Management and entrepreneurship management mechanism of college students based on support vector machine algorithm

Wang, Chao; Dong, Yazhi; Xia, Yuejun; Li, Guoxu; Martínez, Óscar Sanjuán; Crespo, Rubén González

doi:10.1111/coin.12430

Cited by 13 publications

(5 citation statements)

References 15 publications

(16 reference statements)

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“…e statistical analysis methods and relevant conclusions proposed in this thesis are of certain significance to promote the theoretical research on the employment [9,10].…”

Section: Introductionmentioning

confidence: 92%

Prediction of Employment Index for College Students by Deep Neural Network

2022

Mathematical Problems in Engineering

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With the acceleration of popularization of higher education in China and the intensification of employment difficulties for college graduates, the employment field has gradually widened, the number of entrepreneurs has gradually increased, and the regional differences are obvious. The employment difficulty of college graduates has aroused wide-spread concern in the society. Therefore, the convolution neural network (CNN) is used to establish a prediction and evaluation model for the employment development trend of college graduates in this paper. The feasibility and practicability are proved by a case, which is of great significance for the government and colleges to provide decision-making support and suggestions to solve the problem of difficult employment.

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“…e statistical analysis methods and relevant conclusions proposed in this thesis are of certain significance to promote the theoretical research on the employment [9,10].…”

Section: Introductionmentioning

confidence: 92%

Prediction of Employment Index for College Students by Deep Neural Network

2022

Mathematical Problems in Engineering

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“…In the whole process of the nonlinear support vector machine algorithm transforming the low-level nonlinear feature space to the high-level linear special space, there is no change in other constraints. This also highlights the very powerful learning ability and classification prediction function of the support vector machine algorithm model from the side [4]. Some behaviors can also cause violations.…”

Section: Introductionmentioning

confidence: 83%

Artificial intelligence application in enterprise financial management big data calculation based on Virtual network mapping algorithm

2023

Preprint

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In recent years, in order to adapt to the development of the times, a new technology, namely network virtualization technology, has been produced. The research of network virtualization includes many aspects, but the most important thing is the constraint of node and link resources, as well as its topology requirements. Therefore, a new model has been developed in the corresponding era, namely the era of big data. The era of big data can bring us many benefits, and it will also bring us many disadvantages. The era of big data has provided a more comprehensive and accurate data analysis platform for corporate financial management, but it has also brought a huge impact to corporate traditional financial management. The current basic education financial risk prevention methods are relatively backward, traditional financial management mainly relies on manpower. Artificial intelligence is gradually developing and progressing, some artificial intelligence has some learning ability, knowledge reserve ability, and intelligent judgment and decision-making ability. This article uses a basic education group as the research object to analyze artificial intelligence technology in financial risk prevention and identification The application phenomenon in the process, and cited some cases for analysis.

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“…The use of big data technology can accurately reflect the demand situation of enterprises, scientifically grasp the trend of market supply and demand, help graduates understand and grasp the employment situation and employment trend of this major, and guide graduates to actively seek employment. By analyzing the data of 2021 spring campus job fair submission through big data, we can accurately grasp the graduates' tendency degree towards industry, enterprise, position, salary, and so on [11]. The employment management department of the university collects various types of information on graduates' majors, specialties, positions, salaries, working regions, etc.…”

Section: Introductionmentioning

confidence: 99%

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

Huang

2023

Wireless Communications and Mobile Computing

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The increasingly large employment group in colleges and universities has brought huge employment pressure to the society. Therefore, improving the level of professional ability of college students, providing students with personalized career development direction planning, and helping students to establish a correct concept of career selection are feasible ways to solve the structural contradiction of college students’ employment. The arrival of big data era provides new development opportunities for employment work in colleges and universities. Big data drives the high-quality development of precise employment in colleges and universities, which is inevitably required to reach the precise requirements of work development, information mastering, platform docking, information pushing, and help work. The traditional employment guidance work is backward in means and poor in timeliness, which leads to poor intervention effect on students’ employment expectation. With the deepening of digital campus construction and the development of big data technology, massive educational data has been accumulated but not reasonably utilized. Therefore, in this paper, we take the massive student data accumulated in digital campus as the research object, based on the research method of convolutional neural network, to explore the hidden personalized information of students behind it, predict their future career development direction, and provide scientific and technological support for the work of university education and teaching. Thus, the architecture classical DenseNet was improvised to avoid gradient disappearance and guarantee the classification accuracy, thereby targeting to reduce the number of connections. The study also proposed a 2-DenseNet model, wherein the attention module was embedded into 2-DenseNet followed by the conduction of training and validation on the university employment dataset. The experimental results show that the method proposed in this paper could predict the appropriate career development direction timely and effectively based on multiple perspectives of students’ comprehensive quality. This would enable the students to plan and adjust their employment expectations leading to disseminating of efficient employment guidance in colleges and universities.

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Management and entrepreneurship management mechanism of college students based on support vector machine algorithm

Cited by 13 publications

References 15 publications

Prediction of Employment Index for College Students by Deep Neural Network

Prediction of Employment Index for College Students by Deep Neural Network

Artificial intelligence application in enterprise financial management big data calculation based on Virtual network mapping algorithm

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

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