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Assessing the Features on Blood Glucose Level Prediction in Type 1 Diabetes Patients Through Explainable Artificial Intelligence., , , , , и . MetroXRAINE, стр. 278-283. IEEE, (2023)Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems., , , и . Knowl. Based Syst., (2022)Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis., , , , , , и . Sensors, 24 (18): 6110 (сентября 2024)Employment of Domain Adaptation Techniques in SSVEP-Based Brain-Computer Interfaces., , , , , , и . IEEE Access, (2023)A General Approach to Compute the Relevance of Middle-Level Input Features., , , и . ICPR Workshops (3), том 12663 из Lecture Notes in Computer Science, стр. 189-203. Springer, (2020)SHAP-based Explanations to Improve Classification Systems., , , и . XAI.it@AI*IA, том 3518 из CEUR Workshop Proceedings, стр. 76-86. CEUR-WS.org, (2023)Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces., , , , , , , и . MeMeA, стр. 1-5. IEEE, (2022)Towards a general framework for improving the performance of classifiers using XAI methods., , , и . CoRR, (2024)An XAI-Based Masking Approach to Improve Classification Systems., , , , и . BEWARE@AI*IA, том 3615 из CEUR Workshop Proceedings, стр. 79-83. CEUR-WS.org, (2023)A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces., , , , , , , и . I2MTC, стр. 1-5. IEEE, (2022)