scholarly journals Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Yufeng Pan ◽  
Hui Huang
Keyword(s):  
Neural Network ◽  
College Students ◽  
Big Data ◽  
Development Strategy ◽  
Local Governments ◽  
College Graduates ◽  
Strong Support ◽  
Emerging Industries ◽  
Theoretical Support ◽  
Data Integration System

With the strong support of local governments for strategic emerging industries such as high-end equipment manufacturing, new materials, and new energy, strategic emerging industries are playing an increasingly important role in the economy and society. With the increasing enthusiasm of college graduates for independent entrepreneurship, college students’ entrepreneurship is constantly integrated with the development of strategic emerging industries. Based on this background, aiming at the practical problems of the development of strategic emerging industries, this study innovatively puts forward the method of using big data technology and GM model to realize the dynamic model analysis of the development of strategic emerging industries and college students’ entrepreneurial behavior. This article analyzes the correlation between dynamic big data such as industrial scale, industrial market, and industrial direction of local strategic emerging industries and university entrepreneurship, so as to provide theoretical support for the development strategy of strategic emerging industries. Through the neural network algorithm, this article evaluates the entrepreneurship of college students, so as to provide a digital basis for the layout of strategic emerging industries to attract talents and entrepreneurship. Experiments show that the big data integration system established by GM correlation analysis and ant colony Elman regression artificial neural network has high accuracy and can well identify the priority relevance of the industrial direction of strategic emerging industries to college students’ entrepreneurship. It provides theoretical support for regional policy makers to better formulate college students’ entrepreneurship strategy and the development plan of emerging industries.

scholarly journals Construction of entrepreneurship evaluation system for college graduates ——Empirical analysis based on BP neural network

2021 ◽  
Vol 292 ◽  
pp. 02043
Author(s):  
Xiaoyi Wang ◽  
Hui Che
Keyword(s):  
Neural Network ◽  
College Students ◽  
Success Rate ◽  
Bp Neural Network ◽  
Evaluation System ◽  
College Graduates ◽  
Influence Factors ◽  
Entrepreneurial Success ◽  
Training Process ◽  
Input Variables

In order to accurately and efficiently evaluate the entrepreneurial success rate and the risks in the entrepreneurial process of college graduates. BP Neural Network is used to establish the evaluation system of College Students’ entrepreneurship process, making contributions to the underwriting system of entrepreneurship insurance. 12 influence factors are selected as input variables, and the neuron weight and learning rateare adjusted in the training process.

scholarly journals Data Processing and Artificial Neural Network in the Background of Big Data

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Yang You
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Big Data ◽  
Back Propagation ◽  
Human Beings ◽  
Back Propagation Algorithm ◽  
Emerging Industries ◽  
Artificial Intelligence Technology ◽  
Artificial Neural

The existing significance of big data technology lies not only in collecting massive information, but also in professional processing and analysis. It transforms information into data and extracts valuable knowledge from data. The advent of the era of big data has brought us a new development model, but also produced many emerging industries, such as cloud computing, artificial intelligence and so on. Based on this, this paper studies the artificial neural network and back propagation algorithm in this context, so that computer technology can better serve human beings, which is of great significance to promote the further development of artificial intelligence technology.

Legal and regulative foundation of securing the development of consolidated territorial communities in conditions of decentralization

2019 ◽  
pp. 16-19
Author(s):  
Oryslava Korkuna ◽  
Ivan Korkuna ◽  
Oleh Tsilnyk
Keyword(s):  
Development Strategy ◽  
Local Governments ◽  
Local Governance ◽  
Legal Regulation ◽  
Major Elements ◽  
The Development Strategy ◽  
Social Entity ◽  
European Standards ◽  
Definition Of ◽  
State Regional

Development of a territorial community requires efficient use of its capacity taking into account all possible aspects in the course of elaboration and implementation of the development strategy and other local legal and regulative documents. The approach is directly related to maintaining the living activity of a territorial community and should correspond to the interests of population and European standards of state regional policy. In addition to the definition of a community provided by the Law of Ukraine “On Local Governance in Ukraine”, there are also some other. For example, some authors understand territorial community as a single natural and social entity that operates in spatial boundaries of a state and realizes daily needs and interests of population. The paper aims to analyze legal and regulative foundation of the development of territorial communities in conditions of decentralization. The authors analyze current condition of legal and regulative maintenance of local governance reforming in Ukraine in conditions of decentralization of authorities. The paper argues that the major elements of management strategy in CTCs in Ukraine are independence, efficiency, management innovations, quicker and more substantiated decision-making and everything to meet the needs of community’s residents. Management of this sector is grounded on the principles of the provisions of European Charter of Local Self-Government that provides for decentralization of authorities and transfer of resources and responsibilities to local governments. Liabilities of local governments (of consolidated territorial communities) and the mayors are analyzed. The authors prove that in general legal provision of decentralization of local governance corresponds to European requirements and creates reliable ground for practical stage of the reform. The list of issues that require further legal regulation is outlined.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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