scholarly journals Tomato Pests and Diseases Classification Model Based on Optimized Convolutional Neural Network

2020 ◽  
Vol 1437 ◽  
pp. 012052
Author(s):  
Shaopeng Jia ◽  
Hongju Gao ◽  
Xiao Hang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Model ◽  
Pests And Diseases ◽  
Model Based ◽  
Tomato Pests

scholarly journals A Sports Training Video Classification Model Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yunjun Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Camera Calibration ◽  
Classification Accuracy ◽  
Classification Model ◽  
Video Classification ◽  
Sports Training ◽  
Training Video ◽  
Model Based

A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.

scholarly journals An Alzheimer’s Disease Identification and Classification Model Based on the Convolutional Neural Network with Attention Mechanisms

2021 ◽  
Vol 38 (5) ◽  
pp. 1557-1564
Author(s):  
Yin Chen
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Brain Mri ◽  
Brain Regions ◽  
Classification Model ◽  
Model Based ◽  
Disease Identification

MRI image analysis of brain regions based on deep learning can effectively reduce the workload of doctors in reading films and improve the accuracy of diagnosis. Therefore, deep learning models have great application prospects in the classification and prediction of Alzheimer’s patients and normal people. However, the existing research has ignored the correlation between small abnormalities in local brain regions and changes in brain tissues. To this end, this paper studies an Alzheimer’s disease identification and classification model based on the convolutional neural network (CNN) with attention mechanisms. In this paper, the attention mechanisms were introduced from the regional level and the feature level, and the information of brain MRI images was fused from multiple levels to find out the correlation between the slices in brain MRI images. Then, a spatio-temporal graph CNN with dual attention mechanisms was constructed, which made the network model more attentive to the salient channel features while eliminating the impact of certain noise features. The experimental results verified the effectiveness of the constructed model in identification and classification of Alzheimer’s disease.

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