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 20 of 493 for “"model performance"”.
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A model performance index improvement.
Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1977
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A Model Performance Evaluation for the Waco Police Department
No abstract prepared.
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Improving wildlife habitat model performance : sensitivity to the scale and detail of vegetation measurements
Thesis (Ph. D.)--Michigan State University. Department of Fisheries and Wildlife, 2009
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The effects of input data degradation on hydrological model performance for a snowmelt dominated watershed
… data used as input to a hydrologic model is varied and the output compared to observed historical flows. Temperature and precipitation data were used to feed the National Weather Service River Forecast System (NWSRFS); this hydrologic model outputs streamflow and is used daily …
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Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation.
… in alternative methods such as in silico modelling. The QSAR (Quantitative Structure Activity Relationship)-based models are already in use for predicting physicochemical properties, environmental fate, eco-toxicological effects, and specific biological endpoints for a wide range of …
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Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation
… in alternative methods such as in silico modelling. The QSAR (Quantitative Structure Activity Relationship)-based models are already in use for predicting physicochemical properties, environmental fate, eco-toxicological effects, and specific biological endpoints for a wide range of …
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Evaluating Model-Estimated Shoulder Muscle Activity During Overhead Work with Varied Task Demands and Exoskeleton Use
… and resource-intensive. Musculoskeletal modeling could simplify the process of ASE evaluation, by replacing electromyography (EMG) sensors with estimates of muscle activation. However, there is no existing evidence to determine whether model performance with ASE is sufficient or …
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A Case for Pre-trained Language Models in Systems Engineering
… processing techniques. Pre-trained language models, such as BERT, represent state-of-the-art in the field. This thesis seeks to understand if these pre-trained language models can achieve higher model performance at a lower computational and manpower cost than earlier techniques. The results …
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Empirical Evaluation of Graph-Anonymized Metrics for JIT Defect Prediction
… of reverse-engineering training data from shared models further complicates data privacy. To address these challenges, anonymization techniques like MORPH, LACE, and LACE2 provide privacy to defect prediction data. While effective, they often sacrifice metric performance due to a disregard for …
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Engineering TEV Protease Specificity: An Exploration of Machine Learning and High-Throughput Experimentation for Protein Design
… experiments. Most machine learning models do not account for experimental noise, harming model performance and changing model rankings in benchmarking studies. Here we develop FLIGHTED, a Bayesian method of accounting for uncertainty by generating probabilistic fitness landscapes …
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Determining the Influence of Abiotic and Biotic Predictors on Ecological Niche Models
… and are typically excluded from ecological niche models (ENMs). However, the role such interactions play in shaping species distributions is increasingly recognized, sparking the development of methods for their integration into ENMs.</p> <p>Among the most common approaches are those that limit …
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Development of sea ice diagnostic tools for high-resolution simulations of the Climate Model Intercomparison Project (HiResMIP)
… the role that horizontal resolution plays in the performance of climate modelling, this minor-dissertation describes the development and initial testing of a High-Resolution Sea Ice Diagnostics Toolset. This is designed to evaluate the influence increased horizontal resolution has on the ability …
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The Effect of Model Formulation on the Comparative Performance of Artificial Neural Networks and Regression
… used to construct predictive statistical models, relating one or more independent variables (inputs) to a dependent variable (output). Artificial neural networks can also be constructed and trained to learn these complex relationships, and have been shown to perform at least as well as …
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Generating new data points using singular value decomposition
… a Single Value Decomposition (SVD)-based model that draws inspiration from the ability of SVD to estimate a lower rank matrix. This approach seeks to overcome the limitations imposed by sample size constraints by expanding available data. Motivated by challenges faced during algorithm …
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How Data Drives ML Models Performance
… the understanding of the effect of the data on model performance and reliability. First, we study how choice of training data affects model performance. We consider a transfer learning setting and present a framework for selecting from a large pool of data a pretraining subset that improves …
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Modelling impacts of climate change on hydrology of Latonyanda River Quaternary Catchment, Limpopo Province, South Africa
… The Soil and Water Assessment Tool (SWAT) model played a huge role in climate change impact analysis because it helped in improving the understanding of climate change impacts on hydrology as well as in determining mitigation measures. Arc-GIS 10.7 model with a compatible version of …
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Short-term sea level forecasting using machine learning techniques: A case study for South Africa
… efficient in comparison to numerical models, are applied to predict seawater levels. The open-loop NARX model was developed using the Neural Net Time Series application from the Deep Learning Toolbox 14.0 provided by MATLAB® (Mathworks, 2020). A total of five inputs (atmospheric …
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Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues
… notes.</p> <p>The first study investigated the model performance of sequence deep learning models that are widely used in NLP tasks such as RNN, GRU, LSTM, Bi-LSTM, as well as CNN and the novel and more advanced attention-based algorithms such as the Transformer Encoder and BERT-Base. The model …
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