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.
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Showing 1 to 20 of 34 for “"ensemble models"”.
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Ensemble models to assess the risk of exotic plant pathogens in a changing climate
In recent decades, species distribution models (SDMs) have been widely used in many ecological, environmental and climate-change research studies to model invasive species establishment. These models associate recorded locations of species with environmental variables. Nevertheless, the few studies …
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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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Investigating the use of ensemble techniques in predicting object-oriented software maintainability
… to empirically investigate the capability of ensemble models to provide an increased prediction accuracy, compared with individual models, by applying them on several software maintainability datasets using different base models and analysing the impact of parameter tuning.;Method: In the …
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Development of Ensemble Strategies for Generalization in Deepfake Image Detection
The growing accessibility of generative models has enabled the rapid proliferation of deepfake content, posing significant challenges in image-based biometric security and media authenticity. In this thesis, six diverse facial deepfake image datasets are assembled, and four modern detection models …
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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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Integrating Transactive Energy and Machine Learning For Re-Energizing Wastewater Treatment Plants
… (RNN), long short-term memory (LSTM), and ensemble models are adopted as intelligent computing tool for generation forecasting. A regression model was developed to forecast the power output of onsite wind turbines. Managerial insights were obtained regarding the most effective model for …
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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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Towards an Information Theoretic Framework for Evolutionary Learning
… on objectively and explicitly evaluating the ensemble models implicit in evolutionary learning. Information theoretic functionals can provide objective, justifiable, general, computable, commensurate measures of fitness and diversity.</p> <p>We identify information transmission channels …
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Combining measurements with deterministic model outputs: predicting ground-level ozone
… how to combine model outputs from deterministic models with measurements from monitoring stations for air pollutants or other meteorological variables. We consider two different approaches to address this particular problem. The first approach is by using the Bayesian Melding (BM) model proposed …
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On Transferability of Adversarial Examples on Machine-Learning-Based Malware Classifiers
… detection system. However, machine learning models could also be extremely vulnerable and sensible to transferable adversarial example (AE) attacks. The transfer AE attack does not require extra information from the victim model such as gradient information. Researchers explore mainly 2 lines …
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A machine learning approach to predict emotional arousal and aggressive driving from EDA and heart rate signals
… features were used to train four classification models, Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost, as well as an ensemble VotingClassifier. Model performance was evaluated using five-fold cross-validation across accuracy, precision, recall, and F1-score. The …
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Human action recognition with 3D convolutional neural networks
… CNNs and current hardware. Focus is applied to ensemble models and methods such as the Dropout technique, developed by Hinton et al. (2012) to reduce overfitting, and learning rate adaptation techniques. The KTH human action dataset is used to assess the CNN model, which, as a widely used …
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Systems Pharmacology – Machine Learning Approaches in Profiling Oncology Drug Candidates
… In this study, we built in-silico predictive models to predict prospective drug candidates from compound libraries. Robust predictive models help in saving enormous experimental, and resource overheads and compress product cycle times. We used several machine learning algorithms, in building …
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Replay and bogus information attacks : simulation and empirical validation of machine learning-based cybersecurity threat detection in connected and autonomous vehicles
… was generated using the Eclipse MOSAIC platform. Models spanning ensembles (GBM/DRF), deep learning (DL), and classical baselines (DT/NB/SVM/LG) were trained in simulation and deployed on Cohda MK6C hardware; Virtual CAN traffic emulated realistic dynamics to assess real-time performance and …
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Ensemble machine learning to predict family consent for organ donation
… related to family consent. This study uses six Ensemble Machine Learning models to accurately predict family consent outcome (yes/no). All family approaches data between January 2016 and March 2018 from an Organ Procurement Organization (OPO) based in New York city is used to build the family …
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Improving Convective Permitting Model Simulations of Extreme Precipitation using Object-Based Tracking and Data Assimilation
… rainfalls than coarse-grained (e.g. 3 km) ensemble models. Probabilistic verification illustrates that CPM has a much better skill to predict intense convective events especially for the target area of central Texas where the hourly rainfall rates and flooding were extreme. In Appendix B, …
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Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets
… applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to …
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