Essays about: "POST CLASSIFICATION COMPARISON"
Showing result 1 - 5 of 16 essays containing the words POST CLASSIFICATION COMPARISON.
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1. Uncertainty Quantification in Deep Learning for Breast Cancer Classification in Point-of-Care Ultrasound Imaging
University essay from Lunds universitet/Matematik LTHAbstract : Breast cancer is the most common type of cancer worldwide with an estimate of 2.3 million new cases in 2020, and the number one cause of cancer-related deaths in women. READ MORE
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2. Deep Learning for Building Damage Assessment of the 2023 Turkey Earthquakes : A comparison of two remote sensing methods
University essay from KTH/GeoinformatikAbstract : Current disaster response strategies are based on damage assessments carried out on the ground, which can be dangerous following a ä destructive event. Damage assessments can also be performed remotely using satellite imagery, but are usually carried out through visual interpretation, which can take a lot of time. READ MORE
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3. A Comparison of Statistical Methods to Generate Short-Term Probabilistic Forecasts for Wind Power Production Purposes in Iceland
University essay from Uppsala universitet/Luft-, vatten- och landskapsläraAbstract : Accurate forecasts of wind speed and power production are of great value for wind power producers. In Southwest Iceland, wind power installations are being planned by various entities. READ MORE
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4. Using NDVI Time-Series to Examine Post-fire Vegetation Recovery in California
University essay from Uppsala universitet/Institutionen för geovetenskaperAbstract : Over the past couple of decades, fires have experienced changes on a global scale. These changing fire regimes point to an alarming direction where fire-dependent ecosystems are experiencing a decline in burned area, while fire-independent ecosystems are experiencing an increase. READ MORE
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5. Approximating the element matrices in an unfitted finite element method using neural networks
University essay from Umeå universitet/Institutionen för fysikAbstract : This study investigates the possibility of combining an unfitted finite element method, CutFEM, with neural networks, in an attempt to reduce the computational time on evolving domains. Finite element methods are used to solve partial differential equations by fitting a spatial and temporal discretisation to the momentous domain. READ MORE