The transcriptome represents the entirety of RNA molecules within a cell or tissue at a given time. Recent advances have facilitated the production of large-scale, global interrogations of transcriptomes, finding that genomes are extensively transcribed and contain diverse classes of RNAs (Dinger et al., 2009). Information generated by high-throughput analyses of mRNA transcription start sites (TSSs) such as CAGE (Cap Analysis of Gene Expression) indicate that eukaryotic genomes have complex landscapes of transcription initiation. The TSS is important for the annotation of cis-regulatory sequences, because it provides a link between the mRNA transcript and the promoter. The patterns of TSS distributions observed within mRNA 5' end profiling studies prevent straightforward annotation of putative promoters. To address this challenge, we developed a method to identify- on a genome-wide basis- the putative promoter, which we define by TSS distributions and designate the transcription start region (TSR). We applied a clustering method to identify and annotate TSRs within the budding yeast Saccharomyces cerevisiae using a full-length cDNA dataset (Miura et al., 2006). To validate these TSR annotations, we performed an integrative genomic analysis using multiple datasets. Our method identified TSRs at positions consistent with bona fide promoters in S. cerevisiae. In addition, using 5'RACE, we find overall agreement between computationally-defined TSRs and TSSs identified experimentally. From this analysis, we find that a significant proportion of genes exhibiting alternative promoter usage within sporulation are associated with respiration, suggesting that this is regulated on a condition-specific basis in budding yeast. We further developed our TSS clustering method into a bioinformatics tool called TSRchitect, which identifies and annotates TSRs from large-scale TSS profiling information. TSRchitect is capable of handling both tag and sequence-based TSS information and efficiently computes TSRs from global TSS datasets on a desktop computer. We find support for TSRchitect's annotations in human from a CAGE experiment from the ENCODE (Encyclopedia of DNA Elements) project. Finally, we use TSRchitect to identify TSRs from the transcriptomes of diverse eukaryotes. We investigated the conservation of TSRs among orthologous genes. We frequently identify multiple TSRs for a given gene, suggesting that alternative promoter usage is widespread. Overall, using TSS profiling data derived from separate tissues within mouse and human, we find that the positions of TSRs are relatively stable across tissues surveyed; however, a small fraction of genes exhibit tissue-specific differences in TSR use. As transcriptome profiling information continues to be generated at an rapid pace, computational approaches are increasingly important. It is anticipated that the method and approach we describe within this dissertation will contribute to an improved of gene regulation and promoter architecture in eukaryotes.

Since the popularisation of media streaming, a number of video streaming services are continually buying new video content to mine the potential profit. As such, newly added content has to be handled appropriately to be recommended to suitable users. In this dissertation, the new item cold-start problem is addressed by exploring the potential of various deep learning features to provide video recommendations. The deep learning features investigated include features that capture the visual-appearance, as well as audio and motion information from video content. Different fusion methods are also explored to evaluate how well these feature modalities can be combined to fully exploit the complementary information captured by them. Experiments on a real-world video dataset for movie recommendations show that deep learning features outperform hand crafted features. In particular, it is found that recommendations generated with deep learning audio features and action-centric deep learning features are superior to Mel-frequency cepstral coefficients (MFCC) and state-of-the-art improved dense trajectory (iDT) features. It was also found that the combination of various deep learning features with textual metadata and hand-crafted features provide significant improvement in recommendations, as compared to combining only deep learning and hand-crafted features. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021. / The MultiChoice Research Chair of Machine Learning at the University of Pretoria / UP Postgraduate Masters Research bursary / Electrical, Electronic and Computer Engineering / MEng (Computer Engineering) / Unrestricted

The master thesis deals with the performance evaluation of the selected company according to the multi-criteria model START PLUS. Based on the assessment of key performance indicators in individual areas a scoring is created in order to identify strengths and areas for improvement. The last chapter of this thesis is dedicated to proposal of concrete measures and recommendations to increase the performance of the analyzed company.

The diploma thesis deals with the chosen company performance assessment. The company is analyzed on the basis of multi-criteria model which is used for management approaches´ assessment, specifically by means of model Start Plus. After that, the point assessment of this model was made, the strengths and the areas for improvement were identified and the specific steps were suggested. The implementation of these steps should contribute to an improvement in the company performance.

This diploma thesis deals with the company performance assessment of selected company according to multi-criteria model START Plus. Thanks to this model, the final score of the company is realised, strengths and areas for improvement are identified. According to these results, new improvements are proposed.

This diploma thesis deals with company performance assessment of Západoslovenské tlačiarne Skalica, s.r.o. Chosen company is analysed according to multi-criterial model START PLUS. The results of this model are identifications of strengths and areas with possibility of improvement. The conclusion of this work is suggested measurements, which should lead to the company’s higher performance.

The diploma thesis describes the performance evaluation of the organization according to model Start plus. The first part of the thesis is focused on the theoretical description of methods, which are used to evaluate performance of the organization. The second part of thesis describes analysis of the organization evaluation using multi-criteria model Start plus. Base on the findings, each criteria is rated and then strengths and areas for possible improvement are identified.

The master´s thesis analyzes the performance of company Řetězárna Ltd. by using the Model START, which is based on the EFQM Excellence Model principle. The first part is devoted to the theoretical description of performance measurement. The second part of the master´s thesis deals with the application of these theoretical findings in practice, in the form of an analysis according to the Model START. In the last part of the master´s thesis, according to the results of the analytical part, are proposed new possible improvements to increase the performance of Řetězárna Ltd.

This diploma thesis focuses on the evaluation of the performance of the selected company with the help of a multi-criteria model START PLUS. The whole thesis is divided into four fundamental parts that are intertwined. The first part deals with the objectives set and the chosen methodology of this thesis. The theoretical block explains the basic concepts and conventional and unconventional approaches to performance measurement. In the analytical part, the company is characterized, and a complex analysis is carried out based on the chosen model, which results in the identification of strengths and opportunities for improvement, as well as in the point assessment in the studied areas. Based on these findings, real measures are created in the design part, which should contribute to the increase the performance of the company.

As per its theme, this thesis deals with company performance assessment of the selected company according to the multicriterial model START PLUS. The work consists of three main parts: theoretical, analytical and design. The output of performed analyzes via point evaluation is to identify strengths and weaknesses of the company, on basis of what proposals for changes leading to an overall improvement of the company's performance will be presented.