An Integrated Supervised Learning Approach For High-Accuracy String Matching

Abstract

A fundamental problem in computer science is the string matching problem, which is the challenge of locating all instances of one string as a substring of another. Due to several applications in computational biology, this subject has recently got a lot of attention. Different ciphers are considered to speed up the search process in this research, which is a revised form of Horspool's string detection algorithm. The numerous pattern identification algorithms are used to locate all instances of a restricted set of patterns inside an input text or input file in order to examine the information of the documents. String matching can be done in one of two ways: exact matching or approximate matching. The proposed research focuses on employing an exact string matching using Inclusive Supervised Learning Model to develop a Accurate String Matching (ISL-ASM) that is an upgraded form of the Boyer-Moore-Horspool algorithm. When compared to traditional models, the proposed model's string matching accuracy is superior.