Safdar Ali¹, Saad Asad¹, Zeeshan Asghar¹, Atif Ali¹, Dohyeun Kim^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 461-475, 2023, DOI:10.32604/cmc.2023.035672 - 06 February 2023

Abstract The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is… More >

Hyo-Seok Kim¹, Chang-Gyoon Lim², Sang-Joon Lee³, Yong-Min Kim^4,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1749-1763, 2023, DOI:10.32604/cmc.2023.026708 - 22 September 2022

Abstract Recently, Industrial Control Systems (ICSs) have been changing from a closed environment to an open environment because of the expansion of digital transformation, smart factories, and Industrial Internet of Things (IIoT). Since security accidents that occur in ICSs can cause national confusion and human casualties, research on detecting abnormalities by using normal operation data learning is being actively conducted. The single technique proposed by existing studies does not detect abnormalities well or provide satisfactory results. In this paper, we propose a GRU-based Buzzer Ensemble for Abnormal Detection (GBE-AD) model for detecting anomalies in industrial control… More >

Jun Zhao¹, Shuguo Gao¹, Yunpeng Liu^2,3, Quan Wang^2,*, Ziqiang Xu², Yuan Tian¹, Lu Sun¹

Energy Engineering, Vol.119, No.5, pp. 1811-1828, 2022, DOI:10.32604/ee.2022.020490 - 21 July 2022

Abstract Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed. Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence More >

Chin-Shyurng Fahn¹, Chang-Yi Kao^2,*, Meng-Luen Wu³, Hao-En Chueh⁴

Computer Systems Science and Engineering, Vol.42, No.2, pp. 451-463, 2022, DOI:10.32604/csse.2022.022368 - 04 January 2022

Abstract With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy More >