CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection
Authors
Keywords
PCB cosmetic defect detection, Residual convolutional neural network, Class imbalance, Cost-sensitive learning
Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 185, Issue -, Pages 115673
Publisher
Elsevier BV
Online
2021-07-30
DOI
10.1016/j.eswa.2021.115673
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Text Mining Approach for Bottleneck Detection and Analysis in Printed Circuit Board Manufacturing
- (2021) Po-Chien Hao et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Cost-sensitive learning classification strategy for predicting product failures
- (2020) Flavia Dalia Frumosu et al. EXPERT SYSTEMS WITH APPLICATIONS
- Multi-label Thresholding for Cost-sensitive Classification
- (2020) Reem Alotaibi et al. NEUROCOMPUTING
- Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation
- (2020) Anjin Liu et al. IEEE Transactions on Neural Networks and Learning Systems
- An interpretable sequential three-way recommendation based on collaborative topic regression
- (2020) Xiaoqing Ye et al. EXPERT SYSTEMS WITH APPLICATIONS
- Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network
- (2020) Shrouq Alelaumi et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
- (2019) Zifeng Wu et al. PATTERN RECOGNITION
- A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations
- (2019) Abdel-Aziz I. M. Hassanin et al. MULTIMEDIA TOOLS AND APPLICATIONS
- A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance
- (2019) Dina Elreedy et al. INFORMATION SCIENCES
- Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification
- (2018) Bo Luo et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Adaptive Cost-Sensitive Online Classification
- (2018) Peilin Zhao et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Regularized fisher linear discriminant through two threshold variation strategies for imbalanced problems
- (2018) Yujin Zhu et al. KNOWLEDGE-BASED SYSTEMS
- Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
- (2018) Feng Jia et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Multi-scale deep context convolutional neural networks for semantic segmentation
- (2018) Quan Zhou et al. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
- A Cost-Sensitive Deep Belief Network for Imbalanced Classification
- (2018) Chong Zhang et al. IEEE Transactions on Neural Networks and Learning Systems
- A systematic study of the class imbalance problem in convolutional neural networks
- (2018) Mateusz Buda et al. NEURAL NETWORKS
- Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation
- (2018) Xinxin Jiang et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Learning from class-imbalanced data: Review of methods and applications
- (2017) Guo Haixiang et al. EXPERT SYSTEMS WITH APPLICATIONS
- An efficient similarity measure approach for PCB surface defect detection
- (2017) Vilas H. Gaidhane et al. PATTERN ANALYSIS AND APPLICATIONS
- Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
- (2017) Salman H. Khan et al. IEEE Transactions on Neural Networks and Learning Systems
- A Novel Minority Cloning Technique for Cost-Sensitive Learning
- (2015) Liangxiao Jiang et al. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
- Cost-sensitive Bayesian network classifiers
- (2014) Liangxiao Jiang et al. PATTERN RECOGNITION LETTERS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now