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  • Open Access


    End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

    Safdar Ali1, Saad Asad1, Zeeshan Asghar1, Atif Ali1, Dohyeun Kim2,*

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

    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 >

  • Open Access


    GRU-based Buzzer Ensemble for Abnormal Detection in Industrial Control Systems

    Hyo-Seok Kim1, Chang-Gyoon Lim2, Sang-Joon Lee3, Yong-Min Kim4,*

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

    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 >

  • Open Access


    Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

    Jun Zhao1, Shuguo Gao1, Yunpeng Liu2,3, Quan Wang2,*, Ziqiang Xu2, Yuan Tian1, Lu Sun1

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

    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 >

  • Open Access


    SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

    Chin-Shyurng Fahn1, Chang-Yi Kao2,*, 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

    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 >

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