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 148 for “"Deep Learning (DL)"”.
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Comparative Analysis of Machine Learning Models for ERCOT Short Term Load Forecasting
… investigates the efficacy of various machine learning (ML) and deep learning (DL) models for short-term load forecasting (STLF) in the Electric Reliability Council of Texas (ERCOT) grid. A dual comparative approach is employed, evaluating models based on temporal features alone as well as in …
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The Importance of Data in RF Machine Learning
While the toolset known as Machine Learning (ML) is not new, several of the tools available within the toolset have seen revitalization with improved hardware, and have been applied across several domains in the last two decades. Deep Neural Network (DNN) applications have contributed to …
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Computational imaging through deep learning
… objects being imaged. In recent years, machine learning architectures, and deep learning (DL) in particular, have attracted increasing attentions from CI researchers. Unlike traditional inverse algorithms in CI, DL approach learns both the forward operator and the objects' prior implicitly from …
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Computational design of functional cyclic peptides using deep learning
… have led to only a handful of successful cases. Deep learning (DL) networks have recently shown considerable opportunities for accurate structure prediction and design of biomolecules that are potent inhibitors of therapeutically relevant protein interfaces. This work describes the application of …
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Investigations into the role of entropy-selected RF-DNA fingerprint features on ID-verification performance in the presence of rogue emitters
… fingerprinting, a specialized form of SEI, with Deep Learning (DL) techniques to authenticate the identity of authorized emitters. This authentication becomes crucial in the presence of “rogue” emitters who deliberately impersonate authorized emitters using falsified digital credentials. The …
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Deep learning methods applied to anomaly detection in vehicle manufacturing and operations
… vehicles is safety. With the adoption of deep learning (DL) methods, DL-based defect detection and fault detection technology has evolved into a powerful tool with increased accuracy and autonomy compared with traditional detection technology. This thesis presents novel deep learning …
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DEEP LEARNING ALGORITHMS FOR OPTICAL COHERENCE TOMOGRAPHY IMAGES WITH APPLICATIONS IN GLAUCOMA
… glaucoma diagnosis by leveraging on the power of deep learning (DL) to fully exploit the 3D morphological information present in OCT images.
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Addressing the challenges facing deep learning based Specific Emitter Identification via preamble based waveforms
… in-depth experiments that analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of …
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Towards SLO-aware Resource Scheduling for Serverless Inference Workloads
The rapid advancement of Machine Learning (ML) and Deep Learning (DL) has revolutionized various domains, necessitating efficient and cost-effective ML inference capabilities. Function-as-a-Service (FaaS) has emerged as a promising approach for hosting ML inference services, providing a serverless …
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Knowledge-Informed Weakly-Supervised Deep Learning Models for Cancer Applications
In recent decades, deep learning (DL) has emerged as a powerful tool for analyzing complex patterns in large-scale healthcare data, significantly advancing diagnosis, prognosis, and treatment planning. However, the collection of medical data faces inherent limitations, including invasiveness, high …
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A 3D Deep Learning Architecture for Denoising Low-Dose CT Scans
This paper introduces 3D-DDnet, a cutting-edge 3D deep learning (DL) framework designed to improve the image quality of low-dose computed tomography (LDCT) scans. Although LDCT scans are advantageous for reducing radiation exposure, they inherently suffer from reduced image quality. Our novel 3D DL …
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Entropy aided RF-DNA fingerprint learning from Gabor-based images
… (TF) representation and accelerated using the Deep learning (DL) Convolutional Neural Network (CNN). While the classification accuracy has been improved from using raw signals learning the amount of data generated is large and computationally expensive. This work investigate the usage of …
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Machine Learning Application in Energy Storage System’s State Estimation: State of Health (SOH)
… and temperature towards developing different deep learning (DL) models to estimate the cell’s SOH cycled under a variety of extreme fast charging protocols. The results obtained from the different DL models have been compared with those obtained from the conventional feed forward neural …
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Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models
… focuses on evaluating the usefulness of machine learning (ML) and deep learning (DL) models in classifying brain tumor and non-tumor cases using a dataset sourced from Kaggle. After preprocessing, the dataset was analyzed using Support Vector Machines (SVM), VGG-19, and YOLOv10 models. Metrics …
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POCS Augmented CycleGAN for MR Image Reconstruction
… nonlinear optimization problems. By contrast, deep learning (DL)-based reconstruction methods do not need any explicit analytical data model and are robust to noise due to its large data-based training, which both make DL a versatile tool for fast and high-fidelity MR image reconstruction. …
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DEEP LEARNING ALGORITHM FOR MOLECULAR CLASSIFICATION OF ENDOMETRIAL CANCER FROM WHOLE SLIDE HISTOPATHOLOGY IMAGES
… In this study, we developed a fully supervised deep learning (DL) model to classify EC molecular subtypes directly from H&E-stained whole-slide images (WSIs). From an initial cohort of 1,362 cases, 230 FFPE WSIs were selected and annotated to train three sequential binary classifiers (POLEmut vs …
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Continuous Cardiorespiratory Monitoring Using Ballistocardiography From Load Cells Embedded in a Hospital Bed
… is to explore signal processing and machine learning techniques to allow continuous monitoring of cardiorespiratory parameters using the ballistocardiogram (BCG) signals recorded with sensors embedded in a hospital bed. First, the heart rate (HR) estimation algorithms were presented. The …
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Optimising credit card fraud detection through machine learning and deep learning with spatial-temporal imbalance handling
… methodology using sophisticated machine learning (ML) and deep learning (DL) approaches in conjunction with data balancing techniques, including random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and random under sampling. …
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Installectualism: Public Intellectuals in a Digital World
… such as natural language processing (NLP), deep learning (DL), and network analysis. First, intellectuals as social media influencers and digital entrepreneurs. Secondly, how new mediums allowed for positioning and discourse that is more engaged, rapid, and viral, albeit often superficial …
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Advancing Precision Agriculture Through AI and Statistical Modeling: Transforming Crop and Livestock Management
… of Artificial Intelligence (AI), machine learning (ML), and statistical analysis to enhance agricultural practices, focusing on both livestock man- agement and plant biology. The first part investigates automated weight prediction of beef cattle using computer vision techniques, including …
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