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A novel few shot learning derived architecture for long-term HbA1c prediction
Published 2024“…The derived images are then fed into a convolutional neural network (CNN) adapted from a few-shot learning (FSL) model for feature extraction, and all the derived features are fused together. …”
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A multi-pretraining U-Net architecture for semantic segmentation
Published 2025“…In this research, we propose and evaluate a modified version of a deep learning algorithm called U-Net architecture for partitioning histopathological images. …”
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms
Published 2025“…Given the complexity of the LDTP solution for managing online requests, we propose a real-time, lightweight solution using multi-agent meta-reinforcement learning. Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. …”
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An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study
Published 2024“…We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities.…”
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Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE
Published 2023“…An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. …”
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66
Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE
Published 2023“…An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. …”
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Oversampling techniques for imbalanced data in regression
Published 2024“…For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. …”
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Correlation Clustering with Overlaps
Published 2020“…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection
Published 2023“…Evaluated on the PLVP3 dataset of 10,000 images, the DNN designed by MSD-NAS achieves state-of-the-art accuracy (0.9781) and mIoU (0.9542), while being 20.16 times faster and 2.56 times smaller than the current best deep learning model.…”
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Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV
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doctoralThesis -
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review
Published 2024“…It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. …”
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Single-channel speech denoising by masking the colored spectrograms
Published 2025“…<p>Speech denoising (SD) covers the algorithms that remove the background noise from the target speech and thus improve its quality and intelligibility. …”
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Artificial Intelligence for Skin Cancer Detection: Scoping Review
Published 2021“…Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks.…”