Fuzzy Sets and the Holographic Principle: Uncertainty in Black Hole Physics

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Published: Sep 2, 2024
DOI: https://doi.org/10.29070/qf8tsq22
Keywords:
Fuzzy Sets, Holographic Principle, Uncertainty, Black Hole Physics, Gravity, Spacetime, Fundamental Structure, Fuzzy Set Theory, Black Hole Parameter Measurements, Enigmatic Cosmic Entities

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Authors

Nasir Ahamed N

Assistant Professor of Physics, Government College for Women, Chintamani, Karnataka, India

Abstract

Black hole physics, with its profound implications for gravity, spacetime, and the universe'sfundamental structure, remains a challenging field riddled with uncertainties. This study explores theintegration of fuzzy set theory and the holographic principle to address the inherent imprecision in blackhole parameter measurements and advance our understanding of these enigmatic cosmic entities.Hypothetical data sets, characterized by fuzzy set representations of uncertainties, are subjected tofuzzy set-based analysis. Fuzzy entropy calculations reveal the degree of imprecision, while fuzzyclustering suggests the potential existence of distinct black hole classes. This interdisciplinary approachprovides new theoretical insights, challenges existing models, and emphasizes the need for furtherresearch to validate and refine these concepts.

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Issue

Vol. 20 No. 1 (2023): Journal of Advances in Science and Technology (JAST)

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References

  1. Hawking, S. W. (1974). Black hole explosions? Nature, 248(5443), 30-31.
  2. 't Hooft, G. (1993). Dimensional reduction in quantum gravity. arXiv preprint gr-qc/9310026.
  3. Susskind, L. (1995). The world as a hologram. Journal of Mathematical Physics, 36(11), 6377-6396.
  4. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
  5. Hawking, S. W. (1976). Breakdown of predictability in gravitational collapse. Physical Review D, 14(10), 2460.
  6. Maldacena, J. M. (1998). The large N limit of superconformal field theories and supergravity. Advances in Theoretical and Mathematical Physics, 2(2), 231-252.
  7. Preskill, J. (1992). Do black holes destroy information? International Journal of Modern Physics D, 1(02), 185-194.
  8. Yager, R. R. (1994). Fuzzy logic as a basis for reasoning with imprecise information. Fuzzy Sets and Systems, 61(1), 79-101.
  9. Yogeesh, N. (2014). Graphical representation of Solutions to Initial and boundary value problems Of Second Order Linear Differential Equation Using FOOS (Free & Open Source Software)-Maxima. International Research Journal of Management Science and Technology (IRJMST), 5(7), 168-176.
  10. Yogeesh, N. (2019). Graphical Representation of Mathematical Equations Using Open Source Software. Journal of Advances and Scholarly Researches in Allied Education (JASRAE), 16(5), 2204-2209.
  11. Yogeesh, N., & Lingaraju. (2021). Fuzzy Logic-Based Expert System for Assessing Food Safety and Nutritional Risks. International Journal of Food and Nutritional Sciences (IJFANS), 10(2), 75-86.
  12. Yogeesh, N., & F. T. Z. Jabeen (2021). Utilizing Fuzzy Logic for Dietary Assessment and Nutritional Recommendations. International Journal of Food and Nutritional Sciences (IJFANS), 10(3), 149-160.
  13. Yogeesh, N. (2020). Study on Clustering Method Based on K-Means Algorithm. Journal of Advances and Scholarly Researches in Allied Education (JASRAE), 17(1), 2230-7540.
  14. Yogeesh, N. (2018). Mathematics Application on Open Source Software. Journal of Advances and Scholarly Researches in Allied Education [JASRAE], 15(9), 1004-1009.
  15. Yogeesh, N. (2015). Solving Linear System of Equations with Various Examples by using Gauss method. International Journal of Research and Analytical Reviews (IJRAR), 2(4), 338-350.
  16. Yogeesh, N., & Dr. P.K. Chenniappan. (2012). A CONCEPTUAL DISCUSSION ABOUT AN INTUITIONISTIC FUZZY-SETS AND ITS APPLICATIONS. International Journal of Advanced Research in IT and Engineering, 1(6), 45-55.
  17. Yogeesh, N., & Dr. P.K. Chenniappan. (2013). STUDY ON INTUITIONISTIC FUZZY GRAPHS AND ITS APPLICATIONS IN THE FIELD OF REAL WORLD. International Journal of Advanced Research in Engineering and Applied Sciences, 2(1), 104-114.
  18. Yogeesh, N. (2021). Mathematical Approach to Representation of Locations Using K-Means Clustering Algorithm. International Journal of Mathematics And its Applications (IJMAA), 9(1), 127-136.
  19. Yogeesh, N. (2020). Study on Clustering Method Based on K-Means Algorithm. Journal of Advances and Scholarly Researches in Allied Education (JASRAE), 17(1), 2230-7540.
  20. Yogeesh, N. (2021). Mathematical Approach to Representation of Locations Using K-Means Clustering Algorithm. International Journal of Mathematics And its Applications (IJMAA), 9(1), 127-136.