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Impact of enterprise human capital on technological innovation based on machine learning and svm algorithm

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-13页

作者： Liu, Jintao Binzhou Coll Econ & Management Sch Binzhou 256600 Shandong Peoples R China

Human resource is the most important resource of an enterprise, and good talents can lead the enterprise to make continuous breakthroughs. So far, human resource has become the key to the competition of enterprises in the market. The reasonable distribution of human resource can enhance the competitiveness of enterprises. In addition, giving full play to the role of talents can promote the innovation and development of enterprises. Therefore, we should give full play to the role of talents in the development of enterprises. Through the construction of the model, we can see that human resources have a positive role in promoting the development of enterprises. The efficient use of human resources can make the development of enterprises more stable. Enterprises are composed of people, and talents are the main driving force for sustainable development of enterprises. The dynamic ability and technological innovation ability of talents are the key to enhance the innovation ability and market competitiveness of enterprises. This paper mainly studies the influence coefficient of talent's dynamic ability and technological innovation ability. We can judge the influence degree of human resource on enterprise according to the different influence coefficient.

关键词： Machine learning svm algorithm Human capital Technological innovation

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RETRACTED: Quantitative analysis of enterprise chain risk based on svm algorithm and mathematical fuzzy set (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5773-5783页

作者： Sun, Gebing Xinjiang Univ Sch Econ & Management Urumqi 830046 Peoples R China

Under the guidance and practice of lean production, just in time production and other advanced theories, the relationship between enterprises is becoming more and more closely. In order to cope with more fierce market competition, manufacturing enterprises began to strengthen cooperation with partners in the supply chain, gather resources, improve competitiveness and jointly fight against competitors. In these decades, the competition among enterprises is gradually replaced by the competition among supply chains. In this paper, the author makes quantitative analysis of enterprise chain risk based on svm algorithm and mathematical fuzzy set. Support vector machine (svm) is a machine learning method, has strong generalization ability and accuracy. By analyzing dexterity affects the normal operation of the supply chain risk factors, we use simulated annealing - mathematical fuzzy of the risk evaluation, it indicates that the model in risk assessment is applicable through empirical research. According to the data obtained, the simulated annealing - support vector machine evaluation model were trained and tested;the explanation on the choice of kernel function of the process of construction of the evaluation model, the parameters of the model to determine some key problems.

关键词： svm algorithm supply chain risk mathematical fuzzy data accuracy

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Power System Transient Stability Prediction Based on Sample Pre-screening of svm algorithm and AdaBoost algorithm 5

Power System Transient Stability Prediction Based on Sample ...

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5th International Conference on Power and Energy Engineering (ICPEE)

作者： Chen, Xingyun Nie, Kejian State Grid Fujian Econ Res Inst Fuzhou Peoples R China

ISBN: (纸本)9781665420464

As the stability problem of modern power systems is prominent, the stable operation of the power system is more and more popular in power system analysis. To solve the problem of imbalance between the number of unstable and stable samples in transient stability prediction for power system transient prediction using machine learning methods, the paper proposes a transient stability prediction method for power system based on the svm ( Support Vector Regression) algorithm sample pre-screening and AdaBoost (Adaptive Boosting) algorithm. The svm algorithm is used to partition the sample space of the training sample set, obtain the hyperplane for partitioning, calculate the distance of each stable sample to the hyperplane and rank them. Stable samples with the same number of unstable samples are selected at certain intervals to form a new training sample set, and the AdaBoost algorithm is used to perform transient stability prediction of the power system. It is demonstrated in the IEEE39 system that the proposed method is more accurate than general machine learning algorithms and commonly used methods to deal with sample data imbalance.

关键词： power system transient stability machine learning svm algorithm AdaBoost algorithm

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Efficiency of Decision tree algorithm for Brain Tumor MRI Images comparing with svm algorithm 14

Efficiency of Decision tree algorithm for Brain Tumor MRI Im...

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14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Charan, Kappala Sai Chokkalingam, S. P. Sundari, K. K. Shantha Saveetha Univ Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665460712

To prove that the efficiency of the Decision Tree algorithm is high compared with svm algorithm for novel classification of Brain Tumor MRI Images. Materials And Methods: A total of 274 Brain Tumor MRI images are collected and samples are divided into training dataset (n=191 [70%]) and test dataset (n=83[30%]). Novel classification of MRI Images is performed by Decision Tree algorithm and svm algorithm. Results and Discussion : Brain Tumor MRI images are classified to extract the quantitative information by using Decision Tree algorithm and attained accuracy of 97 % and svm algorithm got 89%. Decision Tree algorithm and svm algorithm are statistically significant with the independent sample T-Test value (plt;0.05). The results proved that the Decision Tree algorithm has better efficiency over the svm algorithm in classification of Brain Tumor MRI images.

关键词： Brain Tumor MRI Images svm algorithm Decision Tree algorithm Novel Classification Efficiency Artificial Intelligence Machine Learning Data Mining

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Object Recognition in Substation Alarm Area Based on svm algorithm 34

Object Recognition in Substation Alarm Area Based on SVM Alg...

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34th Chinese Control and Decision Conference (CCDC)

作者： Ding, Peng Zhu, Yuancheng Zheng, Weiliang Gao, Qian Zheng, Wei Yingkou Power Supply Co State Grid Liaoning Elect Power Co Ltd Yingkou 115000 Peoples R China

ISBN: (纸本)9781665478960

As a place with extremely high safety protection factor, substations are often damaged. Although the current substations are equipped with various technical defense systems, the lack of correlation between the various systems results in a high rate of false alarms and false alarms. Protecting the safety of substations can be achieved by accurately identifying the attributes of objects in the alarm area, reducing the rate of false alarms and false alarms. Accurately identifying the attributes of objects is of great significance for the safety protection of substations. In response to such problems, this paper proposes a substation alarm area property recognition based on the svm algorithm, which integrates multiple independent information of video, sound, radar and thermal imaging to obtain multi-dimensional information of the object, and adopts a combination of feature engineering and machine learning technology. The intrusion object recognition classification algorithm realizes the object recognition in the alarm area of the substation. Through experimental analysis, it is verified that the method proposed in this paper can accurately identify the attributes of objects and provide strong support and guarantee for the safety protection of substations..

关键词： Security protection object recognition svm algorithm

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Research on Fault Determination of Active Distribution Network Based on Random Forest-svm algorithm 3rd

Research on Fault Determination of Active Distribution Netwo...

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3rd International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Zhao, Wei Xiao, Shiwu North China Elect Power Univ Sch Elect Power & Elect Engn State Key Lab New Energy Beijing Peoples R China

ISBN: (纸本)9789819905553;9789819905539;9789819905522

After the distributed generation supply is connected to the traditional distribution network, the traditional distribution network changes from "passive" to "active", and the power flowchanges from one-way to two-way. When the active distribution network fails, the short-circuit current is small, inconspicuous fault characteristics and rich harmonics make it difficult to locate faults. In this paper, a method for locating fault sections of active distribution network based on random forest-svm algorithm is proposed. The method first performs fast Fourier transform on the voltage information collected at each measurement point to form the fundamental frequency voltage of each measurement point. And the fault feature set of the 2-7th harmonic voltage, the feature importance evaluation and feature selection of each fault feature are carried out by the random forest algorithm, and finally the fault section is located by the svmalgorithm. By connecting distributed generation and nonlinear loads in the IEEE33 node distribution network, the simulation results show that the method has rapidity, accuracy and fault tolerance for fault section location.

关键词： Component Active distribution network Fault location location Random forest algorithm svm algorithm

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Research on Automatic Classification of Modern Reading Materials in Junior High School Language based on svm algorithm 2

Research on Automatic Classification of Modern Reading Mater...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zhang, Yanjie Fanxian Banqiao Experimental School Puyang China

ISBN: (纸本)9798350317558

Modern reading is a very important part of junior high school language learning. Modern reading material genre classification can allow students to find reading materials more accurately and quickly. Traditional classification methods cannot solve the problem of genre classification of modern reading materials in junior secondary languages. the genre classification of modern reading materials. First of all, the computer is used to classify the genre of modern reading materials, the genre classification of modern reading materials, which is reduced Disturbing factors in the genre classification of modern reading materials. Then, the computer classifies the genre of modern reading materials in junior middle school to form a classification scheme for modern reading materials, and classifies the genres of modern reading materials the svm algorithm classifies the genre of modern reading materials in junior high school under certain evaluation criteria The accuracy and classification time of modern reading materials are better than traditional classification methods. © 2024 IEEE.

关键词： Computer Genre classification of modern reading materials Junior high school language svm algorithm

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Design of Virtual Bass Enhancement algorithm Based on svm algorithm

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Procedia Computer Science 2022年 208卷 231-238页

作者： Jianhua Li

The purpose of target detection is to detect and locate a set kind of target object from still pictures or videos. Most of the existing studies have simplified the target detection problem into a binary classification problem. Considering the superiority of support vector machine in pattern recognition, how to apply it to target detection has become the focus of computer vision. The traditional support vector machine is based on two kinds of problems, and how to effectively extend it to multiple kinds is still a problem to be studied. There are many unique advantages in solving small and nonlinear high-dimensional model recognition problems, and these problems can also be applied to other automatic learning problems, such as the attribute algorithm currently used to define models, bec feedback. The processing process of traditional virtual bass enhancement algorithm is analyzed, and it is found that the processing effect of different frequency components in the sound source is not uniform, which has a great influence on the final bass enhancement effect. Therefore, a new virtual bass enhancement algorithm is proposed based on the idea of sound source separation, which can reduce the distortion and improve the bass enhancement effect by separating the transient and steady components of the sound source before bass enhancement.

关键词： svm algorithm virtual bass target detection sound source separation

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COMPARISON OF svm algorithm AND BP algorithm: STUDY ON THE EVALUATION INDEX SYSTEM OF SCIENTIFIC RESEARCH PERFORMANCE OF VOCATIONAL COLLEGES

ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS

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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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NOVEL MULTI-CLASS svm algorithm FOR MULTIPLE OBJECT RECOGNITION

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INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 2015年第2期8卷 1203-1224页

作者： Wang, Yongqing Zhang, Yanzhou Zhengzhou Inst Aeronaut Ind Management Dept Comp Sci & Applicat Zhengzhou 450015 Peoples R China Henan Aviat Econ Res Ctr Aviat Econ Dev & Aeronaut Mat Technol Collaborat Zhengzhou Peoples R China Henan Polytech Basic Course Dept Zhengzhou 450046 Peoples R China

Object recognition is a fundamental task in applications of computer vision, which aims at detecting and locating the interested objects out of the backgrounds in images or videos, and can be originally formulated as a binary classification problem that can be effectively handled by binary svm. Although the binary technique can be naturally extended to solve the multiple object recognition, which are known as one-vs.-one and one-vs.-all techniques, but the scalability of traditional methods tend to be poor, and limits the wide applications. Inspired by the idea presented by Multi-class Core Vector Machine, we propose a novel Multi-class svm algorithm, which achieves excellent performance on dealing with multiple object recognition. The simulation results on synthetic numerical data and recognition results on real-world pictures demonstrate the validity of the proposed algorithm.

关键词： Object recognition computer vision multi-class svm algorithm classification problem

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