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Enhancing IoT botnet detection with explainable ensemble learning. [PDF]
Front Big DataJoseph L, M S, M V.
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XAI in Biomedical Applications
2023When the correct diagnosis or selection of therapy is made by algorithms by a machine, decisions for doctors, patients, and experts can become non-transparent, leading to the breakdown of relationships. Asking the machine to explain its algorithms is essentially a detailed understanding of the mathematical and statistical details.
Kirboğa, K.K., Küçüksille, E.U.
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Actionable XAI for the Fuzzy Integral
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021The adoption of artificial intelligence (AI) into domains that impact human life (healthcare, agriculture, security and defense, etc.) has led to an increased demand for explainable AI (XAI). Herein, we focus on an under represented piece of the XAI puzzle, information fusion.
Bryce Murray +2 more
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XAI for Communication Networks
2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 2022Sayandev Mukherjee +2 more
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Unsupervised learning algorithms detect inherent patterns and relationships in data without requiring predefined target variables. Although unsupervised learning algorithms have great capabilities, their decisions remain largely opaque, driving the need for explainability.
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Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Knowledge-Based Systems, 2023Waddah Saeed, Christian Omlin
exaly