Explainable AI for Enhancing Efficiency of DL-Based Channel Estimation
The support of artificial intelligence (AI) based decision-making is a key element in future 6G networks. Moreover, AI is widely employed in critical applications such as autonomous driving and medical diagnosis. In such applications, using AI as black-box models is risky and challenging. Hence, it...
Saved in:
| Main Authors: | Abdul Karim Gizzini, Yahia Medjahdi, Ali J. Ghandour, Laurent Clavier |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11115091/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX)
by: Jong-Hwan Jang, et al.
Published: (2025-07-01) -
Strategies for applying interpretable and explainable AI in real world IoT applications
by: Anber Abraheem Shlash Mohammad, et al.
Published: (2025-06-01) -
The effectiveness of explainable AI on human factors in trust models
by: Justin C. Cheung, et al.
Published: (2025-07-01) -
Explainability in machine learning: a pedagogical perspective
by: Andreas Bueff, et al.
Published: (2025-07-01) -
Meta-Ensemble Learning for Heart Disease Prediction: A Stacking-Based Approach With Explainable AI
by: Mehwish Naz, et al.
Published: (2025-01-01)