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Academic literature on the topic 'Plagiarism detector'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Plagiarism detector"

Lanke, Ms Minakshi, Ms Aishwarya Porwal, Ms Sejal Sarolkar, Mr Gaurang Palaskar, and Prof Shital Kakad. "AI Based Student’s Assignments Plagiarism Detector." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 991–99. http://dx.doi.org/10.22214/ijraset.2022.48080.

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Abstract: The increase in plagiarizing academic contents led to efficient plagiarism detectors. In the conventional plagiarism detection tools, we have to put the contents from our paper or assignment and paste it into the input box for checking plagiarism or we have to load our files separately. Software under the domain artificial intelligence is moving towards the creation of a systems that can make tasks hassle-free and it also saves the time. “Smart Assignment Plagiarism Detector” can provide easy and efficient way of checking plagiarised data for teachers just in a single click by upload

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Stone, R. "Court test for plagiarism detector?" Science 254, no. 5037 (1991): 1448. http://dx.doi.org/10.1126/science.1962200.

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Nwohiri, Anthony, Opemipo JODA, and Olasupo Ajayi. "AI-POWERED PLAGIARISM DETECTION: LEVERAGING FORENSIC LINGUISTICS AND NATURAL LANGUAGE PROCESSING." FUDMA JOURNAL OF SCIENCES 5, no. 3 (2021): 207–18. http://dx.doi.org/10.33003/fjs-2021-0503-700.

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Plagiarism of material from the Internet is nothing new to academia and it is particularly rampant. This challenge can range from borrowing a particularly apt phrase without attribution, to paraphrasing someone else’s original idea without citation, to wholesale contract cheating. Plagiarized content can infringe on copyright laws and could incur hefty fines on publishers and authors. Unintentional plagiarism mostly occurs due to inaccurate citation. Most plagiarism checkers ignore this fact. Moreover, plagiarizers are increasingly becoming negatively “smarter”. All these necessitate a plagiar

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Wali, Muhammad, and Safrizal Safrizal. "Similar text sebagai Pengkodean Aplikasi Plagiarisme." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 2, no. 1 (2018): 11. http://dx.doi.org/10.35870/jtik.v2i1.43.

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a b s t r a c tThis research is intended to make a coding technique with a function similar to a plagiarism detector similar_text as text. By using the text function is similar, this research resulted in a detection of the document against the 10 (ten) of selected journal with, the document is the document with extension doc, docx, pdf, and txt. The document will be converted into html form and would henceforth be done making the string with the marker dots (.) And the comma (,) will be fetched a new string. By specifying a percentage string similarity of 90% then produced a text, in this case

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Mahmoodi, Maryam, and Mohammad Mahmoodi Varnamkhasti. "Design a Persian Automated Plagiarism Detector (AMZPPD)." International Journal of Engineering Trends and Technology 8, no. 8 (2014): 465–67. http://dx.doi.org/10.14445/22315381/ijett-v8p280.

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Bouarara, Hadj Ahmed, Reda Mohamed Hamou, and Amine Rahmani. "BHA2." International Journal of Swarm Intelligence Research 8, no. 1 (2017): 30–53. http://dx.doi.org/10.4018/ijsir.2017010102.

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In the last decade, the plagiarism cases were increased and become a topical problem in the modern scientific world, caused by the quantity of textual information available online/offline. The authors' work deals on the development of a new plagiarism detector system called BHA2 which has as input the suspicious text (to be analysed) and the original texts (learning basis). It can detect the different forms of plagiarism based on: Google API to detect the cases of plagiarism with translation; text summarization to detect the plagiarism of idea; conceptual transformation to detect the plagiaris

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Sobowale, Adedayo, Adebimpe Esan, Adebiyi Tomilayo, Bunmi Jooda, and Adekemi Bolajoko. "Automatic plagiarism detection using fuzzy-logic." Dutse Journal of Pure and Applied Sciences 9, no. 3a (2023): 312–18. http://dx.doi.org/10.4314/dujopas.v9i3a.31.

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Plagiarism occurs when a researcher copies fellow researcher’s work verbatim without acknowledging the author. This work developed an automatic plagiarism detector using fuzzy logic. The system developed was tested with 4 different text documents and evaluated using portability, efficiency, functionality, ease of use and accuracy metrics. Results show that the developed plagiarism detector is very easy to use with high functionality and accuracy as well as moderate efficiency and portability based on user’s assessment. However, future work can increase the data size for model training and cons

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Kikuchi, Hiroshi, Takaaki Goto, Mitsuo Wakatsuki, and Tetsuro Nishino. "A Source Code Plagiarism Detecting Method Using Sequence Alignment with Abstract Syntax Tree Elements." International Journal of Software Innovation 3, no. 3 (2015): 41–56. http://dx.doi.org/10.4018/ijsi.2015070104.

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Learning to program is an important subject in computer science courses. During programming exercises, plagiarism by copying and pasting can lead to problems for fair evaluation. Some methods of plagiarism detection are currently available, such as sim. However, because sim is easily influenced by changing the identifier or program statement order, it fails to do enough to support plagiarism detection. In this paper, the authors propose a plagiarism detection method which is not influenced by changing the identifier or program statement order. The authors also explain our method's capabilities

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Ibrahim, Karim Hesham Shaker, Dhari Al Otaibi, and Fadi N. Sibai. "The Robustness of AI-Classifiers in the Face of AI-Assisted Plagiarism." International Journal of Computer-Assisted Language Learning and Teaching 15, no. 1 (2025): 1–27. https://doi.org/10.4018/ijcallt.375428.

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The release of ChatGPT marked the beginning of a new era of artificial intelligence (AI)-assisted plagiarism that disrupted traditional assessment practices. In response to this existential threat, Turnitin added an AI content detector to its platform, which is the most used plagiarism detection software in most academic institutions. However, several early studies suggest that AI detectors have questionable accuracy rates, especially when dealing with English as a second language writings or modified AI texts. To explore this uncharted territory in second language writing research, the presen

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Satish, SVS. "Intellicheck – Plagiarism Detector Using HMM, BERT and Abstract Syntax Tree." International Journal of Science, Engineering and Technology 13, no. 2 (2025): 1–9. https://doi.org/10.61463/ijset.vol.13.issue2.196.

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Dissertations / Theses on the topic "Plagiarism detector"

Arenhardt, Catiane Priscila Barbosa. "MISS MARPLE DESENVOLVIMENTO DE FERRAMENTA PARA AUXILIAR NA VERIFICAÇÃO E DETECÇÃO DE INDÍCIOS DE PLÁGIO COM BASE NO MÉTODO DIP DETECTOR DE INDÍCIOS DE PLÁGIO." Universidade Federal de Santa Maria, 2013. http://repositorio.ufsm.br/handle/1/5418.

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The work aimed to identify the needs that still permeated the tools of analysis and detect plagiarism for further development of a new tool, called Miss Marple, which met the requirements identified in the course of research, besides continuing the work called DIP - Evidence of Plagiarism Detector. The research conducted in this work allowed the survey of the characteristics of each tool enabling trace a comparative study between them and the development of the new tool. The validation of the developed tool was performed in two modes of travel, and distance, and by evaluating the usability of

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Pereira, Rafael Corezola. "Cross-language plagiarism detection." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2010. http://hdl.handle.net/10183/27652.

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Plágio é um dos delitos mais graves no meio acadêmico. É definido como “o uso do trabalho de uma pessoa sem a devida referência ao trabalho original”. Em contrapartida a esse problema, existem diversos métodos que tentam detectar automaticamente plágio entre documentos. Nesse contexto, esse trabalho propõe um novo método para Análise de Plágio Multilíngue. O objetivo do método é detectar casos de plágio em documentos suspeitos baseado em uma coleção de documentos ditos originais. Para realizar essa tarefa, é proposto um método de detecção de plágio composto por cinco fases principais: normaliz

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Mengoli, Chiara. "Plagiarism." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20928/.

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This document aims to provide an overview of the current situation of plagiarism. This survey presents a taxonomy of various plagiarism forms and includes discussion on each of these forms. Moreover, it presents the existence of various tools and systems for detecting plagiarism. The easy access to the Web, its increasing use, and the development of technologies have led to the need to adapt plagiarism laws, implement new mechanisms anti-plagiarism or mechanisms of detection and empower people on the problem of plagiarism. Therefore, there will be a dedicated section for each of these aspects.

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Liaqat, Ahmad Gull, and Aijaz Ahmad. "Plagiarism Detection in Java Code." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-13231.

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Lancaster, Thomas. "Effective and efficient plagiarism detection." Thesis, London South Bank University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.618675.

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Chong, Man Yan Miranda. "A study on plagiarism detection and plagiarism direction identification using natural language processing techniques." Thesis, University of Wolverhampton, 2013. http://hdl.handle.net/2436/298219.

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Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to super cial, brute-force stringmatching techniques. If the text has

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Nilsson, Erik. "Abstract Syntax Tree Analysis for Plagiarism Detection." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80888.

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Today, universities rely heavily on systems for detecting plagiarism in students’essays and reports. Code submissions however require specific tools. A numberof approaches to finding plagiarisms in code have already been tried, includingtechniques based on comparing textual transformations of code, token strings,parse trees and graph representations. In this master’s thesis, a new system, cojac,is presented which combines textual, tree and graph techniques to detect a broadspectrum of plagiarism attempts. The system finds plagiarisms in C, C++ and Adasource files. This thesis discusses the met

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Alsallal, M. "A machine learning approach for plagiarism detection." Thesis, Coventry University, 2016. http://curve.coventry.ac.uk/open/items/7e903a56-4845-4852-b1a8-2849b1cdb08a/1.

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Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and

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Perleka, Erisa. "Plagiarism Detection : An Overview of Text Alignment Techniques." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23404.

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The task of plagiarism detection is to look at a given document and establish whether it is an original or not. More specifically, it concerns the disclosure of which sentences or passages are plagiarised from which respective passages. The strategies of uncovering plagiarism rely on the kind of natural language representation chosen. Plagiarism detection asks for two major tasks. Retrieving a possible source document, and then performing the detailed analysis that would produce the exact detection result. This thesis is mainly concerned with the detailed analysis phase, more specifically with

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Nawab, Rao Muhammad Adeel. "Mono-lingual paraphrased text reuse and plagiarism detection." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/2785/.

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Books on the topic "Plagiarism detector"

Gipp, Bela. Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8.

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Meuschke, Norman. Analyzing Non-Textual Content Elements to Detect Academic Plagiarism. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-42062-8.

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Eaton, Sarah Elaine. Plagiarism in Higher Education. ABC-CLIO, 2021. http://dx.doi.org/10.5040/9798400697142.

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With considerations for students, faculty members, librarians, and researchers, this book will explain and help to mitigate plagiarism in higher education contexts. Plagiarism is a complex issue that affects many stakeholders in higher education, but it isn't always well understood. This text provides an in-depth, evidence-based understanding of plagiarism with the goal of engaging campus communities in informed conversations about proactive approaches to plagiarism. Offering practical suggestions for addressing plagiarism campus-wide, this book tackles such messy topics as self-plagiarism, pl

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Citation-based Plagiarism Detection: Detecting Disguised and Cross-language Plagiarism using Citation Pattern Analysis. Springer Vieweg, 2014.

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Gipp, Béla. Citation-Based Plagiarism Detection: Detecting Disguised and Cross-Language Plagiarism Using Citation Pattern Analysis. Springer Vieweg. in Springer Fachmedien Wiesbaden GmbH, 2014.

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Gipp, Bela. Citation-based Plagiarism Detection: Detecting Disguised and Cross-language Plagiarism using Citation Pattern Analysis. Springer Vieweg, 2014.

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Meuschke, Norman. Analyzing Non-Textual Content Elements to Detect Academic Plagiarism. Springer Fachmedien Wiesbaden GmbH, 2023.

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Wood, John 1704-1754. Origin of Building, or, the Plagiarism of the Heathens Detected: In Five Books. Creative Media Partners, LLC, 2021.

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Wood, John 1704-1754. Origin of Building, or, the Plagiarism of the Heathens Detected: In Five Books. Creative Media Partners, LLC, 2023.

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Velliaris, Donna M. Prevention and Detection of Academic Misconduct in Higher Education. IGI Global, 2019.

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Book chapters on the topic "Plagiarism detector"

Gray, Brenna Clarke. "Ethics, EdTech, and the Rise of Contract Cheating." In Academic Integrity in Canada. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83255-1_9.

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AbstractThis chapter argues that establishing a “culture of academic integrity,” in the era of digitally-situated plagiarism like contract cheating, begins with an institutional approach to student data and student work that is rooted in ethics. If “students cheat when they feel cheated” (Christensen Hughes, 2017, p. 57), then the ethical failures inherent in a system-wide move toward for-profit homework systems and plagiarism checkers sets a dangerous model for students to follow. Paper presented at the University of Calgary, Calgary http://hdl.handle.net/1880/110083), then the ethical failur

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Gipp, Bela. "Plagiarism Detection." In Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8_2.

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Gañan, David. "Plagiarism Detection." In Engineering Data-Driven Adaptive Trust-based e-Assessment Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29326-0_2.

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Weber-Wulff, Debora. "Plagiarism Detection." In False Feathers. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39961-9_4.

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Gipp, Bela. "Introduction." In Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8_1.

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Gipp, Bela. "Citation-based Document Similarity." In Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8_3.

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Gipp, Bela. "Citation-based Plagiarism Detection." In Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8_4.

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Gipp, Bela. "Prototype: CitePlag." In Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8_5.

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Gipp, Bela. "Quantitative and Qualitative Evaluation." In Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8_6.

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Gipp, Bela. "Summary & Future Work." In Citation-based Plagiarism Detection. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06394-8_7.

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Conference papers on the topic "Plagiarism detector"

Fuad, A. Jauhar, Amar Kukuh Wicaksono, M. Auzai Aqib, M. Arif Khoiruddin, Abbas Sofwan Matla’Il Fajar, and Khoirul Mustamir. "AI Hybrid Based Plagiarism Detection System Creation." In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2024. http://dx.doi.org/10.1109/icacite60783.2024.10616945.

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Arefin, Mohammad Shamsul, Yasuhiko Morimoto, and Mohammad Amir Sharif. "Bilingual plagiarism detector." In 2011 14th International Conference on Computer and Information Technology (ICCIT). IEEE, 2011. http://dx.doi.org/10.1109/iccitechn.2011.6164832.

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Kořínek, Lukáš. "Text Document Plagiarism Detector." In STUDENT EEICT 2021. Fakulta elektrotechniky a komunikacnich technologii VUT v Brne, 2021. http://dx.doi.org/10.13164/eeict.2021.73.

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Rahiman, Nik Faris Aiman Nik, Md Gapar Md Johar, and Rabab Alayham Abbas Helmi. "CopyPoppy – A Source Code Plagiarism Detector." In 2022 IEEE 10th Conference on Systems, Process & Control (ICSPC). IEEE, 2022. http://dx.doi.org/10.1109/icspc55597.2022.10001740.

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Anjali, Ashwani Tripathi, Shalini Gupta, and Sudeept Singh Yadav. "Plagiarism Detector using Long Term Common Tracking." In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2021. http://dx.doi.org/10.1109/icacite51222.2021.9404586.

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Kulkarni, Sagar, Sharvari Govilkar, and Dhiraj Amin. "Text Similarity From Image Contentsusing Statistical and Semantic Analysis Techniques." In 4th International Conference on NLP Trends & Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131405.

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Plagiarism detection is one of the most researched areas among the Natural Language Processing(NLP) community. A good plagiarism detection covers all the NLP methods including semantics, named entities, paraphrases etc. and produces detailed plagiarism reports. Detection of Cross Lingual Plagiarism requires deep knowledge of various advanced methods and algorithms to perform effective text similarity checking. Nowadays the plagiarists are also advancing themselves from hiding the identity from being catch in such offense. The plagiarists are bypassed from being detected with techniques like pa

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Apriyani, Nelita, Aldy Atmadja, Rifqi Fuadi, and Yana Gerhana. "The Detector of Plagiarism in Autograder for PHP Programming Languages." In Selected Papers from the 1st International Conference on Islam, Science and Technology, ICONISTECH-1 2019, 11-12 July 2019, Bandung, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.11-7-2019.2298067.

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Ratna, Anak Agung Putri, Paskalis Nandana Yestha Nabhastala, Ihsan Ibrahim, et al. "Cross-Language Automatic Plagiarism Detector Using Latent Semantic Analysis and Self-Organizing Map." In the 2018 International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3293663.3293681.

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Pawelczak, Dieter. "Effects of Plagiarism in Introductory Programming Courses on the Learning Outcomes." In Fifth International Conference on Higher Education Advances. Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/head19.2019.9297.

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We compare two introductory programming courses and the accompanying programming assignments with respect to the learning outcomes and the relation to plagiarism. While in the first course the solutions from the students of their programming assignments are checked directly with a plagiarism detection system to prevent students from plagiarizing, plagiarism is not tracked in the second course. Running a post check against plagiarism after the course reveals a significant higher plagiarism rate with several exact copies. As the number of students handing in copies from fellow students increases

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"Review of Source Code Plagiarism Detection Techniques." In 4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022). Cihan University, 2022. http://dx.doi.org/10.24086/cocos2022/paper.658.

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In the educational sector, where scientific publications and articles are concerned, plagiarism detection systems are critical. Plagiarism occurs when someone copies a piece of content without the author's permission or citation. You'll need a lot of knowledge about plagiarism types and classes to detect such conduct. Thanks to recently developed tools and procedures, many types of plagiarism may now be recognized. Plagiarism detection has become a crucial concern as a result of advancements in information and communication technology tools (ICT Tools), as well as the availability of online sc

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Reports on the topic "Plagiarism detector"

Williams, Jeremy B. Plagiarism: Deterrence, Detection and Prevention. The Economics Network, 2005. http://dx.doi.org/10.53593/n757a.

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Cortinhas, Carlos. Detection and Prevention of Plagiarism in Higher Education. The Economics Network, 2017. http://dx.doi.org/10.53593/n3045a.

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Cortinhas, Carlos, and Szabolcs Deak. Prevention and Detection of Plagiarism in Higher Education: Paper Mills, Online Assessments and AI. The Economics Network, 2023. http://dx.doi.org/10.53593/n3688a.

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Lewellen, Todd, George J. Silowash, and Daniel Costa. Insider Threat Control: Using Plagiarism Detection Algorithms to Prevent Data Exfiltration in Near Real Time. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada610372.

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