Some Results on Specificity of Possibility Distributions

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Published: Oct 30, 2025
DOI: https://doi.org/10.54105/ijam.B1206.05021025
Keywords:
Possibility Distribution, Restriction, Specificity, Hybrid System, Semantic Information

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Dr. Jayesh V. Karanjgaonkar
Assistant Professor, Department of Mathematics, Swami Aatmanand Govt. Eng. Med. Model College, Atari, Raipur (Chhattisgarh).
https://orcid.org/0009-0000-3894-366X

Abstract

Specificity of a possibility distribution is akin to the entropy of a probability distribution. It serves an essential purpose to zero in on the maximum probability observation. However, when we discuss the existing definition of possibility distribution, it lacks applicability in real-world problems; hence, specificity also becomes an underrated measure for gauging the degree of uncertainty in a possibility distribution. In this paper, we present new findings on the specificity of a possibility distribution, resulting from our research on data-based semantic information analysis in hybrid human-machine systems. In this research, we propose a new frequency-based possibility and probability measure and formalise a new method for fitting restrictions on data or information available in the system. We will demonstrate that the proposed formula is superior to existing specificity measures and discuss various applications of specificity measures in solving problems related to hybrid systems. We shall summarise this paper by providing a real-world application of the proposed measure.

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How to Cite
[1]
Dr. Jayesh V. Karanjgaonkar , Tran., “Some Results on Specificity of Possibility Distributions”, IJAM, vol. 5, no. 2, pp. 1–5, Oct. 2025, doi: 10.54105/ijam.B1206.05021025.
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Vol. 5 No. 2 (2025): Volume-5 Issue-2, October 2025
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Articles
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How to Cite

[1]
Dr. Jayesh V. Karanjgaonkar , Tran., “Some Results on Specificity of Possibility Distributions”, IJAM, vol. 5, no. 2, pp. 1–5, Oct. 2025, doi: 10.54105/ijam.B1206.05021025.
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References

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Karanjgaonkar, J., & Jha, P. (2017). On a novel method to measure semantic information through possibilistic restrictions. *IOSR Journal of Mathematics (IOSR-JM), 13*(5), 32-36. DOI: https://doi.org/10.9790/5728-1305033236.