Volume 5 , Issue 1 , PP: 47–57, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Anvar Suleymanov 1 * , Murod Khidoyatov 2
Doi: https://doi.org/10.54216/NIF.050105
Estimating whether ambient air quality exceeds regulatory thresholds requires combining evidence from multiple co-measured pollutants whose concentrations are simultaneously uncertain, interdependent, and subject to instrument noise. This paper introduces a Neutrosophic Cubic Correlation Fusion (NC-CF) model that represents each pollutant observation as a neutrosophic cubic value—a structure that simultaneously encodes an interval-valued membership [𝑇𝐿 , 𝑇𝑈] capturing measurement uncertainty and a crisp neutrosophic triple (𝑡, 𝑖, 𝑓 ) capturing the nominal risk assessment—and then quantifies closeness to ideal pollution profiles through a novel neutrosophic cubic correlation coefficient (NCC). Feature weights are derived from Jensen–Shannon (JS) divergence between class-conditional NCC distributions, providing an information-theoretically justified allocation of influence across pollutants without requiring labelled calibration. Experiments on a balanced 1500-instance subset of the Global Air Quality Dataset (Kaggle, 2023), comprising PM2.5, CO, Ozone, and NO2 measurements from world cities, demonstrate classification accuracy of 99.0% and AUC of 0.9996 under ten-fold cross-validation, matching or exceeding Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Trees. A systematic sensitivity analysis over the interval-to-crisp interpolation parameter 𝜆 ∈ [0, 1] reveals stable performance across the full range, confirming that the NCC’s interval component does not introduce instability. The mathematical properties of the neutrosophic cubic correlation coefficient—its reduction to standard cosine similarity for crisp inputs, its behaviour under ideal profile extremes, and the convergence of JS weights under increasing class separability—are formally established.
Neutrosophic cubic sets , Interval neutrosophic membership , Correlation coefficient , Information fusion , Jensen&ndash , Shannon divergence , Air quality index , Multi-pollutant integration , Ideal profile , Sensitivity analysis
[1] Chai, J. S., Selvachandran, G., Smarandache, F., Gerogiannis, V. C., Son, L. H., Bui, Q.-T., and Vo, B. (2021). New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems. Complex & Intelligent Systems, 7(2):703–723.
[2] Garg, H. and Nancy (2020). Algorithms for single-valued neutrosophic decision making based on TOPSIS and clustering methods with new distance measure. AIMS Mathematics, 5(3):2671–2693.
[3] Li, Y., Cai, Q., and Wei, G. (2023a). PT-TOPSIS methods for multi-attribute group decision making under single-valued neutrosophic sets. Journal of Knowledge Engineering, 26(2):122–135.
[4] Li, Y.-m., Khan, M., Khurshid, A., Gulistan, M., Rehman, A. U., Ali, M., Abdulla, S., and Farooque, A. A. (2023b). Designing pentapartitioned neutrosophic cubic set aggregation operator-based air pollution decision making model. Complex & Intelligent Systems.
[5] Rehman, A. U., Gulistan, M., Ali, M., Al-Shamiri, M. M., and Abdulla, S. (2023). Development of neutrosophic cubic hesitant fuzzy exponential aggregation operators with application in environmental protection problems. Scientific Reports, 13:5484.
[6] Sajid, H. (2023). Global air pollution dataset. Kaggle. https://www.kaggle.com/datasets/ hasibalmuzdadid/global-air-pollution-dataset. Accessed 2024.
[7] Smarandache, F. (1998). Neutrosophy: Neutrosophic Probability, Set, and Logic. American Research Press, Rehoboth, NM.
[8] Wang, H., Smarandache, F., Zhang, Y., and Sunderraman, R. (2010). Single valued neutrosophic sets. Multispace and Multistructure, 4:410–413.
[9] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3):338–353.
[10] Zulqarnain, R. M., Siddique, I., Iampan, A., and Bonyah, E. (2021). Algorithms for multipolar interval-valued neutrosophic soft set with information measures to solve multicriteria decision-making problem. Mathematical Problems in Engineering, 2021:7211399.
