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 153 for “"Deep learning model"”.
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Efficient Deep Learning: Model Design and Algorithmic Innovation
… evolution of Artificial Intelligence (AI) and Deep Learning (DL) has revolutionized numerous domains, from computer vision to natural language processing and intelligent recommendation systems. However, this progress has been accompanied by escalating computational demands that challenge the …
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Software-Hardware Co-design For Deep Learning Model Acceleration
<p>Current deep neural network (DNN) models have shown beyond-human performance in multiple artificial intelligent tasks. However, state-of-the-art DNN models still exhibit great issues on efficiency that pose significant obstacles to their practical application in real-world scenarios. To further …
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Recommending TEE-based Functions Using a Deep Learning Model
… ML-TEE, a recommendation tool that uses a deep learning model to classify whether an input function handles sensitive information or sensitive code. By applying ML-TEE, developers can reduce the burden of manual code inspection and analysis. ML-TEE's model was trained and tested on …
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3D Hand Pose Estimation Via a Lightweight Deep Learning Model
Deep Learning with depth cameras has enabled 3D hand pose estimation from RGBD images. Commercial solutions like Leap Motion and Intel RealSense™ use stereoscopic sensors or IR illumination-based methods to capture the depth in a photograph and further estimate pose using Deep Learning (DL) …
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Profiling and characterization of deep learning model inference on CPU
With the rapid growth of deep learning models and higher expectations for their accuracy and throughput in real-world applications, the demand for profiling and characterizing model inference on different hardware/software stacks is significantly increased. As the model inference characterization on …
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IntelliEdgent: device-server collaborative deep learning model composition for resource-efficient edge intelligence
Deep Learning models have achieved tremendous success lately towards analysing high-dimensional data like images, texts, audio, etc. Despite their phenomenal predictive performance, the high demands on computation resources (e.g., memory requirement is in the order of hundreds of megabytes for …
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Applying Flinet Deep Learning Model to Fluorescence Lifetime Imaging Microscopy for Lifetime Parameter Prediction
… These data may be analyzed by FLINET, a deep learning architecture designed specifically for lifetime parameter prediction. The goal of this study is to train the existing FLINET model on synthetic data that best represents FLIM images on UMSCC74A cells exposed to different mitochondrial …
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You Only Look Twice: An Ensemble Deep Learning Model for Wildfire Detection Using Terrestrial Camera Networks
… normalization layer with a fine-tuned YOLO11 model for precise localization. Using a comprehensive dataset of 33,636 time-sequenced images from terrestrial cameras across the United States and Europe, our system achieves 98% fire detection accuracy and 55% localization mean average precision …
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A Deep Learning Model for Large Coarse Woody Debris Prediction from Aerial LiDAR on the Malheur National Forest.
North Carolina State University Theses Fisheries, Wildlife, and Conservation Biology.
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Missing channel reconstruction for sloan digital sky survey images using linear models and generative adversarial networks
… This thesis implements two methods—the linear model and the deep learning model—on 12,730 SDSS images for missing channel re- construction. Specifically, for the linear model, linear regression and patch- based regression are examined. For the deep learning model, the generative adversarial …
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The Impact of Corporate Crisis on Stock Returns: An Event-driven Approach
… events on firm performance. We build a hybrid deep learning model that utilizes information from financial news, social media, and historical stock prices to predict firm stock performance during firm crisis events. We develop new methodologies that can extract, select, and represent useful …
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Visual Inertial Odometry with Sparse Deep Learning
… with. We also show how the integration of this deep learning model impacts the performance of a real-time VIO system. On the inertial side, a commonly used sensor like an Inertial Measurement Unit (IMU) has noise and bias, which cause errors that grow fast as they get integrated over time. This …
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Autonomous 3D Urban and Complex Terrain Geometry Generation and Micro-Climate Modelling Using CFD and Deep Learning
… requires a clear understanding and realistic modelling of the complex interaction between climate and built environment to create safe and comfortable outdoor and indoor spaces. This necessitates unprecedented urban climate modelling at high temporal and spatial resolution. The interaction …
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Deep Learning Models for Traffic Prediction in Urban Transport Networks.
… we develop a short-term traffic flow prediction model, named EM, on linear roadways based on machine learning technology. EM is able to analyse and extract spatial and temporal features from original traffic data for the final prediction. This short-term traffic prediction could give drivers …
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Deep Transferable Intelligence for Wearable Big Data Pattern Detection
… study, we have performed extensive research on deep learning-based PAD from biomechanical big data, focusing on the challenges raised by the need for real-time edge inference. First, considering there are many places we can place the motion sensors, we have thoroughly compared and analyzed the …
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A citizen science approach for the collection of data to train deep learning models
Machine learning continues to advocate the technological 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 …
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Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics
… increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and …
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A spatial deep network architecture for brain decoding
… to incorporate structured smoothness in a deep learning model. FGL achieves this goal by connecting nodes across layers based on spatial similarity. The inductive bias of structured smoothness implemented by FGL is motivated by applications such as brain image decoding, i.e., predicting …
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Predicting genomic interactions using deep learning
… is still largely not understood. We develop a deep learning model, Deep3DGenome, to predict genomic interactions using both genomic sequence data and chromatin features. We find that a machine learning model that has anchor specific modules and uses rich chromatin features outperforms previous …
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