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

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发表于 2025-3-21 19:08:07 | 显示全部楼层 |阅读模式
书目名称Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery
编辑Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi
视频video
概述Presents the current state of the art on combining artificial intelligence and machine learning with visual analytics.Presents research work on computational intelligence, machine learning, visual ana
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery;  Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2022
描述.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 (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity 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
出版日期Book 2022
关键词Computational Intelligence; Artificial Intelligence; Machine Learning; Visual Analytics; Knowledge Disco
版次1
doihttps://doi.org/10.1007/978-3-030-93119-3
isbn_softcover978-3-030-93121-6
isbn_ebook978-3-030-93119-3Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Visual Analytics for Strategic Decision Making in Technology Managementethods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based o
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Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learningility to the end user to perform data classification as self-service without a machine learning expert. Interactive pattern discovery is challenging for data with hundreds of dimensions/features. To overcome this problem, this chapter proposes an automated classification approach combined with new C
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Gragnostics: Evaluating Fast, Interpretable Structural Graph Features for Classification and Visual ts several new analyses: A deeper analysis showing relationships between features, and showing how individual features separate some graph classes; a new comparison of gragnostics and DDQC to graph kernels, showing that gragnostics has substantially faster runtime than graph kernels and better accur
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ML Approach to Predict Air Quality Using Sensor and Road Traffic Data, and it is combined with other environmental contextual data, namely road traffic mobility data. Estimated air quality data is obtained using a machine learning regression model, that is integrated into the interactive dashboard. The visual analytics solution was designed with the city council deci
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Kawa Nazemi,Tim Feiter,Lennart B. Sina,Dirk Burkhardt,Alexander Kockurfinanzierung macht etwa einen Anteil von 6-7 % der gesamten Kulturfinanzierung in Deutschland aus: Sponsoring (350 Mio. €), kapitalbasierte Stiftungen mit kulturellem Zweck (Erträge p. a. ca. 125 Mio. €), mäzenatische Spenden (50 Mio. €). Die Rahmenbedingungen für die private Kulturfinanzierung ve
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