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 45 for “"ensemble model"”.
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A cumulus ensemble model with simple mesoscale structure
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Meteorology and Physical Oceanography, 1983.
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Development of a Bagging-based Ensemble Model for ECG Classification
… particularly the application of machine learning models in cardiovascular disease classification and recognition, is rapidly growing. CNN, LSTM, and Transformer models have demonstrated in various studies that, when implemented with robust architectures and supported by ample datasets, they can …
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Ensemble regression : using ensemble model output for atmospheric dynamics and prediction
Ensemble regression (ER) is a linear inversion technique that uses ensemble statistics from atmospheric model output to make dynamical inferences and forecasts. ER defines a multivariate regression operator using ensemble forecasts and analyses to determine the most probable predict and …
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Short-term wind power forecasting using artificial neural networks-based ensemble model
… obtained by selecting suitable input features, model parameters, and using forecasting techniques like spatial correlation and ensemble for ANNs. In this research, the effect of input features, model parameters, spatial correlation and ensemble techniques on short-term wind power forecasting …
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Advancements in Word Alignment: Introducing a Novel Count-Based Subword Model Alongside Neural and Ensemble Models
… problem by developing and comparing three models: a count-based subword model, a baseline encoder-decoder neural alignment model, and an ensemble model. The count-based subword model utilizes statistical measures and co-occurrence statistics for word alignment estimation. The neural …
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A stochastic ensemble forecast model for geosynchronous relativistic electron fluxes
A stochastic ensemble model composed of three functional forecasting models has been developed to forecast >2 MeV electron flux at geosynchronous (GEO) orbit. The REFM model is based on a statistical link between electron flux and solar wind speed using empirically derived linear filter …
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Estimating phytoplankton size classes from their inherent optical properties
… phytoplankton size classes. Three separate models were developed, each focusing on a different relationship between absorption and phytoplankton size classes, before being combined into a final ensemble model. It was shown that all of the developed models performed better than the baseline …
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Predicting mergers and acquisitions using machine learning
… objective is the development of a robust ensemble model for potential use in an investment portfolio. The algorithms were trained on a comprehensive historical dataset with diverse financial indicators. Given the considerable amount of missing values in the dataset, imputation was applied …
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Prediction of HPLC Retention Index Using Artificial Neural Networks and IGroup E-State Indices
… create a 10 fold leave 10% out cross validated ensemble model of high performance liquid chromatography retention index (HPLC-RI) for a dataset of 498 diverse drug-like compounds. A 10 fold multiple linear regression (MLR) ensemble model of the same data was developed for comparison. Molecular …
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A Machine Learning-Based Heuristic to Explain Game-Theoretic Models
… an interpretable heuristic for principal-agent models (PAM). We extract solution patterns from ensemble tree models trained on solved instances of a PAM. Using these patterns, we develop a hierarchical tree-based approach that forms an interpretable ML-based heuristic to solve the PAM. This …
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Human action recognition with 3D convolutional neural networks
… and the rationale behind the components of the model are still applicable due to the similarity between image and video data. Previous CNNs have demonstrated good performance upon video datasets, however have not employed methods that have been recently developed and attributed improvements in …
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Advances in Representation and Learning of Temporal Event Sequences
… For point-based event sequences, we build an ensemble model that predicts the time of occurrence of the next point-based event. The ensemble model comprises nine other methods that are able to perform the prediction task. We demonstrate that the prediction results obtained by the ensemble …
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A Cumulus Parameterization Study With Special Attention to the Arakawa-Schubert Scheme (semi-Prognostic, Meso-Scale Simulation)
… substantially when compared with the cumulus ensemble model results (Soong and Tao, 1980; Tao, 1983). An inclusion of the downdraft effects, as formulated by Johnson (1976), appears to alleviate this deficiency.
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Modeling Daily Power Demand in Southern Kentucky: A Single Household Approach
… and compare it to existing aggregate demand models discussed in literature. Of these aggregate demand models, a quadratic autoregressive model was selected to be used as a basis for comparison with the LOESS forecasts. It was our goal to automate the forecasting process by using the goodness …
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Analysis of Chemical Elements in Basalts using Mislabeled Data, a Machine Learning Approach
… to discover any new elements of interest. The models were used with other tools, such as recursive feature elimination and permutations, to increase reliability. Among the scarcely explored chemical elements are Terbium (Tb), Holmium (Ho), <a href="https://en.wikipedia.org/wiki/Samarium" …
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Success Classification for Object Navigation
… We also find no improvement when using a ensemble model for semantic segmentation, although we believe there is more to be tested before arriving at a conclusive judgement.
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Impacts of climate and land use change on fish species distributions in the central United States
Species distribution models are useful tools that can be used to evaluate tradeoffs of management and conservation strategies under scenarios of environmental change. Modeling efforts for fish species have largely focused on cold-water, commercial, and recreationally-valued species, even though …
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A standards-based grading model to predict students' success in a first-year engineering course
<p>Using predictive modeling methods, it is possible to identify at-risk students early in the semester and inform both the instructors and the students. While some universities have started to use standards-based grading, which has educational advantages over common score-based grading, at–risk …
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Deepfake Face Detection: An Ensemble Framework for Generalized Classification in Biometric Verification Systems
… trained on. Current deepfake detection models achieve nearperfect accuracy on benchmark datasets, but do not perform as well on unseen types of deepfakes that were not part of their training dataset. We propose building an ensemble model with multiple base detectors, each trained on …
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Ensemble Modelling of in situ Feature Variables for Printed Electronics Manufacturing with in situ Process Control Potential
… variables in AJP processes. In this research, an ensemble model strategy is proposed to quantify the effect of the process setting variables on the in situ feature variables, and the effect of the in situ feature variables on quality variables in a two-level hierarchical way. By identifying …
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