Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network

被引：0
|
作者：
Ming Liu
Jing Zhao
XiaoZuo Lu
Gang Li
Taixia Wu
LiFu Zhang
机构：
[1] Chinese Academy of Medical Sciences and Peking Union Medical College,Institute of Biomedical Engineering
[2] Tianjin University of Traditional Chinese Medicine,Institute of Chinese Medicine
[3] Tianjin University,State Key Laboratory of Precision Measurement Technology and Instruments
[4] Chinese Academy of Sciences,Institute of Remote Sensing and Digital Earth
来源：
BioMedical Engineering OnLine | / 17卷
关键词：
Reflective spectroscopy; Noninvasive; Blood hyperviscosity diagnosis; Principal component analysis; Artificial neural network;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [31] Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network
    Cao, Jiasheng
    Wang, Jinghan
    SOFT COMPUTING, 2020, 24 (11) : 7851 - 7860
  • [32] Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique
    Yuji Matsuo
    Tatsuo Oyama
    Journal of the Operations Research Society of China, 2020, 8 : 655 - 667
  • [33] Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique
    Matsuo, Yuji
    Oyama, Tatsuo
    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA, 2020, 8 (04) : 655 - 667
  • [34] Development of an optimized multi-biomarker panel for the detection of lung cancer based on principal component analysis and artificial neural network modeling
    Miguel Flores-Fernandez, Jose
    Herrera-Lopez, Enrique J.
    Sanchez-Llamas, Francisco
    Rojas-Calvillo, Antonio
    Anel Cabrera-Galeana, Paula
    Leal-Pacheco, Gisela
    Guadalupe Gonzalez-Palomar, Maria
    Femat, R.
    Martinez-Velazquez, Moises
    EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (12) : 10851 - 10856
  • [35] Pattern classification of acoustic emission signals during wood drying by principal component analysis and artificial neural network
    Kim, KB
    Kang, HY
    Yoon, DJ
    Choi, MY
    ADVANCES IN FRACTURE AND STRENGTH, PTS 1- 4, 2005, 297-300 : 1962 - 1967
  • [36] Identification of Ground Vehicle Aerodynamic Derivatives Application of Neural Network with Principal Component Analysis
    Ramli, Nabilah
    Faris, Waleed Fekry
    Jamaluddin, Hishamuddin Bin
    Mansor, Shuhaimi
    2008 10TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION: ICARV 2008, VOLS 1-4, 2008, : 1678 - +
  • [37] Accelerated convergence of neural network system identification algorithms via principal component analysis
    Hyland, DC
    Davis, LD
    Denoyer, K
    NOVEL OPTICAL SYSTEMS AND LARGE-APERTUR IMAGING, 1998, 3430 : 248 - 259
  • [38] Personal Palm Vein Identification Using Principal Component Analysis and Probabilistic Neural Network
    Perwira, Didi Yudha
    Agung, Tjokorda B. W.
    Sulistiyo, Mahmud Dwi
    2014 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY SYSTEMS AND INNOVATION (ICITSI), 2014, : 99 - 104
  • [39] An Application of Principal Component Analysis - Artificial Neural Network for the Simultaneous Quantitative Analysis of a Binary Mixture System
    Dinc, Erdal
    Sen Koktas, Nigar
    Baleanu, Dumitru
    REVISTA DE CHIMIE, 2009, 60 (07): : 662 - 665
  • [40] Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance
    Arivudainambi, D.
    Kumar, Varun K. A.
    Chakkaravarthy, Sibi S.
    Visu, P.
    COMPUTER COMMUNICATIONS, 2019, 147 : 50 - 57
12345