scholarly journals Multiple Lesions Detection of Fundus Images Based on Convolution Neural Network Algorithm With Improved SFLA

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 97618-97631
Author(s):  
Weiping Ding ◽  
Ying Sun ◽  
Longjie Ren ◽  
Hengrong Ju ◽  
Zhihao Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Fundus Images ◽  
Multiple Lesions ◽  
Network Algorithm ◽  
Neural Network Algorithm

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

scholarly journals Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Juan Zhao
Keyword(s):  
Neural Network ◽  
Task Performance ◽  
Convolution Neural Network ◽  
Sorting Task ◽  
Translation System ◽  
Translation Quality ◽  
Network Algorithm ◽  
Baseline System ◽  
Neural Network Algorithm ◽  
Pseudo Data

In order to effectively optimize the machine online translation system and improve its translation efficiency and translation quality, this study uses the deep separable convolution neural network algorithm to construct a machine online translation model and evaluates the quality on the basis of pseudo data learning. In order to verify the performance of the model, the regression performance experiment of the model, the method performance experiment of generating pseudo data for specific tasks, the sorting task performance experiment of the model, and the machine translation quality comparison experiment are designed. RMSE and MAE were used to evaluate the regression task performance of the model. Spearman rank correlation coefficient and delta AVG value were used to evaluate the sorting task performance of the model. The experimental results show that the MAE and RMSE values of the model are decreased by 2.28% and 1.39%, respectively, compared with the baseline system under the same experimental conditions, and the Spearman and delta AVG values are increased by 132% and 100.7%, respectively, compared with the baseline system. The method of generating pseudo data for specific tasks needs less data and can make the translation system reach a better level faster. When the number of instances is more than 10, the quality score of the model output is higher than that of Google translation whose similarity is more than 0.8.

Autism Spectrum Disorder Risk Gene Prediction Using Improved Salp Swarm Algorithm and Enhanced Convolution Neural Network Algorithm

2021 ◽  
Vol 19 (9) ◽  
pp. 97-109
Author(s):  
Dr.J. Anitha
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Gene Selection ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Convolution Neural Network ◽  
Salp Swarm Algorithm ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Swarm Algorithm

A group of Neuro developmental disorders is Autism Spectrum Disorder (ASD) which is characterized by communication skills and social interaction difficulties. In past few years, there is a rapid increase in ASD and its root symptom’s cause is not yet determined. Comparatively large effort is needed in the existing system using Bayes network and there is no universally accepted technique to construct a network from data. An Enhanced Convolution Neural Network (ECNN) and Improved Salp Swarm Algorithm (ISSA) is proposed to solve these issues and for effective ASD classification. The task corresponds to classifying a long non-coding RNA (lncRNA) gene would cause a disease or not. After class balancing, discretization is applied for converting continuous values into discrete values and for optimal gene selection, ISSA algorithm is used. From genomic data, candidate gene biomarkers are identified using this gene selection. Every possible feature subset is computed for minimizing irrelevant features and error rate in gene data. It is focused to enhance ASD classification model’s accuracy. The Enhanced Convolutional Neural Network algorithm is used for ASD classification. The autism microarray dataset from the benchmark public repository, Gene Expression Omnibus (GEO) (National Center for Biotechnology Information (NCBI) is used for analysis. The proposed work using ISSA and ECNN exhibits better performance in terms of precision, accuracy, specificity, sensitivity and time complexity as indicated in the results.

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