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1. Deep learning-based classification of anti-personnel mines and sub-gram metal content in mineralized soil (DL-MMD).

2. Transparent RFID tag wall enabled by artificial intelligence for assisted living.

3. The use of artificial neural networks in studying the progression of glaucoma.

4. Colon cancer diagnosis by means of explainable deep learning.

5. SIGNIFICANCE deep learning based platform to fight illicit trafficking of Cultural Heritage goods.

6. Fairness-aware recommendation with meta learning.

7. Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm.

8. Conversational AI and equity through assessing GPT-3's communication with diverse social groups on contentious topics.

9. From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.

10. A fast and accurate identification model for Rhinolophus bats based on fine-grained information.

11. A fast and accurate identification model for Rhinolophus bats based on fine-grained information.

12. Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition.

13. Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images.

14. Federated learning with hyper-network—a case study on whole slide image analysis.

15. New explainability method for BERT-based model in fake news detection.

16. Survey on security issues of routing and anomaly detection for space information networks.

17. Evaluation of deep learning models in contactless human motion detection system for next generation healthcare.

18. A deep learning approach for medical waste classification.

19. Training calibration-based counterfactual explainers for deep learning models in medical image analysis.

20. Learning future terrorist targets through temporal meta-graphs.

21. Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.

22. Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.