A deep learning based technique for plagiarism detection: a comparative study

Mostafa, Hambi El; Benabbou, Faouzia

doi:10.11591/ijai.v9.i1.pp81-90

Cited by 20 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5-Word2Vec: is consider a deep learning-based predictive model within the category of unsupervised models that is used to compute and create high-quality dense, distributed. Words are represented as continuous vectors that capture contextual and semantic similarities [61]. 6-AUR-BoW: When user comments are broken into sentences, most user comments are too short, therefor when text is categorized, the text of the workbook is too short.…”

Section: -3 Text Vectorization Models and Feature Extractionmentioning

confidence: 99%

Classification of Software Systems attributes based on quality factors using linguistic knowledge and machine learning: A review.

Ali¹,

Saleem²

2022

JES

View full text Add to dashboard Cite

Both the functionality and the non-functionality for what the software system does and does not do within software systems requirements are documented in a Software Requirements Specification (SRS). In requirements engineering, system requirements classify into several categories such as functional, quality and constraint classes. Therefore, we evaluate several machine learning approaches as well as methodologies mentioned in previous literature in terms of automatic requirements extraction, then classification is performed based on methodically reviewing many previous works on software requirements classification to assist software engineers in selecting the best requirement classification technique. The study aims to obtain answers for several questions: "What were machine learning algorithms used for the classification process of the requirements?", "How do these algorithms work and how are they evaluated?", "What methods were used for extracting features from a text?", "What evaluation criteria were used in comparing results?", and "Which machine learning techniques and methods provided the highest accuracy?".

show abstract

Section: -3 Text Vectorization Models and Feature Extractionmentioning

confidence: 99%

Classification of Software Systems attributes based on quality factors using linguistic knowledge and machine learning: A review.

Ali¹,

Saleem²

2022

JES

View full text Add to dashboard Cite

show abstract

“…El Mostafa and Benabbou (2020) [3] made a comparative study of plagiarism detection. They concluded that most of studies are based on world granularity and use the word2vec method for word vector representation, which is their weak point, since they do not reflect the true meanings of the sentences.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the existing paraphrase systems have performed quite well; however, there are certain challenges with paraphrase detection. For example, existing paraphrase systems deliver relatively good results for clean texts, but they do not perform well when applied to noisy texts [1][2][3]. Moreover, in recent years there was an expansion of deep neural network models' application to the NLP domain, and that opens up a complete new field for experimentation and improvement of the existing approaches.…”

Section: Introductionmentioning

confidence: 99%

Corpus-Based Paraphrase Detection Experiments and Review

Vrbanec

Meštrović

2020

Information

View full text Add to dashboard Cite

Paraphrase detection is important for a number of applications, including plagiarism detection, authorship attribution, question answering, text summarization, text mining in general, etc. In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. We report the results of eight models (LSI, TF-IDF, Word2Vec, Doc2Vec, GloVe, FastText, ELMO, and USE) evaluated on three different public available corpora: Microsoft Research Paraphrase Corpus, Clough and Stevenson and Webis Crowd Paraphrase Corpus 2011. Through a great number of experiments, we decided on the most appropriate approaches for text pre-processing: hyper-parameters, sub-model selection—where they exist (e.g., Skipgram vs. CBOW), distance measures, and semantic similarity/paraphrase detection threshold. Our findings and those of other researchers who have used deep learning models show that DL models are very competitive with traditional state-of-the-art approaches and have potential that should be further developed.

show abstract

“…1635 extreme learning machine (ELM), and bagging classifier were among the machine learning algorithms used to define and classify credit card transaction data into fraudulent and non-fraudulent categories [11]- [14].…”

mentioning

confidence: 99%

Fraudulent credit card transaction detection using soft computing techniques

Priyadarshini

Madichetty²,

Mishra³

et al. 2021

IJEECS

View full text Add to dashboard Cite

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

show abstract

A deep learning based technique for plagiarism detection: a comparative study

Cited by 20 publications

References 28 publications

Classification of Software Systems attributes based on quality factors using linguistic knowledge and machine learning: A review.

Classification of Software Systems attributes based on quality factors using linguistic knowledge and machine learning: A review.

Corpus-Based Paraphrase Detection Experiments and Review

Fraudulent credit card transaction detection using soft computing techniques

Contact Info

Product

Resources

About