Open AccessContext Similarity Strategy for Text Data Plagiarism Detection
Author(s) -
Durga Bhavani Dasari,
Venu Gopala Rao. K
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.32.13517
Subject(s) - computer science , point (geometry) , similarity (geometry) , set (abstract data type) , context (archaeology) , key (lock) , plagiarism detection , world wide web , idiot , data science , information retrieval , artificial intelligence , psychology , computer security , paleontology , geometry , mathematics , image (mathematics) , biology , programming language , psychiatry
Advent development of anti-plagiarism solutions has supported varied range of elementary forms of textual recycling, however, considering the magnum of content that is being generated, a tool alone might be ineffective in preventing complex forms of plagiarism. Some of the issues that are envisaged with the plagiarized articles in many of the open-access journals emphasize the point that critical deficiencies of varied kind of solutions that are existing aren’t being resourceful in identifying the manipulation that is taking place in the form of paraphrasing and editing. Manipulative editing has become a major menace even in the case of predatory journals and is leading to issues of publication ethics. Certain preventive strategies that have evolved in the recent past are relying on semantic solutions, comprehensive texts evaluation, graphics, reference lists, key words, digital technologies. It is right time for enforcing adherence to global editorial guidance and towards implementing a comprehensive set of strategies to address the issue of plagiarism.