Monday, 1 June 2015

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2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015

15 April 2015, Article number 7087030
2015 International Conference on Pervasive Computing, ICPC 2015;
Pune; India; 8 January 2015 through 10 January 2015; Category
numberCFP1583Z-ART; Code 111933

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

Information Technology, B.V.D.U.s College of Engineering, Pune, India

Dept. of Information Technology, B.V.D.U.s College of Engineering, Pune, India

Dept. of Computer Engg., B.V.D.U.s College of Engineering, 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 suitable any more to the task. © 2015

Author keywords

Cosine similarity; Document in Question; EMA; fork;
Inverted Index; join; Mapper; MAS; PMA; Sentence vector; SMA; Term
Vector; Unsupervised Learning; WEMA; WPMA; WSMA

Indexed keywords

Engineering controlled terms: Artificial intelligence;
Grading; Intellectual property; Joining; Learning systems; Multi agent
systems; Natural language processing systems; Semantics; Ubiquitous
computing; Unsupervised learning
Cosine similarity; Document in Question; EMA; fork; Inverted indices; Mapper; MAS; PMA; SMA; WEMA; WPMA; WSMA
Engineering main heading: Vectors

ISBN: 978-147996272-3
Source Type: Conference Proceeding
Original language: English

DOI: 10.1109/PERVASIVE.2015.7087030
Document Type: Conference Paper
Sponsors: Publisher: Institute of Electrical and Electronics Engineers Inc.

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