Deep Anomaly Detection for Automotive Components by Oversampling

Article Properties
  • Language
    English
  • DOI (url)
    10.17929/tqs.9.18
  • Publication Date
    2023/10/10
  • Journal
    Total Quality Science
  • Indian UGC (journal)
  • Refrences
    11
  • Chika Yokocho Nagoya Institute of Technology
  • Hironobu Kawamura Nagoya Institute of Technology
  • Kozaburo Nirasawa Nagoya Institute of Technology
Cite
Yokocho, Chika, et al. “Deep Anomaly Detection for Automotive Components by Oversampling”. Total Quality Science, vol. 9, no. 1, 2023, pp. 18-28, https://doi.org/10.17929/tqs.9.18.
Yokocho, C., Kawamura, H., & Nirasawa, K. (2023). Deep Anomaly Detection for Automotive Components by Oversampling. Total Quality Science, 9(1), 18-28. https://doi.org/10.17929/tqs.9.18
Yokocho C, Kawamura H, Nirasawa K. Deep Anomaly Detection for Automotive Components by Oversampling. Total Quality Science. 2023;9(1):18-2.
Refrences
Title Journal Journal Categories Citations Publication Date
10.1109/ACCESS.2021.3077567
Multi-grade brain tumor classification using deep CNN with extensive data augmentation Journal of Computational Science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 355 2019
10.1109/CVPR.2016.90
GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification Neurocomputing
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 787 2018
10.1109/CVPR.2009.5206848