Generalized rough and fuzzy rough automata for semantic computing

作     者：Yadav, Swati Tiwari, S. P. Kumari, Mausam Yadav, Vijay K. 

作者机构：Indian Inst Technol Dept Math & Comp Indian Sch Mines Dhanbad 826004 Bihar India Cent Univ Rajasthan Dept Math Ajmer 305817 Rajasthan India 

出 版 物：《INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS》 (国际机器学习与控制论杂志)

年 卷 期：2022年第13卷第12期

页      面：4013-4032页

核心收录：

学科分类：08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：UGC, New Delhi, India [F.30-414/2018(BSR)] CSIR, New Delhi, India [JRF F.N. 09/1131(0033)/2019-EMR-1] 

主　　题：Semantic computing Semantic relations Fuzzy finite automata Rough finite state automata Fuzzy finite rough automata 

摘      要：The classical automata, fuzzy finite automata, and rough finite state automata are some formal models of computing used to perform the task of computation and are considered to be the input device. These computational models are valid only for fixed input alphabets for which they are defined and, therefore, are less user-friendly and have limited applications. The semantic computing techniques provide a way to redefine them to improve their scope and applicability. In this paper, the concept of semantically equivalent concepts and semantically related concepts in information about real-world applications datasets are used to introduce and study two new formal models of computations with semantic computing (SC), namely, a rough finite-state automaton for SC and a fuzzy finite rough automaton for SC as extensions of rough finite-state automaton and fuzzy finite-state automaton, respectively, in two different ways. The traditional rough finite-state automata can not deal with situations when external alphabet or semantically equivalent concepts are given as inputs. The proposed rough finite-state automaton for SC can handle such situations and accept such inputs and is shown to have successful real-world applications. Similarly, a fuzzy finite rough automaton corresponding to a fuzzy automaton is also failed to process input alphabet different from their input alphabet, the proposed fuzzy finite rough automaton for SC corresponding to a given fuzzy finite automaton is capable of processing semantically related input, and external input alphabet information from the dataset obtained by real-world applications and provide better user experience and applicability as compared to classical fuzzy finite rough automaton.