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2. Dynamic Adjustment of the Learning Rate Using Gradient

Author

You, Shuai, Gao, Wanyi, Li, Ziyang, Yang, Qifen, Tian, Meng, Zhu, Shuhua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zu, Qiaohong, editor, Tang, Yong, editor, Mladenovic, Vladimir, editor, Naseer, Aisha, editor, and Wan, Jizheng, editor

Published
2022
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9. A Robust Learning Membership Scaling Fuzzy C-Means Algorithm Based on New Belief Peak

Author

Yang, Qifen, Han, Gang, Gao, Wanyi, Yang, Zhenye, Zhu, Shuhua, and Deng, Yuhui

Abstract

Fuzzy C-means clustering (FCM) has been a commonly used algorithm in fuzzy clustering for decades. However, it still faces two problems: how to determine the initial cluster center and how to determine the number of clusters. The recently proposed robust learning fuzzy C-means (RL-FCM) can automatically obtain the optimal number of clusters. However, it assumes that the initial cluster center is the entire dataset, which incurs a significant time cost and involves parameters that are also difficult to determine. Additionally, RL-FCM is unable to handle imbalanced datasets and datasets with a large span of sample attributes. Therefore, we propose a robust learning membership scaling fuzzy C-means algorithm based on new belief peaks (RL-MFCM). Within the framework of the confidence function, the neighbors of the sample points provide evidence for the sample points being cluster centers. Consequently, according to Jiang's combination rule, we consider the new belief peak as the initial cluster center. To avoid excessive interference of the mixing ratio of the cluster to the calculation of membership degree, we employ triangle inequality to improve the influence of the samples in the cluster in the clustering process. We analyze the time complexity of the proposed algorithm and conduct comparative experiments with existing fuzzy clustering algorithms on artificial and real datasets in the article. Experiments demonstrate that our proposed algorithm accurately estimates the number of clusters and exhibits superior clustering performance without needing initialization.

Published
2023
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20. The Impact of COVID-19 Pandemic on Neonatal Admission: An Interrupted Time-Series Study

Author

Liu, Weiqin, Yang, Qifen, Xu, Zhen-e, Hu, Ya, Wang, Yongming, Liu, Zhenqiu, Zhao, Qianqian, Wang, Zhuangcheng, Wei, Hong, and Hua, Ziyu

Subjects
allergology
Abstract

(1) Introduction: The unprecedented coronavirus disease 2019 (COVID-19) epidemic has caused millions of infections worldwide and represents a significant challenge facing modern health care systems. This study was conducted to investigate the impact of lockdown measures on regional neonate services, which might be used to predict the long-term effects of medical behavior. (2) Methods: Using hospital information system (HIS) Statistics data from 4 January 2019 to 27 August 2020, an interrupted time-series analysis was employed to compare changes and trends in hospital admissions and disease spectrum before and after the lockdown interventions. Furthermore, this study was designed to evaluate whether the pandemic influenced newborns' healthcare behavior. (3) Results: Overall, 13,540 infants were admitted to the NICU during the pre-COVID period (n = 12082) and COVID-impacted period (n =4558). The patients' age at admission were younger than that of the pre-COVID-19 period (median age 5d vs. 6d after birth, p＜0.001). The overall number of neonate visits consistently decreased from the first days of the lockdown measures (24 January 2020). The disease spectrum for respiratory system, infectious diseases, and gastrointestinal disease indicated no declined immediately after intervention(p =0.079, p =0.113, p =0.060, respectively). There was an immediate decline in the volume of Jaundice-related conditions (p

Published
2021

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