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Journals / TWMS Journal of Applied and Engineering Mathematics / 2026 / Volume: 16 Issue: 10

GENERAL FUZZY AUTOMATA UNDER SEMANTICALLY SIMILAR ENVIRONMENT: AN APPLICATION TO ENHANCED SEARCH ENGINE PERFORMANCE

Dr. Ranjeet Kaur(Jaypee Institute of Information Technology)0000-0002-1565-7051
Alka Tripathi(Jaypee Institute of Information Technology)0000-0002-4442-3350
Pages
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Publication Date
2026-10-06
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

A practical application of this model is demonstrated in the domain of search engine performance, where the proposed approach improves the handling of queries by mapping them to semantically related states, thereby enhancing the relevance and accuracy of results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Automata theory provides a systematic framework for modelling computational processes; however, many real-world systems require greater adaptability and flexibility.
📊
Key Findings & Data:This paper presents a general fuzzy automata model ($GFA_\mathbb{SE}$) designed to address these challenges, particularly in environments where the set of states is not predetermined.
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Conclusion & Impact:A practical application of this model is demonstrated in the domain of search engine performance, where the proposed approach improves the handling of queries by mapping them to semantically related states, thereby enhancing the relevance and accuracy of results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Fuzzy Automata · General Fuzzy Automata · Semantic Computing

Abstract

Automata theory provides a systematic framework for modelling computational processes; however, many real-world systems require greater adaptability and flexibility. This paper presents a general fuzzy automata model ($GFA_\mathbb{SE}$) designed to address these challenges, particularly in environments where the set of states is not predetermined. The proposed model is capable of dynamically integrating semantically similar states based on contextual relevance, enhancing its robustness in handling real-world data variability. Algorithms are introduced to measure the similarity between states. This advancement extends the capabilities of existing General Fuzzy Automata (GFA) frameworks, allowing them to function effectively in uncertain or evolving environments. A practical application of this model is demonstrated in the domain of search engine performance, where the proposed approach improves the handling of queries by mapping them to semantically related states, thereby enhancing the relevance and accuracy of results.

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Cite This Article

Kaur, D. R., Tripathi, A. (2026). GENERAL FUZZY AUTOMATA UNDER SEMANTICALLY SIMILAR ENVIRONMENT: AN APPLICATION TO ENHANCED SEARCH ENGINE PERFORMANCE. TWMS Journal of Applied and Engineering Mathematics, Volume: 16 Issue: 10. https://ajindex.com/articles/1604189

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References (50)

  1. Astrain, J. J., Garitagoitia, J. R., Gonzalez De Mendivil, J., Villadangos, J., Fari˜na, F., (2004), Approximate string matching using deformed fuzzy automata: A learning experience, Fuzzy Optimization and Decision Making, 3, 141–155.
  2. Cao, Y., Xia, L., Ying, M., (2013), Probabilistic automata for computing with words, Journal of Computer and System Sciences, 79(1), 152–172.
  3. Cao, Y., Ying, M., Chen, G., (2007), Retraction and generalized extension of computing with words, IEEE Transactions on Fuzzy Systems, 15(6), 1238–1250.
  4. Choubey, A., Ravi, K., (2013), Minimization of deterministic finite automata with vague (final) states and intuitionistic fuzzy (final) states, Iranian Journal of Fuzzy Systems, 10(1), 75–88.
  5. Ciric, M., Stamenkovic, A., Ignjatovic, J., Petkovic, T., (2010), Fuzzy relation equations and reduction of fuzzy automata, Journal of Computer and System Sciences, 76(7), 609–633.
  6. Doostfatemeh, M., Kremer, S. C., (2004), Representing generalized fuzzy automata in recurrent neural networks, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No. 04CH37513), 4, 1901–1906.
  7. Doostfatemeh, M., Kremer, S. C., (2005), Developing a new fuzzy controller, NAFIPS 2005 - Annual Meeting of the North American Fuzzy Information Processing Society, 187–192.
  8. Doostfatemeh, M., Kremer, S. C., (2005), New directions in fuzzy automata, International Journal of Approximate Reasoning, 38(2), 175–214.
  9. Doostfatemeh, M., Kremer, S. C., (2006), General fuzzy automata, new efficient acceptors for fuzzy languages, IEEE International Conference on Fuzzy Systems, 2097–2103.
  10. Garitagoitia, J. R., Gonzalez De Mendivil, J. R., Echanobe, J., Astrain, J. J., Farina, F., (2003), Deformed fuzzy automata for correcting imperfect strings of fuzzy symbols, IEEE Transactions on Fuzzy Systems, 11(3), 299–310.
  11. Hopcroft, J. E., Motwani, R., Ullman, J. D., (2001), Introduction to Automata Theory, Languages, and Computation, 2nd edition, ACM Press, New York, NY, USA.
  12. Huang, G., Wasti, S. H., Wei, L., Jiang, Y., (2019), A formal model for semantic computing based on generalized probabilistic automata, Entropy, 21(9), 1–14.
  13. Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., Zhao, L., (2019), Latent dirichlet allocation (LDA) and topic modeling: models, applications, a survey, Multimedia Tools and Applications, 78, 15169–15211.
  14. Jiang, Y., (2018), A general type-2 fuzzy model for computing with words, International Journal of Intelligent Systems, 33(4), 713–758.
  15. Jiang, Y., (2019), A formal model of semantic computing, Soft Computing, 23(14), 5411–5429.
  16. Jiang, Y., Tang, Y., (2014), An interval type-2 fuzzy model of computing with words, Information Sciences, 281, 418–442.
  17. Kaur, R., Tripathi, A., (2021), State minimization of general finite fuzzy automata, International Journal of Mathematical, Engineering and Management Sciences, 6(6), 1709–1718.
  18. Kaur, R., Tripathi, A., (2022), General fuzzy multiset grammar and language, Mathematics in Engineering, Science & Aerospace (MESA), 13(2), 1–10.
  19. Kaur, R., Tripathi, A., (2024), Hybrid model of general fuzzy automata and semantic computing: An application to transportation e-service, Soft Computing, 28, 10519–10530.
  20. Kaur, R., Tripathi, A., (2024), Statistical analysis of lexemes generated in ‘C’ programming using fuzzy automation, Journal of Intelligent & Fuzzy Systems, 46(1), 2537–2546.
  21. Kaur, R., Tripathi, A., (2025), Dynamic state general fuzzy automata under semantically similar environment: An application-based study on air pollution for smart cities 4.0, New Mathematics and Natural Computation, 1–24.
  22. Kim, J., Ostrowski, D. A., Yamaguchi, H., Sheu, P. C.-Y., (2013), Semantic computing and business intelligence, International Journal of Semantic Computing, 7(1), 87–117.
  23. Klir, G. J., Yuan, B., (1994), Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, USA.
  24. Lee, E. T., Zadeh, L. A., (1969), Note on fuzzy languages, Information Sciences, 1(4), 421–434.
  25. Li, Z., Li, P., Li, Y., (2006), The relationships among several types of fuzzy automata, Information Sciences, 176(15), 2208–2226.
  26. Malik, D. S., (2002), Fuzzy Automata and Languages: Theory and Applications, Chapman and Hall/CRC.
  27. Mateescu, A., Salomaa, A., Salomaa, K., Yu, S., (1995), Lexical analysis with a simple finite-fuzzy-automaton model, Journal of Universal Computer Science, 1(5), 292–311.
  28. Mizumoto, M., Toyoda, J., Tanaka, K., (1969), Some considerations on fuzzy automata, Journal of Computer and System Sciences, 3(4), 409–422.
  29. Pennington, J., Socher, R., Manning, C. D., (2014), GloVe: Global vectors for word representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543.
  30. Qiu, D., Wang, H., (2005), A probabilistic model of computing with words, Journal of Computer and System Sciences, 70(2), 176–200.
  31. Santos, E., (1976), Fuzzy automata and languages, Information Sciences, 10(3), 193–197.
  32. Santos, E. S., (1968), Maximin automata, Information and Control, 13(4), 363–377.
  33. Santos, E. S., (1975), Max-product grammars and languages, Information Sciences, 9(1), 1–23.
  34. Shamsizadeh, M., Movahednia, E., De la Sen, M., (2023), Isomorphism between two vector general fuzzy automata, Informatica, 34(3), 617–633.
  35. Shamsizadeh, M., Zahedi, M., (2015), Minimal intuitionistic general l-fuzzy automata, Italian Journal of Pure and Applied Mathematics, 35, 155–186.
  36. Shamsizadeh, M., Zahedi, M. M., (2016), Intuitionistic general fuzzy automata, Soft Computing, 20, 3505–3519.
  37. Shamsizadeh, M., Zahedi, M. M., (2019), Bisimulation of type 2 for bl-general fuzzy automata, Soft Computing, 23(20), 9843–9852.
  38. Sheu, P. C.-Y., Yu, H., Ramamoorthy, C., Joshi, A. K., Zadeh, L. A., (2011), Semantic Computing, John Wiley & Sons.
  39. Sipser, M., (2013), Introduction to the Theory of Computation, 3rd edn, Course Technology, Boston, MA.
  40. Tiwari, S., Gautam, V., Dubey, M., (2016), On fuzzy multiset automata, Journal of Applied Mathematics and Computing, 51, 643–657.
  41. Tripathi, A., Kaur, R., (2019), A review of state minimization and state reduction techniques in fuzzy automata, AIP Conference Proceedings, 2061(1), 1–7.
  42. Wang, H., Qiu, D., (2003), Computing with words via Turing machines: A formal approach, IEEE Transactions on Fuzzy Systems, 11(6), 742–753.
  43. Wee, W. G., Fu, K.-S., (1969), A formulation of fuzzy automata and its application as a model of learning systems, IEEE Transactions on Systems Science and Cybernetics, 5(3), 215–223.
  44. Wei, L., Huang, G., Wasti, S. H., Hussain, M. J., Jiang, Y., (2021), Generalized fuzzy automata with semantic computing, Soft Computing, 25, 5775–5789.
  45. Xia, P., Zhang, L., Li, F., (2015), Learning similarity with cosine similarity ensemble, Information Sciences, 307, 39–52.
  46. Yadav, S., Tiwari, S., Kumari, M., Yadav, V. K., (2022), An interval type-2 fuzzy model of computing with words via interval type-2 fuzzy finite rough automata with application in COVID-19 deduction, New Mathematics and Natural Computation, 18(1), 61–101.
  47. Ying, M., (2002), A formal model of computing with words, IEEE Transactions on Fuzzy Systems, 10(5), 640–652.
  48. Garg, H., Ullah, K., Akram, M., Ali, Z., (2024), A Method To Multi-Attribute Group Decision Making Technique Based On T-Spherical Fuzzy Information Using Bonferroni Mean Operators, TWMS J. Pure Appl. Math., 15(1), pp. 42-64.
  49. Zadeh, L. A., (1965), Fuzzy sets, Information and Control, 8(3), 338–353.
  50. Garg, H., Ullah, K., Akram, M., Ali, Z., (2024), A Method To Multi-Attribute Group Decision Making Technique Based On T-Spherical Fuzzy Information Using Bonferroni Mean Operators, TWMS J. Pure Appl. Math., 15(1), pp. 42-64.