Establishing the Neutrosophic Measurable Space for Astrophysical Signal Classification: A New Methodology for Quantifying Indeterminacy and Contradiction in Radio Telescope Data

Al-Yasari, Ameen; Al-Fayadh, Hassan

doi:Under indexing

Proceedings of International Conference on Applied Innovation in IT · 2026/06/12 · Vol. 14 · Issue 4 · pp. 459–470

Establishing the Neutrosophic Measurable Space for Astrophysical Signal Classification: A New Methodology for Quantifying Indeterminacy and Contradiction in Radio Telescope Data

Ameen Khlaif Sachit Al-Yasari, Hassan H. Ebrahim and Ali Al-Fayadh

📄 Download PDF DOI: Under indexing

Abstract

The classification of radio astrophysical signals presents a significant challenge due to the complex nature of inherent data uncertainty, traditional models, such as probabilistic and Intuitionistic Fuzzy frameworks, fail to adequately represent complex forms of uncertainty, especially when faced with informational conflict or contradiction, this paper introduces a novel framework based on the Neutrosophic Measurable Space (NMS) and presents an empirical validation of its efficacy, through a comparative analysis on a dataset of 10,000 radio astrophysical signals-including canonical, noise, and specially curated anomalous (vague and contradictory) classes-the results demonstrate the clear superiority of the NMS model. Crucially, the analysis reveals a fundamental structural failure in the Intuitionistic Fuzzy model, which misinterprets high-conflict signals as high-certainty events, in contrast, the NMS framework leverages its independent Indeterminacy component to quantitatively measure this contradiction, transforming it from a mere measure of ignorance into a powerful diagnostic tool, this capability establishes the NMS as a superior methodology for anomaly detection, opening new frontiers for scientific discovery in data-driven domains.

Keywords

Neutrosophic Logic Neutrosophic Sets Intuitionistic Fuzzy Sets Astrophysical Signal Processing Signal Classification Uncertainty Quantification Anomaly Detection Radio Astronomy Contradictory Data

References

K. Atanassov and S. Stoeva, “Intuitionistic L-Fuzzy Sets,” in Cybernetics and System Research, R. Trappl, Ed., vol. 2, Amsterdam, Netherlands, Elsevier, 1984, pp. 539-540.
G. Balasubramanin and P. Sundaram, “On Some Generalizations of Fuzzy Continuous Functions,” Fuzzy Sets and Systems, vol. 86, pp. 93-100, 1997, [Online]. Available: https://doi.org/10.1016/S0165-0114(96)00049-2.
M. R. Bivin, N. Saivaraju, and K. S. Ravichandran, “Remedy for Effective Cure of Diseases Using Combined Neutrosophic Relational Maps,” International Journal of Computer Applications, vol. 12, no. 12, pp. 18-23, 2011.
C. L. Chang, “Fuzzy Topological Spaces,” Journal of Mathematical Analysis and Applications, vol. 24, pp. 182-190, 1968, [Online]. Available: https://doi.org/10.1016/0022-247X(68)90057-7.
H. D. Cheng, Y. Guo, and Y. Zhang, “A Novel Image Segmentation Approach Based on Neutrosophic Set and Improved Fuzzy C-Means Algorithm,” New Mathematics and Natural Computation, vol. 7, no. 1, pp. 155-171, 2011, [Online]. Available: https://doi.org/10.1142/S179300571100183X.
D. Coker, “An Introduction to Intuitionistic Fuzzy Topological Space,” Fuzzy Sets and Systems, vol. 88, pp. 81-89, 1997, [Online]. Available: https://doi.org/10.1016/S0165-0114(96)00076-5.
R. Dhavaseelan and S. Jafari, “Generalized Neutrosophic Closed Sets,” in New Trends in Neutrosophic Theory and Application, F. Smarandache and S. Pramanik, Eds., vol. 2, Brussels, Belgium, Pons Editions, 2018, pp. 261-274.
J. Dezert, “Open Questions to Neutrosophic Inferences,” Multiple-Valued Logic, vol. 8, no. 3, pp. 439-472, 2002.
K. Georgiev, “A Simplification of the Neutrosophic Sets,” Notes on Intuitionistic Fuzzy Sets, vol. 11, no. 2, pp. 28-31, 2005.
T. Guerram, R. Maamri, and Z. Sahnoun, “A Tool for Qualitative Causal Reasoning on Complex Systems,” IJCSI International Journal of Computer Science Issues, vol. 7, no. 6, 2010.
D. Jayanthi, “Generalized Closed Sets in Neutrosophic Topological Spaces,” International Journal of Mathematical Trends and Technology, Special Issue, vol. 10, pp. 88-91, 2018.
A.-L. Jousselme and P. Maupin, “Neutrosophy in Situation Analysis,” in Proc. Int. Conf. Information Fusion (FUSION), Stockholm, Sweden, Jun.-Jul. 2004, pp. 400-406.
P. Kraipeerapun, C. C. Fung, W. Brown, and K. W. Wong, “Neural Network Ensembles Using Interval Neutrosophic Sets and Bagging for Mineral Prospectivity Prediction and Quantification of Uncertainty,” in Proc. IEEE, 2006.
J. L. Salmeron and F. Smarandache, “Redesigning Decision Matrix Method With an Indeterminacy-Based Inference Process,” Advances in Fuzzy Sets and Systems, vol. 1, no. 2, pp. 263-271, 2006.
J. L. Salmeron and F. Smarandache, “Processing Uncertainty and Indeterminacy in Information Systems Success Mapping,” arXiv:cs/0512047, 2005.
S. Scellato, L. Fortuna, M. Frasca, J. Gómez-Gardenes, and V. Latora, “Traffic Optimization in Transport Networks Based on Local Routing,” European Physical Journal B, vol. 73, pp. 303-308, 2010, [Online]. Available: https://doi.org/10.1140/epjb/e2010-00019-6.
F. Smarandache, A Unifying Field in Logics: Neutrosophic Logic, 3rd ed., Rehoboth, NM, USA, American Research Press, 1999.
F. Smarandache, “Neutrosophy, a New Branch of Philosophy,” Multiple-Valued Logic, vol. 8, no. 3, pp. 297-384, 2002.
F. Smarandache, “A Unifying Field in Logics: Neutrosophic Field,” Multiple-Valued Logic, vol. 8, no. 3, pp. 385-438, 2002.
F. Smarandache and J. Dezert, “The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information Based on DSmT and Neutro-Fuzzy Inference,” arXiv:cs/0412091, 2004.
F. Smarandache, “Degree of Negation of an Axiom,” Journal of Approximate Reasoning, preprint, [Online]. Available: https://arxiv.org/abs/0905.0719.
F. Smarandache, “Neutrosophic Degree of a Paradoxicity,” Multispace and Multistructure, vol. 4, pp. 605-607, 2010.
F. Smarandache, “Neutrosophic Diagram and Classes of Neutrosophic Paradoxes,” Progress in Physics, vol. 4, pp. 18-23, 2010.
F. Smarandache, “Five Paradoxes and a General Question on Time Traveling,” Progress in Physics, vol. 4, p. 24, 2010.
F. Smarandache, “S-Denying a Theory,” Multispace and Multistructure, vol. 4, pp. 622-629, 2010.
F. Smarandache, “Neutrosophic Research Method,” in Multispace & Multistructure. Neutrosophic Transdisciplinarity, Finland, NESP, 2010, pp. 395-548.
P. Thiruppathi, N. Saivaraju, and K. S. Ravichandran, “A Study on Suicide Problem Using Combined Overlap Block Neutrosophic Cognitive Maps,” International Journal of Algorithms, Computing and Mathematics, vol. 3, no. 4, 2010.
H. Wang, Y. Zhang, R. Sunderraman, and F. Song, “Set-Theoretic Operators on Degenerated Neutrosophic Set,” Georgia State University, Atlanta, 2004.
L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, pp. 338-353, 1965, [Online]. Available: https://doi.org/10.1016/S0019-9958(65)90241-X.
L. Zhou, W. Z. Wu, and W. X. Zhang, “On Characterization of Intuitionistic Fuzzy Rough Sets Based on Intuitionistic Fuzzy Implicators,” Information Sciences, vol. 179, pp. 883-898, 2009, [Online]. Available: https://doi.org/10.1016/j.ins.2008.11.015.