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Showing result 21 - 25 of 899 essays matching the above criteria.
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21. Network Orientation and Segmentation Refinement Using Machine Learning
University essay from Linköpings universitet/Institutionen för medicinsk teknikAbstract : Network mapping is used to extract the coordinates of a network's components in an image. Furthermore, machine learning algorithms have demonstrated their efficacy in advancing the field of network mapping across various domains, including mapping of road networks and blood vessel networks. READ MORE
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22. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. READ MORE
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23. Question-answering chatbot for Northvolt IT Support
University essay from Uppsala universitet/Signaler och systemAbstract : Northvolt is a Swedish battery manufacturing company that specializes in the production of sustainable lithium-ion batteries for electric vehicles and energy storage systems. Established in 2016, the company has experienced significant growth in recent years. READ MORE
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24. Sales forecasting for supply chain using Artificial Intelligence
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Supply chain management and logistics are two sectors currently experiencing a transformation thanks to the advent of AI(Artificial Intelligence) technologies. Leveraging predictive analytics powered by AI presents businesses with novel opportunities to streamline their operations effectively. READ MORE
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25. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. READ MORE