Cost-oriented LSTM methods for possible expansion of control charting signals

A cost-sensitive convolution neural network learning for control chart pattern recognition

(2020) Donovan Fuqua et al.   EXPERT SYSTEMS WITH APPLICATIONS

One-sided control chart based on support vector machines with differential evolution algorithm

(2019) Fu-Kwun Wang et al.   QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Control chart pattern recognition using the convolutional neural network

(2019) Tao Zan et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Control chart pattern recognition using RBF neural network with new training algorithm and practical features

(2018) Abdoljalil Addeh et al.   ISA TRANSACTIONS

Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering

(2018) Munawar Zaman et al.   NEURAL COMPUTING & APPLICATIONS

A novel framework for wind speed prediction based on recurrent neural networks and support vector machine

(2018) Chuanjin Yu et al.   ENERGY CONVERSION AND MANAGEMENT

LSTM network: a deep learning approach for short-term traffic forecast

(2017) Zheng Zhao et al.   IET Intelligent Transport Systems

Convolutional neural networks for time series classification

(2017) Bendong Zhao et al.   Journal of Systems Engineering and Electronics

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

(2016) Guilherme Dean Pelegrina et al.   COMPUTERS & INDUSTRIAL ENGINEERING

A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine

(2016) Aminollah Khormali et al.   ISA TRANSACTIONS

Applying emerging soft computing approaches to control chart pattern recognition for an SPC–EPC process

(2016) Yuehjen E. Shao et al.   NEUROCOMPUTING

A robust unsupervised consensus control chart pattern recognition framework

(2015) Siavash Haghtalab et al.   EXPERT SYSTEMS WITH APPLICATIONS

Development of fuzzy logic-based statistical process control chart pattern recognition system

(2015) Joseph Chen et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing

(2015) Doriana M. D’Addona et al.   JOURNAL OF INTELLIGENT MANUFACTURING

A weighted support vector machine method for control chart pattern recognition

(2014) Petros Xanthopoulos et al.   COMPUTERS & INDUSTRIAL ENGINEERING

A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine

(2014) Ling-Jing Kao et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

(2013) Shichang Du et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Recognition of Concurrent Control Chart Patterns by Integrating ICA and SVM

(2013) Chi-Jie Lu et al.   Applied Mathematics & Information Sciences

A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme

(2012) Wafik Hachicha et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Recognition of control chart patterns using a neural network-based pattern recognizer with features extracted from correlation analysis

(2012) Chuen-Sheng Cheng et al.   PATTERN ANALYSIS AND APPLICATIONS

Control chart pattern recognition using K-MICA clustering and neural networks

(2011) Ataollah Ebrahimzadeh et al.   ISA TRANSACTIONS

A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks

(2009) T.T. El-Midany et al.   EXPERT SYSTEMS WITH APPLICATIONS

CLASSIFICATION OF IMBALANCED DATA: A REVIEW

(2009) YANMIN SUN et al.   INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

Decision tree based control chart pattern recognition

(2008) Chih-Hsuan Wang et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines

(2007) C CHENG et al.   EXPERT SYSTEMS WITH APPLICATIONS