As the "black-box" problem creates challenges for AI, explainable AI can help reshape business-to-business operations by ...

The integration of XAI methods not only improves transparency but also fosters trust among farmers and agricultural experts.

At its core, Explainable AI encompasses a variety of techniques and methods aimed at helping humans understand and trust the ...

Facial expression recognition (FER) has been widely used in healthcare, assistive technologies, and emotion-aware AI systems, ...

To enhance model interpretability and reliability, we integrate a widely accepted XAI method, Local Interpretable Model-agnostic Explanations (LIME). Our proposed framework achieves a peak accuracy of ...

Explainable Artificial Intelligence (XAI) tools, including Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), provide insights into the model’s ...

LIME provides feature importance scores for individual predictions. These scores indicate how much each feature contributes to the prediction for a specific instance. In healthcare, XAI techniques can ...

The study then employed three popular model-agnostic interpretability techniques, namely LIME, Integrated Gradients (IG), and SHAP, to understand the decision-making process of each DL model. The ...

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