{'fraction_non_alphanumeric': 0.018947368421052633, 'fraction_numerical': 0.0, 'mean_word_length': 4.8765432098765435, '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}}
Fire monitoring system based on SVM

The occurrence of fire has a huge threat to people's life, therefore, in order to improve the quality of people's lives, we combines the support vector machine which is hot in recent years with the problem of home fire forecasting. Then a new method based on ensemble empirical mode decomposition and support vector machine is proposed. The results show that EEMD-SVM combination forecasting method has high accuracy for fire forecasting.