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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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.
Details
| Primary Language | English |
|---|---|
| Subjects | Business Administration |
| Journal Section | Articles |
| Authors | |
| Publication Date | June 30, 2019 |
| Published in Issue | Year 2019 Volume: 6 Issue: 2 |
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Research Journal of Business and Management (RJBM) is a scientific, academic, double blind peer-reviewed, quarterly and open-access online journal. The journal publishes four issues a year. The issuing months are March, June, September and December. The publication languages of the Journal are English and Turkish. RJBM aims to provide a research source for all practitioners, policy makers, professionals and researchers working in all related areas of business, management and organizations. The editor in chief of RJBM invites all manuscripts that cover theoretical and/or applied researches on topics related to the interest areas of the Journal. RJBM publishes academic research studies only. RJBM charges no submission or publication fee.
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