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Artifical intelligence to represent and solve 4D Rubic cube
Pech, Vilém ; Vomlelová, Marta (advisor) ; Majerech, Vladan (referee)
The main goal of this thesis is to generalize Micheal Herdy's algorithm for solving Ru- bik's cube using evolutionary strategies for a 4D Rubik's cube. The thesis then studies its characteristics, attempts to improve it, and compares the results with the findings of El- Sourani and Borschbach. The thesis explains the chosen projection of a four-dimensional object into 3D, and slightly suggests the intuition for a better understanding and idea. This work also includes a graphic environment in which everything can be demonstrated visually. 1
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Detecting Misleading Features in Data Visualization
Roubalová, Hana ; Vomlelová, Marta (advisor) ; Červíčková, Věra (referee)
This thesis explores the identification and detection of misleading elements in data visu- alizations. The theoretical portion focuses on understanding various types of misleading features commonly encountered in scientific figures and recognizing them. The imple- mentation introduces an application designed to detect colorblind-unfriendly graphs with the analysis of various algorithms. The thesis raises awareness about misleading visual- izations and demonstrates how software can simplify the detection of misleading features for the everyday user. This thesis highlights the importance of addressing misleading features in data visualizations and introduces an application to assist in their detection. The study advances our understanding of this field and offers insights into reducing the negative effects of misleading data visualizations. 1
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Social Networks: Analysis of Evolution and Sentiment
Fanči, Samuel ; Mrázová, Iveta (advisor) ; Vomlelová, Marta (referee)
Nowadays, social networks form an essential part of our lives. Their analysis helps us better understand various social phenomena, identify individuals influencing society, and model future developments of communities. Often, real-world social networks con- form to power-law degree distribution. We oriented our research toward investigating communities surrounding two well-known companies: GameStop and Enron. Using the data obtained from Reddit and Twitter, we have trained machine learning models like Support vector machines and Neural networks to assess the sentiment of the GameStop community. The results confirm the expected positive sentiment following the GameStop price spike in 2021. We constructed the respective social networks based on the available datasets and identified their vital individuals according to selected centrality measures. Publicly known figures like Ryan Cohen in the case of GameStop and Jeff Skilling in the case of Enron are ranked high according to PageRank and Authority scores. On the other hand, minor influencers from the GameStop community and the upper management of Enron were assigned top ranks of the Hub score and Betweenness centrality. A statistical analysis using the goodness-of-fit test for the power-law degree distribution was performed for both networks. Results...
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Rotation-invariant pattern matching for egg recognition
Bláha, Vojtěch ; Vomlelová, Marta (advisor) ; Harmanec, Adam (referee)
This bachelor's thesis follows up the recognition of eggs in the image. The goal was to create a group of programs that firstly captures image data, than finds eggs in them and finally shows the results in some user environment. We tested gradually different classification methods (template matching, logistic regression and neural network). We tried also different representations of the image such as matrix representation and ring projection, and various pre-processing of the image before the actual finding, we used grayscale, color spectra and edges detected by a high-pass or Kirsche detector. After testing all methods, we selected the best one and created the classification program itself. The most successful method was logistic regression with ring projection. 1
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Active learning in E-Commerce Merchant Classification using Website Information
Borchers, Mitchell ; Vomlelová, Marta (advisor) ; Pilát, Martin (referee)
Data and the collection and analysis of data plays an important role in everyday life even though it often goes unseen. In our case, our partner is using data to classify websites into different categories. We used active learning and other machine learning methods to help classify websites into these categories and to explore the data collection and classification process. We scraped text data from websites, translated the data to English, and then worked with machine learning tools to understand the data and classify it. We found that the xPAL active learning strategy and linear support vector classifiers seemed to perform best with our data. 1
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Graph neural networks and their application to social network analysis
Behún, Marek ; Mrázová, Iveta (advisor) ; Vomlelová, Marta (referee)
Recently, the research on Graph Neural Networks (GNNs) made it possi- ble to apply deep learning techniques to graph-structured data. In this thesis, we explore the application of GNNs to Social Network Analysis (SNA). We build and compare deep learning models for the prediction of hotel review ratings, hotel classes, and hotel scores on data scraped from the Tripadvisor website. We consider the resulting models precise enough to be used by rec- ommender systems. A non-trivial part of this thesis is also the description of the theory behind GNNs and visualization techniques for high-dimensional data. We also provide software suitable for further experimentation on this topic.
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Sequential analysis of transaction data
Lamprecht, Matyáš ; Blažek, Jan (advisor) ; Vomlelová, Marta (referee)
Bank transactions are one of the data sources that banks can use for their marketing decisions and campaigns. In this thesis, we are concerned with the use of bank transac- tions for the prediction of clients who will take out a loan in the following month. The bank can use this information in its direct marketing. We test 2 approaches - logistic regression and recurrent neural network, where both of these approaches use information from transactions for the prediction. These two approaches are compared with each other and also with the currently used approach in the bank. The average AUC of the currently used model in the bank is 0.854, which we improved by our best model to 0.861, which is a significant improvement. Furthermore, our best model also outperforms the bank's current model in other computed metrics. 1
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Probabilistic Models for Recommender Systems
Ahmadli, Aydin ; Vomlelová, Marta (advisor) ; Peška, Ladislav (referee)
Recommender systems are software tools and techniques providing recommendations to users based on their needs. Today, popular e-commerce sites widely use recommender systems to recommend product items, articles, books, music, etc. In this thesis, we discuss various probabilistic models for recommender systems, and put the most focus on implementation of hybrid and interpretable probabilistic content-based collaborative filtering model, called Collaborative Topic model for Poisson distributed ratings (CTMP) augmented with Bernoulli randomness for Online Maximum a Posteriori Estimation (BOPE). Resulting model outperforms the previously existing models significantly with its main competency being in commercial product recommendations. It is a fast, scalable, and efficient in ill-posed cases, including short text and sparse data. The model is trained and tested on well-known MovieLens 20M and NETFLIX datasets, and empirical evaluations such as recall, precision, sparsity and topic interpretations are promising.
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Named Entity Recognition and Its Application to Phishing Detection
Pop, Tomáš ; Skopal, Tomáš (advisor) ; Vomlelová, Marta (referee)
This thesis focuses on named entity recognition applied to email phishing detection. Named entity recognition is a classification task that aims to extract information from a text into a predefined set of categories (named entities), such as organizations, person names, or locations. The thesis describes various named entity recognition approaches, ranging from simple utilizations of neural networks to the current state-of-the-art archi- tectures. The most prevalent libraries and their models in named entity recognition are compared against each other from the computational and predictive performance per- spective on the publicly available Enron email dataset. Moreover, differences in terms of named entities between positive (including phishing) and negative emails are measured on a proprietary dataset. Ultimately, the proprietary dataset is used for an experiment where a phishing email classification workflow is enriched with named entities to conclude whether named entities are helpful for the classifier to improve predictive performance. According to the experiment outcomes, a noticeable dissimilarity was measured regarding named entities in positive and negative emails. However, in the phishing email classifica- tion experiment with the provided dataset, it was concluded that named entities do not offer...
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Raman Microspectroscopy Data Processing
Peška, Filip ; Pilát, Martin (advisor) ; Vomlelová, Marta (referee)
Raman microspectroscopy is a powerful tool combining laser scanning confocal microscopy and Raman spectroscopy which is an analytical tech- nique that provides chemical species-specific vibrational spectra or so-called molecular fingerprint of the measured sample - all at once with microscopic resolution. The main problem of processing of the measured data is its time- consuming nature. Therefore, the thesis aims to develop an application that allows automated batch processing of Raman spectral maps in a user-friendly interface. In the first part of the thesis, the theory of implemented method- ological procedures is described in detail, namely, removal of artifacts caused by cosmic radiation hitting the detector, baseline removal due to residual fluorescence of the sample with regard to the complexity of the Raman spec- trum in terms of position and width of bands, and decomposition methods for the analysis of individual spectral components. Special attention was paid to the development of a new algorithm for the removal of cosmic radiation artifacts. Finally, it is also mentioned how the application is used and how it can be extended it by other methods. 1
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