An Interval Type-2 Fuzzy Model of Computing with Words via Interval Type-2 Fuzzy Finite Rough Automata with Application in COVID-19 Deduction

Abstract

Classical automata, fuzzy automata, and rough automata with input alphabets as numbers or symbols are formal computing models with values. Fuzzy automata and rough automata are computation models with uncertain or imprecise information about the next state and can only process the string of input symbols or numbers. To process words and propositions involved in natural languages, we need a computation model to model real-world problems by capturing the uncertainties involved in a word. In this paper, we have shown that computing with word methodology deals with perceptions rather than measurements and allows the use of words in place of numbers and symbols while describing the real-world problems together with interval type-2 (IT2) fuzzy sets which have the capacity to capture uncertainties involved in word using its footprint of uncertainty. The rough set theory, which has potential of modeling vagueness in the imprecise and ill-defined environment, introduces a computation model, namely, IT2 fuzzy rough finite automata, which is efficient to process uncertainties involved in words. Further, we have shown the application of introduced IT2 fuzzy finite rough automaton in the medical diagnosis of COVID-19 patients.

Generalized rough and fuzzy rough automata for semantic computing

“…But this confining demand for equivalence limits the application scope of RST. For application purposes, many extensions of rough sets like fuzzy rough sets, rough fuzzy sets, IT2 fuzzy rough sets, IT2 rough fuzzy sets, tolerance rough fuzzy sets, rough sets based on Galois connections, soft rough fuzzy sets, and soft fuzzy rough sets have been studied (cf., [11,29,30,39,41,62,67]). RST has been proved an essential method in cognitive sciences, decision making, data mining, and artificial intelligence.…”

“…Tyagi and Tripathi [56,58], introduced rough automata, rough grammar, and rough languages into a fuzzy environment. Recently, Pal et al [34] described fuzzy rough automata corresponding to a fuzzy finite automata based on complete residuated lattices, where transition map, after given an input, returns L-fuzzy rough set of states as next state, and Swati et al [62] introduced interval type-2 fuzzy finite rough automata having application in COVID-19 patient deduction.…”

Fault Diagnosis in Regenerative Braking System of Hybrid Electric Vehicles by Using Semigroup of Finite-State Deterministic Fully Intuitionistic Fuzzy Automata

“…Fuzzy automata have been frequently employed since the introduction of fuzzy technology and neural networks [3][4][5][6][7][8][9][10][11][12][13]. Furthermore, there were a variety of problems to be resolved, for example, medical diagnosis, car anti-crash radar, freeway management, urban road traffic control, and obstacle recognition in front of a vehicle, which required flexible, quick, and accurate decisions, and then, fuzzy neural network automata (FNNA) [14][15][16][17] are an excellent choice. FNNA had an increasingly prominent role, particularly in data communications.…”