Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects

Nature·Dec 27

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Researchers are exploring the use of explainable deep learning models, including convolutional neural networks (CNN), for various applications such as pest and beneficial insect identification, disease detection, and prediction. These models aim to provide transparent and interpretable results, with some studies focusing on specific areas like honeybee apiaries or medical conditions. The range of applications suggests a broader interest in developing explainable AI frameworks for different fields.

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Explainable VGG16 transfer learning with SHAP and grad-CAM for wax moth pest and infestation detection in honeybee apiaries using imaging data

Nature·Mar 17

An explainable deep learning framework for video violence detection using unsupervised keyframe selection and attention-based CNN | Scientific Reports

Nature·Feb 26

Explainable AI based ensemble model for the identification of Schizophrenia prodromal phase

Nature·Apr 18

A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network

Nature·Mar 17

The role of explainability throughout the MLOps lifecycle: review and research agenda

Frontiers·Apr 3

Alzheimer’s disease prediction using deep learning and XAI based interpretable feature selection from blood gene expression data

Nature·Feb 10

TrustNet: a lightweight network with integrated uncertainty quantification and quantitative explainable AI for ischemic stroke detection in CT images

Nature·Feb 19

Deep learning-based battery health prediction for enhancing electric vehicle performance | Scientific Reports