scholarly journals Optimization of the STEP-NC compliant online toolpath generation for T-spline surfaces using convolutional neural network and random forest classifier

2019 ◽  
Vol 658 ◽  
pp. 012015
Author(s):  
O Zavalnyi ◽  
G Zhao ◽  
Y Liu ◽  
W Xiao
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Random Forest Classifier ◽  
Toolpath Generation ◽  
Spline Surfaces

scholarly journals Genetic Algorithm Based Hybrid Model Of convolutional Neural Network And Random Forest Classifier For Sentiment Classification

2021 ◽  
Vol 12 (2) ◽  
pp. 3216-3223
Author(s):  
Siji George C G, Et. al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Random Forest Classifier ◽  
Sentiment Classification ◽  
Experimental Result ◽  
Text Input

Sentiment analysis is one of the active research areas in the field of datamining. Machine learning algorithms are capable to implement sentiment analysis. Due to the capacity of self-learning and massive data handling, most of the researchers are using deep learning neural networks for solving sentiment classification tasks. So, in this paper, a new model is designed under a hybrid framework of machine learning and deep learning which couples Convolutional Neural Network and Random Forest classifier for fine-grained sentiment analysis. The Continuous Bag-of-Word (CBOW) model is used to vectorize the text input. The most important features are extracted by the Convolutional Neural Network (CNN). The extracted features are used by the Random Forest(RF) classifier for sentiment classification. The performance of the proposed hybrid CNNRF model is comparedwith the base model such as Convolutional Neural Network (CNN) and Random Forest (RF) classifier. The experimental result shows that the proposed model far beat the existing base models in terms of classification accuracy and effectively integrated genetically-modified CNN with Random Forest classifier.

Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

2020 ◽  
Vol 230 ◽  
pp. 117451 ◽  
Author(s):  
Tongshu Zheng ◽  
Michael H. Bergin ◽  
Shijia Hu ◽  
Joshua Miller ◽  
David E. Carlson
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Ground Level

scholarly journals Deep Learning-Based Classification of Weld Surface Defects

2019 ◽  
Vol 9 (16) ◽  
pp. 3312 ◽  
Author(s):  
Zhu ◽  
Ge ◽  
Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Recognition Method ◽  
High Level ◽  
Intelligent Recognition ◽  
Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

Predict plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models

2018 ◽  
Author(s):  
Qi Zhao ◽  
Qian Mao ◽  
Zheng Zhao ◽  
Tongyi Dou ◽  
Zhiguo Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Human Body ◽  
Expression Profiles ◽  
Mirna Sequence ◽  
Selective Absorption ◽  
Sequence Specificity ◽  
One Dimensional ◽  
Plant Mirna

AbstractBackgroundAn increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals.ResultsHere, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed.ConclusionsOur study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.

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