Xian Ma¹, Tingyan Lv^2,*, Yingqiang Jin², Rongmin Chen², Dengxian Dong², Yingtao Jia²

Energy Engineering, Vol.118, No.3, pp. 691-705, 2021, DOI:10.32604/EE.2021.012701

Abstract The large number of gas turbines in large power companies is difficult to manage. A large amount of the data from the generating units is not mined and utilized for fault analysis. This study focuses on F-class (9F.05) gas turbine generators and uses unsupervised learning and cloud computing technologies to analyse the faults for the gas turbines. Remote monitoring of the operational status are conducted. The study proposes a cloud computing service architecture for large gas turbine objects, which uses unsupervised learning models to monitor the operational state of the gas turbine. Faults such as chamber seal failure, load abnormality… More >

Yiğit Kültür, Mehmet Ufuk Çağlayan

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 807-817, 2018, DOI:10.1080/10798587.2017.1342415

Abstract Because credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurred from credit card fraud is an important driver for the sector and end-users. In this paper, we focus on analyzing cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is called the Cardholder Behavior Model (CBM). Two focus points are proposed and evaluated for CBMs. The first focus point is building the behavior model using single-card transactions versus multi-card transactions. As the second focus point, we introduce holiday seasons as spending periods that are different… More >

Yu Jiang^{1, 2}, Dengwen Yu¹, Mingzhao Zhao^{1, 2}, Hongtao Bai^{1, 2}, Chong Wang^{1, 2, 3}, Lili He^{1, 2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 207-216, 2020, DOI:10.32604/cmc.2020.09861

Abstract Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning. This paper implements and compares unsupervised and semi-supervised clustering analysis of BOAArgo ocean text data. Unsupervised K-Means and Affinity Propagation (AP) are two classical clustering algorithms. The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range. Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition, and use this data for semi-supervised cluster analysis. Several semi-supervised clustering… More >

Haiwen Chen¹, Guang Yu¹, Fang Liu², Zhiping Cai^{1, *}, Anfeng Liu³, Shuhui Chen¹, Hongbin Huang¹, Chak Fong Cheang⁴

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 917-927, 2020, DOI:10.32604/cmc.2020.05981

Abstract For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need… More >