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Predicting the outcomes of tennis matches. How important is the factor of different surfaces?
Sklenička, Jan ; Kurka, Josef (advisor) ; Palanský, Miroslav (referee)
This thesis focuses on predicting the outcomes of ATP tennis matches with the aim of investigating the effect of different tennis surfaces. We define nine surface variables based on two different clustering methods, and incorporate these variables into models that were estimated using logistic regression. Such approach allows us to robustly observe how the effect of specializing on a spe- cific surface translates into the match winning probability. The accuracy of the models ranges from 63% to 65%, with the models incorporating the effect of surface specialization displaying a superior predictive accuracy. Based on several evaluation metrics, the results confirm that surface specialization is a crucial variable when predicting tennis matches. We are also able to identify that the most accurate metric to measure surface specialization is the difference in normalized winning rate. Moreover, the analysis reveals that the specializa- tion on grass courts is the most important compared to clay or hard courts. Lastly, we formalize betting strategies based on the predicted probabilities from the models, and we are able to achieve positive out-of-sample return on invest- ment (ROI). Accounting for the effect of surface specialization increases ROI on the non-naive betting strategies. JEL Classification...
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Personalization of politics in the Czech presidential election? An analysis of the influence of candidate personality on Czech voters.
Nosková, Pavlína ; Dvořák, Tomáš (advisor) ; Doležalová, Barbora (referee)
NOSKOVÁ, Pavlína. Personalizace politiky v českých prezidentských volbách? Analýza vlivu osobnosti kandidáta na české voliče. Praha, 2024. 48 s. Bakalářská práce (Bc). Univerzita Karlova, Fakulta sociálních věd, Institut sociologických studií, Katedra sociologie. Vedoucí bakalářské práce Mgr. Tomáš Dvořák, Ph.D. Abstract This bachelor thesis deals with what factors influence Czech voters in presidential elections. In particular, it focuses on the influence of the candidate's personality. The thesis is divided into three main parts - theoretical part, methodology and analytical part. In the theoretical part, the path of the Czech Republic to direct presidential elections is first mentioned. Next, the concept of personalisation of politics is introduced, which serves as a theoretical basis for determining the influence of the personality of the presidential candidate on the Czech electorate in the elections. And lastly, the transformation of political communication is mentioned, mainly because of the connection to the aforementioned personalisation of politics in terms of electoral campaigns, which are then dealt with in the analytical part. In the methodological part of the thesis, in particular, the objectives of the thesis and the research questions are presented with hypotheses that will help to answer...
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Who is more prone to depression? Analysis of micro-level data of patients with cancer.
Balážová, Julie Kristýna ; Votápková, Jana (advisor) ; Landovská, Petra (referee)
Depression is very common in cancer patients, affecting about 1 in 5 people with such disease. This thesis uncovers determinants potentially contributing to the development of depression in patients with cancer diagnosis. Special emphasis is placed on how human mental health has responded to the COVID-19 pandemic. The source of our cross-sectional dataset of U.S. population aged 18+ is NHIS. Two dependent variables are examined. The probability of taking medication for depression or anxiety is analyzed using logistic regression, and severity of depres- sion symptoms is estimated by multinomial logistic regression. Several robustness checks are implemented. Additionally to coronavirus symptoms, other regressors include sociodemographic characteristics, household composition, educational at- tainment, health status and life satisfaction. The results show that women are more prone to depression, regardless of cancer diagnosis. The coronavirus symp- toms significantly affect depression among people without cancer, but play no role for people diagnosed with cancer. Older people with cancer are less likely to de- velop depression, and household composition has vital impact on mental health of all respondents, with the exception of cancer survivors. Education is insignificant for patients in cancer...
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Financial Distress Prediction in Digital Finance Platforms
Zhang, Lin ; Kočenda, Evžen (advisor) ; Krištoufek, Ladislav (referee)
Jaké faktory nejvíce přispívají k finanční tísni FinTech firem: kapitálová přiměřenost, provozní činnosti nebo ziskovost? Tato práce se snaží zodpovědět tuto otázku pomocí logistického modelu a zkoumáním účetních dat 973 FinTech firem z celého světa z let 2018 až 2023. Analýza také bere v úvahu nefinanční proměnné a robustnost je testována pomocí modelu uspořádané odezvy a metody Bayesovského průměrování modelů. Výsledky naznačují, že během krizí je finanční tíseň FinTech firem ovlivněna především ziskovostí a provozními činnostmi, přičemž kapitálová přiměřenost hraje méně významnou roli. Klasifikace C52, C53, C58, G21, G32, G33, M41 Klíčová slova FinTech, predikce selhání, CAMELS, logistická regrese, model uspořádané odezvy, ROC, vzácnáudálost, BMA
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Exercise-based predictors of atrial fibrillation recurrence in patients undergoing catheter ablation.
Mátych, Martin ; Pešl, Martin (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation (AF) is the most frequently treated heart arrhythmia. Radiofrequency catheter ablation is a treatment option with a success rate ranging from 60 % to 80 % for paroxysmal AF. This work aimed to determine parameters associated with AF recurrence to identify high-risk patients. Data from 98 patients who underwent pulmonary vein isolation were analyzed. Out of these patients, 19 experienced AF recurrence. Exercise and echocardiographic parameters differed significantly between the recurrence and non-recurrence groups and were used in regression analysis. Peak oxygen consumption (pVO2) was found to be a strong predictor of AF recurrence after adjusting for gender and age (hazard ratio 0.43). Four parameters were identified as the ideal combination in multivariable analysis: pVO2, septal peak late diastolic mitral annulus velocity, post-exercise systolic blood pressure, and left atrial volume index. These findings highlight the importance of stress and echocardiographic parameters in predicting the success of ablation procedures.
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Deep Learning for Image Recognition
Munzar, Milan ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
Neural networks are one of the state-of-the-art models for machine learning today. One may found them in autonomous robot systems, object and speech recognition, prediction and many others AI tasks. The thesis describes this model and its extension which is used in an object recognition. Then explains an application of a convolutional neural networks(CNNs) in an image recognition on Caltech101 and Cifar10 datasets. Using this exemplar application, the thesis discusses and measures efficiency of techniques used in CNNs. Results show that the convolutional networks without advanced extensions are able to reach a 80\% recognition accuracy on Cifar-10 and a 37\% accuracy on Caltech101.
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Bankruptcy Prediction Modelling in the Manufacturing Industry
Tichá, Barbora ; Bartoš, Vojtěch (referee) ; Karas, Michal (advisor)
This diploma thesis deals with the issue of bankruptcy prediction of small and medium-sized enterprises operating in the manufacturing industry in selected Central European countries. The theoretical part of the thesis defines the concepts related to the prediction of bankruptcy and methods of model creation. The analytical part of the work includes testing the accuracy of selected bankruptcy model by other authors and creating a new bankruptcy model. The accuracy of the newly created model is then compared with the accuracy of selected models by other authors.
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Analysis of impact of noise in recordings on the automated detection of hypokinetic dysarthria
Havelková, Nikola ; Galáž, Zoltán (referee) ; Kováč, Daniel (advisor)
This thesis deals with the automated detection of hypokinetic dysarthria by analysing the influence of noise present in recordings. Appropriate single-channel methods, specifically the spectral subtraction and Kalman filter, are selected and implemented in the MATLAB R2022a to enhance speech. These methods are also used for noise-free recordings, to which additive white noise was added. Afterwards, the effectiveness of these methods is objectively evaluated by using signal-to-noise ratio values. After enhancing of speech, interferences are extracted from the recordings. The effect of the presence of noise, as well as its subsequent suppression by individual methods, is then evaluated by statistical analysis, specifically using the Kruskal-Wallis test and the post hoc Dunn’s test. The probability of distributing parameters of clean, noisy and enhanced recordings, for which the effect of noise is significant, according to statistical tests, are plotted using violin and box graphs. Finally, the classification was done by logistic regression with the help of machine learning, where the effect of the presence of noise and subsequent speech enhancement on automated detection of hypokinetic dysarthria was described according to the area values under the ROC curve.
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Laptop Touchpad Palm Detection with AI/ML
Menzyński, Mark Alexander ; Kavetskyi, Andrii (referee) ; Drahanský, Martin (advisor)
Situace ohledně detekci a odmítnutí dlaně na laptopech je méně než ideální. Většina výzkumů se zabývá odmítnutím dotyků na dotykových obrazovkách, a na laptopy probíhá téměř žádný. Patrně nějaký uzavřený výzkům probíhá uvnitř výrobců laptopů, ale i přes to je technologie pozadu. Tato práce prozkoumává několik metod plytkého a hlubokého strojového učení, a výsledná přesnost byla zjištěna jako více než dostačující. Také implementuje aplikaci v reálném čase na demonstraci modelu.
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Statistical Models of Success of Various Techniques of Rugby Kicking
Vrbacká, Kateřina ; Votavová, Helena (referee) ; Bednář, Josef (advisor)
This bachelor thesis is dealing with the testing of statistical hypothesis and their practical use. We model the success of rugby kicking and analyze the dominant factors (ball position, kicking technique, player) and their interactions. We will use some mathematical terms such as chi-square test of independence and logistic regression. The final model will be processed by software MINITAB. The outcome from this thesis will be the exact description of this situation.
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