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Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features
Our approach allows an analyst to detect important features of high-dimensional data and derive adjustments to the AI model based on our global explanation visualization.
Extension of Dictionary-Based Compression Algorithms for the Quantitative Visualization of Patterns from Log Files
This work presents an approach to detect frequent patterns in textual data that can be simultaneously registered during the file compression process with low consumption of resources.
Visualization Of Class Activation Maps To Explain AI Classification Of Network Packet Captures
The classification of internet traffic has become increasingly important due to the rapid growth of today's networks and applications.
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