Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 11 of 11 for “"Regression Machine Learning"”.
-
Using a Single inertial measurement unit to measure the knee flexion angle at touchdown in side cutting movements
… Vicon video motion capture system. Random Forest Regression and Gradient Boosting Regression machine learning algorithms were applied to the IMU data. According to the results, the thigh is the best location for a single IMU sensor, and the knee flexion angle at touchdown can be measured with an …
-
The Development of a Reduced Order Model for Prediction of Haemodynamic and Biochemical Changes in a Computational Cerebral Aneurysm Thrombosis Model
… were used to build reduced-order models using machine learning algorithms. Multiple polynomial regression and logistic regression machine learning algorithms were used to predict clot size in patients. The K-nearest neighbours algorithm was used to develop a model that classifies patients' …
-
LearnAir : toward intelligent, personal air quality monitoring
… if the core principles of the device are robust, machine learning techniques should be able to predict systematic measurement failure based on a handful of related indicators. In this thesis, we test and demonstrate the potential for logistic regression machine learning techniques to predict and …
-
Crowdsourcing traffic data for travel time estimation
… importantly, we present prediction methods using machine learning techniques such as support vector regression.;Machine learning provides an alternative to traditional statistical method such as using averaged historic data for estimation of travel time. Machine Learning techniques played a key …
-
Unconfined compressive strength prediction using drilling parameters and analyzing feature importance through principal components analysis
… methods include but are not limited to basic regression, machine learning, and deep learning algorithms. Data-driven methods to identify patterns in the data to estimate geomechanical parameters are considered to be implemented for drilling operations. This study proposes methods to assist …
-
A Citizen-Science Approach for Urban Flood Risk Analysis Using Data Science and Machine Learning
… Shrinkage and Selection Operator (LASSO) regression analysis, with an embedded Zero-Inflation (ZI) model, the variables statistically significant as predictors, specific to each zip code, are detected. Second, with an intent to understand how factors affect the spatial variability of …
-
Structure-Borne Vehicle Interior Noise Estimation Using Accelerometer Based Intelligent Tires in Passenger Vehicles
With advancements in technology, electric vehicles are dominating the world making Internal Combustion engines less relevant, and hence vehicles are becoming quieter than ever before. But noise levels remain a significant concern for both passengers and automotive manufacturers. The vehicle's …
-
Evaluation of a comprehensive multidimensional model of bipolar spectrum psychopathology through statistical and machine learning methods
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
-
Data-driven prediction of rail neutral temperature for continuously welded rails using rail vibration resonance frequencies
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
-
Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
The present study compared the performance of machine learning classification models against logistic regression in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (UMSC) with scores from the Tailored Adaptive Personality Assessment …
-
NBA Machine Learning for Game Outcome Prediction
… compared, including a baseline team Elo logistic regression, an expanded model incorporating player-level Elo, and a final specification that adds rolling box score summaries and contextual features such as team record and scheduling effects. Model assessment is conducted using a walk-forward …