Effective data alignment and key extraction techniques for generation of regular expressions using machine learning techniques

DINESH D. PURI; G. K. PATNAIK

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Effective data alignment and key extraction techniques for generation of regular expressions using machine learning techniques

Author(s)

DINESH D. PURI Department of Computer Engineering, SSBT’s College of Engineering & Technology Jalgaon.MH, India.

G. K. PATNAIK Department of Computer Engineering, SSBT’s College of Engineering & Technology Jalgaon.MH, India.

Abstract

Machine learning supervised classification plays a significant role in large text classification. Health care data contribute to generating privacy and security for a couple of years. Such electric records might take extensive data from storage devices, so it needs to optimize with some processing techniques. For generation of regular expression, data should be in structured form. The conversion of unstructured data in to structured form is done through modified data alignment and key extraction techniques. The SmithWaterman method compares two sequences to find comparable review statements. Smith-Waterman method employs an evolutionary algorithm to find improved local alignments of pairs. It can identify the best local alignment using the supplied score system. The NLP process has been used to generate the keys from such large text. In this paper, we proposed a sequence alignment generation using Smith-Waterman (SW) algorithm and key extraction from generated sequence using Natural Language Processing (NLP) technique has used for effective regular expression building. The filtration techniques have been used to eliminate redundant features, and the Machine Learning (ML) algorithm has been used as post-processing for classification. The generated regular expression by the SW algorithm gives better classification accuracy using NLP and Machine leavening. Generated regular expressions are filtered using 100% precision as threshold. We applied various Machine learning algorithms out of which SVM gives highest accuracy.

Publication Details

Page(s) 5367-5376

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 17, Year: 2022

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Keywords

NLP, Classification, Sequence Generation, Sentence Alignment, Smith Waterman Algorithm, Key Extraction, Regular Expression

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