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 23 for “"Machine Learning Neural Network"”.
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Vehicle Engine Classification Using of Laser Vibrometry Feature Extraction
… a two-layer feed-forward 20 intermediate-nodes neural network to classify vehicles’ engine, the results are encouraging as they can consistently achieve accuracies over 96%. However, the TPI required a length of 1.25 seconds of vibration, which is a drawback of the TPI, as vehicles generally are …
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Density-Wave Instability Characterization in Boiling Water Reactors under MELLLA+ Domain during ATWS
… profile on the stability boundary. Finally, two machine learning neural network-based models are developed and trained on various subsets of the experimental data. The results from each model showed certain benefits and drawbacks based on model complexity and physicality.
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Optimizing the compliance of pedestrian facilities construction and alteration with accessibility requirements
… to assess the compliance of their pedestrian network with accessibility requirements. Transition plans must include a detailed schedule of all upgrade projects that are required to achieve full compliance with accessibility requirements. These self-evaluation and transition plan requirements …
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Android Application Install-time Permission Validation and Run-time Malicious Pattern Detection
… at both installation and runtime using machine learning. Effective classification techniques with neural networks can be used to verify the application categories on installation. We devise a novel application category verification methodology that involves machine learning the …
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Soluzioni IoT inerenti la Comunicazione, l’Elaborazione e l’Intelligenza Artificiale per l’Industria 5.0
… verrà introdotto un nuovo modello di rete neurale capace di migliorare la produzione di moduli solari, riuscendo a predire il modulo solare risultante prima della sua creazione, permettendo un processo di sviluppo e ricerca senza costi onerosi. Infine, alla fine di ogni capitolo, verranno …
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A linear constraint driven approach to efficiently enhancing branch and bound in neural network verification
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Machine learning surrogate modeling methods in inverse high-speed link design
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01
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Multiscale Modelling of Powder Bed Fusion with Electron Beam Process
L'abstract è presente nell'allegato / the abstract is in the attachment
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Data-driven acceleration of molecular dynamics simulation for nanoscale fluids with coarse-grained and surrogate modeling
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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New techniques for the Reliability Evaluation of AI-oriented Hardware Accelerators
L'abstract è presente nell'allegato / the abstract is in the attachment
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RNN-Based Generation of Polyphonic Music and Jazz Improvisation
… data sources and the character-based recurrent neural network architecture <em>char-rnn</em>. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.</p>
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Neural Network Reduction for Efficient Execution on Edge Devices
As the size of neural networks increase, the resources needed to support their execution also increase. This presents a barrier for creating neural networks that can be trained and executed within resource limited embedded systems. To reduce the resources needed to execute neural networks, weight …
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Visual Analytics and Interactive Machine Learning for Human Brain Data
… multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging …
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Machine Learning-Based Receiver in Multiple Input Multiple Output Communications Systems
Bridging machine learning technologies to multiple-input-multiple-output (MIMO) communications systems is a primary driving force for next-generation wireless systems. This dissertation introduces a variety of neural network structures for symbol detection/equalization tasks in MIMO systems …
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TwinDNN: A tale of two deep neural networks
Compression technologies for deep neural networks (DNNs), such as weight quantization, have been widely investigated to reduce the model size so that they can be implemented on hardware with strict resource restrictions. However, one major disadvantage of model compression is accuracy degradation. …
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Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies
Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The …
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Leveraging Machine Learning Techniques in Power and Transportation Systems
… authenticity. Fortunately, the surge of modern machine learning techniques has enabled us to grapple with seemingly impossible to solve problems, to overcome the computational complexity, and mine the knowledge to guide the operational tasks, especially in complex cyber physical systems such as …
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On Principled Modeling of Inductive Bias in Machine Learning
The inductive bias of a learning algorithm is the set of assumptions that the hypothesis uses to predict unseen data, governing its generalization power. This thesis focuses on principled approaches to modeling inductive bias of learning algorithms. We start with a unifying view on inductive bias …
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Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning
… from a modern point of view. With the help of machine learning techniques, it has become possible to explore regimes that are not accessible with traditional methods: for example, tracking the state of a superconducting transmon qubit continuously with dynamics fast compared with the detector …
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Biophysical dynamical priors in machine learning
The proliferation of machine learning models in biology is due to the great potential of novel discoveries ranging from new medicines to an improved understanding of the development of species. Adding to this, an ever-increasing number of high-resolution biological datasets are providing the fuel …
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