Classification of Bangla Compound Characters Using a HOG-CNN Hybrid Model

2018 ◽  
pp. 403-411 ◽  
Author(s):  
S. M. A. Sharif ◽  
Nabeel Mohammed ◽  
Sifat Momen ◽  
Nafees Mansoor
Keyword(s):  
Hybrid Model

Treatment Implications of a Hybrid Model for the Classification of Alcoholism

1992 ◽  
pp. 257-282
Author(s):  
Leslie C. Morey ◽  
Janice K. Jones
Keyword(s):  
Hybrid Model

A Hybrid Model: DGnet-SVM for the Classification of Pulmonary Nodules

2017 ◽  
pp. 732-741 ◽  
Author(s):  
Yixuan Xu ◽  
Guokai Zhang ◽  
Yuan Li ◽  
Ye Luo ◽  
Jianwei Lu
Keyword(s):  
Hybrid Model ◽  
Pulmonary Nodules

scholarly journals Classification of Microblog Users’ Sentiments Based on BERT-BiLSTM-CBAM

CONVERTER ◽  
2021 ◽  
pp. 281-287
Author(s):  
Juan Chen, Ruyun Chen, Di Yu
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Feature Vectors ◽  
Text Information ◽  
Memory Network ◽  
Long Short Term Memory ◽  
New Feature

This study proposes a method for more accurate classification of microblog users' sentiments based on the BERT-BiLSTM-CBAM hybrid model. First, the text information is pre-trained by the bidirectional encoder representation from transformers (BERT) model to get feature vectors. Then, the feature vectors are spliced and recombined using bidirectional long-short-term memory network (BiLSTM) and CBAM mechanism to obtain new feature vectors. Finally, these new feature vectors are input to the full connection layer and then processed by the softmax function to obtain the sentiment category of the text. The experiment conducted on the sample dataset demonstrates that the model proposed in this study yielded accurate and dependable result in classifying microblog texts in the sample dataset. The model based on BERT-BiLSTM-CBAM algorithm is more efficient than the traditional depth model in processing microblog contents

scholarly journals Detection & Classification of Lung Cancer at an Early Stage by Applying Feature Extraction-Optimization and Neural Network on Hybrid Structure

2019 ◽  
Vol 9 (2) ◽  
pp. 3737-3745
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Hybrid Model ◽  
Error Rate ◽  
Execution Time ◽  
Early Stage ◽  
Support Vector ◽  
Minimum Error

As of now the detection and classification of lung cancer disease is one of the most tedious tasks in the field of medical area. In the diversified sector of medical industry usage of technology plays a very important role. Detection and diagnosis of the lung cancer at an early stage with more accuracy is the most challenging task. So, in this research article 400 set of images has been used for this experiment. Best feature extraction technique and best feature optimization technique has been analyzed on the basis of parameter minimum execution time with minimum error rate. Then finest selection of features leads to an optimal classification. In this context, one of the best classification algorithm the support vector machine has been proposed in this hybrid model for the binary classification. Further Feed forward back propagation neural network has been implemented with SVM. This proposed hybrid model reduces the complexity of the system on the basis of minimum execution time that is 1.94 sec. with minimum error rate 29.25. Further better classification accuracy 99.6507% has been achieved by using this unique hybrid model

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