Proposed System for Plagiarism Detection

Proposed administration for Plagiarism DetectionChapter 3The Proposed System entranceThis chapter introduces ZPLAG as proposed arrangement, and its most important design issues be explained in details.It is very easy for the student to find the documents and magazines utilise advanced search engines, so the paradox of electronic thefts is no longer local or regional, but has become a global problem occurring in m some(prenominal) areas.Due to the Hugging of information, and correlational statistics networks, the discovery of electronic thefts is a difficult task, and the discovery of the thefts started in the Arabic language and the most difficult task no doubt.And in light of the ripening e-learning systems in the Arab countries, this requires special techniques to detect thefts electronic written in Arabic. And although it could use some search engines like Google, it is very difficult to copy and paste the sentences in the search engines to find these thefts.For this reason , it must be develop a good tool for the discovery of electronic thefts written Arabic language to protect e-learning systems, and to relieve and accelerate the learning process, where it can automatically detect electronic thefts automatically by this tool.This dissertation shows, ZPLAG, a system that works on the Internet to alter specialists to detect thefts of electronic texts in Arabic so it can be integrated with e-learning systems to ensure the safety of students and research papers and scientific theses of electronic thefts.The thesis also describes the major components of this system, including stage outfitted, and in the end we volition establish an experimental system on a set of documents and Arabic texts and compared the results obtained with some of the existing systems, particularly TurnItIn.The chapter is organized as follow section 3.2 presents an overview of the Arabic E-Learning, Section 3.3 presents and explains the General Overview of the Proposed System, S ection 3.4 explains in details the system architecture of the proposed system ZPLAG. Section 3.5 gives a summery for this chapter.General Overview of the Proposed System The proposed system consists of three different stagecoachs namely (1) Preparation phase, (2) Processing phase, and (3) Similarity detection phase. prognosticate 3.1 depicts the phases of the proposed system.Figure 3.1 Proposed system phasesPreparation Phases this phase is responsible for collecting and prepares the documents for the next phase. It consists of five mental facultys text editor module, plosive language module, validation spell out module, check grammar module, and Sentences analysis module. textbook editor module allows the user to input a text or upload a text file in document format, these files can be processed in the next phase.The check language module is responsible for checking the input file written language, If it is an Arabic language then use Arabic process, or slope language then u se English process.The check spelling module use to check the countersigns are written settle or there is some misspelling.This phase consists of three modules explained as followsTokenization break up the input text as some token .SWR remove the viridity countersigns that appear in the text but carry little signification.Rooting is the process of removing (prefixes, infixes, or/and suffixes) from words to get the roots or stems of this word reliever of Synonym words are converted to their synonyms.Similarity detection Phases It is consists of three modules Fingerprinting, documents design and similarity detection, this phase discussed as follows To calculate fingerprints of any document, root cut up the text into diminished pieces called chunks, the chunking rule that responsible for cutting up the text will be determined 12. A social unit of chunk could be a sentence or a word. In case of chunking using sentences called sentence- ground, the document can be cutted into s mall chunks based on C parameter. For example, a document containing sentences ds1 ds2 ds3 ds4 ds5, if C=3 then the calculated chunks will be ds1 ds2 ds3, ds2 ds3 ds4, ds3 ds4 ds5. For example, a document containing words dw1 dw2 dw3 dw4 dw5, if C=3 then the calculated chunks will be dw1 dw2 dw3, dw2 dw3 dw4, dw3 dw4 dw5. The chunking using Word gives higher precision in similarity detection than the chunking sentence.The Architecture pf Proposed SystemThe following properties should be satisfied by any system detecting plagiarism in natural languageInsensitivity to small matches.Insensitivity to punctuation, capitalization, etceteraInsensitivity to permutations of the document content.The system briny architecture of ZPLAG is illustrated in Figur1.Preparation text editor, check language, check spelling, and check grammar.Preprocess synonym replacement, tokenization, rooting, and stop-word removal.Fingerprinting the use of n-gram, where the user choses the parameter n.Document repr esentation for each document, create a document tree structure that describes its internal representation.Selection of a similarity use of a similarity metric to find the longest match of two hash strings.As mentioned in the previous section, the system architecture break fell contains three main phases. Each phase will be composed to a set of modules in terms of system functionality. The following section contains the description of each phase and its modules in details.3.4.1 The Preparation PhaseThe main task of this phase is to prepare the data for the next phase. It consists of text editor module, check language module, check spelling module and check grammars module.3.4.1.1. Text editor mental facultyFigure 3.2, illustrates text editor module. The users of the text editor module are faculty members and students, where the users need a text area to upload their files, so the brows helps for file path to make it easy for the users, subsequently that check file format is very im portant , because the serve well upload files with doc or docx format, then after the user upload the file , the text editor module save the file in the database.Figure 3.2 text editor module3.4.1.2 let out Language mental facultyThe raw text of the document is treated separately as well. In order to chicken out terms from text, classic Natural Language Processing (NLP) techniques are applied as. Figure 3.3 illustrates Check Language module and its functions from the system database, whereas all the files are stored, the check language module bring the file and tape it, then check for language either Arabic , English or combo (both Arabic and English), After that mark the document with its written language and save the file again in the system database.Figure 3.3 check language module3.4.1.3 Check recite ModuleFigure 3.4 illustrates Check spelling module and its functions after bringing the document from the system database, whereas all the files are stored, the check spelling module read the file, and use the web spelling checker, then the check spelling module make all the possible replacements for the words in false spelling check , After that save the file again in the system database.Figure 3.4 check spelling module3.4.1.4 Check Grammars ModuleFor English documents, Figure 3.5 illustrates Check grammar module and its functions after bringing the document from the system database, whereas all the files are stored, the check grammar module read the file, and use the web grammar checker, After that the check grammar module mark the sentences with the suitable grammar mark and save the file again in the system database.Figure 3.5 check grammar module3.4.2 The processing Phase3.4.2.1 The Tokenization ModuleIn the Tokenization module after bringing the document from the system database, whereas all the files are stored, the Tokenization module read the file, and brake down the file into paragraphs, after that brake down the paragraphs into sentences, the n brake down the sentence into words. After that save the file again in the system database.3.4.2.2 The Stop Words Removal and Rooting ModuleThe raw text of the document is treated separately as well. In order to extract terms from text, classic Natural Language Processing (NLP) techniques are applied as. Figure 3.6 illustrates Stop Words Removal and rooting module and its functionsFigure 3.6 SWR and Rooting moduleSWR Common stop words in English include a, an, the, in, of, on, are, be, if, into, which etc. Whereas stop words in Arabic include , , , , etc. These words do not provide a significant meaning to the documents . Therefore, they should be removed in order to reduce noise and to reduce the computation time.Word Stemming it will be changed into the words basic form.3.4.2.3 Replacement of SynonymReplacement of Synonym It may help to detect advanced forms of hidden plagiarism. The first synonym in the list of synonyms of a given word is considered as the most frequent one .3.4.3 The Similarity Detection Phase3.4.3.1 The Fingerprinting ModuleIt is consists of three modules Fingerprinting, documents representation and similarity detection, this phase discussed as follows To calculate fingerprints of any document, first cut up the text into small pieces called chunks, the chunking method that responsible for cutting up the text will be determined 12. A unit of chunk could be a sentence or a word. In case of chunking using sentences called sentence-based, the document can be cutted into small chunks based on C parameter. For example, a document containing sentences ds1 ds2 ds3 ds4 ds5, if C=3 then the calculated chunks will be ds1 ds2 ds3, ds2 ds3 ds4, ds3 ds4 ds5. In case of chunking using word called a word-based chunking, the document is cutted into small chunks based on C parameter. For example, a document containing words dw1 dw2 dw3 dw4 dw5, if C=3 then the calculated chunks will be dw1 dw2 dw3, dw2 dw3 dw4, dw3 dw4 dw5. The chunking using Word giv es higher precision in similarity detection than the chunking sentence. ZPLAG is based on a word-based chunking method in every sentence of a document, words are first chunked and then use a hash function for hashing.3.4.3.2 The Document Representation ModuleDocument representation for each document, create a document tree structure that describes its internal representation.3.4.3.3 The Similarity Detection ModuleA tree representation is created for each document to describe its logical structure. The root represents the document itself, the second level represents the paragraphs, and the leaf nodes contain the sentences.SummaryBeing a growing problem, The electronic thefts is generally known as plagiarism and dishonesty academic and they constitute a growing phenomenon, It should be known that way to prevent its spread and protect the ethical principles that control the academic environments, with easy access to information on the World Wide Web and the large number of digital lib raries, electronic thefts get become one of the most important issues that plague universities and scientific centers and research.This chapter presented in detailed description of the proposed system for plagiarism detection in electronic resources and its phases and its functions.