A Comparative Study of the Effeect of Promotion on Employee Career Progression in Academics
February 17, 2026
A Comparative Study of the Effeect of Promotion on Employee
Abstract
This paper presents an innovative software interface for the utilization of widely used machine learning algorithms in a unified Python/R programming environment. This study makes two contributions. First, a more comprehensive and specialized architecture is made available for integrating machine learning into enterprise information systems. Second, a novel software model, Rbox+, is proposed to execute machine learning algorithms by jointly leveraging the capabilities of the Python and R programming languages through an Application Programming Interface (API). The proposed API is tested and evaluated using a publicly available benchmark dataset for regression analysis (Car-sales dataset, available on Kaggle), applying multiple machine learning models and comparative performance metrics. The obtained results demonstrate improved computational efficiency and scalability, with the execution of multiple models completed within a short processing time on standard hardware. Unlike conventional machine learning APIs or isolated ERP analytics tools, Rbox+ enables transparent, languageindependent execution and validation of machine learning models while exposing the underlying source code. The proposed approach supports practical applications in enterprise analytics, reproducible research, and machine learning education, enhancing interoperability between ERP systems, analytics platforms, and statistical programming environments.
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Dr. Antonios Konomos. 2026. “. Global Journal of Computer Science and Technology, Global Journal of Computer Science and Technology – C: Software & Data Engineering GJCST-C Volume 26 (GJCST Volume 26 Issue C1): .
Abstract
This paper presents an innovative software interface for the utilization of widely used machine learning algorithms in a unified Python/R programming environment. This study makes two contributions. First, a more comprehensive and specialized architecture is made available for integrating machine learning into enterprise information systems. Second, a novel software model, Rbox+, is proposed to execute machine learning algorithms by jointly leveraging the capabilities of the Python and R programming languages through an Application Programming Interface (API). The proposed API is tested and evaluated using a publicly available benchmark dataset for regression analysis (Car-sales dataset, available on Kaggle), applying multiple machine learning models and comparative performance metrics. The obtained results demonstrate improved computational efficiency and scalability, with the execution of multiple models completed within a short processing time on standard hardware. Unlike conventional machine learning APIs or isolated ERP analytics tools, Rbox+ enables transparent, languageindependent execution and validation of machine learning models while exposing the underlying source code. The proposed approach supports practical applications in enterprise analytics, reproducible research, and machine learning education, enhancing interoperability between ERP systems, analytics platforms, and statistical programming environments.
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
A Comparative Study of the Effeect of Promotion on Employee
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