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  • Open Access

    ARTICLE

    DL-Powered Anomaly Identification System for Enhanced IoT Data Security

    Manjur Kolhar*, Sultan Mesfer Aldossary

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2857-2879, 2023, DOI:10.32604/cmc.2023.042726 - 26 December 2023

    Abstract In many commercial and public sectors, the Internet of Things (IoT) is deeply embedded. Cyber security threats aimed at compromising the security, reliability, or accessibility of data are a serious concern for the IoT. Due to the collection of data from several IoT devices, the IoT presents unique challenges for detecting anomalous behavior. It is the responsibility of an Intrusion Detection System (IDS) to ensure the security of a network by reporting any suspicious activity. By identifying failed and successful attacks, IDS provides a more comprehensive security capability. A reliable and efficient anomaly detection system… More >

  • Open Access

    ARTICLE

    Identification of an immune classifier for predicting the prognosis and therapeutic response in triple-negative breast cancer

    KUAILU LIN1,2, QIANYU GU2, XIXI LAI2,3,*

    BIOCELL, Vol.47, No.12, pp. 2681-2696, 2023, DOI:10.32604/biocell.2023.043298 - 27 December 2023

    Abstract Objectives: Triple-negative breast cancer (TNBC) poses a significant challenge due to the lack of reliable prognostic gene signatures and an understanding of its immune behavior. Methods: We analyzed clinical information and mRNA expression data from 162 TNBC patients in TCGA-BRCA and 320 patients in METABRIC-BRCA. Utilizing weighted gene coexpression network analysis, we pinpointed 34 TNBC immune genes linked to survival. The least absolute shrinkage and selection operator Cox regression method identified key TNBC immune candidates for prognosis prediction. We calculated chemotherapy sensitivity scores using the “pRRophetic” package in R software and assessed immunotherapy response using the… More >

  • Open Access

    ARTICLE

    DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

    Xiaoqi Xin*, Dezhi Han, Mingming Cui

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2407-2425, 2023, DOI:10.32604/cmc.2023.042308 - 29 November 2023

    Abstract Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect… More >

  • Open Access

    ARTICLE

    Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

    Wei Li1,2, Benjian Zou1, Yuxiang Luo2, Ning Yang2, Faye Zhang1,*, Mingshun Jiang1, Lei Jia1

    Structural Durability & Health Monitoring, Vol.17, No.6, pp. 485-500, 2023, DOI:10.32604/sdhm.2023.025989 - 17 November 2023

    Abstract As a critical structure of aerospace equipment, aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system. In this study, a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft. Firstly, together with numerical simulation, the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed, to establish the damage data. Subsequently, the amplitude-frequency characteristics of impact damage signals are extracted and put… More >

  • Open Access

    Identification of STAT5B as a biomarker for an early diagnosis of endometrial carcinoma

    XUELIAN CHEN1,*, YUNZHENG ZHANG2, JUNJIAN HE3, YIBING LI4

    BIOCELL, Vol.47, No.10, pp. 2283-2300, 2023, DOI:10.32604/biocell.2023.030086 - 08 November 2023

    Abstract Background: The late detection of endometrial carcinoma (EC) at an advanced stage often results in a poor patient prognosis. It is hence important to identify reliable biomarkers to facilitate early detection of EC. Signal transducer and activator of transcription (STAT) family members play an important role in several tumors, however, their impact on EC development and progression remains unclear. Methods: Machine learning methods were used to investigate the importance of STAT5B in EC. Results: Hence, we explored the UALCAN data mining platform and found that while STAT1 and STAT2 were upregulated, STAT5A, STAT5B, and STAT6 were… More >

  • Open Access

    ARTICLE

    Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of Key Regions

    Wenqiu Zhu1,2, Yongsheng Li1,2, Qiang Liu1,2,*, Zhigao Zeng1,2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1373-1392, 2023, DOI:10.32604/cmc.2023.037216 - 31 October 2023

    Abstract Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs… More >

  • Open Access

    ARTICLE

    Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

    Xueting Cheng1, Ziqi Zhang2,*, Yueshuang Bao1, Huiping Zheng1

    Energy Engineering, Vol.120, No.11, pp. 2517-2529, 2023, DOI:10.32604/ee.2023.042633 - 31 October 2023

    Abstract At present, the proportion of new energy in the power grid is increasing, and the random fluctuations in power output increase the risk of cascading failures in the power grid. In this paper, we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering (DEC) algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids. First, considering the real-time operation status and system structure of new energy power grids, the scenario cascading failure risk More >

  • Open Access

    ARTICLE

    Efficient Remote Identification for Drone Swarms

    Kang-Moon Seo1, Jane Kim1, Soojin Lee1, Jun-Woo Kwon1, Seung-Hyun Seo1,2,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2937-2958, 2023, DOI:10.32604/cmc.2023.039459 - 08 October 2023

    Abstract With the advancement of unmanned aerial vehicle (UAV) technology, the market for drones and the cooperation of many drones are expanding. Drone swarms move together in multiple regions to perform their tasks. A Ground Control Server (GCS) located in each region identifies drone swarm members to prevent unauthorized drones from trespassing. Studies on drone identification have been actively conducted, but existing studies did not consider multiple drone identification environments. Thus, developing a secure and effective identification mechanism for drone swarms is necessary. We suggested a novel approach for the remote identification of drone swarms. For… More >

  • Open Access

    ARTICLE

    An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

    Hui Xu, Yalin Hu*, Weidong Cao, Longjie Han

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.039227 - 08 October 2023

    Abstract The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimization-seeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the… More >

  • Open Access

    ARTICLE

    3-D Gait Identification Utilizing Latent Canonical Covariates Consisting of Gait Features

    Ramiz Gorkem Birdal*, Ahmet Sertbas

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2727-2744, 2023, DOI:10.32604/cmc.2023.032069 - 08 October 2023

    Abstract Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’ walking patterns to be recognized. Existing research in this area has primarily focused on feature analysis through the extraction of individual features, which captures most of the information but fails to capture subtle variations in gait dynamics. Therefore, a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced. The gait features extracted from body halves divided by anatomical planes on vertical, horizontal, and diagonal… More >

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