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Neural networks have an increasingly important role in the financial market, by offering a solution to stationarity and non-linearity whilst also providing robustness and predictive power. Options and option pricing are a fundamental area of interest in the daily activities of investment banks, hedge funds and trading firms in the financial market. Implied volatility is the focal point of these op

In this thesis, the consequences of extending the internal symmetries of the standard model with a complex singlet scalar field, are investigated in terms of dark matter phenomenology and the possibility of primordial gravitational wave detection utilizing space based laser interferometry. The scalar potential of the model is constructed with a Z2 symmetry, which allows for the retainment of a lin

In this thesis we study the width of the Higgs boson in the process pp → t ̄t 4l at LO using MadGraph5_aMC@NLO to generate the events. The contributions including the Higgs signal, continuum background and interference were considered in order to calculate the expected number of events in a broad range of four-lepton invariant masses. Due to strongly enhanced off-shell contributions an upper bound

In this thesis, a Glauber Monte-Carlo event generator is developed and used to analyze proton-Ion (p -$^{63}$Cu and p -$^{197}$Au) and Ion-Ion ($^{63}$Cu -$^{63}$Cu and $^{197}$Au -$^{197}$Au) collisions. Three different sub-collision models are implemented, the black disk, grey disk and oscillating grey disk models, and their validity is compared. The predicted nucleon-nucleon cross-sections by t

The width of the Higgs boson is a quantity that cannot be measured directly from the on-shell Higgs peak; it would have to be measured indirectly. In 2013, Caola and Melnikov developed a method to determine the Higgs width by using off-shell and on-shell cross-sections and applying the narrow-width approximation. In this thesis, we will use the indirect method of finding the Higgs width for the pr

The growing popularity of Artificial Neural Networks (ANN) demands continuous improvement and optimization of the training process to achieve higher-performing algorithms at a lower computational cost. During training an ANN learns to solve a problem by looking at examples, and will iteratively go over a dataset to reach an optimal performance. Usually the user needs to define a fixed number of it

There are many unknowns regarding the interaction between fungi and their surroundings. In this project, we took a closer look at hyperspectral images of several fungal strains on two different substrates. The project mainly consisted of developing a code for the classification of fungal strains and the extraction of information from it. The classifier used hyperspectral images in infrared of four

Barcoding is a coarse-grained optical DNA sequencing method, where DNA molecules are stained with fluorescent dyes to create sequence-dependent patterns similar to barcodes. For DNA adsorbed onto a glass surface, we need to be able to trace out these molecules for barcode extraction. We show that B-splines can be used to approximate the shape of adsorbed DNA molecules with curvature and present a

Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common forms of lung cancer. These two subtypes of lung cancer are usually classified by visual inspection clinically. Our aim is to design an effective strategy based on convolutional neural networks to classify histopathology slides of these two types of lung cancer. With augmentation of the histopathology slides, different cl

This Bachelor project will study convolutional neural networks created for image classification. Furthermore, it will specifically use an explanatory model for how the network decided a certain classification output. This is to increase the interpretability of the network. However, the completeness of the explanatory model needs to be high for it to be useful. A saliency map of how valuable each i

Infrared absorption microscopy is a powerful chemometric tool with a wide variety of applications. It is, however, subject to considerable disturbances from atmospheric gases and scattering effects. As experimental fixes are not always applicable or advisable, data therefore needs to be computationally corrected before any attempt at an interpretation. This work designed, improved and validated se

Understanding the patterns of evolutionary sequence divergence is fundamental for comparative analyses like phylogenetics or genomics. The rate at which the different sites of protein sequences evolve is multifactorial and the causes of variation among them are highly convoluted. Inference methods have been developed to estimate site-specific evolution rates from sequence alignments. Moreover, sev

Two deep learning techniques for classification on corrupt data are investigated and compared by performance. A simple imputation before classification is compared to imputation using a Variational Autoencoder (VAE). Both single and multiple imputation using the VAE are considered and compared in classification performance for different types and levels of corruption, and for different sample size

A lattice model for fibril formation on the basis of general peptide interactions is presented. Fibrils are well-structured protein aggregates and a key phenomenon in many diseases, among them Alzheimer. We study the model’s equilibrium and kinetic properties using Wang-Landau sampling and a Metropolis Monte Carlo algorithm with cluster moves. The peptides are shown to form ordered fibrilar aggreg