Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), ...
Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and Remaining Useful Life (RUL) prediction. However, accompanied by CNN’s increasing performance is a deeper network structure and growing parameter size. This preve
Real-time crash risk prediction using long short-term memory recurrent neural network Transp. Res. Rec., 2673 (4) (2019), pp. 314-326 Google Scholar Further Reading Ahmed et al., 2012 M.M. Ahmed, M. Abdel-Aty, R. Yu Bayesian updating approach for real-time safety evaluation with aut...
Article CAS Google Scholar Wang J, Wang L. Deep learning of the back-splicing code for circular RNA formation. Bioinformatics. 2019;35(24):5235–42. Article CAS PubMed Google Scholar Jia C, Bi Y, Chen J, Leier A, Li F, Song J. PASSION: an ensemble neural network approach for ide...
Axillary lymph node evaluation utilizing convolutional neural networks using MRI dataset J Digit Imaging, 31 (6) (2018), pp. 851-856 CrossrefView in ScopusGoogle Scholar 34 T. Ren, S. Lin, P. Huang, et al. Convolutional neural network of multiparametric MRI accurately detects axillary lymph ...
The Convolutional Neural Network (CNN) [44] is inspired by the animal visual cortex. It consists of convolution, activation, and max-pool layers. The one-hot encoding matrix derived from RNA sequences and structures are the inputs to the CNNs and are used to learn the weight parameters of...
Optimal Design of Convolutional Neural Network Architectures Using Teaching–Learning-Based Optimization for Image Classification by Koon Meng Ang Koon Meng Ang SciProfiles Scilit Preprints.org Google Scholar 1, El-Sayed M. El-kenawy El-Sayed M. El-kenawy SciProfiles Scilit Preprints.org Google ...
[Google Scholar] [CrossRef] Wei, L.F.; Wang, K.; Lu, Q.K.; Liang, Y.J.; Li, H.B.; Wang, Z.X.; Wang, R.; Cao, L.Q. Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning. Remote Sens. 2021, 13, 2917. [Google Scholar...
A convolutional neural network (CNN) is a supervised deep learning algorithm developed based on the multi-layer perceptron (MLP), and its basic theory is outlined in this section. 2.1Multi-layer perceptron The MLP is a machine learning model known as a fully-connected feedforward neural network...
Google Scholar Wang, W., Zhu, M., Zeng, X.-W., Ye, X.-Z., Sheng, Y.-Q.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking, pp. 712–717. IEEE Press, Da Nang (2017) Google Scholar...