Titlebook: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery; Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2024 The

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Relative Confusion Matrix: An Efficient Visualization for the Comparison of Classification Modelsiminated classes and problematic classes of a single classifier, the very few works leverage the matrix structure of this visualization to compare several models at a class scale. In this paper, we present the Relative Confusion Matrix (RCM), a matrix-based visualization leveraging a color encoding

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Road Traffic Flow Prediction with Visual Analyticsedictive model. The model uses XGBoost to create a short-term time delay estimation for a region-of-interest. We find that our approach is able to achieve high Mean Squared Error (R.) results and low Mean Absolute Error (MAE). Additionally, we perform a user study to assess the quality of traffic fl

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Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Scienceons, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the

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Designing and Evaluating Context-Sensitive Visualization Models for Deep Learning Text Classifierso gauge the usefulness of the extracted insights for explaining the models. Additionally, visualizing discrepancies in the knowledge extracted by different models becomes crucial for effective ranking purposes. This is an area of research with very few available results. In this work, we investigate

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