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 14 of 14 for “"Convolution Neural Networks"”.
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Recommendations in text-rich heterogeneous networks
… relevant background and illustrate heterogeneous networks along with related tasks through the help of examples. We choose the setting of Bibliographic Heterogeneous Network and devise a Citation Recommendation system that integrates the various sources of information present in the network. We …
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Decoding Invisible 3D Printed Tags with Convolutional Neural Networks
… out all sets of parameters. It will instead use convolution neural networks (CNNs) to quickly convert an IR image into a binary image, from which the embedded code can be readily read.
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Building a similarity engine
… ability in downstream tasks. They also discuss a neural network architecture that many of todays approaches are built upon. In 2013, Mikolov created word2vec, a toolkit that enabled the training and use of pre-trained embeddings. In 2014, Pennington introduced GloVe, a competitive set of …
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Deep Multimodal Physiological Learning of Cerebral Vasoregulation Dynamics on Stroke Patients Towards Precision Brain Medicine
… employs various Deep learning techniques like Convolution Neural Networks (CNN), Mo bileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood …
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Fast and Accurate Image Feature Detection for On-The-Go Field Monitoring Through Precision Agriculture. Computer Predictive Modelling for Farm Image Detection and Classification with Convolution Neural Network (CNN)
… cycle. Then, an ensemble of classifiers with Convolution Neural Networks (CNN) was used as off the shelf feature extractor to train images to develop an end-to-end feature detection and multiclass classification system for plant overall health’s conditions. Whereby previous works have …
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Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs
… impervious surface classifier based on fully convolution neural networks (FCNN) is trained to monitor the urbanization in five Nile basin cities between 2013 and 2019. For SDG 14, the studies focus on marine pollution (SDG 14.1.1) through monitoring and exploring the atmospheric and …
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Automated Detection and Analysis of Low Latitude Nightside Equatorial Plasma Bubbles
… this dissertation tests the possibility of using convolution neural networks for detection of EPBs with the end goal of reducing the amount of preprocessing necessary. Further, data from the Ionospheric Connection Explorer's (ICON's) ion velocity meter (IVM) are compared to EPBs detected via GOLD …
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Near-Memory Processing for Low-precision Deep Neural Networks
Deep neural networks (DNNs) provide many application domains with state-of-the-art performance and accuracy. However, they are compute-heavy and data-intensive which makes deploying them on resource-constrained edge devices challenging. Transforming the real-valued parameters of a DNN into a …
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Satellite change detection in the albany thicket biome
… identifying clearings of Thickets using Temporal Convolution Neural Networks and comparing it against the Continuous Change Detection and Classification (CCDC) algorithm. Finally chapter three sets out to develop a Domain adaptive Temporal Convolution Neural Network for continuous change detection …
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An Explainable Artificial Intelligence Approach Based on Deep Type-2 Fuzzy Logic System
… complex (or Black box) AI systems such as Deep Neural Networks, support vector machines, etc., could lead to a lack of transparency. This lack of transparency is not specific to deep learning or complex AI algorithms; other interpretable AI algorithms such as kernel machines, logistic …
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Using Siamese neural networks to identify individual animals
… to the other image in the pair. Mask-Regional Convolution Neural Networks (Mask - RCNN) [He et al., 2017] are used for the object detection and instance segmentation which answers (1). This is a modern deep learning approach which has been used for tasks such as identifying breast cancer tumors …
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Deep neural networks for medical image super-resolution
… the past decades, the rapid development of deep neural networks has ensured high reconstruction fidelity and photo-realistic super-resolution image generation. However, challenges still exist in the medical domain, requiring novel network architectures, training tricks, and SR image evaluation …
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Development of Surrogate Model for FEM Error Prediction using Deep Learning
… solve an image-to-image regression problem using convolutional neural networks (CNN) that takes a 256 × 256 colored image of von mises stress contour and outputs the required error indicator. To train this model with good generalization performance we have developed four different geometries for …
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Machine learning based digital image forensics and steganalysis
… topics are covered: (1) architectural design of convolutional neural networks (CNNs) for steganalysis, (2) statistical feature extraction for camera model classification, and (3) real-world tampering detection and localization. For covert communications, steganography is used to embed secret …