A Heuristic Ranking of Different Characteristic Mining Based Mathematical Formulae Retrieval Models
K.N. Brahmaji Rao1, G.Srinivas2, P.V.G.D. Prasad Reddy3,T.Surendra4

1K.N.Brahmaji Rao*, CS&SE Department, AUCE, Andhra University, Visakhapatnam, India.
2Dr.G. Srinivas, CSE Department, GITAM Deemed to be University, Visakhapatnam, India.
3Dr.Prasad Reddy P.V.G.D., Senior Professor, CS&SE Department, AUCE, Vice-Chancellor, Andhra University, Visakhapatnam, India.
4T. Surendra,  Assistant Professor in the Department of Applied Mathematics, Visakhapatnam, India. 
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 893-901 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9412109119/2019©BEIESP | DOI: 10.35940/ijeat.A9412.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The significant difficulty in the present circumstances is how to classify the math related keywords from a given text file and group them in one math file. Through this article a heuristic ranking model was developed and was evaluated on different mathematical formulae retrieval algorithms based on Characteristic mining. Our proposed heuristic ranking model was developed using the performance metrics of exiting retrieval algorithms such as NMF clustering, Levenstein distance, Sequence matcher, Fuzzy-wuzzy and Tensor flow. Performance metrics such as sensitivity, specificity, efficiency, accuracy and retrieval time were used in developing our heuristic ranking model.
Keywords: Sensitivity, Specificity, Efficiency, Accuracy.