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Clustering of Biological Sequences
Kubiš, Radim ; Burgetová, Ivana (referee) ; Martínek, Tomáš (advisor)
One of the main reasons for protein clustering is prediction of structure, function and evolution. Many of current tools have disadvantage of high computational complexity due to all-to-all sequence alignment. If any tool works faster, it does not reach accuracy as other tools. Further disadvantage is processing on higher rate of similarity but homologous proteins can be similar with less identity. The process of clustering often ends when reach the condition which does not reflect sufficient quality of clusters. Master's thesis describes the design and implementation of new tool for clustering of protein sequences. New tool should not be computationally demanding but it should preserve required accuracy and produce better clusters. The thesis also describes testing of designed tool, evaluation of results and possibilities of its further development.
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Data Mining on Oracle Database Server and MS SQL Server
Opršal, Martin ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
This bachelor's thesis deals with issue of knowledge discovery in databases. This document is focused in getting rules from relation databases based on Microsoft SQL server or Oracle Data mining server. The practical part of this document is about design applications that run on both servers. These applications are programmed in asp.NET, C# for Microsoft SQL server and Java for Oracle server.
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Computational tasks for Parallel data processing course
Horečný, Peter ; Rajnoha, Martin (referee) ; Mašek, Jan (advisor)
The goal of this thesis was to create laboratory excercises for subject „Parallel data processing“, which will introduce options and capabilities of Apache Spark technology to the students. The excercises focus on work with basic operations and data preprocessing, work with concepts and algorithms of machine learning. By following the instructions, the students will solve real world situations problems by using algorithms for linear regression, classification, clustering and frequent patterns. This will show them the real usage and advantages of Spark. As an input data, there will be databases of czech and slovak companies with a lot of information provided, which need to be prepared, filtered and sorted for next processing in the first excercise. The students will also get known with functional programming, because the are not whole programs in excercises, but just the pieces of instructions, which are not repeated in the following excercises. They will get a comprehensive overview about possibilities of Spark by getting over all the excercices.
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Knowledge Discovery in Multimedia Databases
Málik, Peter ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
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Optimal Deployment of Multiple Hypotheses Generators for Detecting Traffic Lights in Camera Images
Bajus, Tomáš ; Richter, Miloslav (referee) ; Petyovský, Petr (advisor)
Táto práca sa zaoberá detekciou semafórov na snímkoch z kamery. Cieľom je nájsť vhodné nastavenie a kombináciu dostupných detektorov. V prvej časti práce je vysvetlený princíp funkcie použitých detektorov. Nasleduje zhodnotenie vlastností jednotlivých detektorov pred ich optimalizáciou. V ďalšej časti práce je popísaný proces testovania a evaluácie detektorov a predstavený nový systém pre zefektívnenie hľadania optimálneho nastavenia detektorov. V rámci optimizácie je popísaný effekt jednotlivých parametrov na chovanie systému a sú navrhnuté rozpätia vhodných hodnôt pre každý parameter. Taktiež je predstavené nové zapojenie detektorov sú nájdené optimálne pracovné body systému. Posledná časť sa zaoberá použitím vhodných metód na filtorvanie a zhlukovanie hypotéz. Nakoniec je prezentovaná celková funkčnosť systému pred a po optimizácii a výsledky sú zhodnotené.
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Knowledge Discovery from Databases with Use of the R Language
Krutý, Peter ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from databases. Main objective is to research possibilities of R language and its support in this area. Support is researched by experiments using appropriate data sets. More detailed attention is given to the methods of classification, clustering and association rules learning. The output of the thesis is comparison of methods application in R and defining the suitability of using language for knowledge discovery from databases.
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Increasing Reliability of Communication Networks
Hausner, Richard ; Komosný, Dan (referee) ; Koton, Jaroslav (advisor)
The bachelor's thesis deals with selected options for increasing reliability of communication networks. The basic protocols and network topologies are described in the thesis. In the second section the technologies of cascading, clustering and stacking network switches are discussed. The practical experiments and scenerios of connections, which are described in the aforementioned section, form a basis of their practical use proposal.
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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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Interactive 3D CT Data Segmentation Based on Deep Learning
Trávníčková, Kateřina ; Hradiš, Michal (referee) ; Kodym, Oldřich (advisor)
This thesis deals with CT data segmentation using convolutional neural nets and describes the problem of training with limited training sets. User interaction is suggested as means of improving segmentation quality for the models trained on small training sets and the possibility of using transfer learning is also considered. All of the chosen methods help improve the segmentation quality in comparison with the baseline method, which is the use of automatic data specific segmentation model. The segmentation has improved by tens of percents in Dice score when trained with very small datasets. These methods can be used, for example, to simplify the creation of a new segmentation dataset.
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