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An explainable deep learning framework for skin lesion classification (EDLF-SLC) is developed and makes use of various approaches in machine learning and explainable artificial intelligence to ensure improvements in terms of both accuracy and interpretability in a three-stage manner.
A temporal transformer-based system for ASD classification using eye-tracking sequences that can represent a scalable and developmentally appropriate approach to gaze-based ASD classification and support the implementation of trusted neurodevelopmental screening systems is presented.
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