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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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Guardado en:
Formato: Online
Lenguaje:inglés
Publicado: MDPI - Multidisciplinary Digital Publishing Institute 2024
Materias:
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
Acceso en línea:ONIX_20240514_9783725802838_215
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