Forskningsfrågan i denna avhandling var att utvärdera och jämföra två ML-algoritmer som var Support Vector Machine (SVM) och Artificial Neural Network (ANN) i termer av noggrannhet, precision, återkallelse, f1-poäng och förutsägelse när de tränades för att klassificera binära datamängder. Datauppsättningen hämtades från Ladok och bestod av anonyma högskolestudenter från en mängd kurser. Algoritmerna kördes på TensorFlow med Keras som API och byggdes, tränades och kördes för utvärdering, allt på Google Colab. Källkoden skrevs i Python. Det icke-tekniska målet med studien var att försöka hitta ett förutsägelsemönster för studentprestationer och tillhandahålla ett tekniskt ramverktyg för att ge feedback till studenter och universitetsfakulteten. Forskningsfrågan delades upp i tre separata delfrågor. Den första var om ML-algoritmerna var ett lämpligt sätt att hitta dessa elevmönster och den kunskap man fick var att ja eftersom dessa algoritmer var lämpliga för den lilla datauppsättningsstorleken. Den andra handlade om hur man implementerar SVM och ANN och det löstes med TensorFlow med Keras API. Den tredje handlade om mängden som behövdes för att dra slutsatserna och förutsäga dessa algoritmer, och det fastställdes att storleken var tillräcklig på grund av att den tränade noggrannheten var högre än baslinjenoggrannheten i båda algoritmerna. Den huvudsakliga forskningsfrågan resulterade i att SVM-modellen överträffade ANN-modellen vad gäller alla nämnda parametrar. Detta teoretiserades på grund av att SVM har linjärt ökande multiparameter som matchade de ökade ingångarna. Detta var inte fallet med strukturen för ANN. / The research question of this thesis was to evaluate and compare two ML algorithms which were Support Vector Machine (SVM) and Artificial Neural Network (ANN) in terms of accuracy, precision, recall, f1 score, and prediction when trained for classifying binary datasets. The dataset was fetched from Ladok and consisted of anonymous higher education student credit from a multitude of courses. The algorithms were run on TensorFlow with Keras as an API and were built, trained, and run for evaluation all on Google Colab. The source-code was written in Python. The non-technical goal of the study was to try to find a prediction pattern for student performance and provide a technical framework tool to provide feedback for student and university faculty. The research question was broken down into three separate sub questions. The first one was if the ML algorithms were an appropriate way to find these student patterns and the knowledge gained was that yes because theses algorithms were appropriate for the small dataset size. The second one was about how to implement SVM and ANN and that was solved using TensorFlow with Keras API. The third one was about the amount needed to draw the conclusions and prediction these algorithms would make, and it was determined that the size was sufficient due to the trained accuracy being higher that the baseline accuracy in both algorithms. The main research question resulted in the SVM model outperforming the ANN model in terms of all the parameters mentioned. This was theorized due to the nature of SVM having linearly increasing multiparameter that that matched the increased inputs. This was not the case with the structure of the ANN.

The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.

Vid stor mängd data i form av kundomdömen kan det vara ett relativt tidskrävande arbeteatt bedöma varje omdömes sentiment manuellt, om det är positivt eller negativt laddat. Denna avhandling har utförts för att automatiskt kunna klassificera kundomdömen efter positiva eller negativa omdömen vilket hanterades med hjälp av maskininlärning. Tre olika djupa neurala nätverk testades och jämfördes med hjälp av två olika ramverk, TensorFlow och Keras, på både större och mindre datamängder. Även olika inbäddningsmetoder testades med de neurala nätverken. Den bästa kombination av neuralt nätverk, ramverk och inbäddningsmetod var ett Convolutional Neural Network (CNN) som använde ordinbäddningsmetoden Word2Vec, var skriven i ramverket Keras och gav en träffsäkerhetpå ca 88.87% med en avvikelse på ca 0.4%. CNN gav bäst resultat i alla olika tester framför de andra två neurala nätverken, Recurrent Neural Network (RNN) och Convolutional Recurrent Neural Network (CRNN) / With large amount of data in the form of customer reviews, it could be time consuming to manually go through each review and decide if its sentiment is positive or negative. This thesis have been done to automatically classify client reviews to determine if a review is positive or negative. This was dealt with by machine learning. Three different deep neural network was tested on greater and lesser datasets, and compared with the help of two different frameworks, TensorFlow and Keras. Different embedding methods were tested on the neural networks. The best combination of a neural network, a framework and anembedding was the Convolutional Neural Network (CNN) which used the word embedding method Word2Vec, was written in Keras framework and gave an accuracy of approximately 88.87% with a deviation of approximately 0.4%. CNN scored a better result in all of the tests in comparison with the two other neural networks, Recurrent NeuralNetwork (RNN) and Convolutional Recurrent Neural Network (CRNN).

This thesis explores the possibility of applying sentiment analysis to extract tonality of user reviews on the Nordic languages. Data processing is performed in the form of preprocessing through tokenization and padding. A model is built in a framework called Keras. Models for classification and regression were built using LSTM and GRU architectures. The results showed how the dataset influences the end result and the correlation between observed and predicted values for classification and regression. The project shows that it is possible to implement NLP in the Nordic languages and how limitations in input and performance in hardware affected the result. Some questions that arose during the project consist of methods for improving the dataset and alternative solutions for managing information related to big data and GDPR. / Denna avhandling undersöker möjligheten att tillämpa sentiment analys för att extrahera tonalitet av användarrecensioner på nordiska språk. Databehandling utförs i form av förprocessering genom tokenisering och padding. En modell är uppbyggd i en ramverkad Keras. Modeller för klassificering och regression byggdes med LSTM och GRU-arkitekturer. Resultaten visade hur datasetet påverkar slutresultatet och korrelationen mellan observerade och förutspådda värden för klassificering och regression. Projektet visar att det är möjligt att implementera NLP på de nordiska språken och hur begränsningar i input och prestanda i hårdvara påverkat resultatet. Några frågor som uppstod under projektet består av metoder för att förbättra datasetet och alternativa lösningar för hantering av information relaterad till stora data och GDPR.

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Convolutional Neural Networks (CNNs) are successful tools in image classification. CNNs are inspired by the animal visual cortex using a similar connectivity pattern as between neurons. The purpose of this thesis is to create a classifier, using transfer learning, that manages to classify images of traffic signs from the German Traffic Sign Recognition Benchmark (GTSRB) with good accuracy and to improve the performance further by tuning the hyperparameters. The pre-trained CNN used is the VGG16 network from the paper "Very deep convolutional networks for large-scale image recognition". The result showed that the VGG16 network got an accuracy of 74.5\% for the hyperparameter set where the learning rate was 1e-6, the batch size was 15 and the dropout rate 0.3. The conclusion was that transfer learning using the bottleneck features is a good tool for building a classifier with only a small amount of training data available and that the results probably could be further improved using more real data or data augmentation both for training and testing and by tuning more of the hyperparameters in the network.

The thesis deals with methods for analysis of financial markets, focused on cryptocurrencies. The theoretical part, in a context of virtual currencies, describes block-chain technology, financial indicators and neural networks with recurrent architectures. Main goal is to create a system for giving a recommendation either for buy, or sell the currency. The system consists of designed financial strategy and predicted value of the currency, for which is used financial indicators and LSTM neural network. Tests were performed on Bitcoin, Litecoin and Ethereum historical data from year 2017.

This master's thesis deals with the classification of the retinal blood vessels in retinal image data. The thesis contains a description of anatomy of the human eye with focus on the blood circulation, and imaging and diagnostic methods of the retina are briefly mentioned further. The thesis also summarizes methods of the blood circulation classification with emphasis on the deep learning. The practical section was implemented in Python programming language and describes the pre-processing of the data with determination of AV ratio. Based on a literature search, the U-net architecture was chosen for the classification of the retinal blood vessels. The architecture was modified using the open-source Keras library and tested on images from the experimental video-ophthalmoscope. The modified architecture was initially used for classification of vessels into the corresponding classes and because of unsatisfying results was modified another architecture segmenting retinal vessels, arteries or veins and a proposition of a method of the blood vessels classification.

The aim of this diploma thesis is to study and design experimental improvement of the convolutional neural network for disease detection. Another goal is to extend the classifier with a new type of detection. he new type of detection is damage fingerprint by pressure. The experimentally improved convolutional network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. Synthetic fingerprints are used when training the net. Real fingerprints are added to the synthetic fingerprints.

Functional magnetic resonance imaging (fMRI) technology has been used in the investigation of human brain functionality and assist in brain disease diagnosis. While fMRI can be used to model both spatial and temporal brain functionality, the analysis of the fMRI images and the discovery of patterns for certain brain diseases is still a challenging task in medical imaging. Deep learning has been used more and more in medical field in an effort to further improve disease diagnosis due to its effectiveness in discovering high-level features in images. Convolutional neural networks (CNNs) is a class of deep learning algorithm that have been successfully used in medical imaging and extract spatial hierarchical features. The application of CNNs in fMRI and the extraction of brain functional patterns is an open field for research. This project focuses on how fMRIs can be used to improve Autism Spectrum Disorders (ASD) detection and diagnosis with 3D resting-state functional MRI (rs-fMRI) images. ASDs are a range of neurodevelopment brain diseases that mostly affect social function. Some of the symptoms include social and communicating difficulties, and also restricted  and repetitive  behaviors. The  symptoms appear on early childhood and tend to develop in time thus an early diagnosis is required. Finding a proper model for identifying between ASD and healthy subject is a challenging task and involves a lot of hyper-parameter tuning. In this project a grid search approach is followed in the quest of the optimal CNN architecture. Additionally, regularization and augmentation techniques are implemented in an effort to further improve the models performance.