Results 51 to 60 of about 27,667 (170)
Comparative LegilinguisticsArtificial intelligence has profound implications for the filed of clinical practices, and also for semiotics and law. In this article, we articulate and explain the different types of Clinical artificial intelligence (CAIs) as their normativity often ...
CAROLE SENECHAL, Nicholas Léger-Riopel
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Transparent Task Delegation in Multi-Agent Systems Using the QuAD-V Framework
Applied SciencesTask delegation in multi-agent systems (MASs) is crucial for ensuring efficient collaboration among agents with different capabilities and skills. Traditional delegation models rely on social mechanisms such as trust and reputation to evaluate potential ...
Jeferson José Baqueta +2 more
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IEEE AccessMedical chest X-ray (CXR) classification necessitates balancing detailed local feature extraction with capturing broader, long-range dependencies, especially when working with limited and heterogeneous datasets.
Omid Almasi Naghash, Nam Ling, Xiang Li
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Communications of the ACMWhen the decisions of ML models impact people, one should expect explanations to offer the strongest guarantees of rigor. However, the most popular XAI approaches offer none.
João Marques-Silva 0001 +1 more
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Proceedings of the National Academy of Sciences, 2003 What do social survey data tell us about the determinants of happiness? First, that the psychologists' setpoint model is questionable. Life events in the nonpecuniary domain, such as marriage, divorce, and serious disability, have a lasting effect on happiness, and do not simply deflect the average person temporarily above or below a setpoint given by ...
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Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
AlgorithmsThe Patient Rule Induction Method is a data mining technique used for identifying patterns in datasets, particularly focusing on discovering regions of the chosen input space where the response variable is unusually high or low.
Rym Nassih, Abdelaziz Berrado
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Frontiers in OncologyIntroductionThe early identification of brain tumors is essential for optimal treatment and patient prognosis. Advancements in MRI technology have markedly enhanced tumor detection yet necessitate accurate classification for appropriate therapeutic ...
Novsheena Rasool +8 more
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How Explainable Is Explainability? Towards Better Metrics for Explainable AI
Despite the fact that machine learning has been applied in innumerable domains, its models have usually operated in a black box fashion, i.e. without revealing the rationale behind their decisions. For human users, insufficient model transparency may result in the lack of trust in the technology, effectively hindering its development and adoption.This ...
Pawlicka, Aleksandra +4 more
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An Explainable Scheme for Memorization of Noisy Instances by Downstream Evaluation
Applied SciencesDeep learning models are often perceived as black boxes, making it challenging to analyze the causal relationships between inputs and outputs. For this reason, the explainability of model learning has garnered increasing attention in recent years.
Chun-Yi Tsai +2 more
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LCNN: Lightweight CNN Architecture for Software Defect Feature Identification Using Explainable AI
IEEE AccessSoftware defect identification (SDI) is a key part of improving the quality of software projects and lowering the risks that along with maintenance. It does identify the software defect causes that have not been reached yet to get sufficient results.
Momotaz Begum +7 more
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