This thesis presents three distinct methodologies aimed at exploring pivotal aspects within the domain of fiber optics and piezoelectric materials. The first approach offers a comprehensive exploration of three pivotal aspects within the realm of fiber optics and piezoelectric materials. The study delves into the influence of voltage variation on piezoelectric displacement, examines the effects of bending multimode fiber (MMF) on data transmission, and scrutinizes the performance of an Autoencoder in MMF image reconstruction with and without additional noise. To assess the impact of voltage variation on piezoelectric displacement, experiments were conducted by applying varying voltages to a piezoelectric material, meticulously measuring its radial displacement. The results revealed a notable increase in displacement with higher voltage, presenting implications for fiber stability and overall performance. Additionally, the investigation into the effects of bending MMF on data transmission highlighted that the bending process causes the fiber to become leaky and radiate power radially, potentially affecting data transmission. This crucial insight emphasizes the necessity for further research to optimize data transmission in practical fiber systems. Furthermore, the performance of an Autoencoder model was evaluated using a dataset of MMF images, in diverse scenarios. The Autoencoder exhibited impressive accuracy in reconstructing MMF images with high fidelity. The results underscore the significance of ongoing research in these domains, propelling advancements in fiber optic technology.The second approach of this thesis entails a comparative investigation involving three distinct neural network models to assess their efficacy in improving image quality within optical transmissions through multimode fibers, with a specific focus on mitigating speckle patterns. Our proposed methodology integrates multimode fibers with a piezoelectric source, deliberately introducing noise into transmitted images to evaluate their performance using an autoencoder neural network. The autoencoder, trained on a dataset augmented with noise and speckle patterns, adeptly eliminates noise and reconstructs images with enhanced fidelity. Comparative analyses conducted with alternative neural network architectures, namely a single hidden layer (SHL) model and a U-Net architecture, reveal that while U-Net demonstrates superior performance in terms of image reconstruction fidelity, the autoencoder exhibits notable advantages in training efficiency. Notably, the autoencoder achieves saturation SSIM in 450 epochs and 24 minutes, surpassing the training durations of both U-Net (210 epochs, 1 hour) and SHL (160 epochs, 3 hours and 25 minutes) models. Impressively, the autoencoder's training time per epoch is six times faster than U-Net and fourteen times faster than SHL. The experimental setup involves the application of varying voltages via a piezoelectric source to induce noise, facilitating adaptation to real-world conditions. Furthermore, the study not only demonstrates the efficacy of the proposed methodology but also conducts comparative analyses with prior works, revealing significant improvements. Compared to Li et al.'s study, our methodology, particularly when utilizing the pre-trained autoencoder, demonstrates an average improvement of 15% for SSIM and 9% for PSNR in the worst-case scenario. Additionally, when compared to Lai et al.'s study employing a generative adversarial network for image reconstruction, our methodology achieves slightly superior SSIM outcomes in certain scenarios, reaching 96%. The versatility of the proposed method is underscored by its consistent performance across varying voltage scenarios, showcasing its potential applications in medical procedures and industrial inspections. This research not only presents a comprehensive and innovative approach to addressing challenges in optical image reconstruction but also signifies significant advancements compared to prior works. The final approach of this study entails employing Hermit Gaussian Functions with varying orders as activation functions within a U-Net model architecture, aiming to evaluate its effectiveness in image reconstruction. The performance of the model is rigorously assessed across five distinct voltage scenarios, and a supplementary evaluation is conducted with digit 5 excluded from the training set to gauge its generalization capability. The outcomes offer promising insights into the efficacy of the proposed methodologies, showcasing significant advancements in optical image reconstruction. Particularly noteworthy is the robust accuracy demonstrated by the higher orders of the Hermit Gaussian Function in reconstructing MMF images, even amidst the presence of noise introduced by the voltage source. However, a decline in accuracy is noted in the presence of voltage-induced noise, underscoring the imperative need for further research to bolster the model's resilience in real-world scenarios, especially in comparison to the utilization of the Rectified Linear Unit (ReLU) function.

Distibuted denial of service (DDoS) attacks are getting more and more common in society as the number of devices connected to the Internet is increasing. To reduce the impact of such attacks it is important to detect them as soon as possible. Many papers have investigated how well different machine learning algorithms can detect DDoS attacks. However, most papers are focusing on supervised learning algorithms which require a lot of labeled data, which is hard to find. This thesis compares two unsupervised learning algorithms, an autoencoder and principal component analysis (PCA), in how well they detect DDoS attacks. The models are implemented in the Python libraries Keras, Tensorflow and scikit-learn. They are then trained and tested with data that has its origin in the CICDDOS2019 dataset. There are normal data and nine different types of DDoS attacks in the used dataset. The models are compared by computing the Receiver Operating Characteristic (ROC) curve and its Area Under the Curve (AUC) score, and the F1 score of the models. For both measures the mean value of the results of all attack types are used. The computations show that the autoencoder perform better than PCA with respect to both the mean AUC score (0.981 compared to 0.967) and the mean F1 score (0.987 compared to 0.978). The thesis goes on to discussing why the autoencoder performs better than PCA and, finally draws conclusions based on the insights of the analysis.

For an economically sustainable fuel cell commercialization, robust manufacturing processes are essential. As current quality control is time-consuming and costly for manufacturers, standardized solutions are required that reduce cycle times needed to determine cell quality. With existing studies examining durability in field use, little is known about end-of-line detection of cell malfunctions. Applying machine learning algorithms to analyse performance measures of 3600 PEM fuel cells, this work presents a concept to automatically classify produced fuel cells according to cell performance indicators. Using a deep learning autoencoder and the extreme gradient boosting algorithm for anomaly detection and cell classification, models are created that detect cells associated with potential cell malfunctions. The work shows that the models developed predict key performance features in an early stage of the quality control phase and contributes to the overall goal of achieving cycle time reduction for manufacturers quality control procedures. / Für eine wirtschaftlich nachhaltige Kommerzialisierung von Brennstoffzellen sind robuste Herstellungsprozesse unerlässlich. Da die derzeitige Qualitätskontrolle zeitaufwändig und kostenintensiv ist, sind standardisierte Lösungen erforderlich. Während bisherige Arbeiten vorwiegend Lebensdaueruntersuchungen durchführen, ist nur wenig über die Erkennung von Zellfehlfunktionen am Ende der Produktionslinie bekannt. Durch die Anwendung von Algorithmen des maschinellen Lernens zur Analyse der Leistungsdaten von 3600 PEM-Brennstoffzellen wird in dieser Arbeit ein Konzept zur automatischen Klassifizierung produzierter Brennstoffzellen anhand von Leistungsindikatoren der Zellen vorgestellt. Unter Verwendung eines Deep-Learning-Autoencoders und des Extreme-Gradient-Boosting-Algorithmus zur Erkennung von Anomalien und zur Klassifizierung von Zellen werden Modelle erstellt, die Zellen erkennen, die mit potenziellen Zellfehlfunktionen in Verbindung stehen. Die Arbeit zeigt, dass die entwickelten Modelle wichtige Leistungsmerkmale in einem frühen Stadium der Qualitätskontrollphase vorhersagen und zum Gesamtziel der Reduzierung der Zykluszeit für die Qualitätskontrollverfahren der Hersteller beitragen.

Fraud is a common crime within the insurance industry, and insurance companies want to quickly identify fraudulent claimants as they often result in higher premiums for honest customers. Due to the digital transformation where the sheer volume and complexity of available data has grown, manual fraud detection is no longer suitable. This work aims to automate the detection of fraudulent claimants and gain practical insights into fraudulent behavior using unsupervised anomaly detection, which, compared to supervised methods, allows for a more cost-efficient and practical application in the insurance industry. To obtain interpretable results and benefit from the temporal dependencies in human behavior, we propose two variations of LSTM based autoencoders to classify sequences of insurance claims. Autoencoders can provide feature importances that give insight into the models' predictions, which is essential when models are put to practice. This approach relies on the assumption that outliers in the data are fraudulent. The models were trained and evaluated on a dataset we engineered using data from a Swedish insurance company, where the few labeled frauds that existed were solely used for validation and testing. Experimental results show state-of-the-art performance, and further evaluation shows that the combination of autoencoders and LSTMs are efficient but have similar performance to the employed baselines. This thesis provides an entry point for interested practitioners to learn key aspects of anomaly detection within fraud detection by thoroughly discussing the subject at hand and the details of our work. / <p>Gjordes digitalt via Zoom. </p>

The mortality rate is high for cancer patients and treatments are only efficient in a fraction of patients. To be able to cure more patients, new treatments need to be invented. Immunotherapy activates the immune system to fight against cancer and one treatment targets immune checkpoints. If more targets are found, more patients can be treated successfully. In this project, interactions between immune and cancer cells that drive tumour growth were investigated in an attempt to find new potential targets. This was achieved by creating a machine learning model that finds genes expressed in cells involved in tumour-driving interactions. Single-cell RNA sequencing and spatial transcriptomic data from breast cancer patients were utilised as well as single-cell RNA sequencing data from healthy patients. The tumour rate was based on the cumulative expression of G2/M genes. The G2/M related genes were excluded from the analysis since these were assumed to be cell cycle genes. The machine learning model was based on a supervised variational autoencoder architecture. By using this kind of architecture, it was possible to compress the input into a low dimensional space of genes, called a latent space, which was able to explain the tumour rate. Optuna hyperparameter optimizer framework was utilised to find the best combination of hyperparameters for the model. The model had a R2 score of 0.93, which indicated that the latent space was able to explain the growth rate 93% accurately. The latent space consisted of 20 variables. To find out which genes that were in this latent space, the correlation between each latent variable and each gene was calculated. The genes that were positively correlated or negatively correlated were assumed to be in the latent space and therefore involved in explaining tumour growth. Furthermore, the correlation between each latent variable and the growth rate was calculated. The up- and downregulated genes in each latent variable were kept and used for finding out the pathways for the different latent variables. Five of these latent variables were involved in immune responses and therefore these were further investigated. The genes in these five latent variables were mapped to cell types. One of these latent variables had upregulated immune response for positively correlated growth, indicating that immune cells were involved in promoting cancer progression. Another latent variable had downregulated immune response for negatively correlated growth. This indicated that if these genes would be upregulated instead, the tumour would be thriving. The genes found in these latent variables were analysed further. CD80, CSF1, CSF1R, IL26, IL7, IL34 and the protein NF-kappa-B were interesting finds and are known immune-modulators. These could possibly be used as markers for pro-tumour immunity. Furthermore, CSF1, CSF1R, IL26, IL34 and the protein NF-kappa-B could potentially be targeted in immunotherapy.

Paraphrase generation refers to the task of automatically generating a paraphrase given an input sentence or text. Paraphrase generation is a fundamental yet challenging natural language processing (NLP) task and is utilized in a variety of applications such as question answering, information retrieval, conversational systems etc. In this study, we address the problem of paraphrase generation of questions in Swedish by evaluating two different deep generative models that have shown promising results on paraphrase generation of questions in English. The first model is a Conditional Variational Autoencoder (C-VAE) and the other model is an extension of the first one where a discriminator network is introduced into the model to form a Generative Adversarial Network (GAN) architecture. In addition to these models, a method not based on machine-learning was implemented to act as a baseline. The models were evaluated using both quantitative and qualitative measures including grammatical correctness and equivalence to source question. The results show that the deep generative models outperformed the baseline across all quantitative metrics. Furthermore, from the qualitative evaluation it was shown that the deep generative models outperformed the baseline at generating grammatically correct sentences, but there was no noticeable difference in terms of equivalence to the source question between the models. / Parafrasgenerering syftar på uppgiften att, utifrån en given mening eller text, automatiskt generera en parafras, det vill säga en annan text med samma betydelse. Parafrasgenerering är en grundläggande men ändå utmanande uppgift inom naturlig språkbehandling och används i en rad olika applikationer som informationssökning, konversionssystem, att besvara frågor givet en text etc. I den här studien undersöker vi problemet med parafrasgenerering av frågor på svenska genom att utvärdera två olika djupa generativa modeller som visat lovande resultat på parafrasgenerering av frågor på engelska. Den första modellen är en villkorsbaserad variationsautokodare (C-VAE). Den andra modellen är också en C-VAE men introducerar även en diskriminator vilket gör modellen till ett generativt motståndarnätverk (GAN). Förutom modellerna presenterade ovan, implementerades även en icke maskininlärningsbaserad metod som en baslinje. Modellerna utvärderades med både kvantitativa och kvalitativa mått inklusive grammatisk korrekthet och likvärdighet mellan parafras och originalfråga. Resultaten visar att de djupa generativa modellerna presterar bättre än baslinjemodellen på alla kvantitativa mätvärden. Vidare, visade the kvalitativa utvärderingen att de djupa generativa modellerna kunde generera grammatiskt korrekta frågor i större utsträckning än baslinjemodellen. Det var däremot ingen större skillnad i semantisk ekvivalens mellan parafras och originalfråga för de olika modellerna.

The goal of this thesis is to explore a new clustering algorithm, VAE-Clustering, and examine if it can be applied to find differences in the distribution of stock returns and augment the distribution of a current portfolio of stocks and see how it performs in different market conditions. The VAE-clustering method is as mentioned a newly introduced method and not widely tested, especially not on time series. The first step is therefore to see if and how well the clustering works. We first apply the algorithm to a dataset containing monthly time series of the power demand in Italy. The purpose in this part is to focus on how well the method works technically. When the model works well and generates proper results with the Italian Power Demand data, we move forward and apply the model on stock return data. In the latter application we are unable to find meaningful clusters and therefore unable to move forward towards the goal of the thesis. The results shows that the VAE-clustering method is applicable for time series. The power demand have clear differences from season to season and the model can successfully identify those differences. When it comes to the financial data we hoped that the model would be able to find different market regimes based on time periods. The model is though not able distinguish different time periods from each other. We therefore conclude that the VAE-clustering method is applicable on time series data, but that the structure and setting of the financial data in this thesis makes it to hard to find meaningful clusters. The major finding is that the VAE-clustering method can be applied to time series. We highly encourage further research to find if the method can be successfully used on financial data in different settings than tested in this thesis. / Syftet med den här avhandlingen är att utforska en ny klustringsalgoritm, VAE-Clustering, och undersöka om den kan tillämpas för att hitta skillnader i fördelningen av aktieavkastningar och förändra distributionen av en nuvarande aktieportfölj och se hur den presterar under olika marknadsvillkor. VAE-klusteringsmetoden är som nämnts en nyinförd metod och inte testad i stort, särskilt inte på tidsserier. Det första steget är därför att se om och hur klusteringen fungerar. Vi tillämpar först algoritmen på ett datasätt som innehåller månatliga tidsserier för strömbehovet i Italien. Syftet med denna del är att fokusera på hur väl metoden fungerar tekniskt. När modellen fungerar bra och ger tillfredställande resultat, går vi vidare och tillämpar modellen på aktieavkastningsdata. I den senare applikationen kan vi inte hitta meningsfulla kluster och kan därför inte gå framåt mot målet som var att simulera olika marknader och se hur en nuvarande portfölj presterar under olika marknadsregimer. Resultaten visar att VAE-klustermetoden är väl tillämpbar på tidsserier. Behovet av el har tydliga skillnader från säsong till säsong och modellen kan framgångsrikt identifiera dessa skillnader. När det gäller finansiell data hoppades vi att modellen skulle kunna hitta olika marknadsregimer baserade på tidsperioder. Modellen kan dock inte skilja olika tidsperioder från varandra. Vi drar därför slutsatsen att VAE-klustermetoden är tillämplig på tidsseriedata, men att strukturen på den finansiella data som undersöktes i denna avhandling gör det svårt att hitta meningsfulla kluster. Den viktigaste upptäckten är att VAE-klustermetoden kan tillämpas på tidsserier. Vi uppmuntrar ytterligare forskning för att hitta om metoden framgångsrikt kan användas på finansiell data i andra former än de testade i denna avhandling

Dedicating less transport resources by grouping goods to be shipped together, or pooling as we name it, has a very crucial role in saving costs in transport networks. Nonetheless, it is not so easy to estimate pooling among different groups of shipments or understand why these groups are poolable. The typical solution would be to consider all shipments of both groups as one and use some Vehicle Routing Problem (VRP) software to estimate costs of the new combined group. However, this brings with it some drawbacks, such as high computational costs and no pooling explainability. On this work we build a tool that estimates the different types of pooling using demand data. This solution includes mapping shipment data to a lower dimension, where each poolability trait corresponds to a latent dimension. We tested different dimensionality reduction techniques and found that the best performing are the autoencoder models based on neural networks. Nevertheless, comparing shipments on the latent space turns out to be more challenging than expected, because distances in these latent dimensions are sometimes uncorrelated to the distances in the real shipment features. Although this limits the use cases of this approach, we still manage to build the full poolability tool that incorporates the autoencoders and uses metrics we designed to measure each poolability trait. This tool is then compared to a VRP software and proves to have close accuracy, while being much faster and explainable. / Att optimera transportresurser genom att gruppera varor som ska skickas tillsammans, även kallat poolning, spelar en avgörande roll för att spara kostnader i transportnätverk. Trots detta är det inte så enkelt att uppskatta poolning mellan olika grupper av försändelser eller förstå varför dessa grupper kan poolas. Den vanliga lösningen skulle vara att betrakta alla försändelser från båda grupperna som en enda enhet och använda mjukvara för att lösa problemet med fordonsschemaläggning (Vehicle Routing Problem, VRP) för att uppskatta kostnaderna för den nya sammanslagna gruppen. Detta medför dock vissa nackdelar, såsom höga beräkningskostnader och bristande förklarbarhet när det kommer till poolning. I detta arbete bygger vi ett verktyg som med hjälp av efterfrågedata uppskattar olika typer av poolning. Lösningen innefattar kartläggning av försändelsedata till en lägre dimension där varje egenskap för poolbarhet motsvarar en dold dimension. Vi testade olika tekniker för att minska dimensionerna och fann att de bäst presterande är autoencoder-modeller baserade på neurala nätverk. Trots detta visade det sig vara mer utmanande än förväntat att jämföra försändelser i det dolda rummet eftersom avstånden i dessa dolda dimensioner ibland inte korrelerar med avstånden i de faktiska försändelseegenskaperna. Trots att detta begränsar användningsområdena för denna metod lyckades vi ändå bygga ett komplett verktyg för poolbarhet som inkluderar autoencoders och använder metriker som vi har utformat för att mäta varje egenskap för poolbarhet. Detta verktyg jämförs sedan med en VRP-mjukvara och visar sig ha liknande noggrannhet samtidigt som det är betydligt snabbare och mer förklarligt. / Dedicare meno risorse di trasporto raggruppando insieme le merci da spedire, o creando un pool come lo chiamiamo noi, svolge un ruolo cruciale nel risparmio dei costi nelle reti di trasporto. Tuttavia, non è facile stimare il grado di aggregazione tra diversi gruppi di spedizioni o comprendere perché tali gruppi siano aggregabili. La soluzione tipica consisterebbe nel considerare tutte le spedizioni di entrambi i gruppi come una sola entità e utilizzare un software di Problema di Routing dei Veicoli (VRP) per stimare i costi del nuovo gruppo combinato. Tuttavia, ciò comporta alcuni svantaggi, come elevati costi computazionali e la mancanza di spiegazioni riguardo all'aggregazione. In questo lavoro abbiamo sviluppato uno strumento che stima i diversi tipi di aggregabilità utilizzando i dati di domanda. Questa soluzione prevede la mappatura dei dati delle spedizioni in una dimensione inferiore, in cui ciascuna caratteristica di aggregabilità corrisponde a una dimensione. Abbiamo testato diverse tecniche di riduzione dimensionale e abbiamo constatato che i modelli autoencoder basati su reti neurali sono i più efficaci. Tuttavia, confrontare le spedizioni nello spazio latente si è rivelato più complesso del previsto, poiché le distanze in queste dimensioni latenti talvolta non sono correlate alle distanze nelle caratteristiche reali delle spedizioni. Sebbene ciò limiti le applicazioni di questo approccio, siamo comunque riusciti a sviluppare uno strumento completo per l'aggregabilità che incorpora gli autoencoder e utilizza metriche da noi progettate per misurare ciascuna caratteristica di aggregabilità. Successivamente, abbiamo confrontato questo strumento con un software VRP e dimostrato che presenta un'accuratezza simile, pur essendo più veloce e fornendo spiegazioni chiare.

Today, a large amount of time series data is being produced from a variety of different devices such as smart speakers, cell phones and vehicles. This data can be used to make inferences and predictions. Neural network based methods are among one of the most popular ways to model time series data. The field of neural networks is constantly expanding and new methods and model variants are frequently introduced. In 2018, a new family of neural networks was introduced. Namely, Neural Ordinary Differential Equations (Neural ODEs). Neural ODEs have shown great potential in modelling the dynamics of temporal data. Here we present an investigation into using Neural Ordinary Differential Equations for anomaly detection. We tested two model variants, LSTM-ODE and latent-ODE. The former model utilises a neural ODE to model the continuous-time hidden state in between observations of an LSTM model, the latter is a variational autoencoder that uses the LSTM-ODE as encoding and a Neural ODE as decoding. Both models are suited for modelling sparsely and irregularly sampled time series data. Here, we test their ability to detect anomalies on various sparsity and irregularity ofthe data. The models are compared to a Gaussian mixture model, a vanilla LSTM model and an LSTM variational autoencoder. Experimental results using the Human Activity Recognition dataset showed that the Neural ODEbased models obtained a better ability to detect anomalies compared to their LSTM based counterparts. However, the computational training cost of the Neural ODE models were considerably higher than for the models that onlyutilise the LSTM architecture. The Neural ODE based methods were also more memory consuming than their LSTM counterparts. / Idag produceras en stor mängd tidsseriedata från en mängd olika enheter som smarta högtalare, mobiltelefoner och fordon. Denna datan kan användas för att dra slutsatser och förutsägelser. Neurala nätverksbaserade metoder är bland de mest populära sätten att modellera tidsseriedata. Mycket forskning inom området neurala nätverk pågår och nya metoder och modellvarianter introduceras ofta. Under 2018 introducerades en ny familj av neurala nätverk. Nämligen, Neurala Ordinära Differentialekvationer (NeuralaODE:er). Neurala ODE:er har visat en stor potential i att modellera dynamiken hos temporal data. Vi presenterar här en undersökning i att använda neuralaordinära differentialekvationer för anomalidetektion. Vi testade två olika modellvarianter, en som kallas LSTM-ODE och en annan som kallas latent-ODE.Den förstnämnda använder Neurala ODE:er för att modellera det kontinuerliga dolda tillståndet mellan observationer av en LSTM-modell, den andra är en variational autoencoder som använder LSTM-ODE som kodning och en Neural ODE som avkodning. Båda dessa modeller är lämpliga för att modellera glest och oregelbundet samplade tidsserier. Därför testas deras förmåga att upptäcka anomalier på olika gleshet och oregelbundenhet av datan. Modellerna jämförs med en gaussisk blandningsmodell, en vanlig LSTM modell och en LSTM variational autoencoder. Experimentella resultat vid användning av datasetet Human Activity Recognition (HAR) visade att de Neurala ODE-baserade modellerna erhöll en bättre förmåga att upptäcka avvikelser jämfört med deras LSTM-baserade motsvarighet. Träningstiden förde Neurala ODE-baserade modellerna var dock betydligt långsammare än träningstiden för deras LSTM-baserade motsvarighet. Neurala ODE-baserade metoder krävde också mer minnesanvändning än deras LSTM motsvarighet.

In large scale productions of metal sheets, it is important to maintain an effective way to continuously inspect the products passing through the production line. The inspection mainly consists of detection of defects and tracking of ID numbers. This thesis investigates the possibilities to create an automatic inspection system by evaluating different machine learning algorithms for defect detection and optical character recognition (OCR) on metal sheet data. Digit recognition and defect detection are solved separately, where the former compares the object detection algorithm Faster R-CNN and the classical machine learning algorithm NCGF, and the latter is based on unsupervised learning using a convolutional autoencoder (CAE). The advantage of the feature extraction method is that it only needs a couple of samples to be able to classify new digits, which is desirable in this case due to the lack of training data. Faster R-CNN, on the other hand, needs much more training data to solve the same problem. NCGF does however fail to classify noisy images and images of metal sheets containing an alloy, while Faster R-CNN seems to be a more promising solution with a final mean average precision of 98.59%. The CAE approach for defect detection showed promising result. The algorithm learned how to only reconstruct images without defects, resulting in reconstruction errors whenever a defect appears. The errors are initially classified using a basic thresholding approach, resulting in a 98.9% accuracy. However, this classifier requires supervised learning, which is why the clustering algorithm Gaussian mixture model (GMM) is investigated as well. The result shows that it should be possible to use GMM, but that it requires a lot of GPU resources to use it in an end-to-end solution with a CAE.