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Sunday, 10 May 2015

MAS a scalable framework for research effort evaluation by unsupervised machine learning-Hybrid plag...


MAS a scalable framework for research effort evaluation by unsupervised machine learning-Hybrid plagiarism model

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3 Author(s) Shinde, S.V.; Inf. Technol., B.V.D.U.s Coll. of Eng., Pune, India
In the era of web new information is upcoming day by day.
Researches add their work for their research domains. Detecting of
originality of research work is in hype. In Academic sector students
researchers bring in innovative ideas, algorithms stating that their
work outperforms prior research. They may implement NULL Hypothesis or
alternative Hypothesis, detecting their effort is a challenge. By means
of plagiarism detectors such academic efforts can be evaluated or
graded. This reflects the essence of research in the field of
Plagiarized content detection and grading. Some of our research issue
highlights to technical scenario to design an algorithm which is
adaptable to changing nature of dataset. The dataset grows, as new
research work is added in due course of time. Data extraction from
unstructured information is challenging, as no standard pattern is yet
defined. Such patterns vary from research to research and are domain
specific. A document in question i.e plagiarized or not? Is a join of
one or more sentences that originate by the authors research or
referenced from previous publications. Authors to prove originality use
paraphrasing which may have semantic similarity, also some of the
contents act as metaphor for upcoming research work. It is complex task
point out such an activity. Methodology states that a document in
question is a join of sentences, whereas each sentence is a join of
terms. Thus we conclude by fork and join operations; plagiarism
detection is possible in effective way. Document in question is split to
produce a sentence vector. A term vector is generated by forking
sentence to terms for each sentence in sentence vector. Mapper is
implemented that maps term to sentence and sentence to source document.
To enhance the accuracy of the model a Multi Agent Based System MAS
frame is recommended to adapt varying similarity functions. Achieve
parallelism in system and adaptability of new similarity measures as
well remove one which are not sui- able any more to the task.

Published in:


Pervasive Computing (ICPC), 2015 International Conference on


Date of Conference:8-10 Jan. 2015



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