EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 56 (2022) RAIRO-Theor. Inf. Appl., 56 (2022) 6 References
Open Access
Issue
RAIRO-Theor. Inf. Appl.
Volume 56, 2022
Article Number 6
Number of page(s) 16
DOI https://doi.org/10.1051/ita/2022005
Published online 21 June 2022
  1. R. Bača, M. Krátkỳ, I. Holubová, M. Nečaskỳ, T. Skopal, M. Svoboda and S. Sakr Structural XML query processing. ACM Comput. Surv. 50 (2017) 1–41. [Google Scholar]
  2. T. Ballard, H. Palada, M. Griffin and A. Neal An integrated approach to testing dynamic, multilevel theory: using computational models to connect theory, model, and data. Org. Res. Methods 24 (2021) 251–284. [CrossRef] [Google Scholar]
  3. S. Borgwardt and R.P. Naloza Reasoning in fuzzy description logics using automata. Fuzzy Sets Syst. 298 (2016) 22–43. [CrossRef] [Google Scholar]
  4. S. Bozapalidis and O.L. Bozapalidoy Fuzzy tree language recognizability. Fuzzy Sets Syst. 161 (2010) 716–734. [CrossRef] [Google Scholar]
  5. S. Chehida, A. Baouya, S. Bensalem and M. Bozga Learning and analysis of sensors behavior in loT systems using statistical model checking. Softw. Quality J. (2021) 1–22. Available from: https://doi.org/10.1007/s11219-021-09559-w [Google Scholar]
  6. K.C. Clarke, Mathematical Foundations of Cellular Automata and Complexity Theory, in The Mathematics of Urban Morphology. Springer (2019) 163–170. [Google Scholar]
  7. H. Comon, M. Dauchet, R. Gilleron, F. Jacquemard, D. Lugiez, C. Loding, S. Tison and M. Tommasi, Tree automata: techniques and applications (2007). Preprint https://hal.inria.fr/hal-03367725 [Google Scholar]
  8. Y. Du and P. Zhu Fuzzy approximations of fuzzy relational structures. Int. J. Approx. Reas. 98 (2018) 1–10. [CrossRef] [Google Scholar]
  9. Z. Esik and L. Guangwu Fuzzy tree automata. Fuzzy Sets Syst. 158 (2007) 1450–1460. [CrossRef] [Google Scholar]
  10. M.K. Fallah, S. Moghari, E. Nazemi and M.M. Zahedi, Fuzzy ontology based document feature vector modification using fuzzy tree transducer, in Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems (2008) 38–44. [CrossRef] [Google Scholar]
  11. A.-J. Fougères and E. Ostrosi Fuzzy engineering design semantics elaboration and application. Soft Comput. Lett. 3 (2021) 100025. [CrossRef] [Google Scholar]
  12. H. Frenkel, O. Grumberg and S. Sheinvald An automata-theoretic approach to model-checking systems and specifications over infinite data domains. J. Autom. Reas. 63 (2019) 1077–1101. [CrossRef] [Google Scholar]
  13. M. Ghorani, S. Garhwal and S. Moghari Lattice-valued tree pushdown automata: pumping lemma and closure properties. Int. J. Approx. Reas. 142 (2022) 307–323. [Google Scholar]
  14. M. Ghorani and S. Moghari Decidability of the minimization of fuzzy tree automata with membership values in complete lattices. J. Appl. Math. Comput. 68 (2022) 461–478. [CrossRef] [MathSciNet] [Google Scholar]
  15. E. González-Caballero, R.A. Espin-Andrade, W. Pedrycz, L. Martinez and L.A. Guerrero-Ramos, Continuous linguistic variables and their applications to data mining and time series prediction. Int. J. Fuzzy Syst. (2021) 1–22. [Google Scholar]
  16. P. Grzegorzewski Metrics and orders in space of fuzzy numbers. Fuzzy Sets Syst. 97 (1998) 83–94. [CrossRef] [Google Scholar]
  17. M. Hachicha and J. Darmont, A survey of XML tree patterns. IEEE Trans. Knowl. Data Eng. 25 (2011) 29–46. [Google Scholar]
  18. J. He, X. Li, Y. Yao, Y. Hong and Z. Jinbao Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. Int. J. Geogr. Inf. Sci. 32 (2018) 2076–2097. [CrossRef] [Google Scholar]
  19. M. He and S. Kazi, Data structures for categorical path counting queries, in 32nd Annual Symposium on Combinatorial Pattern Matching (CPM 2021), Schloss Dagstuhl-Leibniz-Zentrum fur Informatik (2021). [Google Scholar]
  20. J.E. Hopcroft, Introduction to automata theory, languages, and computation. Pearson Education India (2008). [Google Scholar]
  21. F. Howar and B. Steffen, Active automata learning in practice, in Machine Learning for Dynamic Software Analysis: Potentials and Limits. Springer (2018) 123–148. [Google Scholar]
  22. X. Ji, L. Wang, H. Xue and Y. Gao Decision-making method of qualitative and quantitative comprehensive evaluation of talents based on probability hesitation fuzzy language. Math. Probl. Eng. 2021 (2021) 8903427. [Google Scholar]
  23. K. Johannisson, Disambiguating implicit constructions in OCL, in Workshop on OCL and Model Driven Engineering at UML2004, Lisbon (2004). [Google Scholar]
  24. A. Kundu and E. Bertino Structural signatures for tree data structures. Proc. VLDB Endow. 1 (2008) 138–150. [CrossRef] [Google Scholar]
  25. I. Lamrani, A. Banerjee and S.K. Gupta, Hymn: Mining linear hybrid automata from input output traces of cyber-physical systems, in 2018 IEEE Industrial Cyber-Physical Systems (ICPS), IEEE (2018) 264–269. [CrossRef] [Google Scholar]
  26. X. Li and A. Gar-On Yeh, Data mining of cellular automata’s transition rules. Int. J. Geogr. Inf. Sci. 18 (2004) 723–744. [CrossRef] [Google Scholar]
  27. Y. Li Approximation and robustness of fuzzy finite automata. Int. J. Approx. Reas. 47 (2008) 247–257. [CrossRef] [Google Scholar]
  28. D.L. Ly and H. Lipson Learning symbolic representations of hybrid dynamical systems. J. Mach. Learn. Res. 13 (2012) 3585–3618. [MathSciNet] [Google Scholar]
  29. R. Medhat, S. Ramesh, B. Bonakdarpour and S. Fischmeister, A framework for mining hybrid automata from input/output traces, in Proceedings of the 12th International Conference on Embedded Software. IEEE Press (2015) 177–186. [Google Scholar]
  30. S. Moghari and M. Ghorani, A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling. Chaos Solitons Fract. 155 (2022) 111660. [CrossRef] [Google Scholar]
  31. S. Moghari and M. Zahedi (1711-4091), Multidimensional fuzzy finite tree automata. Iran. J. Fuzzy Syst. 16 (2019) 155–167. [MathSciNet] [Google Scholar]
  32. S. Moghari and M.M. Zahedi Similarity-based minimization of fuzzy tree automata. J. Appl. Math. Comput. 50 (2016) 417–436. [CrossRef] [MathSciNet] [Google Scholar]
  33. S. Moghari, M.M. Zahedi and R. Ameri New direction in fuzzy tree automata. Iranian J. Fuzzy Syst. 8 (2011) 59–68. [MathSciNet] [Google Scholar]
  34. S. Mohammed, A.F. Barradah and E.-S.M. El-Alfy, Selectivity estimation of extended XML query tree patterns based on prime number labeling and synopsis modeling. Simul. Model. Practice Theory 64 (2016) 30–42. [CrossRef] [Google Scholar]
  35. S. Mohammed, E.-S.M. El-Alfy and A.F. Barradah, Improved selectivity estimator for XML queries based on structural synopsis. World Wide Web 18 (2015) 1123–1144. [CrossRef] [Google Scholar]
  36. J. Mordeson and D.S. Malik, Fuzzy automata and languages: theory and applications. Chapman & Hall, London (2002). [Google Scholar]
  37. L.D. Nguyen and D.Q. Tran Measurement of fuzzy membership functions in construction risk assessment. J. Constr. Eng. Manag. 147 (2021) 04021005. [CrossRef] [Google Scholar]
  38. B.E. Reddy, R.O. Reddy and E.K. Reddy Pattern analysis and texture classification using finite state automata scheme. Int. J. Adv. Intell. Parad. 14 (2019) 30–45. [Google Scholar]
  39. M.S. Roodposhti, J. Aryal and B.A. Bryan, A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change. Environ. Model. Softw. 112 (2019) 70–81. [CrossRef] [Google Scholar]
  40. M.G. Soto, T.A. Henzinger, C. Schilling and L. Zeleznik, Membership-based synthesis of linear hybrid automata, in International Conference on Computer Aided Verification. Springer (2019) 297–314. [Google Scholar]
  41. J.L. Verdegay, Vol. 377 of Uncertainty Management with Fuzzy and Rough Sets. Springer (2019). [Google Scholar]
  42. L. Wang, Y. Wang, D. Cai, D. Zhang and X. Liu, Translating a math word problem to an expression tree. Preprint arXiv:1811.05632 (2018). [Google Scholar]
  43. L.-G. Wang, T.T.-Y. Lam, S. Xu, Z. Dai, L. Zhou, T. Feng, P. Guo, C.W. Dunn, B.R. Jones, T. Bradley et al., Treeio: an R package for phylogenetic tree input and output with richly annotated and associated data. Mol. Biol. Evol. 37 (2020) 599–603. [CrossRef] [PubMed] [Google Scholar]
  44. X. Wu and D. Theodoratos Template-based bitmap view selection for optimizing queries over tree data. Int. J. Cooperative Inf. Syst. 25 (2016) 1650005. [CrossRef] [Google Scholar]
  45. C. Yang and Y. Li Approximate bisimulations and state reduction of fuzzy automata under fuzzy similarity measures. Fuzzy Sets Syst. 391 (2020) 72–95. [CrossRef] [Google Scholar]
  46. H. Ying and F. Lin Online self-learning fuzzy discrete event systems. IEEE Trans. Fuzzy Syst. 28 (2020) 2185–2194. [CrossRef] [Google Scholar]
  47. M. Yulduz Lexico-grammatical parts of speech expressing the indefiniteness of the subject. JournalNX 7 (2021) 323–327. [Google Scholar]
  48. L.A. Zadeh Fuzzy sets. Inf. Control 8 (1965) 338–353. [Google Scholar]
  49. L.A. Zadeh The concept of a linguistic variable and its application to approximate reasoning-I. Inf. Sci. 8 (1975) 199–249. [CrossRef] [Google Scholar]
  50. L.A. Zadeh The concept of a linguistic variable and its application to approximate reasoning-II. Inf. Sci. 8 (1975) 301–357. [CrossRef] [Google Scholar]
  51. H.J. Zimmermann, Fuzzy set theory and its applications, 3rd edn. Springer Science & Business Media (2011). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.