{'fraction_non_alphanumeric': 0.025522041763341066, 'fraction_numerical': 0.002320185614849188, 'mean_word_length': 4.910958904109589, '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}}
Data Monitoring and Anomaly Analysis for Information Systems based on Balanced Scorecard and Fuzzy Neural Network

Data monitoring and anomaly analysis for the HR information systems based on the balanced scorecard and fuzzy neural network is conducted in this paper. From the technique level, the novelty are as follows. (1) The labeling data used for training to confirm abnormal models is very difficult to obtain, and manual labeling is also time-consuming and laborious. Therefore, the fuzzy neural network is combined to recognize the data features. (2) The proportion of big data is large, with fast speed, many styles, and low value density. Big data is also different from ordinary goods. The clustering model is combined to enhance the robustness of the proposed model. The experiment compared with the state-of-the-art has validated the effectiveness.