RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism

2022 ◽  
Vol 193 ◽  
pp. 106644
Author(s):  
Yun Zhao ◽  
Cheng Sun ◽  
Xing Xu ◽  
Jiagui Chen
Keyword(s):  
Plant Disease ◽  
Attention Mechanism ◽  
Disease Classification ◽  
Classification Model ◽  
Residual Structure ◽  
Model Based

Detection and Classification of Leaf Disease Using Deep Neural Network

2022 ◽  
pp. 51-77
Author(s):  
Meeradevi ◽  
Monica R. Mundada ◽  
Shilpa M.
Keyword(s):  
Neural Network ◽  
Plant Disease ◽  
Early Stage ◽  
Crop Productivity ◽  
Attention Mechanism ◽  
Disease Classification ◽  
Production Plant ◽  
Leaf Disease ◽  
Proposed Model

Modern technologies have improved their application in field of agriculture in order to improve production. Plant diseases are harmful to plant growth, which leads to reduced quality and quantity of crop. Early identification of plant disease will reduce the loss of the crop productivity. So, it is necessary to identify and diagnose the disease at an early stage before it spreads to the entire field. In this chapter, the proposed model uses VGG16 with attention mechanism for leaf disease classification. This model makes use of convolution neural network which consist of convolution block, max pool layer, and fully connected layer with softmax as an activation function. The proposed approach integrates CNN with attention mechanism to focus more on the diseased part of leaf and increase the classification accuracy. The proposed model design is a novel deep learning model to perform the fine tuning in the classification of nine different type of tomato plant disease.

scholarly journals A multi-label text classification model based on ELMo and attention

2020 ◽  
Vol 309 ◽  
pp. 03015
Author(s):  
Wenbin Liu ◽  
Bojian Wen ◽  
Shang Gao ◽  
Jiesheng Zheng ◽  
Yinlong Zheng
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Data Set ◽  
Model Based ◽  
Related Information ◽  
Related Text ◽  
Common Application

Text classification is a common application in natural language processing. We proposed a multi-label text classification model based on ELMo and attention mechanism which help solve the problem for the sentiment classification task that there is no grammar or writing convention in power supply related text and the sentiment related information disperses in the text. Firstly, we use pre-trained word embedding vector to extract the feature of text from the Internet. Secondly, the analyzed deep information features are weighted according to the attention mechanism. Finally, an improved ELMo model in which we replace the LSTM module with GRU module is used to characterize the text and information is classified. The experimental results on Kaggle’s toxic comment classification data set show that the accuracy of sentiment classification is as high as 98%.

Sample imbalance disease classification model based on association rule feature selection

2020 ◽  
Vol 133 ◽  
pp. 280-286 ◽  
Author(s):  
Chenxi Huang ◽  
Xin Huang ◽  
Yu Fang ◽  
Jianfeng Xu ◽  
Yi Qu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Association Rule ◽  
Disease Classification ◽  
Classification Model ◽  
Model Based

Alzheimer's Disease Classification Model Based on MED-3D Transfer Learning

2021 ◽  
Author(s):  
YanMei Li ◽  
WeiWu Ding ◽  
XingYu Wang ◽  
LiHong Li ◽  
JingHong Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transfer Learning ◽  
Disease Classification ◽  
Classification Model ◽  
Model Based

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

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