American Psychological Association 6th edition

Sewal, P., & Singh, H. (2024). Analyzing distributed Spark MLlib regression algorithms for accuracy, execution efficiency and scalability using best subset selection approach. Multimedia Tools & Applications, 83(15), 44047-44066. https://doi.org/10.1007/s11042-023-17330-5

ISO-690 (author-date, English)

SEWAL, Piyush und SINGH, Hari, 2024. Analyzing distributed Spark MLlib regression algorithms for accuracy, execution efficiency and scalability using best subset selection approach. Multimedia Tools & Applications. 1 Mai 2024. Vol. 83, no. 15, p. 44047-44066. DOI 10.1007/s11042-023-17330-5.

Modern Language Association 9th edition

Sewal, P., und H. Singh. „Analyzing Distributed Spark MLlib Regression Algorithms for Accuracy, Execution Efficiency and Scalability Using Best Subset Selection Approach.“. Multimedia Tools & Applications, Bd. 83, Nr. 15, Mai 2024, S. 44047-66, https://doi.org/10.1007/s11042-023-17330-5.

Mohr Siebeck - Recht (Deutsch - Österreich)

Sewal, Piyush/Singh, Hari: Analyzing distributed Spark MLlib regression algorithms for accuracy, execution efficiency and scalability using best subset selection approach., Multimedia Tools & Applications 2024, 44047-44066.

Emerald - Harvard

Sewal, P. und Singh, H. (2024), „Analyzing distributed Spark MLlib regression algorithms for accuracy, execution efficiency and scalability using best subset selection approach.“, Multimedia Tools & Applications, Vol. 83 No. 15, S. 44047-44066.

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