Retraction Note: Soft multimedia anomaly detection based on neural network and optimization driven support vector machine

被引：0
|
作者：
Dong Liang [1 ]
Chen Lu [1 ]
Hao Jin [1 ]
机构：
[1] Beijing University of Posts and Telecommunications (BUPT),Key Laboratory of Universal Wireless Communication (Ministry of Education)
来源：
Multimedia Tools and Applications | 2024年 / 83卷 / 23期
关键词：
D O I：
10.1007/s11042-024-19569-y
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：64061 / 64061
相关论文
共 50 条
  • [31] Congestion Detection in Wireless Sensor Networks Based on Artificial Neural Network and Support Vector Machine
    Alejandrino, Jonnel
    Concepcion, Ronnie
    Lauguico, Sandy
    Palconit, Maria Gemel
    Bandala, Argel
    Dadios, Elmer
    2020 IEEE 12TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM), 2020,
  • [32] Developing an Automatic Asbestos Detection Method Based on a Convolutional Neural Network and Support Vector Machine
    Matsuo, Tomohito
    Takimoto, Mitsuteru
    Tanaka, Suzuyo
    Futamura, Ayami
    Shimadera, Hikari
    Kondo, Akira
    APPLIED SCIENCES-BASEL, 2024, 14 (20):
  • [33] One class support vector machine for anomaly detection in the communication network performance data
    Zhang, Rui
    Zhang, Shaoyan
    Muthuraman, Sethuraman
    Jiang, Jianmin
    TOPICS IN APPLIED ELECTROMAGNETICS AND COMMUNICATIONS: PROCEEDINGS OF THE 5TH WSEAS INTERNATIONAL CONFERENCE ON APPLIED ELECTROMAGNETICS, WIRELESS AND OPTICAL COMMUNICATIONS (ELECTROSCIENCE '07), 2007, : 31 - 36
  • [34] Research on Network Intrusion Detection Based on Support Vector Machine Optimized with Grasshopper Optimization Algorithm
    Ye, Zhiwei
    Sun, Yiheng
    Sun, Shuang
    Zhan, Sikai
    Yu, Han
    Yao, Quanfeng
    PROCEEDINGS OF THE 2019 10TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS - TECHNOLOGY AND APPLICATIONS (IDAACS), VOL. 1, 2019, : 378 - 383
  • [35] Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine
    LI Zhanchun
    Wuhan University Journal of Natural Sciences, 2006, (06) : 1769 - 1772
  • [36] Anomaly-Based Intrusion Detection System Using Support Vector Machine
    Krishnaveni, S.
    Vigneshwar, Palani
    Kishore, S.
    Jothi, B.
    Sivamohan, S.
    ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, 2020, 1056 : 723 - 731
  • [37] Support vector machine based optimal model selection method in anomaly detection
    Zhang X.-Q.
    Gu C.-H.
    Wu J.-Y.
    Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2011, 40 (04): : 559 - 563
  • [38] Retraction Note: Hybrid approaches of convolutional network and support vector machine for American sign language prediction
    Beena M V
    Agnisarman Namboodiri
    Rani Thottungal
    Multimedia Tools and Applications, 2024, 83 (22) : 62495 - 62495
  • [39] DETECTION OF MAMMOGRAPHIC CANCER USING SUPPORT VECTOR MACHINE AND DEEP NEURAL NETWORK
    Krishna, Timmana Hari
    Rajabhushnam, C.
    JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, 2019, 14 (06): : 156 - 167
  • [40] Face Recognition Based on Convolutional Neural Network and Support Vector Machine
    Guo, Shanshan
    Chen, Shiyu
    Li, Yanjie
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), 2016, : 1787 - 1792
12345