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 37 for “"Applied Machine Learning."”.
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Applied Machine Learning for Electrocatalysis
… energy conversion reactions. First, supervised machine learning models are applied to identify kinetic trends in the nitrogen reduction reaction. Next, a novel computer vision pipeline is introduced to extract electrochemical parameters from cyclic voltammograms, enabling faster, automated …
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Applied Machine Learning with Latent Space Representation and Manipulation
Machine learning is one of the most promising fields of study nowadays. It is applied to various types of industry including image classification, object detection, and time-series signals prediction, etc. Latent space is a concept that is hidden but significant to machine learning, which helps …
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SmartPitch: Applied Machine Learning for Professional Baseball Pitching Strategy
… programming techniques have previously been applied to this space in order to research the areas of offensive player selection (lineup creation) and player substitution, but they have rarely been studied in the context of one of the most complicated parts of the sport: the minigame between …
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Automated precision computational image analysis and applied machine learning for experimental and clinical hematology applications
… pipelines for image processing coupled with applied machine learning interpretation represent a solution by providing methods that balance high-throughput automation with precise, quantitative results. To this end, this work has developed a series of computational workflows to provide greater …
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UNCOVERING FUNDAMENTAL PHYSICS OF ENERGY MATERIALS WITH QUANTUM MECHANICS, MOLECULAR DYNAMICS, AND APPLIED MACHINE LEARNING
… leverage quantum mechanical simulations and machine learning algorithms to discover new materials for CO2 conversion and for improved sensors and communication devices. Next, we expand on these methods to develop a machine learned force field for large scale simulations of ferroelectric …
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Using machine learning for hydrocarbon prospecting in Reconcavo Basin, Brazil
Machine Learning techniques are being widely used in Social Sciences to find connections amongst various variables. Machine Learning connects features across different fields that do not seem to have known mathematical relationships with each other. In natural resource prospecting, machine learning …
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The Objective Function: Science and Society in the Age of Machine Intelligence
<p>Machine intelligence, or the use of complex computational and statistical practices to make predictions and classifications based on data representations of phenomena, has been applied to domains as disparate as criminal justice, commerce, medicine, media and the arts, mechanical engineering, …
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Robust graph representation learning with structure-aware attention and self-supervised contrastive frameworks.
… and information networks, yet effectively learning from such data remains a fundamental challenge in machine learning. My dissertation focuses on developing novel graph representation learning methods that enhance predictive performance, robustness, and interpretability for node and graph …
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Apply Machine Learning on Cattle Behavior Classification Using Accelerometer Data
… acceleration from the cows. For the traditional Machine learning approach, we segmented the data to calculate features, selected the important features, and applied machine learning algorithms for classification. We compared the performance of various models and found a robust model with …
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Improving complex sale cycles and performance by using machine learning and predictive analytics to understand the customer journey
… research examines the benefits and challenges of applied machine learning and predictive analytics to improve critical stages in the sales and marketing process by making assisted decisions that accelerate the sales cycle and increase performance. This thesis focuses on methodologies for promoting …
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Machine Learning Algorithms for Improved Glaucoma Diagnosis
… of advanced statistical techniques based on machine learning for automated classification of tests from visual field examinations and retinal nerve fibre measurements to detect glaucoma. Diagnostic performance of the applied machine learning classification algorithms was shown to depend …
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Towards understanding and simplifying human-in-the-loop machine learning
"Machine learning application developers and data scientists spend inordinate amount of time iterating on machine learning (ML) workflows, by modifying the data pre-processing, model training, and post-processing steps, via trial-and-error to achieve the desired model performance. As a result, …
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GraphDHT: Scaling Graph Neural Networks' Distributed Training on Edge Devices on a Peer-to-Peer Distributed Hash Table Network
… domains and structures. This contributes to applied machine learning, especially in optimizing distributed learning on edge devices.
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Automation of Drosophila gene expression pattern image annotation : development of web-based image annotation tool and application of machine learning methods
… also enabling automation of the process using machine learning methods. First, a tool called LabelLife was developed to provide a systematic and flexible way of annotating images, groups of images, and shapes within images using terms from a controlled vocabulary. Second, machine learning …
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Using application generated data to provide personalized user experience in software applications
… purposes and create one fits all solution. Machine/application data, which is continuously generated by the software applications, tracking each and every user activity, can be extremely useful in understanding the user behavior and thus giving companies the ability to create more …
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A High-quality Digital Library Supporting Computing Education: The Ensemble Approach
… YouTube and SlideShare for an educational DL. We applied machine learning techniques to transfer what we learned from the ACM Digital Library dataset. We built classifiers to catalog resources according to the ACM Computing Classification System from the two new domains that were evaluated using …
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Multilevel frameworks for studying protein energy landscapes
… of complex proteins over extended timescales. Applied to the bovine pancreatic trypsin inhibitor (BPTI), lwONIOM achieves a balance between computational cost and precision, providing deep insights into structural stability and dynamic behaviour. Energy landscapes described by the AMBER and …
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Advancing Emergency Department Efficiency, Infectious Disease Management at Mass Gatherings, and Self-Efficacy Through Data Science and Dynamic Modeling
… in preventing health crises. The third essay applied machine learning methods to predict student self-efficacy in Muslim societies, revealing the importance of socio-emotional traits, cognitive abilities, and regulatory competencies. It provided a basis for identifying students with varying …
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Modeling Convergence Undercurrents in Brain Science via Statistics and Machine Learning
… awkwardly within researchers through expansive learning. Our models reveal three key findings: First, brain researchers tend to tackle subject areas beyond their core expertise, especially when these areas are epistemically close - a convergence shortcut. Second, this expansive learning behavior …
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The Science and Art of Human and Artificial Intelligence Collaboration
… surpass) the performance of either humans or the machine alone? This dissertation addresses various dimensions of this guiding question by conducting large-scale, digital experiments across three distinct tasks and domains: deepfake detection, dermatology diagnosis, and Wordle. First, the …
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