Titlebook: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery; Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2022

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Nuno Datia,M. P. M. Pato,Ruben Taborda,Jo?o Moura Pires

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Book 2022ty in this domain.?.This book is a collection of 25 extended works of over 70 scholarspresented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level.? The sections of this book cover i

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“Negative” Results—When the Measured Quantity Is Outside the Sensor’s Range—Can Help Data Processing of the measuring instrument. Usually, such cases are ignored. In this paper, we show that taking these cases into account can help data processing—by improving the accuracy of our estimates of . and thus, by improving the accuracy of the resulting predictions of ..

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VisIRML: Visualization with an Interactive Information Retrieval and Machine Learning Classifierng. The resulting classifier produces high quality labels better than comparable semi-supervised learning techniques. While multiple visualization approaches were considered to depict these articles, users exhibited a strong preference for a map-based representation.

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1860-949X on computational intelligence, machine learning, visual ana.This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence

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Augmented Classical Self-organizing Map for Visualization of Discrete Data with Density Scalingzation depicting a SOM by allowing for the proportion of each output node’s instances of a discrete variable to be visualized, allowing distribution to be ascertained. This chapter extends that research by addressing visual noise that can arise out of dense hSOM visualizations and by adding an additional case study to evaluate hSOM’s performance.

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