{'fraction_non_alphanumeric': 0.029850746268656716, 'fraction_numerical': 0.016417910447761194, 'mean_word_length': 5.451923076923077, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Using Transfer Learning and BPDFHE to Improve Ocular Image Recognition Accuracy

We used image enhancement algorithms along with transfer learning to fine-tune a deep convolutional neural network to perform ocular image recognition. To enhance the input images, we used a novel color image histogram equalization technique called Brightness Preserving Dynamic Fuzzy Histogram Equalization, which showed significant accuracy improvements: on the test data, using AlexNet, the ROC Area Under the Curve (AUC) increased to over 0.99, Equal Error Rate (EER) decreased 4-fold and dropped below 4%, and decidability (a measure of class separability) increased from 1.89 to 4.17