Sökning: "machine learning models"
Visar resultat 1 - 5 av 2017 uppsatser innehållade orden machine learning models.
1. Feature Selection for Microarray Data via Stochastic Approximation
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : This thesis explores the challenge of feature selection (FS) in machine learning, which involves reducing the dimensionality of data. The selection of a relevant subset of features from a larger pool has demonstrated its effectiveness in enhancing the performance of various machine learning algorithms. LÄS MER
2. Exploring Automated Early Problem Identification Based on Diagnostic Trouble Codes
Master-uppsats, Institutionen för tillämpad informationsteknologiSammanfattning : In the current automotive industry, problem identification is a reactive process. It starts when the customer experiences a vehicle problem and goes to the workshop. Subsequently, all the problem-related data will be collected from the workshop and forwarded to the vehicle manufacturer. LÄS MER
3. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience
M1-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : 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
4. Predictive Modeling of Pipetting Dynamics. Multivariate Regression Analysis: PLS and ANN for Estimating Density and Volume from Pressure Recordings
Master-uppsats, Lunds universitet/Avdelningen för Biomedicinsk teknikSammanfattning : Thermo Fisher Scientific manufacture automatic pipetting instruments for diagnostic tests. These tests are sensitive to abnormalities and changes in e.g. volume or density could potentially lead to less precision or other issues in the pipetting work flow. LÄS MER
5. Predicting True Sepsis and Culture-positive Sepsis in Intensive Care Unit with Machine Learning Techniques
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : 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