Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors
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Di Wang | Zhiqin Zhu | Fengchun Tian | Simon X. Yang | Simon X Yang | Daiyu Jiang | Bin Cai | Zhiqin Zhu | Daiyu Jiang | Di Wang | F. Tian | Bin Cai
[1] Yi Wang,et al. [Tobacco quality analysis of producing areas of Yunnan tobacco using near-infrared (NIR) spectrum]. , 2013, Guang pu xue yu guang pu fen xi = Guang pu.
[2] Antonis Nikitakis,et al. Tensor-Based Classification Models for Hyperspectral Data Analysis , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[3] Di Wang,et al. Automatic Prediction of leave Chemical Compositions based on NIR spectroscopy with Machine Learning , 2019, Int. J. Robotics Autom..
[4] Rashid Mehmood,et al. Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs , 2019, Sensors.
[5] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[6] Michael S. Lew,et al. Deep learning for visual understanding: A review , 2016, Neurocomputing.
[7] Yuhua Qin,et al. NIR models for predicting total sugar in tobacco for samples with different physical states , 2016 .
[8] Jannat Yasmin,et al. Classification Method for Viability Screening of Naturally Aged Watermelon Seeds Using FT-NIR Spectroscopy , 2019, Sensors.
[9] S. Ekren,et al. The contents of some major and trace elements for quality groups of Aegean Region tobaccos , 2011 .
[10] Pil Un Kim,et al. Non-Destructive Classification of Diversely Stained Capsicum annuum Seed Specimens of Different Cultivars Using Near-Infrared Imaging Based Optical Intensity Detection , 2018, Sensors.
[11] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[12] Lin Xie,et al. Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors , 2018, Italian National Conference on Sensors.
[13] Nikolaos Doulamis,et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[14] Nanning Zheng,et al. Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network , 2018, Sensors.
[15] Shaikh Anowarul Fattah,et al. Biometric Authentication Using CNN Features of Dorsal Vein Pattern Extracted from NIR Image , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.
[16] Tao Dong,et al. Spectral Analysis and Sensitive Waveband Determination Based on Nitrogen Detection of Different Soil Types Using Near Infrared Sensors , 2018, Sensors.
[17] Kang Ryoung Park,et al. Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor , 2019, Sensors.
[18] Simon X. Yang,et al. Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches , 2019, Sensors.
[19] Geoffrey E. Hinton,et al. On rectified linear units for speech processing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[20] Tien Dat Nguyen,et al. Spoof Detection for Finger-Vein Recognition System Using NIR Camera , 2017, Sensors.
[21] Yuan-yuan Chen,et al. Feature selection based convolutional neural network pruning and its application in calibration modeling for NIR spectroscopy , 2019, Chemometrics and Intelligent Laboratory Systems.
[22] Yue Huang,et al. Determination of 27 chemical constituents in Chinese southwest tobacco by FT-NIR spectroscopy , 2012 .
[23] Elena Marchiori,et al. Convolutional neural networks for vibrational spectroscopic data analysis. , 2017, Analytica chimica acta.
[24] Q. Su,et al. [Research on the nonlinear model of near infrared spectroscopy and the total sugar of tobacco samples]. , 2004, Guang pu xue yu guang pu fen xi = Guang pu.
[25] S LewMichael,et al. Deep learning for visual understanding , 2016 .
[26] Amy Loutfi,et al. A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..
[27] Ezzeddine Zagrouba,et al. Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain , 2018, Neural Computing and Applications.
[28] Kang Ryoung Park,et al. Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor , 2018, Sensors.
[29] Yizeng Liang,et al. A modified random forest approach to improve multi-class classification performance of tobacco leaf grades coupled with NIR spectroscopy , 2016 .
[30] Tom Fearn,et al. Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration , 2018, Chemometrics and Intelligent Laboratory Systems.
[31] Jens Meiler,et al. Improving quantitative structure–activity relationship models using Artificial Neural Networks trained with dropout , 2016, Journal of Computer-Aided Molecular Design.
[32] J. C. Dias,et al. Fast inline tobacco classification by near-infrared hyperspectral imaging and support vector machine-discriminant analysis , 2019, Analytical Methods.
[33] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[34] Kai Xie,et al. Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning , 2018, Sensors.
[35] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[36] Lei Chen,et al. FPGA-Based Hybrid-Type Implementation of Quantized Neural Networks for Remote Sensing Applications , 2019, Sensors.
[37] Nikolaos Doulamis,et al. Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..
[38] Tara N. Sainath,et al. Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[39] Kang Ryoung Park,et al. Convolutional Neural Network-Based Classification of Driver’s Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors , 2018, Sensors.
[40] Jin Wang,et al. The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN , 2019, Sensors.