To Predict Employability of Student by Using Artificial Neural Network-Reference-Cited by-同舟云学术

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To Predict Employability of Student by Using Artificial Neural Network

Published:2021-11-09 Issue: Volume: Page:675-682
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
Short-container-title:

Author:

Laddha Manjushree D.,Kiwelekar Arvind W.,Netak Laxman D.,Mahajan Prasad C.

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-3690-5_61

Reference8 articles.

1. Aravind T, Reddy BS, Avinash S, Jeyakumar G (2019) A comparative study on machine learning algorithms for predicting the placement information of under graduate students. In: 2019 third international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC). IEEE, pp 542–546

2. Joy LC, Raj A (2019) A review on student placement chance prediction, pp 542–545

3. Lundberg GM, Krogstie BR, Krogstie J (2020) Becoming fully operational: employability and the need for training of computer science graduates, pp 644–651

4. Marling C, Juedes D (2016) CS0 for computer science majors at Ohio University, pp 138–143

5. Mavani U, Lobo VB, Pednekar A, Pereira NC, Mishra R, Ansari N (2020) Naïve Bayes classification on student placement data: a comparative study of data mining tools. Information and communication technology for sustainable development. Springer, Germany, pp 363–372

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1. Cognitive Science: An Insightful Approach;Proceedings of International Conference on Data Science and Applications;2023

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