Title: Harnessing core-accelerated machine learning for enhanced knowledge acquisition in system integration

Authors: Biswaranjan Senapati; Swati Vashisht; Basant Kumar; Mohammad Sajid Mohammadi; V. Kamalakannan; Karuna Markam

Addresses: Department of Computer and Information Science, Parker Hannifin Corp, IL, 60503, USA ' Department of ACSE, GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, 201306, India ' Department of Computer Science, Modern College of Business and Science, P.O. Box 100 PC 133, Muscat, Sultanate of Oman ' Department of Computer Science, College of Engineering and Information Technology, Onaizah Colleges, Qassim, 56447, Saudi Arabia ' Department of Mathematics, Saveetha Engineering College, Chennai, Tamil Nadu, India ' Department of Electronics Engineering, MITS, Gwalior, 474002, India

Abstract: In machine learning, the integrity of labelled datasets is paramount for achieving high model accuracy. This study introduces an innovative method leveraging accelerated machine learning (AML) to rectify mislabelled data autonomously, a common issue that detrimentally affects model efficacy. AML's fast training features allow iterative label modifications and enhancements, enhancing model accuracy dynamically. Initial results show a considerable boost in model precision, especially in datasets with significant initial mislabelling errors. This breakthrough underscores our methodology's capacity to tackle a fundamental obstacle in machine learning: maintaining the quality of training data. Implementing our AML-based label correction strategy also diminishes the necessity for labour-intensive and prone-to-error manual label checks. Efficiency simplifies data preparation and model training. This research increases machine learning by merging AML's sophisticated features with automatic label correction. It boosts accuracy, efficiency, and reliability in data-intensive companies. This new phase in machine learning improves model performance with fewer resources, demonstrating the benefits of integrating AML with automated data quality processes.

Keywords: AML; accelerated machine learning; knowledge acquisition; dataset integrity; iterative refinement; autonomous labelling; machine learning efficiency; label verification; real-time optimisation.

DOI: 10.1504/IJSSE.2026.154881

International Journal of System of Systems Engineering, 2026 Vol.16 No.3, pp.361 - 385

Received: 29 Dec 2023
Accepted: 18 Apr 2024
Published online: 17 Jul 2026 *

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