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Evaluating the Performance of AI-Based Software Tools in Intelligent Decision-Making Systems

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  • Abdinasir Ismael Hashi

  • Mr.Osman Abdullahi Jama

Abstract

This paper evaluates the performance of AI-based software tools within intelligent decision-making systems, emphasizing their application in Industry 4.0 environments. Various AI techniques, including machine learning, deep learning, and natural language processing—are assessed across domains such as predictive maintenance, quality control, supply chain optimization, and energy management. To advance this field, we introduce a novel framework, RAISE-DM (Real-time Adaptive Intelligence Software Evaluation for Decision-Making), which combines real-time data acquisition from IoT devices with adaptive AI models for continuous decision optimization. Performance evaluation considers key parameters such as scalability, response time, accuracy, and interpretability. The study also highlights critical technical barriers like data heterogeneity and integration complexity, offering targeted strategies to address them. By providing a structured performance analysis and proposing a scalable evaluation model, this research contributes to the design of more efficient, transparent, and resilient AI-driven decision support systems applicable across industrial and cross-sector settings.

Suggested Citation

  • Abdinasir Ismael Hashi & Mr.Osman Abdullahi Jama, 2025. "Evaluating the Performance of AI-Based Software Tools in Intelligent Decision-Making Systems," International Journal of Computing and Engineering, CARI Journals Limited, vol. 7(22), pages 1-20.
    • Handle: RePEc:bhx:ojijce:v:7:y:2025:i:22:p:1-20:id:3315
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    File URL: https://carijournals.org/journals/IJCE/article/view/3315
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    References listed on IDEAS

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    1. Xiao Han & Shumei Xiao & Jun Sheng & Guangtao Zhang, 2025. "Enhancing Efficiency and Decision-Making in Higher Education Through Intelligent Commercial Integration: Leveraging Artificial Intelligence," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 1546-1582, March.
    2. Manfren, Massimiliano & Gonzalez-Carreon, Karla M., 2025. "Tracking decarbonisation: Scalable and interpretable data-driven methods for district energy systems," Applied Energy, Elsevier, vol. 391(C).
    3. Savindu Herath Pathirannehelage & Yash Raj Shrestha & Georg von Krogh, 2025. "Design principles for artificial intelligence-augmented decision making: An action design research study," European Journal of Information Systems, Taylor & Francis Journals, vol. 34(2), pages 207-229, March.
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