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ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2018年第40期 244-255页

作者： Feng, Jiayin Jia, Dongyan Cui, Li Cao, Jing Lin, Zhuo Zhang, Min Hebei Normal Univ Sci & Technol Qinhuangdao Haigang Dist 066000 Heibei Peoples R China

As the education in China develops rapidly, the scientific research of higher vocational education has gradually drawn extensive attentions. In order to construct a reasonable evaluation model of scientific research performance to further enhance the research enthusiasm of teachers, this study constructed a model based on relevant theories of the support vector machine (svm) algorithm and the back propagation (BP) algorithm. In addition, the simulation of the model was performed and the accuracy rate and errors of these two algorithms were compared and analyzed. Then the most appropriate algorithm was applied to the evaluation index system. The simulation results showed that, simplified data of scientific research evaluation could be applied as the input data of the svm algorithm to accurately and effectively construct an evaluation index system of scientific research performances of vocational colleges. Thus a more reasonable and accurate evaluation system was constructed.

关键词： svm algorithm BP algorithm vocational colleges scientific research performance evaluation

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Sustainable Development Model of New Culture in Rural Community based on svm Intelligent algorithm 9

Sustainable Development Model of New Culture in Rural Commun...

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

作者： He, Dongming Hubei Inst Fine Arts Wuhan 430205 Hubei Peoples R China

ISBN: (纸本)9781509048687

In this paper, a new sustainable development model of new culture in rural community based on svm intelligent algorithm is provided. Through nonlinear mapping, economic and cultural factors are grouped together, comprehensive consideration of various factors, vegetation selection and cultural infrastructure construction site, the results show the model is more effective.

关键词： sustainable space svm algorithm new culture comprehensive location

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Plateau underground engineering geological safety risk assessment based on Bootstrap-svm-BPNN

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PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2024年 1-13页

作者： Chen, Yongling Hu, Junfeng Yang, Hu Zhan, Chun Wang, Jia China Geol Survey Res Ctr Appl Geol Chengdu 610036 Peoples R China

Underground engineering has become an extremely complex, highly uncertain and dynamic system problem due to the influence of various complex natural geological conditions and many unknown factors. Based on Bootstrap-svm-BPNN optimization model, this paper predicts the underground engineering situation of plateau and analyzes the geological safety risk. The principle of Bootstrap algorithm and svm algorithm is introduced, and the possibility of combining them is realized. The pseudo-sample set was built based on Bootstrap method, and the svm model was used to train and predict it, and the mean and variance of the predicted value were calculated. The random error data set is constructed, and MEA-BPNN is used to train and predict the deviation of random error, so as to realize the hybrid algorithm of Bootstrap-svm-BPNN geological safety prediction. Comparing and analyzing various models, the mixed learning model in this paper has the best prediction effect, R2 is up to 98%, RMSE value is 7.2. Finally, the Bootstrap-svm-BPNN model was applied to practical engineering, and it was found that micro-filled and semi-filled types were still the most common positive samples in the real positive samples. The number of positive samples correctly predicted by interference type increased by 6, and AP increased by 56.15%. The accuracy of the micro-filling model was 41.41%, and the recall rate was 89.13%. The accuracy of the semi-filling type is 57.45%, and the recall rate is 96.43%. The use of the Bootstrap-svm-BPNN optimization model for geological exploration can truly predict various disasters and reduce the negative impact of risk factors, thereby overall lowering the probability level of disaster occurrence.

关键词： Bootstrap-svm-BPNN model MEA-BPNN svm algorithm Pseudo sample set Geological safety

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Design and verification of use case generation algorithm based on multiple combination tests

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第2期23卷 69-79页

作者： Zheng, Genrang Sch Informat Engn Zhongshan Polytech Zhongshan 528404 Peoples R China

To solve the problem of low accuracy of interface test cases, an AdaBoost + svm use case generation algorithm based on multiple combination tests is proposed. Multiple svm classifiers are homogeneously integrated by using serial AdaBoost integration methods, and they are simulated by using the PICT combination test tools. The results show that the proposed AdaBoost + svm use case generation algorithm can generate multiple combination interface test cases automatically, and it has a strong interface defect detection capability. Compared with the use case generation algorithm of single svm classifier and parallel bagging integrated svm classifier and the current popular use case generation algorithm, the proposed AdaBoost + svm algorithm performs better in accuracy, precision, recall, F1 value and AUC values, which has certain advantages and can improve the accuracy of interface test.

关键词： combination test interface test use case generation svm algorithm ensemble learning

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Corrosion inhibition by Myristica fragrans extract in neem biodiesel: Evaluation by machine learning and traditional methods

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第17期238卷 8829-8837页

作者： Iyyappan, S. Kumar, K. P. Vinod Ponram, P. Ponjesly Coll Engn Dept Mech Engn Nagercoil Tamil Nadu India Univ Coll Engn Dept Chem Nagercoil 629004 Tamil Nadu India Univ Coll Engn Dept Elect & Commun Engn Nagercoil Tamil Nadu India

Ethyl acetate extract of Myristica fragrans mace flowers was investigated as an effective inhibitor for copper in neem biodiesel. Employing the ASTM D130 method to assess copper corrosion in biodiesel, a remarkable inhibition efficiency of 98.04% was achieved. Inhibitor adsorption on the surface of copper metal was substantiated by FTIR spectral evaluation. Surface morphology studies by SEM confirmed the protective nature of the inhibitor for copper metal corrosion prevention. Further, several support vector machine (svm) learning algorithms were employed with three different classes of corrosion to study the corrosion inhibition of which, the medium Gaussian procedure gave a maximum accuracy of 92.4%. The work reveals the applicability of the svm algorithm for accurate corrosion estimation and classification not only in biodiesel medium but also in various environments.

关键词： svm algorithm corrosion inhibitors machine learning biodiesel corrosion

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Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-svm model

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第5期44卷 3778-3791页

作者： Huang, He Zhong, Liwei Shen, Ting Wang, Huixin Univ Shanghai Sci & Technol Business Sch Shanghai Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Municipal Hosp Tradit Chinese Med Shanghai Peoples R China

The coronavirus disease (COVID-19) pandemic has caused significant changes in the external environment of enterprises, resulting in tremendous negative impacts. Accordingly, the irregular fluctuation of business data poses a critical challenge to traditional approaches. Therefore, to combat the effects of the COVID-19 pandemic, an effective model is required to proactively predict an enterprise's performance and simultaneously generate scientific performance optimization solutions. Consequently, at the intersection of artificial intelligence algorithms, operations research, and management science, an intelligent DEA-svm model, which has a theoretical contribution, is developed in this study. The capabilities of this model are verified through sufficient numerical experiments. On the one hand, this model outperforms traditional algorithms in prediction accuracy. On the other hand, effective performance optimization solutions for low-performance enterprises are obtained from the input-output perspective. Moreover, the application value of this model is reflected in its successful implementation in the healthcare industry. Thus, it is a user-friendly tool for realizing the stable operation of enterprises in the context of the COVID-19 pandemic.

关键词： svm algorithm Data envelopment analysis Healthcare management Performance prediction Performance optimization

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Credit risk assessment of small and medium-sized enterprises in supply chain finance based on svm and BP neural network

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NEURAL COMPUTING & APPLICATIONS 2022年第15期34卷 12467-12478页

作者： Zhao, Jingfeng Li, Bo North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450046 Henan Peoples R China

Our country's market economy is composed of enterprises. However, due to their inherent credit deficiencies and high risks of management, it is very difficult for them to obtain financing support. Based on this, this article studies Error Back Propagation (BP) to establish (SMEs). Based on the relevant concepts of the supply chain management budget model, it explores the main factors influencing the financial impact of SMEs and the benefits of the supply chain budget in solving problems expenditure of SMEs, support vector machine is mainly based on solving the main credit risks of small and medium-sized enterprises, such as poor information transparency, low credit and various risk unknown factors. BP neural network is an algorithm that takes into account the components of supply chain financial financing. This article first gives a simple background and theoretical introduction to the under the current supply chain finance model, and then proposes to use svm and BP neural network algorithms to build and the model has been trained and tested. After collecting relevant references, we will establish authoritative risk assessment rules in accordance with this article according to these standards. These experts are mainly people with many years of experience in the financial industry, and they also have a certain influence in the industry. The risk assessment established for this can be the analysis of factors such as risk indicators and government intervention in this experiment.

关键词： svm algorithm BP neural network algorithm Supply chain finance Small and medium enterprises Credit risk assessment

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Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method

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OPEN GEOSCIENCES 2024年第1期16卷

作者： Zhao, Longhui Qin, Yigen Yang, Genlan Zhang, Wuzhou Lu, Kunpeng Guizhou Water Resources & Hydropower Survey Design Guiyang 550004 Peoples R China Sun Yat Sen Univ Sch Earth Sci & Engn Guangdong Prov Key Lab Geodynam & Geohazards Guangzhou Peoples R China Guizhou Univ Coll Resource & Environm Engn Guiyang 550025 Peoples R China Guizhou Zhiheng Engn Survey & Design Consulting Co Guiyang 550004 Peoples R China

This study aims to develop a comprehensive method for automated rockfall mapping and susceptibility assessment using unmanned aerial vehicle (UAV) tilt photography and the support vector machine (svm) algorithm. By employing Jinzhong Town in the mountainous forest landscapes of Southwest China as a case study, we leverage photogrammetry principles and computer vision algorithms to generate high-precision, high-resolution digital surface models (DSMs), and digital orthophoto maps through a UAV remote sensing system. The rockfall inventory is accurately and automatically mapped using the object-based classification method and svm algorithm. The automated rockfall identification method achieves a 93% accuracy with a Kappa coefficient of 0.7967. Statistical analyses of spatial distribution characteristics reveal a significant correlation between rockfall volume and area with a correlation coefficient (R-2) of 0.92316 under logarithmic coordinates. In addition, a power function relationship describes the link between rockfall volume and slope height (R-2 = 0.87), while the relationship with sliding distance is characterized by a weaker linear correlation (R-2 = 0.65). Rockfall runout distance also shows a significant linear correlation with slope height (R-2 = 0.79) but exhibits a less-pronounced association with rockfall volume (R-2 = 0.58). The svm model employed to assess rockfall susceptibility indicates high accuracy (area under the curve = 0.896), affirming its efficacy in rockfall susceptibility assessment. Our findings underscore the utility of UAV remote sensing for rockfall information extraction and susceptibility evaluation, particularly in challenging mountainous forest environments characterized by intricate topography and geological complexities.

关键词： UAV object-based image analysis svm algorithm rockfall susceptibility evaluation mountainous forest

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Footwork recognition and trajectory tracking in track and field based on image processing

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Zhu, Jiaju Zhang, Zhong Liu, Runnan Liu, Junyi Northeast Normal Univ Sch Phys Educ Changchun 130024 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130015 Peoples R China Harbin Engn Univ Sch Ship Engn Harbin 150006 Peoples R China

In track and field sports, footwork can greatly affect the effect and performance of sports. Accurate footwork can effectively improve the performance of professional athletes, and for ordinary trainers, it can reduce the probability of training injuries. To solve the problem that traditional footwork is inaccurate and not well accepted by people, this paper has used an image processing method based on support vector machine (svm) algorithm to identify and track the footwork. In this paper, a 13-s video image was extracted frame by frame from the athletes' videos in Olympic sports competitions, and the athletes' footwork was used as a benchmark to track their motion trajectories, extracting the corresponding feature points and categorizing them. 10 school athletes, 6 males and 4 females, were selected to track their movement pace and trajectory with a camera. The behaviors were standardized according to the extracted features, and the behaviors before and after standardization were compared. The results showed that the svm algorithm had the most stable classification accuracy, higher recognition accuracy and better performance compared with other classification algorithms. Image processing of standardized track and field movements was effective in improving athletes' performance, with all 10 athletes tested improving their performance between 0.4 and 0.6. The svm algorithm-based image processing method is more acceptable after validation of its effectiveness, and the method can be extended more easily.

关键词： Track and field sports Footwork recognition SIFT (scale-invariant feature transform) feature extraction svm algorithm

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Fall Monitoring System Based on Wearable Device and Improved KNN

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2024年第4期58卷 366-378页

作者： Tan, Diyuan Yao, Binbin Wang, Zhe Henan Agr Univ Coll Landscape Architecture & Art Zhengzhou 450002 Peoples R China Zhongyuan Inst Sci & Technol Sch Electromech Engn Xuchang 461000 Peoples R China

For the elderly, falls can be extremely fatal. However, due to the physical decline of the elderly, it is difficult to avoid falls. Therefore, to the greatest extent feasible lessen the harm that falls on the elderly inflict, so that they can be found in the first time of falls, this study based on wearable devices, proposed a fall monitoring system using an improved K-nearest neighbor algorithm. The improved fuzzy K-nearest neighbor algorithm combined with support vector machine algorithm is applied to improve the efficiency and accuracy of the algorithm, and reduce the false positive rate and false negative rate as much as possible. The suggested model's average precision in the simulation experiment is 97.5%. The specificity was 97.6%. The sensitivity was 97.5%. The convergence performance is also good, 24 iterations can reach the optimal. In the actual experiment, the average accuracy reached 98.7%;The false alarm rate is only 0.7%;The negative rate was 2.5%;Its performance is superior to other two algorithms. This shows that the proposed method has excellent accuracy, false positive rate and false negative rate in practical application, which has important significance for the health and safety of the elderly.

关键词： fall detection system KNN algorithm svm algorithm fuzzy set

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