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Nushrat Praveen,
Waris Sarwar Imam,
Chandana Kumari,
- Professor, AlKabir Institute of Technology and Management, Jharkhand, India
- Professor, AlKabir Institute of Technology and Management, Jharkhand, India
- Professor, AlKabir Institute of Technology and Management, Jharkhand, India
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology reshaping the way organizations process information, analyze data, and support decision-making. With the rapid growth of digital data across sectors, traditional analytical methods often face limitations in handling large, complex, and dynamic datasets. AI-based techniques, particularly machine learning and intelligent decision support systems, provide advanced capabilities for identifying hidden patterns, predicting outcomes, and assisting in data-driven decisions. This paper examines the role of AI in developing intelligent decision support systems that improve efficiency and accuracy of data analysis. Five publicly available benchmark datasets spanning healthcare, financial services, education, industrial management, and retail are used to evaluate and compare the performance of five machine learning algorithms under a unified experimental framework. Results demonstrate that AI-driven decision support systems achieve decision accuracy of 91–95%, outperforming traditional methods by approximately 26 percentage points while reducing error rates by 77%. The study also highlights challenges including data quality, algorithmic transparency, and ethical concerns, and concludes with directions for future research in responsible AI-DSS deployment.
Keywords: Artificial Intelligence, Machine Learning, Decision Support Systems, Data Analysis, Intelligent Systems, Benchmark Datasets
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Journal of Artificial Intelligence Research & Advances
| Volume | 13 | |
| 03 | ||
| Received | 10/06/2026 | |
| Accepted | 19/08/2026 | |
| Published | 30/09/2026 | |
| Publication Time | 112 Days |