American Psychological Association 6th edition

Sapkota, S. C., Yadav, A., Khatri, A., Singh, T., & Dahal, D. (2024). Explainable hybridized ensemble machine learning for the prognosis of the compressive strength of recycled plastic-based sustainable concrete with experimental validation. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7(6), 6073-6096. https://doi.org/10.1007/s41939-024-00567-4

ISO-690 (author-date, English)

SAPKOTA, Sanjog Chhetri, YADAV, Ajay, KHATRI, Ajaya, SINGH, Tushar und DAHAL, Dipak, 2024. Explainable hybridized ensemble machine learning for the prognosis of the compressive strength of recycled plastic-based sustainable concrete with experimental validation. Multiscale and Multidisciplinary Modeling, Experiments and Design. 1 November 2024. Vol. 7, no. 6, p. 6073-6096. DOI 10.1007/s41939-024-00567-4.

Modern Language Association 9th edition

Sapkota, S. C., A. Yadav, A. Khatri, T. Singh, und D. Dahal. „Explainable Hybridized Ensemble Machine Learning for the Prognosis of the Compressive Strength of Recycled Plastic-Based Sustainable Concrete With Experimental Validation“. Multiscale and Multidisciplinary Modeling, Experiments and Design, Bd. 7, Nr. 6, November 2024, S. 6073-96, https://doi.org/10.1007/s41939-024-00567-4.

Mohr Siebeck - Recht (Deutsch - Österreich)

Sapkota, Sanjog Chhetri/Yadav, Ajay/Khatri, Ajaya/Singh, Tushar/Dahal, Dipak: Explainable hybridized ensemble machine learning for the prognosis of the compressive strength of recycled plastic-based sustainable concrete with experimental validation, Multiscale and Multidisciplinary Modeling, Experiments and Design 2024, 6073-6096.

Emerald - Harvard

Sapkota, S.C., Yadav, A., Khatri, A., Singh, T. und Dahal, D. (2024), „Explainable hybridized ensemble machine learning for the prognosis of the compressive strength of recycled plastic-based sustainable concrete with experimental validation“, Multiscale and Multidisciplinary Modeling, Experiments and Design, Vol. 7 No. 6, S. 6073-6096.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.