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Classified human and machine generated text using 1) a single score threshold classifier and 2) a neural network classifier approach, based on perplexities and probability scores generated from n-grams. Best results are 77% for the single score classifier and 80% for the ANN classifier.
This study tries to compare the detection of lung diseases using xray scans from three different datasets using three different neural network architectures using Pytorch and perform an ablation study by changing learning rates. The dimensional understanding is visualised using t-SNE and Grad-CAM for visualisation of diseases in x-ray scans.
Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis