scholarly journals Empirical Study on Theories and Techniques of Adolescent Physical Health Promotion under the Background of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haolun Xu
Keyword(s):  
Health Promotion ◽  
Big Data ◽  
Physical Health ◽  
Classification Accuracy ◽  
Health Data ◽  
Training Set ◽  
Accuracy Rate ◽  
Standing Long Jump ◽  
Long Jump ◽  
Accuracy Rates

In order to make my country’s youth health management more scientific, more standardized, and more effective, it is necessary to conduct in-depth research on the management of youth physical health promotion. Through the investigation and analysis of the physical health data of adolescents in my country, this paper proposes that the results of health intervention training as part of the empirical research on the construction of adolescent health big data management service system can effectively improve the relationship hypothesis of the physical health of adolescents and by selecting the example of our country’s “Adolescent Physical Health Data Analysis in 2020” for regression analysis. The research results show that the theory of adolescent physical health promotion can improve the physical health of adolescents by interfering with students’ physical exercise. In the processing of data, GBDT is suitable when the training set is relatively large, and as the sample size increases, the accuracy rate can reach 79.79%. In terms of the classification accuracy of male sitting forward bending promotion, the accuracy of the RF method is higher than that of GBDT. In terms of the promotion classification effect of boys’ 1000 m running, the RF method achieved the highest promotion accuracy rate of 77.62%. In the male pull-ups to promote the classification effect, when the proportion of the training set is 60%, the RF method gets the highest accuracy rate, which is 92.04%. The results of the classification effect for girls standing long jump promotion show that the classification accuracy rate for girls standing long jump promotion is between 51% and 56%. When the training set is less than 60%, the RF method is the best, the highest is 53.93%, and the rest is the GBDT method, the highest is 55.46%; in Macro-F1, the RF and GBDT indicators have their own advantages. In the promotion of the classification effect on the final fitness level of girls, the accuracy rates of RF and GBDT methods range from 90% to 96%, and the accuracy rates of the NN method range from 80% to 87%; when the practice rate reaches 80%, the GBDT method achieves the highest accuracy rate of 95.06%; on the Macro-F1 index, the GBDT method is obviously the best.

scholarly journals Health data analysis based on multi-calculation of big data during COVID-19 pandemic

2020 ◽  
Vol 39 (6) ◽  
pp. 8775-8782
Author(s):  
Yang Bo ◽  
Wang Chunli
Keyword(s):  
College Students ◽  
Big Data ◽  
Physical Health ◽  
Reference Value ◽  
Health Data ◽  
Digital Information ◽  
Medical Institutions ◽  
Asymptomatic Infections ◽  
And Control ◽  
Analysis Platform

Under the influence of the COVID-19, the analysis of physical health data is helpful to grasp the physical condition in time and promote the level of prevention and control of the epidemic. Especially for novel corona virus asymptomatic infections, the initial analysis of physical health data can help to detect the possibility of virus infection to some extent. The digital information system of traditional hospitals and other medical institutions is not perfect. For a large number of health data generated by smart medical technology, there is a lack of an effective storage, management, query and analysis platform. Especially, it lacks the ability of mining valuable information from big data. Aiming at the above problems, the idea of combining Struts 2 and Hadoop in the system architecture of the platform is proposed in this paper. Data mining association algorithm is adopted and improved based on MapReduce. A service platform for college students’ physical health is designed to solve the storage, processing and mining of health big data. The experiment result shows that the system can effectively complete the processing and analysis of the big data of College students’ physical health, which has a certain reference value for college students’ physical health monitoring during the COVID-19 epidemic.

scholarly journals Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Hou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Physical Fitness ◽  
Physical Health ◽  
Convolutional Neural Network ◽  
Deep Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Standing Long Jump ◽  
Long Jump

The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short-Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull-up, 50 m sprint, and 1000 m long-distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.

scholarly journals PO-303 The Physical Health Characteristics and Its Causes Analysis of Female Dance Students

2018 ◽  
Vol 1 (5) ◽  
Author(s):  
Rou Wen ◽  
JingJing Xue
Keyword(s):  
Physical Health ◽  
Vital Capacity ◽  
Female Students ◽  
The Body ◽  
Test Results ◽  
Lung Capacity ◽  
Dance Training ◽  
Standing Long Jump ◽  
Long Jump

Objective Dance is an art form with the body as the carrier, the long-term systematic dance training will make a certain effect on the physical health. By comparing the physical health test results of the female college students majoring in dance with female general students’, this research will analyze the characteristics of physical health of female dance students and its influencing factors, and provide the basis of dance can promote the physical health of women, which can help dance to play a role in promoting the national physical and mental health. Methods The physical health data of female students majoring in dance in Beijing dance academy and female students majoring in liberal arts in a normal university in Beijing in 2018 were analyzed and studied. The physical test indexes include: (1) body morphology  indexes: height, weight; (2) body function indexes: vital capacity; (3) Physical Fitness indicators: reaction selection time, sit and reach, standing long jump, 50m run, 800m run, sit-ups. SPSS17.0 software was used to process the data, and independent sample T test was used to compare and analyze the data of the two groups, and P<0.05 was taken as a significant difference. Results The physical health test results of female students majoring in dance and ordinary female students are as follows:(1) BMI: The former is 19.58±1.72kg/m2; the latter is 20.43±2.86kg/m2.(2)Vital Capacity: The former is 2613.19±546.7ml; the latter is 2597.12±449.34 ml.（3）50m Run：The former is 8.94±0.952s;the latter is 9.48±0.62s; 800m Run：The former is 244.4±30.6s;the latter is 238.1±22.2s; Sit-and-Reach：The former is 28.34±4.14cm; the latter is 18.86±6.62cm; Standing Long Jump: The former is 180.04±17.79cm,the latter is 162.73±16.39cm. One Min Sit-Ups：The former is 40.52±6.5times; the latter is 29.44±8.02times.In addition to the Vital Capacity, the test results of other indicators all showed significant differences (P<0.05). Conclusions (1) Compared with ordinary college students, female students majoring in dance have a lower BMI. As a physical activity, dance can stimulate bone growth; At the same time, the daily dance training increases the energy consumption of the body, besides that, dance students must limit the daily diet to control the weight to meet the special dance figure requirements.（2）The lung capacity of female students majoring in dance is slightly higher than that of ordinary students, but the difference is not significant, which indicates that the training of dance has no obvious effect on the lung capacity.（3）The strength, speed, flexibility and other physical indicators of female students majoring in dance were all significantly higher than those of ordinary students. As far as strength is concerned, lower limb ability is an important part of dance training, long-term dance training will improve the explosive power of lower limb and body coordination, so female dancers performed well in the standing long jump. Dance major students have a higher score of one-minute sit-ups, because the strength of lumbar and abdominal muscle group is also an important content of dance training, it has been improved to a certain extent after a long period of training. The complex and diverse changes in the speed and spatial position of dance movements are conducive to the improvement of the speed quality and the flexibility of the nervous system. Therefore, female students majoring in dance are relatively faster in the 50-meter running. Dance has a really high requirement for flexibility, which is also an important content of dance training. After a long-term training, the flexibility of students majoring in dance has been greatly improved, which is reflected an obvious advantages in the value of the sit-and-reach. However, female dance majors did not have an advantage in lung capacity and 800-meter running, indicating that their lung functions and endurance capacity were at a general level. In the dance training, there are more intervals during the movements and less continuous movements for a long time, which has little effect on improving the function of the aerobic metabolism system. This suggests that students majoring in dance should carry out targeted aerobic exercise to improve their endurance. To sum up, on the whole, female students majoring in dance have a relatively high level of physical health, especially with advantages in body shape, muscle strength and flexibility. It shows that the beneficial effect of long-term dance training on physical health. Therefore, how to incorporate dance into the national physical health system as an important means to promote national health, and how to take certain measures to encourage the public to actively participate in dance activities to bring the health functions of dance fully play are worthy of more attention and deeper research.

scholarly journals The Influence of Football Training Based on Big Data on Physical Function and Football Skills

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Changjun Hu ◽  
Yang Sun
Keyword(s):  
Big Data ◽  
Physical Function ◽  
T Test ◽  
The Third ◽  
Standing Long Jump ◽  
Significant Difference ◽  
Before And After ◽  
Long Jump ◽  
Football Training ◽  
The Impact

In order to actively respond to the government’s call to scientifically create campus football culture, combine the characteristics of football sports, and improve people’s understanding of the mental and intellectual functions of football, this article focuses on the impact of football training on physical function and football technology. Based on the understanding of related theories, the experiment on the impact of football training on physical function and football technology was carried out. The experimental results showed that the weight, height, and BMI increased significantly during the period of football training ( P < 0.05 ). The independent sample T test showed that there were no significant differences in height, weight, and BMI between the two groups before and after training; the standing long jump performance of the control group after training showed an upward trend, but the significance level was not statistically significant. Three months later, the time for the experimental team to complete the eight-character dribble test in football training was reduced from 20.51 seconds to 15.57 seconds. The independent sample T test found that there was no significant difference in the physical fitness of the two groups before training and the changes in football skills of the subjects before and after training. Then, the clustering algorithm in the big data was used to analyze the data of the experimental group. The standing long jump has the highest performance; the second category belongs to the third level, and the third category belongs to the second level.

scholarly journals Research and Implementation on Power Analysis Attacks for Unbalanced Data

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaoyi Duan ◽  
Dong Chen ◽  
Xiaohong Fan ◽  
Xiuying Li ◽  
Ding Ding ◽  
...  
Keyword(s):  
Random Forest ◽  
Classification Accuracy ◽  
Power Analysis ◽  
Prediction Accuracy ◽  
Random Forest Algorithm ◽  
Training Set ◽  
Accuracy Rate ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Weight Model

In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation data, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic minority oversampling technique (SMOTE) is proposed. It compensates for the problem that the random forest algorithm is affected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall classification accuracy rate. The experimental results show that when the training set data is 800, the random forest algorithm predicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest algorithm improves the prediction accuracy of the same set of test data by 91%. The classification accuracy rate of a few categories has increased from 0% to 100%.

scholarly journals An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Hakan Gunduz
Keyword(s):  
Reduction Method ◽  
Feature Reduction ◽  
The Other ◽  
Recursive Feature Elimination ◽  
Accuracy Rate ◽  
Stock Performance ◽  
Attention Model ◽  
Feature Sets ◽  
Accuracy Rates ◽  
F Measure

AbstractIn this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models. These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics. While the first experiments directly used the own stock features as the model inputs, the second experiments utilized reduced stock features through Variational AutoEncoders (VAE). In the last experiments, in order to grasp the effects of the other banking stocks on individual stock performance, the features belonging to other stocks were also given as inputs to our models. While combining other stock features was done for both own (named as allstock_own) and VAE-reduced (named as allstock_VAE) stock features, the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination. As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model, the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675. Although the classification results achieved with both feature types was close, allstock_VAE achieved these results using nearly 16.67% less features compared to allstock_own. When all experimental results were examined, it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features. It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.

Sign in / Sign up

Export Citation Format

Share Document