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 12 of 12 for “"Augmentation Technique"”.
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Satellite Image Analysis and Sidewalk Classification using Deep Learning Models
… the top of pretrained layers, employing various techniques to improve the classification accuracy.</p> <p>The dataset comprises 4,731 images of sidewalk based on occlusion levels, including images where the sidewalk is visible from overhead and instances where sidewalk is occluded by tree …
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RF Fingerprinting Unmanned Aerial Vehicles
… UAV. To achieve this goal, AlexNet with the data augmentation technique was employed.</p>
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IDENTIFICATION OF RISK OF MAXILLARY SINUS PERFORATION IN ASSOCIATION WITH THE SINUS ANGLE
… sinus anatomy. Lateral wall maxillary sinus augmentation technique was utilized to increase bone height and width for posterior region presented with atrophic ridges. The highest post-operative complication rate for lateral wall sinus augmentation technique is perforation of the maxillary …
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Augmented Surface Integral Equation Method for Low-Frequency Electromagnetic Analysis
… interconnects on chip and package levels. The augmentation technique is a simple and efficient remedy for the low-frequency breakdown, which is the bottleneck of the broadband simulation. Based on the augmented formulations, very complicated problems in the real world can be efficiently solved …
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Unsupervised Text Translation Through the Application of Generative Adversarial Networks
… This requires us to focus on unsupervised techniques for text translation that depend only on access to large domains of unpaired data. For unsupervised translation, one approach involves the use of generative adversarial techniques for sequence generation. Unfortunately, prior work using …
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Predictive and Prescriptive Trees for Optimization and Control Problems
… with OCT-H and introduce a novel feature augmentation technique to handle nonlinearities. In Chapter 4, we propose a machine learning framework for solving two-stage linear adaptive robust optimization problems with binary here-and-now decisions and polyhedral uncertainty sets. We also …
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Learning from small and imbalanced dataset of images using generative adversarial neural networks.
… Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and semi-supervised learning have not lived up to expectations. Some of these techniques do not account for other …
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A citizen science approach for the collection of data to train deep learning models
… progress of nature studies. Machine learning techniques that give good predictions require a considerable amount of data, which can sometimes be a challenge to collect. Due to the size of the island, the study of Maltese flora is one of such fields that lacks available data causing little …
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Classification of multiwavelength transients with machine learning
… to such small datasets by making use of data augmentation. We develop a novel data augmentation technique based on Gaussian processes, able to generate new data statistically consistent with the original. As the dataset is currently small, three studies were done on the effect of the training …
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An enhanced volume integral equation method and augmented equivalence principle algorithm for low frequency problems
Two techniques based on integral equation methods are addressed. Firstly, a novel volume integral equation method is proposed to characterize the scattering properties of dielectric objects involving inhomogeneous and anisotropic permittivity and permeability. Two algorithms are available: …
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Disaster and infrastructure scene understanding
… 10 to 20 images). A scale-space theoretic data augmentation technique is proposed in this framework. To deal with the scene complexities, the deep transfer learning mechanism is adopted. Several convolutional neural network (CNN) models based on the seminal Faster R-CNN models are tested and …
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Accurate and Trustworthy Recommender Systems: Algorithms and Findings
… issue, we devise an influence-guided data augmentation technique DAIN that augments important data points for reducing training loss to the original data. With DAIN, we can enhance the recommendation model’s generalization ability and mitigate cold-start and popularity-bias problems. Apart …