Advances in Explainable Artificial Intelligence

Machine Learning (ML)-based Artificial Intelligence (AI) algorithms have the capability to learn from known examples, creating various abstract representations and models. When applied to unfamiliar examples, these algorithms can perform a range of tasks, including classification, regression, and fo...

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Bibliographische Detailangaben
Format:	Online
Sprache:	Englisch
Veröffentlicht:	MDPI - Multidisciplinary Digital Publishing Institute 2024
Schlagworte:	activation function ReLU family activation function test psychological profiling predictive modeling behavioral data explainable artificial intelligence rule extraction counterfactual explanations fairness bias artificial intelligence machine learning psychiatry health mental health federated learning 6G vehicle-to-everything (V2X) quality of service quality of experience interactive machine learning decision tree classifiers transparent-by-design parallel coordinates natural language generation fact-checking explainable AI deep learning LSTM Arabic sentiment analysis Explainable AI text mining random forest multi-layer perceptron protein data bank neural network artificial neural networks knowledge representation source code analysis text classification uncertainty quantification efficiency electroencephalography convolutional variational autoencoder latent space interpretation spectral topographic maps thema EDItEUR::U Computing and Information Technology::UY Computer science
Online-Zugang:	ONIX_20240514_9783725802838_215
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Advances in Explainable Artificial Intelligence

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