Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

Maize is an important crop cultivated in Sub-Saharan Africa, essential for food security.
However, its cultivation faces significant challenges due to debilitating diseases such as …

Beyond high accuracy, good interpretability is very critical to deploy a face forgery detection
model for visual content analysis. In this paper, we propose learning patch-channel …

In the field of chest X-ray (CXR) diagnosis, existing works often focus solely on determining
where a radiologist looks, typically through tasks such as detection, segmentation, or …

Artificial intelligence and its derivative technologies are not only playing a role in the fields of
medicine, economy, policing, transportation, and natural science computing today but also …

In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional
neural network (DCNN) image classifiers, called L-CAM-Fm and L-CAM-Img, are proposed …

Timely and accurate building damage map** is essential for supporting disaster response
activities. While RS satellite imagery can provide the basis for building damage map …

The higher requirements for deep neural networks are driving researchers to have a deeper
understanding of the internals of neural networks. The class activation map (CAM) based …

Artificial Intelligence (AI) integration in healthcare is revolutionizing medical analysis and
disease prediction, enhancing diagnostic accuracy and patient care. However, with the …