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Visar resultat 1 - 5 av 145 uppsatser som matchar ovanstående sökkriterier.

1. 1. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Habib Jabeli; [2024]
   Nyckelord :Machine Learning; Flight Comparison; Flygresor.se; Neural Networks; Flight Ranking; Random Forest; XGBoost;

   Sammanfattning : Flygresor.se, a leading flight comparison platform, uses machine learning to rankflights based on their likelihood of being clicked. The main goal of this project was toimprove this flight sorting to obtain a better user experience. The platform's existingmodel is based on a neural network approach and a limited set of features. LÄS MER

3. 2. Predicting True Sepsis and Culture-positive Sepsis in Intensive Care Unit with Machine Learning Techniques

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Zeyuan Wu; [2024]
   Nyckelord :Machine Learning; Diagnosis of Sepsis; XGBoost; Logistic Regression; Mathematics and Statistics;

   Sammanfattning : Sepsis, a serious medical condition often leading to patients requiring intensive care, has prompted numerous scientists to employ mathematical techniques to aid in its diagnosis. This thesis uses logistic regression and a machine learning technique, XGBoost, to predict true sepsis (as opposed to sepsis mimics) and culture-positive sepsis (among true sepsis) in critical care using blood test results, physiological measurements and other patient characteristics. LÄS MER

4. 3. On The Evaluation of District Heating Load Predictions

   Master-uppsats, Lunds universitet/Institutionen för energivetenskaper

   Författare :Herman Hansson; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : District Heating is a technology with the potential to enable a fossil-free society. However, to realize this potential, some improvements need to be made in order to improve District Heating operation at large, decrease losses in the systems, and thus increase the competitiveness of District Heating as a technology. LÄS MER

5. 4. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Robert Iain Salter; [2023]
   Nyckelord :Behavioural Credit Scoring; Deep Learning; Machine Learning; Long Short-Term Memory; Default Prediction;

   Sammanfattning : Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. LÄS MER

7. 5. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för beräkningsvetenskap

   Författare :Stella Jarlöv; Anton Svensson Dahl; [2023]
   Nyckelord :demand forecasting; data augmentation; time series data; machine learning; restaurant industry; generative adversarial networks; TimeGAN; XGBoost;

   Sammanfattning : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. LÄS MER