseras förmaksflimmer med hjälp av till exempel pulspalpation och auskultation av hjärtat, men diagnosen brukar fastställas med en EKG-undersökning. Det finns idag flertalet algoritmer för att detektera arytmin genom att analysera ett EKG. En av de vanligaste metoderna är att undersöka variabiliteten av hjärtrytmen (HRV) och utföra olika sorters statistiska beräkningar som kan upptäcka episoder av förmaksflimmer som avviker från en normal sinusrytm. I detta projekt har två metoder för att detektera förmaksflimmer utvärderats i Matlab, en baseras på beräkningar av variationskoefficienten och den andra använder sig av logistisk regression. EKG som kommer från databasen Physionet MIT används för att träna och testa modeller av algoritmerna. Innan EKG-signalen kan användas måste den behandlas för att ta bort olika typer av brus och artefakter. Vid test av algoritmen med variationskoefficienten blev resultatet en sensitivitet på 91,38%, en specificitet på 93,93% och en noggrannhet på 92,92%. För logistisk regression blev sensitiviteten 97,23%, specificiteten 93,79% och noggrannheten 95,39%. Algoritmen med logistisk regression presterade bättre och valdes därför för att implementeras i Java, där uppnåddes en sensitivitet på 91,31%, en specificitet på 93,47% och en noggrannhet på 95,25%. Atrial Fibrillation AF Detection algorithm AF detection Algorithm evaluation Matlab Java Electrocardiogram ECG Heart rate variability HRV Signal processing Pre processing Noise reduction Baseline wander powerline interference wavelet transform DWT R-peak detection Pan-tompkins HRV cleaning Feature selection U-test SDANN SDNN CV RMSSD PNN50 TINN LF HF Logistic regression CV algorithm Receiver operating characteristics ROC Classification Confusion matrix Leave-one-out cross validation Statgraphics Förmaksflimmer detekteringsalgoritm evaluering Matlab Java elektrokardiogram EKG Hjärtfrekvens variabilitet Signalbehandling Brusreducering Logistisk regression Variationskoefficient Klassificering Medical Engineering Medicinteknik

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