The field of explainable artificial intelligence (XAI) is gaining increasing importance in recent
years. As a consequence, several surveys have been published to explore the current state …

Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …

The rise of artificial intelligence (AI) is accompanied by a growing need for methods to
explain the decisions of AI models. In the last decade, new explanation methods have been …

Fault diagnosis is crucial to guarantee safe operation and extend the operating time while
preventing the thermal runaway of the lithium-ion battery. This study presents a data-driven …

The field of neurosymbolic AI aims to benefit from the combination of neural networks and
symbolic systems. A cornerstone of the field is the translation or encoding of symbolic …

The field of neuro-symbolic AI aims to benefit from the combination of neural networks and
symbolic systems. A cornerstone of the field is the translation or encoding of symbolic …

The paper proposed the method for neural network cyberbullying detection in text content
with visual analytic, designed to explain the neural network's decisions regarding identified …

As artificial intelligence systems become integral across domains, the demand for
explainability grows, the called eXplainable artificial intelligence (XAI). Existing efforts …

Disasters present enormous obstacles for humanity and demand prompt and precise action.
In this paper, we explore a critical intersection of disaster image classification and …

As artificial intelligence (AI) continues to become more integrated into healthcare systems,
establishing trust is crucial for its successful implementation and widespread use. This paper …