AI can Check the Authenticity of submitted students’ work. It can do that by detecting exact word matches, word rearrangements, word substitutions, and hidden symbols. It can also detect changes from active to passive voice, and synonymization.Â
Doing that minimizes the time spent reviewing assignments and research papers, improves the accuracy of detecting plagiarism, and generates less false positives
Understand the Use-case under 5 minutes
Video (2.3 minutes)
AI-powered plagiarism detection platform that is used by educational institutions and businesses around the world, using advanced technology which performs extensive searches in any language across the web.
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Article (5 minutes)
Amira assesses oral reading fluency and analyzes errors to pinpoint strengths and weaknesses. Every time a student and Amira read together, Amira generates diagnostic reports that empower teachers and parents
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Article (6 minutes)
Using Plagiarism software will check students' work against not just Wikipedia but 16+ billion web pages. AI leverages deep Search technology so all matches are re-validated allowing teachers to get accurate results.
ReadCase studies, Organizational Aspects, Return on Investment examples
More details on the technical aspects of the use-case
Article (6 minutes)
Natural Language Processing plays a decisive role in establishing a link between human language and computer language. See how the NLP algorithm works to curtail Plagiarism.
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paper
This paper proposes a new method implemented in a program, where we utilize a text set to identify the copied part by comparing with some existing multiple files (using a KNN Model)
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.
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This repository contains code and associated files for deploying a plagiarism detector using AWS SageMaker. The model examines a text file labeling that file as either plagiarized or not.
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The purpose of this study is to give an overview of different propositions for plagiarism detection based on the deep learning algorithms.
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Video (13 minutes)
Build your own Plagiarism checker in python language just by using Scikit learn Machine learning library in a few lines of code.
Video
Data Sets you can use to build Demos, POCs, or test Algorithms
200,767 paragraphs (98,282 original, 102,485paraphrased) extracted from 8,024 Wikipedia (English) articles (4,012 original, 4,012 paraphrased using the SpinBot API).
Off-the-Shelf Products using AI for Plagiarism Detection
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