scholarly journals Analysis of Tai Chi Ideological and Political Course in University Based on Big Data and Graph Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chun yan Li ◽  
Lu Zheng
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Tai Chi ◽  
Image Features ◽  
Basic Module ◽  
Semantic Categories ◽  
Interactive Classroom ◽  
Spatio Temporal ◽  
Low Dimensional

Tai Chi martial arts education is one of the components of school education. Its educational value is not only to require students to master basic Tai movement technical skills and improve their physical fitness but also to bring students’ ideological progress and cultivate students to respect teachers and lectures. Excellent moral qualities such as politeness, keeping promises, observing the rules, and acting bravely, as well as the cultivation of the spirit of unity and cooperation, and the quality of will also have a certain meaning. However, the scientific Tai Chi ideological and political courses and the construction of Wude education interactive classrooms lack relevant research. Therefore, this article builds a Tai Chi ideological and political interactive classroom system based on big data technology and graph neural network. First, the spatio-temporal graph convolutional neural network is used to reason about the relationship between Tai Chi action categories and strengthen the low-dimensional features of semantic categories and their co-occurrence expressions used for semantic enhancement of current image features. In addition, in order to ensure the efficiency of the Tai Chi scene analysis network, an efficient dual feature extraction basic module is proposed to construct the backbone network, reducing the number of parameters of the entire network and the computational complexity. Experiments show that this method can obtain approximate results, while reducing the amount of floating-point operations by 42.5% and the amount of parameters by 50.2% compared with the work of the same period, and achieves a better balance of efficiency and performance. Secondly, based on the big data of historical Tai Chi classrooms, this article constructs an interactive classroom system that can effectively improve the quality of Tai Chi ideological and political courses.

scholarly journals ASSESSING THE SEMANTIC SIMILARITY OF IMAGES OF SILK FABRICS USING CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 641-648
Author(s):  
D. Clermont ◽  
M. Dorozynski ◽  
D. Wittich ◽  
F. Rottensteiner
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Features ◽  
Training Data ◽  
Loss Functions ◽  
Silk Fabrics ◽  
Semantic Properties ◽  
The Eu

Abstract. This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (http://silknow.eu/), two variants of training were developed, one based on a Siamese CNN and one based on a triplet architecture. We propose different definitions of similarity and different loss functions for both training strategies, some of them also allowing the use of incomplete information about the training data. We assess the quality of the trained model by using the learned image features in a k-NN classification. We achieve overall accuracies of 93–95% and average F1-scores of 87–92%.

scholarly journals Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xi Chen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Big Data ◽  
Convolutional Neural Network ◽  
Language Teaching ◽  
Chinese Language ◽  
Chinese Language Teaching ◽  
Text Information ◽  
Analysis System

Chinese language is also an important way to understand Chinese culture and an important carrier to inherit and carry forward Chinese traditional culture. Chinese language teaching is an important way to inherit and develop Chinese language. Therefore, in the era of big data, data mining and analysis of Chinese language teaching can effectively sum up experience and draw lessons, so as to improve the quality of Chinese language teaching and promote Chinese language culture. Text clustering technology can analyze and process the text information data and divide the text information data with the same characteristics into the same category. Based on big data, combined with convolutional neural network and K-means algorithm, this paper proposes a text clustering method based on convolutional neural network (CNN), constructs a Chinese language teaching data mining analysis system, and optimizes it so that the system can better mine Chinese character data in Chinese language teaching data in depth and comprehensively. The results show that the optimized k-means algorithm needs 683 iterations to achieve the target accuracy. The average K-measure value of the optimized system is 0.770, which is higher than that of the original system. The results also show that K-means algorithm can significantly improve the clustering effect, optimize the data mining analysis system of Chinese language teaching, and deeply mine the Chinese data in Chinese language teaching, so as to improve the quality of Chinese language teaching.

scholarly journals Prediction of Future Terrorist Activities Using Deep Neural Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
M. Irfan Uddin ◽  
Nazir Zada ◽  
Furqan Aziz ◽  
Yousaf Saeed ◽  
Asim Zeb ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Single Layer ◽  
Machine Learning Algorithms ◽  
Suitable Model ◽  
Law And Order ◽  
The People

One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

scholarly journals MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation

2020 ◽  
Vol 10 (19) ◽  
pp. 6823
Author(s):  
Hongwei Ding ◽  
Xiaohui Cui ◽  
Leiyang Chen ◽  
Kun Zhao
Keyword(s):  
Neural Network ◽  
Blood Vessel ◽  
Feature Fusion ◽  
Image Data ◽  
Retinal Vessel ◽  
Image Features ◽  
Segmentation Method ◽  
Low Contrast ◽  
Low Sensitivity ◽  
Low Dimensional

Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis. Feature information from the retinal blood vessel image is relatively complicated, and the existing algorithms are sometimes difficult to perform effective segmentation with. Aiming at the problems of low accuracy and low sensitivity of the existing segmentation methods, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. Firstly, the image enhancement algorithm and random segmentation method are used to solve the problems of low contrast and insufficient image data of the original image. Moreover, smaller image blocks after random segmentation are helpful to reduce the complexity of the U-shaped neural network model; secondly, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature use and to reduce information loss, and a feature fusion module is introduced between the encoder and decoder to extract image features with different granularities; and finally, a feature balancing module is added to the skip connections to resolve the semantic gap between low-dimensional features in the encoder and high-dimensional features in decoder. Experimental results show that our method has better accuracy and sensitivity on the DRIVE and STARE datasets (accuracy (ACC) = 0.9611, sensitivity (SE) = 0.8613; STARE: ACC = 0.9662, SE = 0.7887) than some of the state-of-the-art methods.

scholarly journals Application of Convolution Network Model Based on Deep Learning in Sports Image Information Detection

2021 ◽  
Vol 233 ◽  
pp. 02024
Author(s):  
Xiaoqiao Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Image Features ◽  
Convolution Neural Network ◽  
Image Information ◽  
Model Based ◽  
Wide Range ◽  
Information Detection

In recent years, convolution neural network has achieved great success in single image super-resolution detection. Compared with the traditional method, this method achieves better reconstruction detection effect. However, the network structure of the existing reconstruction model is shallow, and the convolution kernel has a small acceptance, so it is difficult to learn a wide range of motion image features, which affects the quality of motion image information detection. Aiming at the problems and shortcomings of the existing sports image information detection based on convolution neural network, this paper proposes the application of convolution network model based on deep learning in sports image information detection. In this paper, we get the average SSIM value from the data of set5, set14, bsd100 and urban100 by using the X4 model of different algorithms. The average SSIM value of set5 is 0.865, which shows that the quality of sports image reconstruction and the reconstruction efficiency of the model can be improved by using the local image features of different scales, which provides technical support for sports image information detection. The research in this paper has important practical significance for the further development of the two and the reform of the convolution network model in sports image information detection.

scholarly journals Analysing the Quality of Phaseolus Vulgaris Family of Legumes using Artificial Neural Network and “Bag of Features” Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 1270-1274
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Phaseolus Vulgaris ◽  
Development Program ◽  
The Philippines ◽  
Image Features ◽  
Support Vector ◽  
Bag Of Features ◽  
Artificial Neural

The Philippine Council for Agriculture, Forestry and National Resources Research and Development-Department of Science and Technology (PCAARRD-DOST) have recognized the importance of cultivating legumes as priority crop among others in the vegetable industry under the National Vegetable Research & Development Program. They have further emphasized the need for innovating the methods to improve the processes in terms of producing better quality of products. The study developed a prototype compiled application based on the trained and validated dataset using ANN (Artificial Neural Network) machine. The BoF (Bag of Features) technique was utilized for image features extraction in the SVM (Support Vector Machine) environment for quality classification of Phaseolus Vulgaris family of legumes. These are commonly cultivated in the Philippines. The combined methods yielded an accuracy of 90.2%.

Tai Chi Improved HF Patients' Quality of Life

2011 ◽  
Vol 41 (10) ◽  
pp. 19
Author(s):  
KERRI WACHTER
Keyword(s):  
Quality Of Life ◽  
Tai Chi

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

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