Results 71 to 80 of about 78,288 (166)

Explainable clustering: Methods, challenges, and future opportunities

Journal of Intelligent Systems
In recent years, artificial intelligence (AI) has increasingly relied on subsymbolic techniques like machine learning (ML). Despite their widespread use, these techniques often lack transparency, leading to potential distrust.
Dewoprabowo Ridhwan, Stefanus Lim Yohanes, Saptawijaya Ari   +2 more
doaj   +1 more source

Explainable Artificial Intelligence

, 2022
Every decade technology makes revolutionary shifts that become the new platforms on which application technology is built. Artificial intelligence AI is no different. AI has moved from 1st Generation shallow learning and handcrafted features to 2nd Generation deep learning, which has been effective at learning patterns.
Averchin, A., Averkin, A.
openaire   +1 more source

Audio Explainable Artificial Intelligence: A Review

Intelligent Computing
Artificial intelligence (AI) capabilities have grown rapidly with the introduction of cutting-edge deep-model architectures and learning strategies. Explainable AI (XAI) methods aim to make the capabilities of AI models beyond accuracy interpretable by ...
Alican Akman, Björn W. Schuller
doaj   +1 more source

Clustered Partial Explanation: A Multilevel Framework for Contextual Model Interpretation in Clinical Decision Support

Applied Computational Intelligence and Soft Computing
The increasing integration of artificial intelligence into clinical decision-making underscores the need for transparent and trustworthy predictive models.
Khadija Letrache, Mohammed Ramdani
doaj   +1 more source

Using slisemap to interpret physical data.

PLoS ONE
Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry.
Lauri Seppäläinen, Anton Björklund, Vitus Besel, Kai Puolamäki   +3 more
doaj   +1 more source

Application of Artificial Intelligence in Support of NAFLD Diagnosis

Applied Sciences
A comprehensive system for automated medical data analysis and diagnosis of non-alcoholic fatty liver disease using artificial intelligence has been developed.
Jakub Płudowski, Jan Mulawka
doaj   +1 more source

Explainability and artificial intelligence in medicine

The Lancet Digital Health, 2022
openaire   +4 more sources

Exploring explainable AI: a bibliometric analysis

Discover Applied Sciences
Over the past few years, explainable artificial intelligence (XAI) has become increasingly popular as a result of the demand for AI systems that are simpler to comprehend and with greater interpretability. This study provides a conceptual framework and a
Chetan Sharma, Shamneesh Sharma, Komal Sharma, Ganesh Kumar Sethi, Hsin-Yuan Chen   +4 more
doaj   +1 more source

Study on the Helpfulness of Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) is essential for building advanced machine learning-powered applications, especially in critical domains such as medical diagnostics or autonomous driving. Legal, business, and ethical requirements motivate using effective XAI, but the increasing number of different methods makes it challenging to pick the ...
Tobias Labarta, Elizaveta Kulicheva, Ronja Froelian, Christian Geißler, Xenia Melman, Julian von Klitzing   +5 more
openaire   +2 more sources

Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions. [PDF]

Health Sci Rep
Shiddik MAB.
europepmc   +1 more source