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Visar resultat 6 - 10 av 4572 uppsatser som matchar ovanstående sökkriterier.

1. 6. 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. 7. 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 teknik

   Författare :Lisa Linard Pedersen; [2024]
   Nyckelord :Technology and Engineering;

   Sammanfattning : 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

4. 8. Cross project Just-In-Time bug detection

   Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Axel Pettersson; [2024]
   Nyckelord :JITLine; Bug detection; Software Development; JITLine; Bugg identifiering; Mjukvaruutveckling;

   Sammanfattning : Software is present in almost all aspects of our lives, and with more parts of life beingdriven by code, the importance of limiting bugs is critical. Studies have shown that thelonger a bug is present in a system increases the complexity of finding and handlingthe bug. LÄS MER

5. 9. 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

7. 10. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Nikolaos Staikos; [2024]
   Nyckelord :Physical Geography; Ecosystem Analysis; Bike-sharing demand; Machine learning; Deep learning; Spatial regression; Graph Convolutional Neural Network; Multiple Linear Regression; Multilayer Perceptron Regressor; Support Vector Machine; Random Forest Regressor; Urban environment; Micro-mobility; Station planning; Geomatics; Earth and Environmental Sciences;

   Sammanfattning : Public Bike-Sharing systems have been employed in many cities around the globe. Shared bikes are an efficient and convenient means of transportation in advanced societies. Nonetheless, station planning and local bike-sharing network effectiveness can be challenging. LÄS MER