Criteria for Measuring Intelligent Systems Quality in the Context of Contemporary International Standards

Authors

  • Mansoor N. Ali, Motea M. Aljafare

Keywords:

Intelligent Systems, International Standards, Measuring, Quality.

Abstract

Intelligent systems are more common in many facets of modern life, and whether they succeed or fail largely depends on how well they are made and how strictly they follow standards. First, standards must be established and evaluated for Intelligent systems. Organizations struggle to deploy itelligent systems efficiently because they lack explicit quality requirements. A crucial component of quality assurance is selecting the appropriate standards. quality metrics for intelligent systems will be defined. This research study examines the traits, creation, and development processes of Intelligent systems. It defines the term quality Intelligent Systems. It discusses the fundamental standards and procedures for determining the caliber of quality metrics for intelligent systems and the factors that have the most significant bearing on that caliber. This study addresses intelligent system quality, measures it and how it may be regulated, and illustrates the necessity for quality ISs as they become essential to everyday interactions and activities.

Downloads

Download data is not yet available.

References

Wanner, Jonas, et al. The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electronic Markets, 2022, 1-24.‏

Agarwal, Avinash; Agarwal, Harsh; Agarwal, Nihaarika. Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems. AI and Ethics, 2023, 3.1: 267-279.‏

Smith-Renner, Alison Marie, et al. TExSS: Transparency and explanations in intelligent systems. In: 26th International Conference on Intelligent User Interfaces-Companion. 2021. p. 24-25.‏

Abidin, Wan Yusran Naim Wan Zainal, and Zulkefli Mansor. "The Criteria for Software Quality in Information System: Rasch Analysis." Editorial Preface From the Desk of Managing Editor… 10.9 (2019).‏

Pérez, L. S. V., Tornés, A. F. G., Riverón, E. M. F., Hernández, A. M. S., & Galeana, N. I. P. (2018). Software Quality Methodology to Train Engineers as Evaluators of Information Systems Development Tools. Universal Journal of Educational Research, 6(12), 2942-2951.‏

Aouhassi, S., & Hanoune, M. (2018). Information System Quality: Managers Perspective. International Journal Of Advanced Computer Science And Applications, 9(8), 493-502.‏

GAO, Rong; Tsoukalas, L. Performance metrics for intelligent systems: An engineering perspective. Nist Special Publication SP, 2002, 5-10.‏

Russia, Evgeniy Bryndin. Standardization of Artificial Intelligence for the Development and Use of Intelligent Systems. Advances in Wireless Communications and Networks, 2020, 6.1: 1.‏

YU, Rong. Security Framework of Artificial Intelligence System. In: Journal of Physics: Conference Series. IOP Publishing, 2021. p. 012011.‏

Dahiya, Pawan, et al. Intelligent systems: Features, challenges, techniques, applications & future scopes. Intelligent Systems & Mobile Adhoc Networks, 2007.‏

Schneider, Florian; Berenbach, Brian. A literature survey on international standards for systems requirements engineering. Procedia Computer Science, 2013, 16: 796-805.‏

Dahar, Hind; Roudies, Ounsa. Mapping of Quality Standards. IJITEE, 2019, 8: 2406-2412.‏

Kopyltsov, A. V. (2020, April). Selection of metrics in software quality evaluation. In Journal of Physics: Conference Series (Vol. 1515, No. 3, p. 032018). IOP Publishing.‏

Hansen, Lars Kai; Rieger, Laura. Interpretability in intelligent systems–a new concept?. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019, 41-49.‏

Barmer, Hollen, et al. Robust and Secure AI. 2021.‏

Hamon, Ronan; Junklewitz, Henrik; Sanchez, Ignacio. Robustness and explainability of artificial intelligence. Publications Office of the European Union, 2020, 207.‏

Dingli, Alexiei; CASSAR, Sarah. An intelligent framework for website usability. Advances in Human-Computer Interaction, 2014, 2014: 5-5.‏

Knauer, T., Nikiforow, N., & Wagener, S. (2020). Determinants of information system quality and data quality in management accounting. Journal of Management Control, 31(1), 97-121

RUDAS, Imre J.; FODOR, János. Intelligent systems. International Journal of Computers, Communications & Control, 2008, 3.3: 132-138.‏

Li, Z., Wu, S., Zhou, H., Zou, S., & Dong, T. (2019, August). Analytic model and assessment framework for data quality evaluation in state grid. In Journal of Physics: Conference Series (Vol. 1302, No. 2, p. 022083). IOP Publishing.‏

Hong, Yili, et al. Statistical perspectives on reliability of artificial intelligence systems. Quality Engineering, 2023, 35.1: 56-78.‏

A Deep Learning Framework for Predicting Tumor Proliferation Score of Breast Histopathological Images" by Yujing Wang et al., published in the IEEE Journal of Biomedical and Health Informatics in 2020.

Downloads

Published

24.03.2024

How to Cite

Mansoor N. Ali. (2024). Criteria for Measuring Intelligent Systems Quality in the Context of Contemporary International Standards. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 3190–3199. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5924

Issue

Section

Research Article