In a modern diesel engine, a fuel injector is a vital component. The injectors control the fuel dosing into the combustion chambers. The accuracy in the fuel dosing is very important as inaccuracies have negative effects on engine out emissions and the controllability. Because of this, a diagnosis that can classify the conditions of the injectors with good accuracy is highly desired. A signal that contains information about the injectors condition, is the engine angular velocity. In this thesis, the classification performance of six common machine learning methods is evaluated. The input to the methods is the engine angular velocity. In addition to the classification performance, also the computational cost of the methods, in a deployed state, is analysed. The methods are evaluated on data from a Scania truck that has been run just like any similar commercial vehicle. The six methods evaluated are: logistic regression, kernel logistic regression, linear discriminant analysis, quadratic discriminant analysis, fully connected neural networks and, convolutional neural networks. The results show that the neural networks achieve the best classification performance. Furthermore, the neural networks also achieve the best classification performance from a, in a deployed state, computational cost effectiveness perspective. Results also indicate that the neural networks can avoid false alarms and maintain high sensitivity.

Background: Over the past 4-5 months, the Coronavirus has rapidly spread to all parts of the world. Research is continuing to find a cure for this disease while there is no exact reason for this outbreak. As the number of cases to test for Coronavirus is increasing rapidly day by day, it is impossible to test due to the time and cost factors. Over recent years, machine learning has turned very reliable in the medical field. Using machine learning to predict COVID-19 in patients will reduce the time delay for the results of the medical tests and modulate health workers to give proper medical treatment to them. Objectives: The main goal of this thesis is to develop a machine learning model that could predict whether a patient is suffering from COVID-19. To develop such a model, a literature study alongside an experiment is set to identify a suitable algorithm. To assess the features that impact the prediction model. Methods: A Systematic Literature Review is performed to identify the most suitable algorithms for the prediction model. Then through the findings of the literature study, an experimental model is developed for prediction of COVID-19 and to identify the features that impact the model. Results: A set of algorithms were identified from the Literature study that includes SVM (Support Vector Machines), RF (Random Forests), ANN (Artificial Neural Network), which are suitable for prediction. Performance evaluation is conducted between the chosen algorithms to identify the technique with the highest accuracy. Feature importance values are generated to identify their impact on the prediction. Conclusions: Prediction of COVID-19 by using Machine Learning could help increase the speed of disease identification resulting in reduced mortality rate. Analyzing the results obtained from experiments, Random Forest (RF) was identified to perform better compared to other algorithms.

Recent research in bioinformatics has presented multiple cell type identification meth- dologies using single cell RNA sequence data (scRNA-seq). However, a consensus on which cell typing methodology consistently demonstrates superior performance remains absent. Additionally, very few studies approach cell type identification through a semi- supervised learning study, whereby the information in unlabeled data is leveraged to train an enhanced classifier. This paper presents cell annotation methodologies through self- learning and graph-based semi-supervised learning, in both raw count scRNA-seq data as well as in a latent embedding. I find that a self-learning framework enhances perfor- mance compared to a solely supervised learning classifier. Additionally, modelling on the latent data representations consistently outperforms modelling on the original data. The results show an overall accuracy of 96.12%, whereas additional models achieve an average precision rate of 95.12% and an average recall rate of 94.40%. The semi-supervised learn- ing approaches in this thesis compare favourable to scANVI in terms of accuracy, average precision rate, average recall rate and average f1-score. Moreover, results for alternative scenarios, in which cell types among training and test data do not perfectly overlap, are reported in this thesis.

Machine learning is a growing area of artificial intelligence that is widely used in our world today. Training machine learning models requires knowledge and computing power. Machine Learning as a Service (MLaaS) tries to solve these issues. By storing the datasets and using virtual computing instances in the cloud, one can create machine learning models without writing a single line of code. When selecting an MLaaS platform to use, the natural question of which one to use arises. This thesis conducts controlled experiments to compare the image classification capabilities of Microsoft Azure ML, Amazon Web Services SageMaker, and Google Cloud Platform Vertex AI. The prediction accuracy, training time, and cost will be measured with three different datasets. Some subjective comments about the user experience while conducting these experiments will also be provided. The results of these experiments will be used to make recommendations as to which MLaaS platform to use depending on which metric is most suitable. This thesis found that Microsoft Azure ML performed best in terms of prediction accuracy, and training cost, across all datasets. Amazon Web Services SageMaker had the shortest time to train but performed the worst in terms of accuracy and had trouble with two of the three datasets. Google Cloud Platform Vertex AI did achieve the second-bestprediction accuracy but was the most expensive platform by far as it had the largest time to train. It did, however, provide the smoothest user experience.Overall, Azure ML would be the platform of choice for image classification tasks after weighing together the results of the experiments as well as the subjective user experience.

In this thesis, we investigate the usefulness of a group of features in genre classification problems for news. We choose a diverse feature set, covering features related to content and styles of the texts. The features are divided into two groups: semantic and stylistic. More specifically, the semantic features include genre-exclusive words, emotional words and synonyms. The stylistic features include character-level and document-level features. We use three traditional machine learning classification models and one neural network model to evaluate the effects of our features: Support Vector Machine, Complement Naive Bayes, k-Nearest Neighbor, and Convolutional Neural Networks. The results are evaluated by F1 score, precision and recall (both micro- and macro-averaged). We compare the performance of different models to find the optimal feature set for this news genre classification task, and meanwhile seek the most suitable classifier. We show that genre-exclusive words and synonyms are beneficial to the classification task, in that they are the most informative features in the training process. Emotional words have negative effect on the results. We present the best result of 0.97 by macro-average F1 score, precision and recall on the feature set combining the preprocessed dataset and its synonym sets generated based on contexts classified by the Complement Naive Bayes model. We discuss the results achieved from the experiments and the best-performing models, answer the research questions, and provide suggestions for future studies.

Making predictions nowadays is of high importance for any company, whether small or large, as thanks to the possibility to analyze the data available, new market opportunities can be found, risks and costs can be reduced, among others. Machine learning algorithms for time series can be used for predicting future values of interest. However, choosing the appropriate algorithm and tuning its metaparameters require a great level of expertise. This creates an adoption barrier for small and medium enterprises which could not afford hiring a machine learning expert to their IT team. For these reasons, this project studies different possibilities to make good predictions based on machine learning algorithms, but without requiring great theoretical knowledge from the users. Moreover, a software package that implements the prediction process has been developed. The software is an ensemble method that first predicts a value taking into account different algorithms at the same time, and then it combines their results considering also the previous performance of each algorithm to obtain a final prediction of the value. Moreover, the solution proposed and implemented in this project can also predict according to a concrete objective (e.g., optimize the prediction, or do not exceed the real value) because not every prediction problem is subject to the same constraints. We have experimented and validated the implementation with three different cases. In all of them, a better performance has been obtained in comparison with each of the algorithms involved, reaching improvements of 45 to 95%.

Network attacks remain a constant threat to organizations around the globe. Intrusion detection systems provide a vital piece of the protection needed in order to fend off these attacks. Machine learning has become a popular method for developing new anomaly-based intrusion detection systems, and in recent years, deep learning has followed suit. Additionally, data fusion is often applied to intrusion detection systems in research, most often in the form of feature reduction, which can improve the accuracy and training times of classifiers. Another less common form of data fusion is decision fusion, where the outputs of multipe classifiers are fused into a more reliable result. Recent research has produced some contradictory results regarding the efficiency of traditional machine learning algorithms compared to deep learning algorithms. This study aims to investigate this problemand provide some clarity about the relative performance of a selection of classifier algorithms, namely artificial neural network, long short-term memory and random forest. Furthermore, two feature selection methods, namely correlation coefficient method and principal component analysis, as well as one decision fusion method in D-S evidence theory are tested. The majority of the feature selection methods fail to increase the accuracy of the implemented models, although the accuracy is not drastically reduced. Among the individual classifiers, random forest shows the best performance, obtaining an accuracy of 87,87%. Fusing the results with D-S evidence theory further improves this result, obtaining an accuracy of 88,56%, and proves particularly useful for reducing the number of false positives.

Log messages are one of the most valuable sources of information in the cloud and other software systems. These logs can be used for audits and ensuring system security. Many millions of log messages are produced each day which makes anomaly detection challenging. Automating the detection of anomalies can save time and money as well as improve detection performance. In this dissertation, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for log message anomaly detection. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB. The first three are popular log data sets while the fourth is a movie review data set which is used for sentiment classification. The results obtained show that Auto-LSTM, Auto-BLSTM and Auto-GRU perform better than other well-known algorithms. Dealing with imbalanced data is one of the main challenges in Machine Learning (ML)/DL algorithms for classification. This issue is more important with log message data as it is typically very imbalanced and negative logs are rare. Hence, a model is proposed to generate text log messages using a Sequence Generative Adversarial Network (SeqGAN) network. Then features are extracted using an Autoencoder and anomaly detection is done using a GRU network. The proposed model is evaluated with two imbalanced log data sets, namely BGL and Openstack. Results are presented which show that oversampling and balancing data increases the accuracy of anomaly detection and classification. Another challenge in anomaly detection is dealing with unlabeled data. Labeling even a small portion of logs for model training may not be possible due to the high volume of generated logs. To deal with this unlabeled data, an unsupervised model for log message anomaly detection is proposed which employs Isolation Forest and two deep Autoencoder networks. The Autoencoder networks are used for training and feature extraction, and then for anomaly detection, while Isolation Forest is used for positive sample prediction. The proposed model is evaluated using the BGL, Openstack and Thunderbird log message data sets. The results obtained show that the number of negative samples predicted to be positive is low, especially with Isolation Forest and one Autoencoder. Further, the results are better than with other well-known models. A hybrid log message anomaly detection technique is proposed which uses pruning of positive and negative logs. Reliable positive log messages are first identified using a Gaussian Mixture Model (GMM) algorithm. Then reliable negative logs are selected using the K-means, GMM and Dirichlet Process Gaussian Mixture Model (BGM) methods iteratively. It is shown that the precision for positive and negative logs with pruning is high. Anomaly detection is done using a Long Short-Term Memory (LSTM) network. The proposed model is evaluated using the BGL, Openstack, and Thunderbird data sets. The results obtained indicate that the proposed model performs better than several well-known algorithms. Last, an anomaly detection method is proposed using radius-based Fuzzy C-means (FCM) with more clusters than the number of data classes and a Multilayer Perceptron (MLP) network. The cluster centers and a radius are used to select reliable positive and negative log messages. Moreover, class probabilities are used with an expert to correct the network output for suspect logs. The proposed model is evaluated with three well-known data sets, namely BGL, Openstack and Thunderbird. The results obtained show that this model provides better results than existing methods. / Graduate

Negation is a complex phenomenon present in all human languages, allowing for the uniquely human capacities of denial, contradiction, misrepresentation, lying, and irony. It is in the first place a phenomenon of semantical opposition. Sentences containing negation are generally (a) less informative than affirmative ones, (b) morphosyntactically more marked—all languages have negative markers while only a few have affirmative markers, and (c) psychologically more complex and harder to process. Negation often conveys positive meaning. This meaning ranges from implicatures to entailments. In this dissertation, I develop a system to reveal the underlying positive interpretation of negation. I first identify which words are intended to be negated (i.e, the focus of negation) and second, I rewrite those tokens to generate an actual positive interpretation. I identify the focus of negation by scoring probable foci along a continuous scale. One of the obstacles to exploring foci scoring is that no public datasets exist for this task. Thus, to study this problem I create new corpora. The corpora contain verbal, nominal and adjectival negations and their potential positive interpretations along with their scores ranging from 1 to 5. Then, I use supervised learning models for scoring the focus of negation. In order to rewrite the focus of negation with its positive interpretation, I work with negations from Simple Wikipedia, automatically generate potential positive interpretations, and then collect manual annotations that effectively rewrite the negation in positive terms. This procedure yields positive interpretations for approximately 77% of negations, and the final corpus includes over 5,700 negations and over 5,900 positive interpretations. I then use sequence-to-sequence neural models and provide baseline results.

Background: Traffic Sign Recognition (TSR) is particularly useful for novice driversand self-driving cars. Driver Assistance Systems(DAS) involves automatic trafficsign recognition. Efficient classification of the traffic signs is required in DAS andunmanned vehicles for safe navigation. Convolutional Neural Networks(CNN) isknown for establishing promising results in the field of image classification, whichinspired us to employ this technique in our thesis. Computer vision is a process thatis used to understand the images and retrieve data from them. OpenCV is a Pythonlibrary used to detect traffic sign images in real-time. Objectives: This study deals with an experiment to build a CNN model which canclassify the traffic signs in real-time effectively using OpenCV. The model is builtwith low computational cost. The study also includes an experiment where variouscombinations of parameters are tuned to improve the model’s performance. Methods: The experimentation method involve building a CNN model based onmodified LeNet architecture with four convolutional layers, two max-pooling layersand two dense layers. The model is trained and tested with the German Traffic SignRecognition Benchmark (GTSRB) dataset. Parameter tuning with different combinationsof learning rate and epochs is done to improve the model’s performance.Later this model is used to classify the images introduced to the camera in real-time. Results: The graphs depicting the accuracy and loss of the model before and afterparameter tuning are presented. An experiment is done to classify the traffic signimage introduced to the camera by using the CNN model. High probability scoresare achieved during the process which is presented. Conclusions: The results show that the proposed model achieved 95% model accuracywith an optimum number of epochs, i.e., 30 and default optimum value oflearning rate, i.e., 0.001. High probabilities, i.e., above 75%, were achieved when themodel was tested using new real-time data.