Оригинал (Original)
Автори: Уйкани, Б. Т., Минковска, Д. В., Стоянова, Л. Й.
Заглавие: Application of Logistic Regression Technique for Predicting Student Dropout
Ключови думи: student dropout, higher education, machine learning, logisti

Библиография

    Издание

    XXXI International Scientific Conference Electronics - ET2022, 2022, България, Sozopol, DOI 10.1109/ET55967.2022.9920280
    		Autors: Ujkani, B. T., Minkovska, D. V., Stoyanova, L. Y.
    Title: Application of Logistic Regression Technique for Predicting Student Dropout
    Keywords: student dropout, higher education, machine learning, logistic regression, numpy, scikit-learn

    References

      Issue

      XXXI International Scientific Conference Electronics - ET2022, 2022, Bulgaria, Sozopol, DOI 10.1109/ET55967.2022.9920280

      Цитирания (Citation/s):
      1. Yunga Pedraza, Joel Omar, Estudio del estado del arte sobre la predicción de deserción universitaria usando machine learning, UNIVERSIDAD POLITÉCNICA SALESIANA SEDE QUITO, CARRERA DE COMPUTACIÓN, Quito, Ecuador, 2023 - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
      2. Spassov, S., Cyber Threats to Nuclear Security and the Role of Education, 2023 11th International Scientific Conference on Computer Science, COMSCI 2023 – Proceedings, ISBN 979-835032525-6, DOI 10.1109/COMSCI59259.2023.10315941 - 2023 - в издания, индексирани в Scopus или Web of Science
      3. Alghamdi, S., Soh, B., Li, A., A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms, Multimodal Technologies and Interaction, Volume 9, Issue 1 January 2025, Article number 3, ISSN 24144088, DOI 10.3390/mti9010003 - 2025 - в издания, индексирани в Scopus или Web of Science

      Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus