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Raman, R., & Pramod, D. (2022). The role of predictive analytics to explain the employability of management graduates. Benchmarking: An International Journal, 29(8), 2378-2396.Search in Google Scholar
Shenoy, S. S., & Shailashri, V. T. (2023). Impact of Skill Enhancement Training on Quality of Work Life–A Review. International Journal of Case Studies in Business, IT and Education (IJCSBE), 7(1), 74-94.Search in Google Scholar
Gowsalya, G., & Kumar, M. (2015). Employability skill: A literature review. International Journal of Advance Research in Computer Science and Management Studies, 3(3).Search in Google Scholar
Casuat, C. D., Festijo, E. D., & Alon, A. S. (2020). Predicting students’ employability using support vector machine: a smote-optimized machine learning system. International Journal, 8(5), 2101-2106.Search in Google Scholar
Patro, C., & Pan, I. (2021). Decision tree-based classification model to predict student employability. In Proceedings of Research and Applications in Artificial Intelligence: RAAI 2020 (pp. 327-333). Springer Singapore.Search in Google Scholar
Garcia, J. A., & Murcia, J. V. (2023). Comparison of Supervised Machine Learning Approaches in Predicting Employability of Students. Business and Organization Studies e-Journal, 1(1), 121-139.Search in Google Scholar
Taeza-Cruz, M. E. L., & Capili-Kummer, M. G. (2023). Decision Support System to Enhance Students’ Employability using Data Mining Techniques for Higher Education Institutions. International Journal of Computing and Digital Systems.Search in Google Scholar
Green, N., Liu, M., & Murphy, D. (2020). Using an Electronic Resume Analyzer Portal (eRAP) to Improve College Graduates Employability. Information Systems Education Journal, 18(3), 28-37.Search in Google Scholar
Metilda, R. M., & Neena, P. C. (2017). Impact of digital technology on learning to enhance the employability skills of business management graduates. The online Journal of distance Education and E-Learning, 5(2), 35-41.Search in Google Scholar
Nathan, S. K., & Rajamanoharane, S. (2016). Enhancement of skills through e-learning: prospects and problems. The Online Journal of Distance Education and e‐Learning, 4(3), 24.Search in Google Scholar
Patil, A. K., Chavan, P. C., & Patil, S. C. S. D. (2023). Enhancement in Student Employability by Providing Internship and Project Track. Journal of Engineering Education Transformations, 36(Special Issue 2).Search in Google Scholar
Tian, J., & He, G. (2024). Research on innovative teaching to enhance the employability of college students based on the “five Links and Six steps” method. Ain Shams Engineering Journal, 15(5), 102677.Search in Google Scholar
Tang, Y. (2023). Research on the Current Situation and Enhancement Strategies of Employability of General College Graduates. Journal of Education and Educational Research, 5(2), 15-18.Search in Google Scholar
Cruz, M. E. L. T., & Encarnacıon, R. E. (2021). Analysis and prediction of students’ academic performance and employability using data mining techniques: A research travelogue. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 16, 117-131.Search in Google Scholar
Thakar, P., & Mehta, A. (2017). A unified model of clustering and classification to improve students’ employability prediction. International Journal of Intelligent Systems and Applications, 9(9), 10.Search in Google Scholar
Wang, Y. (2024). Prediction of Student Employability through Internship based on Big Data Analysis. Journal of Electrical Systems, 20(3s), 2749-2761.Search in Google Scholar
Mezhoudi, N., Alghamdi, R., Aljunaid, R., Krichna, G., & Düştegör, D. (2023). Employability prediction: a survey of current approaches, research challenges and applications. Journal of Ambient Intelligence and Humanized Computing, 14(3), 1489-1505.Search in Google Scholar
Bai, A., & Hira, S. (2021). An intelligent hybrid deep belief network model for predicting students employability. Soft Computing, 25(14), 9241-9254.Search in Google Scholar
Saini, B., Mahajan, G., & Sharma, H. (2021, March). An analytical approach to predict employability status of students. In IOP conference series: materials science and engineering (Vol. 1099, No. 1, p. 012007). IOP Publishing.Search in Google Scholar
Saidani, O., Menzli, L. J., Ksibi, A., Alturki, N., & Alluhaidan, A. S. (2022). Predicting student employability through the internship context using gradient boosting models. Ieee Access, 10, 46472-46489.Search in Google Scholar
He, S., Li, X., & Chen, J. (2021, May). Application of data mining in predicting college graduates employment. In 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 65-69). IEEE.Search in Google Scholar
Zhang, Y. C., Zhang, Y., Xiong, X. L., Liu, J. B., & Zhai, R. B. (2022). An empirical study on the improvement of college students’ employability based on university factors. Frontiers in Psychology, 13, 793492.Search in Google Scholar
Shen, J. (2023). A bilateral employment situation prediction model for college students using GCN and LSTM. PeerJ Computer Science, 9, e1494.Search in Google Scholar
Xi, C., Xiao, M., Wu, L., & Cui, H. (2023). Evaluation of college students’ entrepreneurial employability improvement based on machine learning neural network. REVIEWS OF ADHESION AND ADHESIVES, 11(2).Search in Google Scholar
Brinker Nils. (2024). Identification and demarcation—A general definition and method to address information technology in European IT security law.Computer Law & Security Review. The International Journal of Technology Law and Practice105927-.Search in Google Scholar
Pepper Ian,Rogers Colin,Turner James,Louis Nick & Williams Bronwen. (2024). Enabling student employability through volunteering: insights from police volunteers studying professional policing degrees in Wales. Higher Education, Skills and Work-Based Learning(5),1135-1148.Search in Google Scholar
Santos Devisson Mesquita dos,Lopes Fernanda Leandra Leal,Melo André Cristiano Silva,Nunes Denilson Ricardo de Lucena,Rampasso Izabela Simon & Martins Vitor William Batista. (2024). How to promote resilience in the supply chain in the context of COVID-19? An exploratory study using the Delphi method. Modern Supply Chain Research and Applications(3),303-329.Search in Google Scholar
Mobin Saremi,Abbas Maghsoudi,Zohre Hoseinzade & Ahmad Reza Mokhtari. (2024). Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran. Earth Science Informatics(prepublish),1-16.Search in Google Scholar
Yang Liu,Mingqiang Hao,Ran Bi,Chaoliang Bian & Xiaoqing Wang. (2024). Research on Gas Channeling Identification Using the Fuzzy Comprehensive Evaluation Method. Energies(16),3908-3908.Search in Google Scholar