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Generalized rough and fuzzy rough automata for semantic computing.

Swati Yadav1, S P Tiwari1, Mausam Kumari2

  • 1Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004 India.

International Journal of Machine Learning and Cybernetics
|September 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces new semantic computing (SC) models for rough finite-state automata and fuzzy finite automata. These enhanced models improve computational flexibility by handling semantically related inputs, expanding their real-world applications.

Keywords:
Fuzzy finite automataFuzzy finite rough automataRough finite state automataSemantic computingSemantic relations

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Area of Science:

  • Computer Science
  • Formal Methods
  • Artificial Intelligence

Background:

  • Classical automata models (finite automata, fuzzy finite automata, rough finite state automata) are limited by fixed input alphabets.
  • These limitations restrict user-friendliness and applicability in real-world scenarios.
  • Semantic computing offers a method to redefine these models for broader scope.

Purpose of the Study:

  • To introduce and investigate two novel formal computation models incorporating semantic computing (SC).
  • These models are extensions of rough finite-state automata and fuzzy finite automata.
  • The goal is to enhance their ability to process diverse and semantically related inputs.

Main Methods:

  • Developed a rough finite-state automaton for SC capable of handling external or semantically equivalent concepts.
  • Created a fuzzy finite rough automaton for SC to process semantically related and external input alphabets.
  • Utilized real-world application datasets to validate the proposed models.

Main Results:

  • The proposed rough finite-state automaton for SC successfully handles inputs with semantically equivalent concepts.
  • The fuzzy finite rough automaton for SC effectively processes semantically related and external input alphabets.
  • Both models demonstrate successful real-world applications and improved user experience.

Conclusions:

  • The new semantic computing-enhanced automata models significantly overcome the limitations of traditional fixed-alphabet automata.
  • These enhanced models offer greater flexibility and applicability in real-world computational tasks.
  • The research provides a foundation for more adaptive and user-friendly formal computation systems.