FPSS: Fingerprint-based semantic similarity detection in big data environment

Elhoseny, Mohamed; Zaher, Mahmoud A.; Shehab, Abdulaziz; Hassanien, Aboul Ella

doi:10.1109/intelcis.2017.8260066

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The exams monitor that the LSTM model accomplished the very best price of semantic similarity and outperformed considerably the contemporary cutting-edge strategies. A fingerprint-based semantic similarity detection gadget, so-known as FPSS, was also provided to hit upon plagiarism within the arabic texts [49].…”

Section: B Semantic-based Similaritymentioning

confidence: 99%

Arabic Language Documents’ Similarity and its Challenges (A Review)

Saleh¹,

Fadzli²,

El-Ebiary³

2020

IJETT

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Measuring the similarity of documents is of great importance and has influences on many areas and subjects such as Information Retrieval (IR), redrafting discovered, classified documents, and conversational agent programs. Recently, several scientific studies have been interested in implementing the similarity of documents in Arabic language applications. In this study, we review most of the techniques used in measuring the similarity between documents in Arabic; these techniques are classified into three types, mainly lexical, semantic, and finally hybrid. Hence, most of the techniques and methods adopted to measure the similarity between documents were reviewed to determine the most appropriate ones to measure the similarity in Arabic. However, the measurement of the percentage of similarity in documents in the Arabic language represents a major challenge faced by most of the researchers due to the nature of the Arabic language, such as complex morphological processes, ambiguity, and a general lack of resources.

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Section: B Semantic-based Similaritymentioning

confidence: 99%

Arabic Language Documents’ Similarity and its Challenges (A Review)

Saleh¹,

Fadzli²,

El-Ebiary³

2020

IJETT

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Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

Zaher

Shehab

Elhoseny

et al. 2020

Journal of Organizational and End User Computing

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Due to the rapid increase of internet-based data, there is urgent need for a robust intelligent documents security mechanism. Although there are many attempts to build a plagiarism detection system in natural language documents, the unlimited variation and different writing styles of each character in Arabic documents make building such systems challenging. Based on its position in a word, the same Arabic letter can be written three different ways, which makes the handwritten character recognition a cumbersome process. This article proposes an intelligent unsupervised model to detect plagiarism in these documents called ASTAP. First, a handwritten Arabic character recognition system is proposed using the Grey Wolf Optimization (GWO) algorithm. Then, a modified Abstract Syntax Tree (AST) is used to match the contents of the Arabic documents to detect any similarity. Compared to the state-of-the-art methods, ASTAP improves the effectiveness of the plagiarism detection in terms of the matched similarity ratio, the precision ratio, and the processing time.

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FPSS: Fingerprint-based semantic similarity detection in big data environment

Cited by 2 publications

References 6 publications

Arabic Language Documents’ Similarity and its Challenges (A Review)

Arabic Language Documents’ Similarity and its Challenges (A Review)

Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

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