Essays about: "GCN"
Showing result 1 - 5 of 22 essays containing the word GCN.
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1. Generative adversarial network for point cloud upsampling
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : Point clouds are a widely used system for the collection and application of 3D data. But most timesthe data gathered is too scarce to reliably be used in any application. READ MORE
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2. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. READ MORE
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3. Estimation of Voltage Drop in Power Circuits using Machine Learning Algorithms : Investigating potential applications of machine learning methods in power circuits design
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. READ MORE
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4. Highway Traffic Forecasting with the Diffusion Model : An Image-Generation Based Approach
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Forecasting of highway traffic is a common practice for real traffic information system, and is of vital importance to traffic management and control on highways. As a typical time-series forecasting task, we want to propose a deep learning model to map the historical sensory traffic values (e.g., speed, flow) to future traffic forecasts. READ MORE
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5. Graph Neural Network for Traffic Flow Forecasting : Does an enriched adjacency matrix with low dimensional dataenhance the performance of GNN for traffic flow forecasting?
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : Nowadays, machine learning methods are used in many applications and deployed in manyelectronic devices to solve problems and predict future states. One of the challenges mostbig cities confront is traffic jams since the roads are crammed with more and more vehicles, which will easily cause traffic congestion. READ MORE