{'fraction_non_alphanumeric': 0.023519163763066203, 'fraction_numerical': 0.006968641114982578, 'mean_word_length': 5.313186813186813, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SEPARATION OF DOMESTIC WASTE WITH DEEP LEARNING TECHNIQUES

Thanks to the rapid development of deep learning technology, smart systems used in almost every part of our daily life are being developed. Developed applications not only made our lives easier, but also contributed positively to nature. Traditional waste separation methods fall short in terms of efficiency and accuracy. In addition to its high cost, it can also cause problems in terms of environmental risks. In recent years, artificial intelligence, machine learning and the deep learning techniques it brings have become a popular method for solving complex problems such as organic, household and packaging waste sorting. In this study, the problem of separation of domestic wastes, which is of great importance in terms of both human and living life and the protection of nature, is discussed. In the artificial intelligence cluster; Classification performances were compared by using popular conventional neural network (CNN) based ResNet-50, DenseNet-121, Inception-V3, VGG16 architectures to detect and sort household waste with deep learning, a sub-branch of machine learning.