scholarly journals Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8 ◽  
Author(s):  
Zhongxiao Wang
Keyword(s):  
Deep Learning ◽  
Physical Training ◽  
Evaluation System ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Sports Training ◽  
Deep Learning Algorithm ◽  
The Impact ◽  
Physical Index

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.

scholarly journals Research on the Design of Government Affairs Platform in the Context of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qian Huang ◽  
Xue Wen Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Algorithm ◽  
The Internet ◽  
Chinese Government ◽  
Development Status ◽  
Deep Learning Algorithm ◽  
Government Management ◽  
The Government ◽  
The Impact

Big data is a massive and diverse form of unstructured data, which needs proper analysis and management. It is another great technological revolution after the Internet, the Internet of Things, and cloud computing. This paper firstly studies the related concepts and basic theories as the origin of research. Secondly, it analyzes in depth the problems and challenges faced by Chinese government management under the impact of big data. Again, we explore the opportunities that big data brings to government management in terms of management efficiency, administrative capacity, and public services and believe that governments should seize opportunities to make changes. Brainlike computing attempts to simulate the structure and information processing process of biological neural network. This paper firstly analyzes the development status of e-government at home and abroad, studies the service-oriented architecture (SOA) and web services technology, deeply studies the e-government and SOA theory, and discusses this based on the development status of e-government in a certain region. Then, the deep learning algorithm is used to construct the monitoring platform to monitor the government behavior in real time, and the deep learning algorithm is used to conduct in-depth mining to analyze the government's intention behavior.

scholarly journals Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3424
Author(s):  
Xujia Liang ◽  
Zhonghua Huang ◽  
Liping Lu ◽  
Zhigang Tao ◽  
Bing Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Mobile Robotics ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Detection Methods ◽  
Signal Recognition ◽  
Echo Signal ◽  
Deep Learning Algorithm ◽  
Recognition Criterion

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

scholarly journals Research on Network Security Application Based on Deep Learning

CONVERTER ◽  
2021 ◽  
pp. 598-605
Author(s):  
Zhao Jianchao
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Internet Security ◽  
Detection Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Internet Industry

Behind the rapid development of the Internet industry, Internet security has become a hidden danger. In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology. Therefore, this paper attempts to apply deep learning to intrusion detection to learn and classify network attacks. Aiming at the nsl-kdd data set, this paper first uses the traditional classification methods and several different deep learning algorithms for learning classification. This paper deeply analyzes the correlation among data sets, algorithm characteristics and experimental classification results, and finds out the deep learning algorithm which is relatively good at. Then, a normalized coding algorithm is proposed. The experimental results show that the algorithm can improve the detection accuracy and reduce the false alarm rate.

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

scholarly journals Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yan Guo ◽  
Jin Zhang ◽  
Chengxin Yin ◽  
Xiaonan Hu ◽  
Yu Zou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Agricultural Production ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Deep Learning Algorithm ◽  
Rust Diseases ◽  
Ecological Protection ◽  
Smart Farming ◽  
Bacterial Plaque

The identification of plant disease is the premise of the prevention of plant disease efficiently and precisely in the complex environment. With the rapid development of the smart farming, the identification of plant disease becomes digitalized and data-driven, enabling advanced decision support, smart analyses, and planning. This paper proposes a mathematical model of plant disease detection and recognition based on deep learning, which improves accuracy, generality, and training efficiency. Firstly, the region proposal network (RPN) is utilized to recognize and localize the leaves in complex surroundings. Then, images segmented based on the results of RPN algorithm contain the feature of symptoms through Chan–Vese (CV) algorithm. Finally, the segmented leaves are input into the transfer learning model and trained by the dataset of diseased leaves under simple background. Furthermore, the model is examined with black rot, bacterial plaque, and rust diseases. The results show that the accuracy of the method is 83.57%, which is better than the traditional method, thus reducing the influence of disease on agricultural production and being favorable to sustainable development of agriculture. Therefore, the deep learning algorithm proposed in the paper is of great significance in intelligent agriculture, ecological protection, and agricultural production.

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user’s personality

2016 ◽  
Vol 2 ◽  
pp. e81 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Semantic Information ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Heuristic Analysis

Due to the rapid development of information technology, the Internet has gradually become a part of everyday life. People would like to communicate with friends to share their opinions on social networks. The diverse behavior on socials networks is an ideal reflection of users’ personality traits. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic analysis. Although they work fairly well, they are hard to extend and maintain. In this paper, we utilize a deep learning algorithm to build a feature learning model for personality prediction, which could perform an unsupervised extraction of the Linguistic Representation Feature Vector (LRFV) activity without supervision from text actively published on the Sina microblog. Compared with other feature extractsion methods, LRFV, as an abstract representation of microblog content, could describe a user’s semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using a linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of the prediction model, respectively. The results show that LRFV performs better in microblog behavior descriptions, and improves the performance of the personality prediction model.

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