Research on SVM Analysis Model of Influencing Factors of Employability of Graduates from Higher Vocational Colleges and Universities in Jiangxi Province
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Othman, Z., Shan, S. W., Yusoff, I., & Kee, C. P. (2018). Classification techniques for predicting graduate employability. International Journal on Advanced Science, Engineering and Information Technology, 8(4-2), 1712-1720.Search in Google Scholar
ElSharkawy, G., Helmy, Y., & Yehia, E. (2022). Employability prediction of information technology graduates using machine learning algorithms. International Journal of Advanced Computer Science and Applications, 13(10).Search in Google Scholar
Sun, T., & He, Z. (2023). Develope intelligent hybrid DNN model for predicting students’ employability–A Machine Learning approach. Journal of Education, Humanities and Social Sciences, 18, 235-248.Search in Google Scholar
Su, J., & Sun, X. (2023, August). Research on the Employment Prediction Method of College Students Under the Background of Mass Entrepreneurship and Innovation Education. In International Conference on E-Learning, E-Education, and Online Training (pp. 341-353). Cham: Springer Nature Switzerland.Search in Google Scholar
Mpia, H. N., Mburu, L. W., & Mwendia, S. N. (2023). Applying Data Mining in Graduates’ Employability: A Systematic Literature Review. International Journal of Engineering Pedagogy, 13(2).Search in Google Scholar
Maaliw, R. R., Quing, K. A. C., Lagman, A. C., Ugalde, B. H., Ballera, M. A., & Ligayo, M. A. D. (2022, January). Employability prediction of engineering graduates using ensemble classification modeling. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0288-0294). IEEE.Search in Google Scholar
Bora, M., & Baruah, R. (2024). A study on employment sustainability among Engineering students using a Statistical and Deep Learning framework. Educational Administration: Theory and Practice, 30(5), 6011-6018.Search in Google Scholar
Awujoola, O., Odion, P. O., Irhebhude, M. E., & Aminu, H. (2021). Performance evaluation of machine learning predictive analytical model for determining the job applicants employment status. Malaysian Journal of Applied Sciences, 6(1), 67-79.Search in Google Scholar
Haque, R., Quek, A., Ting, C. Y., Goh, H. N., & Hasan, M. R. (2024). Classification Techniques Using Machine Learning for Graduate Student Employability Predictions. International Journal on Advanced Science, Engineering & Information Technology, 14(1).Search in Google Scholar
Jayachandran, S., & Joshi, B. (2024). Customized support vector machine for predicting the employability of students pursuing engineering. International Journal of Information Technology, 16(5), 3193-3204.Search in Google Scholar
Kumar, M. S., & Babu, G. P. (2019). Comparative study of various supervised machine learning algorithms for an early effective prediction of the employability of students. Journal of Engineering Sciences, 10(10), 240-251.Search in Google Scholar
Tu, J., Lin, A., Chen, H., Li, Y., & Li, C. (2019). Predict the entrepreneurial intention of fresh graduate students based on an adaptive support vector machine framework. Mathematical Problems in Engineering, 2019(1), 2039872.Search in Google Scholar
Wei, Y., Rao, X., Fu, Y., Song, L., Chen, H., & Li, J. (2023). Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction. Plos one, 18(11), e0294114.Search in Google Scholar
Sutriawan, Muljono, Khairunnisa, Zumhur Alamin, Teguh Ansyor Lorosae& Sahrul Ramadhan. (2024). Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine. Mathematical Modelling of Engineering Problems(2).Search in Google Scholar
Wenhao Wu, Weiwei Wang, Xixi Jia & Xiangchu Feng. (2024). Transformer Autoencoder for K-means Efficient clustering. Engineering Applications of Artificial Intelligence(PF),108612-.Search in Google Scholar
Melaku Bitew Haile, Yelkal Mulualem Walle & Abebech Jenber Belay. (2024). Enhanced Image-Based Malware Multiclass Classification Method with the Ensemble Model and SVM. Open Information Science(1).Search in Google Scholar
Lara Vankelecom, Tom Loeys & Beatrijs Moerkerke. (2024). How to Safely Reassess Variability and Adapt Sample Size? A Primer for the Independent Samples t Test. Advances in Methods and Practices in Psychological Science(1).Search in Google Scholar
Petre Stoica & Prabhu Babu. (2024). Pearson–Matthews correlation coefficients for binary and multinary classification. Signal Processing109511-.Search in Google Scholar
Zhang Jiaying, Huang Tianwen, Jia Zhenyu, Yang Yangyang, Tsai Tsung Yuan & Li Pingyue. (2024). Factors influencing the posterior cruciate ligament buckling phenomenon—a multiple linear regression analysis of bony and soft tissue structures of the knee joint. Journal of Orthopaedic Surgery and Research(1),277-277.Search in Google Scholar