Research on Convolutional Neural Network Based on Deep Learning Framework in Big Data Education

2021 ◽  
pp. 109-114
Author(s):  
Xuan Luo
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Framework

scholarly journals Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129889-129898
Author(s):  
Xin Dong ◽  
Yizhao Zhou ◽  
Lantian Wang ◽  
Jingfeng Peng ◽  
Yanbo Lou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Liver Cancer ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Learning Framework

scholarly journals A structure-based deep learning framework for protein engineering

2019 ◽  
Author(s):  
Raghav Shroff ◽  
Austin W. Cole ◽  
Barrett R. Morrow ◽  
Daniel J. Diaz ◽  
Isaac Donnell ◽  
...  
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Deep Learning ◽  
Protein Engineering ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
A Priori ◽  
Novel Proteins ◽  
Learning Framework

AbstractWhile deep learning methods exist to guide protein optimization, examples of novel proteins generated with these techniques require a priori mutational data. Here we report a 3D convolutional neural network that associates amino acids with neighboring chemical microenvironments at state-of-the-art accuracy. This algorithm enables identification of novel gain-of-function mutations, and subsequent experiments confirm substantive phenotypic improvements in stability-associated phenotypes in vivo across three diverse proteins.

Deep Multi-Instance Multi-Label Learning for Image Annotation

2017 ◽  
Vol 32 (03) ◽  
pp. 1859005 ◽  
Author(s):  
Hai-Feng Guo ◽  
Lixin Han ◽  
Shoubao Su ◽  
Zhou-Bao Sun
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Supervised Classification ◽  
Image Annotation ◽  
Learning Framework ◽  
Recent Advancement ◽  
Real World Datasets

Multi-Instance Multi-Label learning (MIML) is a popular framework for supervised classification where an example is described by multiple instances and associated with multiple labels. Previous MIML approaches have focused on predicting labels for instances. The idea of tackling the problem is to identify its equivalence in the traditional supervised learning framework. Motivated by the recent advancement in deep learning, in this paper, we still consider the problem of predicting labels and attempt to model deep learning in MIML learning framework. The proposed approach enables us to train deep convolutional neural network with images from social networks where images are well labeled, even labeled with several labels or uncorrelated labels. Experiments on real-world datasets demonstrate the effectiveness of our proposed approach.

scholarly journals A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2335 ◽  
Author(s):  
Asif Khan ◽  
Jae Kyoung Shin ◽  
Woo Cheol Lim ◽  
Na Yeon Kim ◽  
Heung Soo Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Composite Laminates ◽  
Laminated Composite ◽  
Test Accuracy ◽  
Smart Composite ◽  
Manufacturing Defects ◽  
Learning Framework ◽  
Unseen Data

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

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