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Interpretable Artificial Intelligence: A Perspective of Granular Computing

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Cover of 'Interpretable Artificial Intelligence: A Perspective of Granular Computing'

Table of Contents

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    Book Overview
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    Chapter 1 Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
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    Chapter 2 Use of Visual Analytics (VA) in Explainable Artificial Intelligence (XAI): A Framework of Information Granules
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    Chapter 3 Visualizing the Behavior of Convolutional Neural Networks for Time Series Forecasting
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    Chapter 4 Beyond Deep Event Prediction: Deep Event Understanding Based on Explainable Artificial Intelligence
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    Chapter 6 Factual and Counterfactual Explanation of Fuzzy Information Granules
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    Chapter 7 Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model
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    Chapter 8 Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations
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    Chapter 10 Designing Explainable Text Classification Pipelines: Insights from IT Ticket Complexity Prediction Case Study
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    Chapter 11 A Granular Computing Approach to Provide Transparency of Intelligent Systems for Criminal Investigations
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    Chapter 12 RYEL System: A Novel Method for Capturing and Represent Knowledge in a Legal Domain Using Explainable Artificial Intelligence (XAI) and Granular Computing (GrC)
Attention for Chapter 3: Visualizing the Behavior of Convolutional Neural Networks for Time Series Forecasting
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Chapter title
Visualizing the Behavior of Convolutional Neural Networks for Time Series Forecasting
Chapter number 3
Book title
Interpretable Artificial Intelligence: A Perspective of Granular Computing
Published by
Springer, Cham, January 2021
DOI 10.1007/978-3-030-64949-4_3
Book ISBNs
978-3-03-064948-7, 978-3-03-064949-4
Authors

Henze, Janosch, Sick, Bernhard

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