Master of Science / Department of Computing and Information Sciences / William H. Hsu / In this thesis, I examine the Articles for Deletion (AfD) system in /Wikipedia/, a large-scale collaborative editing project. Articles in Wikipedia can be nominated for deletion by registered users, who are expected to cite criteria for deletion from the Wikipedia deletion. For example, an article can be nominated for deletion if there are any copyright violations, vandalism, advertising or other spam without relevant content, advertising or other spam without relevant content. Articles whose subject matter does not meet the notability criteria or any other content not suitable for an encyclopedia are also subject to deletion. The AfD page for an article is where Wikipedians (users of Wikipedia) discuss whether an article should be deleted. Articles listed are normally discussed for at least seven days, after which the deletion process proceeds based on community consensus. Then the page may be kept, merged or redirected, transwikied (i.e., copied to another Wikimedia project), renamed/moved to another title, userfied or migrated to a user subpage, or deleted per the deletion policy. Users can vote to keep, delete or merge the nominated article. These votes can be viewed in article’s view AfD page. However, this polling does not necessarily determine the outcome of the AfD process; in fact, Wikipedia policy specifically stipulates that a vote tally alone should not be considered sufficient basis for a decision to delete or retain a page. In this research, I apply machine learning methods to determine how the final outcome of an AfD process is affected by factors such as the difference between versions of an article, number of edits, and number of disjoint edits (according to some contiguity constraints). My goal is to predict the outcome of an AfD by analyzing the AfD page and editing history of the article. The technical objectives are to extract features from the AfD discussion and version history, as reflected in the edit history page, that reflect factors such as those discussed above, can be tested for relevance, and provide a basis for inductive generalization over past AfDs. Applications of such feature analysis include prediction and recommendation, with the performance goal of improving the precision and recall of AfD outcome prediction.

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Six new structural parameters £rB, £pB, £rC, £pC, £rS, and £pS are proposed to enhance the side chain actions in protein structures. Programs for calculating these new parameters based on phi and psi torsion angles vector algebra calculation method are established. A bivariate model with von Mises marginal distributions are applied to establish models of phi and psi in protein class Ophiophagus hannah neurotoxins and alpha-bungarotoxins respectively. 11 global structural parameters include phi and psi torsion angles, bond lengths of C-N, C-O, C£\ -C, and N-C£\, and bond angles of C-N-C£\, C£\-C-N, C£\-C-O, N-C£\-C, and O-C-N are considered to classify long alpha-neurotoxins by Ward's cluster method and LIBSVM program package. Those global structural parameters of loop II Trp residues of alpha-neurotoxins are discussed.

This work deals with development of an application for optical music recognition. This application is designed for mobile phones with Android operating system. The work includes a brief introduction to the problem and introduces some existing solutions. There are described methods for image processing and classification, which are used in final application. It also shows the design and implemenation of the final aplication, where used methods for detection and removal of staff lines, detection and processing of musical symbols and their classification are described. The evaluation of the final application and a summary of achieved results are shown in the end of this work.

This work focuses on classification and recognition of traffic signs in image. It describes briefly some used methods a deeply describes chosen system including extensions and method for creating models needed for classification. There's described implementation of library and demonstration program including important pieces of knowledge discovered during development. There're also results of some experiments and possible enhancements in conclusion.

This thesis is focused on knowledge discovery from databases, especially on methods of classification and prediction. These methods are described in detail.  Furthermore, this work deals with multimedia databases and the way these databases store data. In particular, the method for processing low-level image and video data is described.  The practical part of the thesis focuses on the implementation of this GMM method used for extracting low-level features of video data and images. In other parts, input data and tools, which the implemented method was compared with, are described.  The last section focuses on experiments comparing extraction efficiency features of high-level attributes of low-level data and the methods implemented in selected classification tools LibSVM.

This master's thesis deals with knowledge discovery in databases, especially basic methods of classification and prediction used for data mining are described here. The next chapter contains introduction to multimedia databases and knowledge discovery in multimedia databases. The main goal of this chapter was to focus on extraction of low level features from video data and images. In the next parts of this work, there is described data set and results of experiments in applications RapidMiner, LibSVM and own developed application. The last chapter summarises results of used methods for high level feature extraction from low level description of data.

This master's thesis is dedicated to theme of knowledge discovery in Multimedia Databases, especially basic methods of classification and prediction used for data mining. The other part described about extraction of low level features from video data and images and summarizes information about content-based search in multimedia content and indexing this type of data. Final part is dedicated to implementation Gaussian mixtures model for classification and compare the final result with other method SVM.