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 15 of 15 for “"Ensemble machine learning"”.
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Ensemble machine learning to predict family consent for organ donation
… the factors associated with family consent. Machine Learning approach had been used in very few literature to understand factors related to family consent. This study uses six Ensemble Machine Learning models to accurately predict family consent outcome (yes/no). All family approaches data …
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Enhancement of digital elevation models using tree-based ensemble machine learning algorithms
… This research proposes an explainable tree-based ensemble feature-level fusion framework for enhancing satellite DEMs using Cape Town, South Africa as a case study. The enhancement methodology combines elevation and terrain features data alignment (co-registration and resampling) with …
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Early prediction of students at risk in a virtual learning environment using ensemble machine learning techniques
Submitted in fulfillment of the requirements for the Degree of Masters of Information and Communication Technology, Durban University of Technology, Durban, South Africa, 2021.
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Diversity tradeoff curves in personnel selection: Evaluating local study, meta-analysis, Bayes-analysis, and ensemble machine learning
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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DEVELOPMENT AND APPLICATION OF ENSEMBLE MACHINE LEARNING ALGORITHMS TO ENABLE A PRIORI, HIGH-FIDELITY, AND PROMPT PREDICTIONS AND OPTIMIZATIONS OF COMPLEX MATERIALS AND SYSTEMS
… years, scientists have harnessed the power of machine learning (ML) and “Big” data to uncover the underlying mixture design-property correlations and produce high-fidelity predictions of properties of complex materials. The ML models autonomously learn cause-effect correlations from the …
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A Machine Learning Approach for Next Step Prediction in Walking using On-Body Inertial Measurement Sensors
… presents the development and implementation of a machine learning prediction model for concurrently aggregating interval linear step distance predictions before future foot placement. Specifically, on-body inertial measurement units consisting of accelerometers, gyroscopes, and magnetometers, …
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An improved algorithm for iris classification by using support vector machine and binary random machine learning
In machine learning, there are three type of learning branch that can used in classification procedures for data mining. Those branch are consist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classify all the Iris …
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A Comparative Study of Machine Learning and Traditional Techniques for Grade Prediction and Grade-Tonnage Evaluation in a Small VMS Deposit
… structures. This thesis evaluates whether machine learning methods can improve grade prediction and tonnage estimation compared to traditional methods. A three-dimensional block model with 5 x 5 x 5 m resolution was constructed in Vulcan, and grade estimation was performed using Inverse …
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What Text Information Helps to Reduce Default Risk
… this research employs logistic regression and ensemble machine learning algorithms, including forward and backward stepwise selection and random forests, to rank the importance of various factors in predicting loan default.</p> <p>By including text information, this paper improves the accuracy …
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Exploratory Data Analysis (EDA) and Predictive Machine Learning (ML) for Buildings’ Energy Fault Detection
… usage analysis. It helps uncover energy wastage, machinery/appliance degradation or inefficiency, and failures or faults in buildings’ HVAC (heating, ventilation, and air conditioning) systems. Early identification of machinery failure and energy wastages due to operational maintenance negligence …
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Systems Biology of Host-Pathogen Protein-Protein Interactions
… proteomic approaches. First, automated machine learning (ML)-based computational workflows with different algorithmic strategies were devised to generate high-quality tissue-specific and tissue-common SARS-CoV-2-human PPIs. Subsequent clustering of highly conserved networks using an …
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Translating biomedical research data to knowledge through bioinformatics
… and knowledge discovery, we developed a novel ensemble data analysis method to improve the predictive ability of classic bagging and AdaBoost methods. By evaluating forty-one online datasets, we demonstrate the ability of our ensemble method in increasing predictive accuracy, which could be …
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Artificial Intelligence in Digital Agriculture. Towards In-Field Grapevine Monitoring using Non-invasive Sensors
… deal with data. Within artificial intelligence, machine learning has evolved greatly during the last decades providing tools to make computers learn, and these algorithms are used in many different fields due to their high versatility for many data-related tasks, generating knowledge and …
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Temporal Data Mining in a Dynamic Feature Space
… issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.
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Investigating Ensembles of Single-class Classifiers for Multi-class Classification
… methods of multi-class classification in machine learning involve the use of a monolithic feature extractor and classifier head trained on data from all of the classes at once. These architectures (especially the classifier head) are dependent on the number and types of classes, and are …