{'fraction_non_alphanumeric': 0.018975332068311195, 'fraction_numerical': 0.0, 'mean_word_length': 5.947368421052632, '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}}
Neural techniques for image segmentation

We present new neural techniques including unsupervised technology and fuzzy logic foundations. We realized a hybrid neural network and applied three different unsupervised learning algorithms that we developed specially for it: fuzzy MLSOM, fuzzy hierarchical "neural gas" and fuzzy hierarchical "maximum entropy". The experiments presented deal with image segmentation. The results obtained show that neural networks are a valid instrument for image processing and shape recognition.