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An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT

TECHNOLOGY AND HEALTH CARE 2024年第4期32卷 2657-2671页

作者： Li, Jiahao Beijing Sports Univ Competit Sports Coll Beijing Peoples R China

BACKGROUND: The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. OBJECTIVE: The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. METHODS: A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and random forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times RESULTS: The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. CONCLUSION: All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.

关键词： Discrete wavelet transform random forest algorithm three-dimensional mechanics gyroscope computational efficiency

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Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China

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NATURAL HAZARDS 2021年第1期107卷 331-353页

作者： Huang, Xiao-yan He, Li Zhao, Hua-sheng Huang, Ying Wu, Yu-shuang Guangxi Res Inst Meteorol Sci Nanning Peoples R China

The recent emergence of satellite detection and imaging technologies has increased the demand for the application of satellite cloud images to current weather forecasting. However, approaches based on nonlinear prediction technology to forecast satellite images are lacking, and forecasting timelines are relatively short, e.g., 1-3 h in advance. In the present study, a nonlinear dimensionality reduction approach based on Laplacian eigenmaps (LEs) was combined with a random forest (RF) algorithm to construct an intelligent computing prediction model for rainstorm satellite images obtained from the first annual rainy season (April-June) in South China from 2010 to 2018. Results showed that the proposed forecasting model based on nonlinear intelligent calculation can accurately predict the key features and trends of the development and movement of strong precipitation clouds. The predicted satellite images described by the model were also consistent with the major features of the observed satellite images. This study then used a multiple linear regression (MLR) method based on the same prediction factors to establish a model for predicting satellite images for the same modeling and forecasting samples. Comparative results of the two prediction schemes showed that the LE + RF algorithm satellite image prediction scheme yields more samples exhibiting a high correlation with observed satellite images than the MLR method. Compared with that of the proposed scheme, the amount of samples of the MLR scheme in the low-correlation area was significantly larger. In general, the nonlinear intelligent computing scheme developed in this study is superior to the MLR method for predicting satellite cloud images. Thus, the LE + RF algorithm satellite image prediction scheme provides an objective and practical method for observed satellite cloud image predictions.

关键词： Satellite cloud images Heavy rainfall Laplacian eigenmaps random forest algorithm Multiple linear regression

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Predicting the postmortem interval using human intestinal microbiome data and random forest algorithm

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SCIENCE & JUSTICE 2021年第5期61卷 516-527页

作者： Hu, Lai Xing, Yu Jiang, Pu Gan, Li Zhao, Fan Peng, Wenli Li, Weihan Tong, Yanqiu Deng, Shixiong Chongqing Med Univ Dept Forens Med 1 Yixueyuan Rd Chongqing 400016 Peoples R China Chongqing Jiaotong Univ Sch Humanities 66 Xuefu Rd Chongqing 400016 Peoples R China

Gradual changes in microbial communities in a human body after death can be used to determine postmortem interval (PMI). In this study, gut microflora samples were collected from the vermiform appendix and the transverse colon of human cadavers with PMIs between 5 and 192 h. The results revealed that the appendix might be an excellent intestinal sampling site and the appendix flora had an inferred succession rule during human body decomposition. Firmicutes, Bacteroidetes, and their respective subclasses showed a predictable succession rule in relative abundance over time. A random forest regression model was developed to correlate human gut microbiota with PMI. We believe that our findings have increased the knowledge of the composition and abundance of the gut microbiota in human corpses, and suggest that the use of the human appendix microbial succession may be a potential method for forensic estimation of the time of death.

关键词： Human gut microbiome High-throughput sequencing Postmortem interval random forest algorithm

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Influencing Factors of Students' Online Learning Satisfaction During the COVID-19 Outbreak: An Empirical Study Based on random forest algorithm 19th

Influencing Factors of Students' Online Learning Satisfactio...

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19th International Conference on Web-Based Learning / 5th International Symposium on Emerging Technologies for Education ICWL/SETE)

作者： Zhang, Yuzhuo Zhang, Po Yang, Hongji Zhao, Kangrui Han, Chunmei Peoples Publ Secur Univ China Beijing Peoples R China Northeast Agr Univ Key Lab Dairy Sci Minist Educ Harbin Peoples R China

ISBN: (纸本)9783030669058;9783030669065

New coronavirus is wreaking havoc around the world and has a profound impact on the international community, especially on higher education. Online teaching provides an effective path for higher education to avoid the risk of cross-contagion in traditional classroom education under epidemic condition. In order to ensure the quality of online teaching during the epidemic, this study takes the students' satisfaction of online education learning as a measurement object. 1120 online learners from 126 colleges and universities in 26 provinces were investigated through 50 questions survey in 10 dimensions. First, the chi-square test is used to pre-process all the characteristics of the factors, and 30 influencing factors with the highest feature correlation are selected. random forest algorithm is used to establish a satisfaction classification model in the training set. The accuracy in the test set is 0.72. Through the ranking of feature contribution, the influential factors with higher weight are obtained. The results show that in online learning, the attractiveness of teachers' teaching methods is the most influential factor, while curriculum arrangement and learning environment rank second and third. Finally, according to the research results, this paper puts forward some suggestions and counter measures.

关键词： Online learning Online teaching Learning satisfaction Machine learning random forest algorithm COVID-19

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Taxi-Out Time Prediction at a Busy Airport using random forest algorithm 40

Taxi-Out Time Prediction at a Busy Airport using Random Fore...

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IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

作者： Kim, Jihoon Baik, Hojoing Korea Aerosp Univ Sch Air Transport & Logist Seoul South Korea

ISBN: (纸本)9781665434201

Taxi-out time prediction is one of the key elements required for successful implementation of Airport Collaborative Decision Making (A-CDM) system. In this paper, we discuss an application of a Machine Learning technique called random forest algorithm to predict taxi-out time. Using information retrieved from Incheon International airport's A-CDM and METAR databases, the model is trained and tested. This paper presents the process of selecting explanatory variables (i.e., features) using feature importance and demonstrates the prediction accuracy of the resulting model.

关键词： Taxi-Out Time Prediction random forest algorithm Feature Importance

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Research on the Classification of High Dimensional Imbalanced Data Based on the Optimizational random forest algorithm 9

Research on the Classification of High Dimensional Imbalance...

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9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Bo, Su Guizhou Inst Technol Guiyang 550025 Guizhou Peoples R China

ISBN: (纸本)9781509048687

The random forest algorithm is a new classification and prediction model algorithm. So far, there is not much research on the problem of unbalanced data for random forest classification, ditto, no direct and effective method. On the basis of feature selection algorithm based on correlation measure, the integration feature selection method was helpful to increase the selection probability of classification feature of positive class samples, which used the subspace selection algorithm based on stratified sampling, sampling the feature subset generated by the integration feature selection method, respectively, which ensured the importance of the feature and the difference of the generated model. The simulation results show that the proposed method is effective.

关键词： random forest algorithm High dimension Unbalanced data Classification

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Research on network intrusion detection method of power system based on random forest algorithm 13

Research on network intrusion detection method of power syst...

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13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Zhu, Guowei Yuan, Hui Zhuang, Yan Guo, Yue Zhang, Xianfei Qiu, Shuang Hubei Elect Power Co Ltd State Grid Informat & Commun Branch Wuhan 430077 Peoples R China

ISBN: (纸本)9781665438926

Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision free. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.

关键词： random forest algorithm Power system Network intrusion Intrusion detection

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Research on Non-Intrusive Load Monitoring Based on random forest algorithm 4

Research on Non-Intrusive Load Monitoring Based on Random Fo...

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4th International Conference on Smart Grid and Smart Cities (ICSGSC)

作者： Wu Guohua Yuan Diping Yin Jiyao Zeng Wenhua Deng Peng Xiao Yiqing Harbin Inst Technol Shenzhen 518000 Peoples R China Shenzheng Urban Publ Safety & Technol Inst Shenzhen 50080 Guangdong Peoples R China

ISBN: (数字)9781728194042

ISBN: (纸本)9781728194042

It is of great significance for load monitoring to monitor illegal use of electricity. Load monitoring can provide supervision for government and improve residents' awareness of safety. Compared with the intrusive load monitoring, non-intrusive load monitoring has the advantages of good economy, high reliability, and quickly realizing the electricity decomposition. Many scholars have carried out this research, but there still exists the problems: it is difficult to obtain key information from big data and the diagnostic results are inaccuracy. Therefore, load monitoring is conducted in this paper. To overcome the above shortcomings, firstly this paper obtains key information of sample based on harmonic analysis. Secondly, random forest based on multiple decision trees has better accuracy in recognition. So the random forest algorithm is applied to machine learning and pattern recognition. Finally, the proposed method is identified and analyzed. The results show that the accuracy of the online electrical detection based on the harmonic analysis and random forest algorithm is greater than 86%, which shows the effectiveness of the method.

关键词： non-intrusive load monitoring harmonic analysis random forest algorithm

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The reservoir screening standard of CO2 huff-n-puff based on orthogonal analysis method and random forest algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第11期14卷 3111-3123页

作者： Wang, Xiaoyan Li, Dongping Zhang, Yang Wang, Haifeng Liu, Shuangfeng Li, Lingling Pang, Zhanxi CNPC Dagang Oilfield Tianjin 300280 Peoples R China China Univ Petr State Key Lab Petr Resources & Engn Beijing 102249 Peoples R China

In heavy oil reservoirs, favorable reservoir properties have a positive impact on the production performance during CO2 huff-n-puff. It is significant to study the screening method of applicable conditions for CO2 huff-n-puff in actual reservoirs. To solve these problems, this paper introduced the orthogonal design method to analyze the main factors based on numerical simulation. The technical analysis and the economic evaluation were both employed to obtain the applicable conditions of selecting oil layers or injection wells during CO2 huff-n-puff. And a new algorithms of machine learning, the random forest algorithm, was introduce to find the weighted factors and the scoring standards that were suitable for CO2 huff-n-puff. Finally, a set of method for screening suitable reservoir conditions was established. Based on the introduction of orthogonal analysis method and random forest algorithm, a software was established to achieve the purpose of analyzing the feasibility of CO2 huff-n-puff considering different reservoir geological parameters. This method increased the accuracy and efficiency in screening reservoir conditions that was suitable for CO2 huff-n-puff.

关键词： CO2 huff-n-puff Orthogonal design method random forest algorithm Numerical simulation Screening method

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random forest algorithm and support vector machine for nondestructive assessment of mass moisture content of brick walls in historic buildings

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AUTOMATION IN CONSTRUCTION 2023年第1期149卷

作者： Hola, Anna Czarnecki, Slawomir Wroclaw Univ Sci & Technol Fac Civil Engn Dept Mat Engn & Construct Proc Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

The article presents the results of experimental research and numerical analyses, and also shows the usefulness of the random forest algorithm and the support vector machine for the non-destructive identification of the moisture content of brick walls in historic buildings. To train and test the models, a representative dataset, including 290 sample sets and 7 predictor variables, was used. These variables are: Y -describing the year of construction of the building;XD, XM, Um -describing the wall's moisture;XC, XS, XA -describing the concentration of salts in the wall. The analyses showed that the random forest algorithm is the most predisposed model for the non-destructive assessment of the Umc mass moisture content of brick walls in historic buildings. This is proved by the highest value of the R2, amounting to 0.968, and the lowest values of RMSE, MAE and MAPE, amounting to 1.080%, 0.791 and 14.28%, respectively.

关键词： Historic buildings Brick walls Mass moisture Non-destructive methods random forest algorithm Support vector machine Artificial neural networks

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