Background: With the increase in automating the performance testing strategies, many efforts have been made to detect the Software Performance Antipatterns (SPAs). These performance antipatterns have become a major threat to software platforms at the enterprise level, and detecting these anomalies is essential in any company dealing with performance-sensitive software as these processes should be performed quite often. Due to the complexity of the process, the manual identification of performance issues has become challenging and time-consuming. Objectives: The thesis aims to address and solve the issues mentioned above by developing a tool that automatically Characterizes and Detects Software Performance Antipatterns. The goal is to automate the parameterization process of the existing approach that helps characterize SPAs and improve the interpretation of detection of SPAs. These two processes are integrated into the tool designed to be deployed in the CI/CD pipeline. The developed tool is named Chanterelle. Methods: A case study and a survey has been used in this research. A case study has been conducted at Ericsson. A similar process as in the existing approach has been automated using python. A literature review is conducted to identify an appropriate approach to improve the interpretation of the detection of SPAs. A static user validation has been conducted with the help of a survey consisting of Chanterelle feasibility and usability questions. The responses are provided by Ericsson staff (developers and tester in the field of Software performance) after the tool is presented. Results: The results indicate that the automated parameterization and detection process proposed in this thesis have a considerable execution time compared to the existing approaches and helps the developers interpret the detection results easily. Moreover, it does not include domain experts t run the tests. The results of the static user validation show that Chanterelle is feasible and usable as a tool to be used by the developers. Conclusions: The validation of the tool suggests that Chanterelle helps the developers to interpret the performance-related bugs easily. It performs the automated parameterization and detection process in a considerable time when compared with the existing approaches.

Forecasting the weather remains a challenging task because of the atmosphere's complexity and unpredictable nature. A few of the factors that decide weather conditions, such as rain, clouds, clear skies, and sunshine, include temperature, pressure, humidity, wind speed, and direction. Currently, sophisticated, and physical models are used to forecast weather, but they have several limitations, particularly in terms of computational time. In the past few years, supervised machine learning algorithms have shown great promise for the precise forecasting of meteorological events. Using historical weather data, these strategies train a model to predict the weather in the future. This study employs supervised machine learning techniques, including k-nearest neighbors (KNNs), support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs), for better weather forecast accuracy. To conduct this study, we employed historical weather data from the Weatherstack API. The data spans several years and contains information on several meteorological variables, including temperature, pressure, humidity, wind speed, and direction. The data is processed beforehand which includes normalizing it and dividing it into separate training and testing sets. Finally, the effectiveness of different models is examined to determine which is best for producing accurate weather forecasts. The results of this study provide information on the application of supervised machine learning methods for weather forecasting and support the creation of better weather prediction models. / Att förutsäga vädret är fortfarande en utmanande uppgift på grund av atmosfärens komplexitet och oförutsägbara natur. Några av faktorerna som påverkar väderförhållandena, som regn, moln, klart väder och solsken, inkluderar temperatur, tryck, luftfuktighet, vindhastighet och riktning. För närvarande används sofistikerade fysiska modeller för att förutsäga vädret, men de har flera begränsningar, särskilt när det gäller beräkningstid. Under de senaste åren har övervakade maskininlärningsalgoritmer visat stor potential för att noggrant förutsäga meteorologiska händelser. Genom att använda historiska väderdata tränar dessa strategier en modell för att förutsäga framtida väder. Denna studie använder övervakade maskininlärningstekniker, inklusive k-nearest neighbors (KNNs), support vector machines (SVMs), random forests (RFs) och artificial neural networks (ANNs), för att förbättra noggrannheten i väderprognoser. För att genomföra denna studie använde vi historiska väderdata från Weatherstack API. Data sträcker sig över flera år och innehåller information om flera meteorologiska variabler, inklusive temperatur, tryck, luftfuktighet, vindhastighet och riktning. Data bearbetas i förväg, vilket inkluderar normalisering och uppdelning i separata tränings- och testset. Slutligen undersöks effektiviteten hos olika modeller för att avgöra vilken som är bäst för att producera noggranna väderprognoser. Resultaten av denna studie ger information om tillämpningen av övervakade maskininlärningsmetoder för väderprognoser och stödjer skapandet av bättre väderprognosmodeller.

A kayak is a small watercraft that moves over the water. The kayak is propelled by a person sitting inside of the hull and paddling using a double-bladed paddle. While kayaking can be casual, it is used as a competitive sport in races and even the Olympic games. Therefore, it is important to be able to analyse athletes’ performance during the race. To study the race better, some kayaking teams and organizations have attached sensors to their kayaks. These sensors record various data, which is later used to generate performance reports. However, to generate such reports, the coach must manually pinpoint the beginning of the race because the sensors collect data before the actual race begins, which may include practice runs, warming-up sessions, or just standing and waiting position. The identification of the race start and the race sequence in the data is tedious and time-consuming work and could be automated. This project proposes an approach to identify kayak races from velocity signal data with the help of a machine learning algorithm. The proposed approach is a combination of several techniques: signal preprocessing, a machine learning algorithm, and a programmatic approach. Three machine learning algorithms were evaluated to detect the race sequence, which are Support Vector Machine (SVM), k-Nearest Neighbour (kNN), and Random Forest (RF). SVM outperformed other algorithms with an accuracy of 95%. Programmatic approach was proposed to identify the start time of the race. The average error of the proposed approach is 0.24 seconds. The proposed approach was utilized in the implemented web-based application with a user interface for coaches to automatically detect the beginning of a kayak race and race signal sequence.

In a changing global energy market where the decarbonization of the economy and the demand growth are pushing to look for new models away from the existing centralized non-renewable based grid. To do so, households have to take a ‘prosumer’ role; to help them take optimal actions is needed a multi-step ahead forecast of their expected energy production and consumption. In multi-step ahead forecasting there are different strategies to perform the forecast. The single-output: Recursive, Direct, DirRec, and the multi-output: MIMO and DIRMO. This thesis performs a comparison between the performance of the differents strategies in a ‘prosumer’ household; using Artificial Neural Networks, Random Forest and K-Nearest Neighbours Regression to forecast both solar energy production and grid input. The results of this thesis indicates that the methodology proposed performs better than state of the art models in a more detailed household energy consumption dataset. They also indicate that the strategy and model of choice is problem dependent and a strategy selection step should be added to the forecasting methodology. Additionally, the performance of the Recursive strategy is always far from the best while the DIRMO strategy performs similarly. This makes the latter a suitable option for exploratory analysis.

This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.

The thesis is focused on analysis of polysomnographic signals based on extraction of chosen parameters in time, frequency and time-frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. The classification is realized by artificial neural networks, k-NN classifier and linear discriminant analysis. The program with a graphical user interface was created using Matlab.

The selection of machine learning algorithm used to solve a problem is an important choice. This paper outlines research measuring three performance metrics for eight different algorithms on a prediction task involving under- graduate admissions data. The algorithms that were tested are k-nearest neighbours, decision trees, random forests, gradient tree boosting, logistic regression, naive bayes, support vector machines, and artificial neural net- works. These algorithms were compared in terms of accuracy, training time, and execution time. / Thesis / Master of Applied Science (MASc)

In this dissertation a method for the classification of emotion in speech recordings made in a customer service call centre of a large business is presented. The problem addressed here is that customer service analysts at large businesses have to listen to large numbers of call centre recordings in order to discover customer service-related issues. Since recordings where the customer exhibits emotion are more likely to contain useful information for service improvement than “neutral” ones, being able to identify those recordings should save a lot of time for the customer service analyst. MTN South Africa agreed to provide assistance for this project. The system that has been developed for this project can interface with MTN’s call centre database, download recordings, classify them according to their emotional content, and provide feedback to the user. The system faces the additional challenge that it is required to classify emotion notwith- standing the fact that the caller may have one of several South African accents. It should also be able to function with recordings made at telephone quality sample rates. The project identifies several speech features that can be used to classify a speech recording according to its emotional content. The project uses these features to research the general methods by which the problem of emotion classification in speech can be approached. The project examines both a K-Nearest Neighbours Approach and an Artificial Neural Network- Based Approach to classify the emotion of the speaker. Research is also done with regard to classifying a recording according to the gender of the speaker using a neural network approach. The reason for this classification is that the gender of a speaker may be useful input into an emotional classifier. The project furthermore examines the problem of identifying smaller segments of speech in a recording. In the typical call centre conversation, a recording may start with the agent greeting the customer, the customer stating his or her problem, the agent performing an action, during which time no speech occurs, the agent reporting back to the user and the call being terminated. The approach taken by this project allows the program to isolate these different segments of speech in a recording and discard segments of the recording where no speech occurs. This project suggests and implements a practical approach to the creation of a classifier in a commercial environment through its use of a scripting language interpreter that can train a classifier in one script and use the trained classifier in another script to classify unknown recordings. The project also examines the practical issues involved in implementing an emotional clas- sifier. It addresses the downloading of recordings from the call centre, classifying the recording and presenting the results to the customer service analyst. AFRIKAANS : n Metode vir die klassifisering van emosie in spraakopnames in die oproepsentrum van ’n groot sake-onderneming word in hierdie verhandeling aangebied. Die probleem wat hierdeur aangespreek word, is dat kli¨entediens ontleders in ondernemings na groot hoeveelhede oproepsentrum opnames moet luister ten einde kli¨entediens aangeleenthede te identifiseer. Aangesien opnames waarin die kli¨ent emosie toon, heel waarskynlik nuttige inligting bevat oor diensverbetering, behoort die vermo¨e om daardie opnames te identifiseer vir die analis baie tyd te spaar. MTN Suid-Afrika het ingestem om bystand vir die projek te verleen. Die stelsel wat ontwikkel is kan opnames vanuit MTN se oproepsentrum databasis verkry, klassifiseer volgens emosionele inhoud en terugvoering aan die gebruiker verskaf. Die stelsel moet die verdere uitdaging kan oorkom om emosie te kan klassifiseer nieteenstaande die feit dat die spreker een van verskeie Suid-Afrikaanse aksente het. Dit moet ook in staat wees om opnames wat gemaak is teen telefoon gehalte tempos te analiseer. Die projek identifiseer verskeie spraak eienskappe wat gebruik kan word om ’n opname volgens emosionele inhoud te klassifiseer. Die projek gebruik hierdie eienskappe om die algemene metodes waarmee die probleem van emosie klassifisering in spraak benader kan word, na te vors. Die projek gebruik ’n K-Naaste Bure en ’n Neurale Netwerk benadering om die emosie van die spreker te klassifiseer. Navorsing is voorts gedoen met betrekking tot die klassifisering van die geslag van die spreker deur ’n neurale netwerk. Die rede vir hierdie klassifisering is dat die geslag van die spreker ’n nuttige inset vir ’n emosie klassifiseerder mag wees. Die projek ondersoek ook die probleem van identifisering van spraakgedeeltes in ’n opname. In ’n tipiese oproepsentrum gesprek mag die opname begin met die agent wat die kli¨ent groet, die kli¨ent wat sy of haar probleem stel, die agent wat ’n aksie uitvoer sonder spraak, die agent wat terugrapporteer aan die gebruiker en die oproep wat be¨eindig word. Die benadering van hierdie projek laat die program toe om hierdie verskillende gedeeltes te isoleer uit die opname en om gedeeltes waar daar geen spraak plaasvind nie, uit te sny. Die projek stel ’n praktiese benadering vir die ontwikkeling van ’n klassifiseerder in ’n kommersi¨ele omgewing voor en implementeer dit deur gebruik te maak van ’n programeer taal interpreteerder wat ’n klassifiseerder kan oplei in een program en die opgeleide klassifiseerder gebruik om ’n onbekende opname te klassifiseer met behulp van ’n ander program. Die projek ondersoek ook die praktiese aspekte van die implementering van ’n emosionele klassifiseerder. Dit spreek die aflaai van opnames uit die oproep sentrum, die klassifisering daarvan, en die aanbieding van die resultate aan die kli¨entediens analis, aan. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

An automated manufacturing industry makes use of many interacting moving parts and sensors. Data from these sensors generate complex multidimensional data in the production environment. This data is difficult to interpret and also difficult to find patterns in. This project provides tools to get a deeper understanding of Swedsafe’s production data, a company involved in an automated manufacturing business. The project is based on and will show the potential of the multidimensional production data. The project mainly consists of predicting deviations from predefined threshold values in Swedsafe’s production data. Machine learning is a good method of finding relationships in complex datasets. Supervised machine learning classification is used to predict deviation from threshold values in the data. An investigation is conducted to identify the classifier that performs best on Swedsafe's production data. The technique sliding window is used for managing time series data, which is used in this project. Apart from predicting deviations, this project also includes an implementation of live graphs to easily get an overview of the production data. A steady production with stable process values is important. So being able to monitor and predict events in the production environment can provide the same benefit for other manufacturing companies and is therefore suitable not only for Swedsafe. The best performing machine learning classifier tested in this project was the Random Forest classifier. The Multilayer Perceptron did not perform well on Swedsafe’s data, but further investigation in recurrent neural networks using LSTM neurons would be recommended. During the projekt a web based application displaying the sensor data in live graphs is also developed.

The thesis is aimed at an analysis and automatic detection of logopaedic defects in speech utterance. Its objective is to facilitate and accelerate the work of logopaedists and to increase percentage of detected logopaedic defects in children of the youngest possible age followed by the most successful treatment. It presents methods of speech work, classification of the defects within individual stages of child development and appropriate words for identification of the speech defects and their subsequent remedy. After that there are analyses of methods of calculating coefficients which reflect human speech best. Also classifiers which are used to discern and determine whether it is a speech defect or not. Classifiers exploit coefficients for their work. Coefficients and classifiers are being tested and their best combination is being looked for in order to achieve the highest possible success rate of the automatic detection of the speech defects. All the programming and testing jobs has been conducted in the Matlab programme.