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EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK

Year 2019, , 109 - 120, 30.06.2019
https://doi.org/10.17261/Pressacademia.2019.1051

Abstract

Purpose- This study intends to investigate the factors that affect the enrollment in Taiwan's colleges and universities. The subjects were selected by random sampling methods from senior high school graduates who were about to enter colleges.

Methodology- By implementing the Alyuda NeuroIntelligence software, this study applied neural network simulation and prediction analysis on the data of 100 questionnaires.

Findings- The results showed that the influencing factors of school enrollment and their degree of relevance and importance are: (1) curriculum, (2) chance of oversea study, (3) faculty, (4) scholarship, (5) tuition, (6) location, (7) internship, (8) career, (9) campus and (10) reputation.

Conclusion- It is hoped that the research results discovered in this study can help relevant schools to understand students' total evaluation of schools and willingness to study, and serve as an important reference for schools to strengthen enrollment strategy and improve the quality of school operation in the future.

References

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  • Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459.
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  • Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception. Cambridge, MA: Academic Press.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
  • Kelemen, J. (2007). From artificial neural networks to emotion machines with marvin minsky. Acta Polytechnica Hungarica, 4(4), 1-12.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
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  • O'Leary, D. E. (1997). The Internet, intranets, and the AI renaissance. IEEE Computer, 30(1), 71-78.
  • Ramos, C., Augusto, J. C., & Shapiro, D. (2008). Ambient intelligence—the next step for artificial intelligence. IEEE Intelligent Systems, 23(2), 15- 18.
  • Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386-408.
  • Rumelhart, D. E., Durbin, R., Golden, R., & Chauvin, Y. (1995). Backpropagation: The basic theory. Backpropagation: Theory, architectures and applications, 1-34.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive modeling, 5(3), 1.
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia: Pearson Education Limited.
  • Turing, A. M. (2009). Computing machinery and intelligence. In Parsing the Turing Test. Dordrecht: Springer.
Year 2019, , 109 - 120, 30.06.2019
https://doi.org/10.17261/Pressacademia.2019.1051

Abstract

References

  • Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends in Machine Learning, 2(1), 1-127.
  • Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459.
  • Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design. Boston, MA: Pws Pub.
  • Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception. Cambridge, MA: Academic Press.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
  • Kelemen, J. (2007). From artificial neural networks to emotion machines with marvin minsky. Acta Polytechnica Hungarica, 4(4), 1-12.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  • McCarthy, J. (1989). Artificial intelligence, logic and formalizing common sense. In Philosophical logic and artificial intelligence. Dordrecht: Springer.
  • O'Leary, D. E. (1997). The Internet, intranets, and the AI renaissance. IEEE Computer, 30(1), 71-78.
  • Ramos, C., Augusto, J. C., & Shapiro, D. (2008). Ambient intelligence—the next step for artificial intelligence. IEEE Intelligent Systems, 23(2), 15- 18.
  • Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386-408.
  • Rumelhart, D. E., Durbin, R., Golden, R., & Chauvin, Y. (1995). Backpropagation: The basic theory. Backpropagation: Theory, architectures and applications, 1-34.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive modeling, 5(3), 1.
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia: Pearson Education Limited.
  • Turing, A. M. (2009). Computing machinery and intelligence. In Parsing the Turing Test. Dordrecht: Springer.
There are 15 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Kuang-tai Liu This is me 0000-0003-3371-6884

Pin-chang Chen This is me 0000-0001-7609-825X

Chiu-chi Wei This is me 0000-0002-9433-9114

Publication Date June 30, 2019
Published in Issue Year 2019

Cite

APA Liu, K.-t., Chen, P.-c., & Wei, C.-c. (2019). EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK. Research Journal of Business and Management, 6(2), 109-120. https://doi.org/10.17261/Pressacademia.2019.1051
AMA Liu Kt, Chen Pc, Wei Cc. EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK. RJBM. June 2019;6(2):109-120. doi:10.17261/Pressacademia.2019.1051
Chicago Liu, Kuang-tai, Pin-chang Chen, and Chiu-chi Wei. “EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK”. Research Journal of Business and Management 6, no. 2 (June 2019): 109-20. https://doi.org/10.17261/Pressacademia.2019.1051.
EndNote Liu K-t, Chen P-c, Wei C-c (June 1, 2019) EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK. Research Journal of Business and Management 6 2 109–120.
IEEE K.-t. Liu, P.-c. Chen, and C.-c. Wei, “EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK”, RJBM, vol. 6, no. 2, pp. 109–120, 2019, doi: 10.17261/Pressacademia.2019.1051.
ISNAD Liu, Kuang-tai et al. “EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK”. Research Journal of Business and Management 6/2 (June 2019), 109-120. https://doi.org/10.17261/Pressacademia.2019.1051.
JAMA Liu K-t, Chen P-c, Wei C-c. EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK. RJBM. 2019;6:109–120.
MLA Liu, Kuang-tai et al. “EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK”. Research Journal of Business and Management, vol. 6, no. 2, 2019, pp. 109-20, doi:10.17261/Pressacademia.2019.1051.
Vancouver Liu K-t, Chen P-c, Wei C-c. EXPLORING INFLUENCING FACTORS OF UNIVERSITY ENROLLMENT USING NEURAL NETWORK. RJBM. 2019;6(2):109-20.

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