Sökning: "Mathematics and Statistics"
Visar resultat 6 - 10 av 1284 uppsatser innehållade orden Mathematics and Statistics.
6. Comparison of VADER and Pre-Trained RoBERTa: A Sentiment Analysis Application
Kandidat-uppsats, Lunds universitet/Statistiska institutionenSammanfattning : Purpose: The purpose of this study is to examine how the overall sentiment results from VADER and a pre-trained RoBERTa model differ. The study investigates potential differences in terms of the median and shape of the two distributions. Data: The sustainability reports of 50 independent random companies are selected as the sample. LÄS MER
7. Stock Price Predictions for FAANG Companies Using Machine Learning Models
Kandidat-uppsats, Lunds universitet/Statistiska institutionenSammanfattning : The financial industry is one of the highest grossing sectors in the world as it is estimated to represent 24\% of the global economy. As most companies want their asset value to increase, it is of high interest to make good investments which will increase in either the short or long run. LÄS MER
8. Uncertainty Quantification in Deep Learning for Breast Cancer Classification in Point-of-Care Ultrasound Imaging
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : Breast cancer is the most common type of cancer worldwide with an estimate of 2.3 million new cases in 2020, and the number one cause of cancer-related deaths in women. LÄS MER
9. Tau-Leaping Implementations outside of chemistry
Kandidat-uppsats, Lunds universitet/Statistiska institutionenSammanfattning : Tau-leaping is an algorithm for model simulations most often used in kinetic chemistry. It was created to make simulations more efficient at the cost of some accuracy. However, its uses outside of chemistry are limited but could help make some model simulations more efficient. LÄS MER
10. Virtual H&E Staining Using PLS Microscopy and Neural Networks
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : Histopathological examination, crucial in diagnosing diseases such as cancer, traditionally relies on time- and resource-consuming, poorly standardized chemical staining for tissue visualization. This thesis presents a novel digital alternative using generative neural networks and a point light source (PLS) microscope to transform unstained skin tissue images into their stained counterparts. LÄS MER