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

Main aim of our thesis is fatigue and stress detection from biological signals of a driver. Introduction contains information on published methods of detection and thoroughly informs readers about theoretical background necessary for our thesis. In the practical application we have firstly worked with a database of measured rides and subsequently chose their most relevant sections. Extraction and selection of features followed afterward. Five different classification models for tiredness and stress detection were used in the thesis and prediction was based on actual data. Lastly, the final section compares the best model of our thesis with the already published results.

Mechanical properties of thermal spray deposited coatings are highly influenced by their microstructural characteristics. The objective of this investigation is to evaluate the mechanical properties of thermally sprayed coatings consisted of aluminum and zinc based on the coating microstructure, using an image based computational scheme. Microstructural images of coating samples were subjected to image-based finite element analysis and the results were validated by experimental tests and analytical models. Comparison of the experimental data with FEA was used to explain the microstructural basis of the mechanical characteristics of Al-Zn coatings and the differences between two methods of thermal spray techniques. It was concluded that the cold spraying technique produces higher-quality coatings with less porosity and higher hardness compared to wire arc deposition. An isotropic behavior was observed in the cold sprayed coating. Finally, the electrochemical tests showed that the coating with a higher amount of zinc had better anti-corrosion properties.

Written skills are an essential evaluation criterion for a student’s creativity, knowledge, and intellect. Consequently, academic writing is a common part of university and college admissions applications, standardized tests, and classroom assessments. However, the task for teachers is quite daunting when it comes to essay scoring. Then Automated Essay Scoring may be a helpful tool in the decision-making by the teacher.  There have been many successful models with supervised or unsupervised machine learning algorithms in the eld of Automated Essay Scoring. This thesis work makes a comparative study among various neural network models with supervised machine learning algorithms and different linguistic feature combinations. It also proves that the same linguistic features are applicable to more than one language.  The models studied in this experiment include TextCNN, TextRNN_LSTM, Tex- tRNN_GRU, and TextRCNN trained with the essays from the Automated Student Assessment Prize (ASAP) from Kaggle competitions. Each essay is represented with linguistic features measuring linguistic complexity. Those features are divided into four groups: count-based, morphological, syntactic, and lexical features, and the four groups of features can form a total of 14 combinations.  The models are evaluated via three measurements: Accuracy, F1 score, and Quadratic Weighted Kappa. The experimental results show that models trained only with count-based features outperform the models trained using other feature combinations. In addition, TextRNN_LSTM performs best, with an accuracy of 54.79%, an F1 score of 0.55, and a Quadratic Weighted Kappa of 0.59, which beats the statistically-based baseline models.

Performance of automatic speech recognition (ASR) systems utilizing only acoustic information degrades significantly in noisy environments such as a car cabins. Incorporating audio and visual information together can improve performance in these situations. This work proposes a lip detection and tracking algorithm to serve as a visual front end to an audio-visual automatic speech recognition (AVASR) system. Several color spaces are examined that are effective for segmenting lips from skin pixels. These color components and several features are used to characterize lips and to train cascaded lip detectors. Pre- and post-processing techniques are employed to maximize detector accuracy. The trained lip detector is incorporated into an adaptive mean-shift tracking algorithm for tracking lips in a car cabin environment. The resulting detector achieves 96.8% accuracy, and the tracker is shown to recover and adapt in scenarios where mean-shift alone fails.

This master‘s thesis describes the identification faces in real terms. It includes an overview of current methods of detection faces by the classifiers. It also includes various methods for detecting faces. The second part is a description of two programs designed to identify persons. The first program operates in real time under laboratory conditions, where using web camera acquires images of user's face. This program is designed to speed recognition of persons. The second program has been working on static images, in real terms. The main essence of this method is successful recognition of persons, therefore the emphasis on computational complexity. The programs I used a staged method of PCA, LDA and kernel PCA (KPCA). The first program only works with the PCA method, which has good results with respect to the success and speed of recognition. In the second program to compare methods, which passed the best method for KPCA.

The thesis discusses in detail some concepts of speech and prosody that can contribute to build a speech corpus for the speaker segmentation purpose. Moreover, the Elan multimedia annotator used for labeling is described. The theoretical part highlights some frequently used speech features such as MFCC, PLP and LPC and deals with currently most popular speech segmentation methods. Some classification algorithms are also mentioned. The practical part describes implementation of Bayesian information criterium algorithm in system for automatic speaker segmentation. For classification of speaker change point in speech, were used different speech features. The results of tests were evaluated by the graphic method of receiver operating characteristic (ROC) and his quantitative indices. As the best speech features for this system were provided MFCC and HFCC.

Laboratory source is basic and necessary part of almost every modern electronics laboratories. Development cannot be done without good laboratory resources. It is main function is supply stabilized DC voltage and current. It appears in a simpler version without the continuous regulation of voltage and current, with only predetermined switching voltage. Of course there are also more demanding applications, where stable voltage adjustable fluently and where is continuous of stable voltage for example LCD applications. This term project deals with a connection of microprocessor and stabilizing and regulating circuits for voltage and current of laboratory sources output. That microprocessor controls performance features by DA converter, which provide a stable output voltage which required power.

This diploma thesis is focused on detection in images using extended set of Haar-like features and histogram-based method. At first is introduced a basic concept of extraction and classification image features. The next part bring own concept of image features based on Diffusion distance. Result of this work is implementation this methods in Rapidminer.

This thesis deals with human body detection and gestures tracking in videosequences. First, processing of videosequences in general is described. Further, different methods of human body detection are described and represented by significant papers. The most of the attention is focused on detection by real AdaBoost algorithm based on Haar-like features and Edgelet features. The practical part starts with selection of method that is implemented in this thesis. This method is detection by real AdaBoost based on Haar-like features. Further, different options of videosequence processing in JAVA are researched with justification of choice OpenCV library with JavaCV wrapper, which is used in this thesis. In the end, application itself is described, including description of GUI and description of each class and its functionality.