An Algorithm Search Engine for Extracting Algorithm From PDF Document

Authors

  • Akshata R. Sanas  Computer Science Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  • Pallavi S. Patil  Computer Science Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT195454

Keywords:

Algorithms, Pseudo codes, Scholarly big data, Sentence Extractor, Steaming, TFIDF

Abstract

Algorithms are used to developing, analysing, and applying in computer field and used for developing new application. It is used for finding solutions to any problems in different condition. It transforms the problems into algorithmic ones on which standard algorithms are applied. Day by day Scholarly Digital documents are increasing. AlgorithmSeer is a search engine used for searching algorithms. The main aim of it providing a large algorithm database. It is used to automatically encounter and take these algorithms in this big collection of documents that enable algorithm indexing, searching, discovery and analysis. An original set to identify and pull out algorithm representations in a big collection of scholarly documents is proposed, of scale able techniques used by AlgorithmSeer. Along with this, particularly important and relevant textual content can be accessed the platform and highlight portions by anyone with different levels of knowledge. In support of lectures and self-learning, the highlighted documents can be shared with others. However, different levels of learners cannot use the highlighted part of text at same understanding level. We can solve the problem of guessing new highlights of partially highlighted documents.

References

  1. Sumit Bhatia, Prasenjit Mitra and C. Lee Giles.2016 “AlgorithmSeer: A System for Extracting and Searching for Algorithms in Scholarly Big Data”, IEEE Transactions On Big Data 2332-7790 (c) IEEE 2016.
  2. Elena Baralis, and Luca Cagliero. 2017. “Highlighter: Automatic highlighting of electronic learning documents”, IEEE Transactions on Emerging Topics in Computing 2168-6750 (c) IEEE 2017.
  3. S. Kataria, W. Browuer, P. Mitra, and C. L. Giles. 2008 “Automatic extraction of data points and text blocks from 2-dimensional plots in digital documents”, Proceedings of the 23rd national conference on Artificial intelligence - Volume 2,AAAI08, pages 11691174. AAAI Press, 2008
  4. S. Bhatia, S. Tuarob, P. Mitra, and C. L. Giles. 2011. “An Algorithm Search Engine for Software Developers”, 2011
  5. J. B. Baker, A. P. Sexton, V. Sorge, and M. Suzuki. 2011, “Comparing approaches to mathematical document anal-ysis from pdf”, ICDAR 11, pages 463467, 2011.
  6. S. Bhatia, P. Mitra, and C. L. Giles. 2010. “Finding algorithms in scientific articles”,  2010.
  7. D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. “Latent dirichlet allocation”, Journal of Machine Learning Research 3 (2003) 993-1022, Mar. 2003.
  8. J.Kittler, M. Hatef, R. P. W. Duin, and J. Matas. 1998. “On combining classifiers”, IEEE Trans. Pattern Anal. Mach. Intell., 20(3):226239, Mar. 1998.
  9. T.A. Asuncion, M. Welling, P. Smyth, and Y. W. Teh. 2009. “On smoothing and inference for topic models”, In Proceedings of the Twenty-Fifth Conference on Uncertainty in Articial Intelligence, UAI.2009.

Downloads

Published

2019-09-30

Issue

Section

Research Articles

How to Cite

[1]
Akshata R. Sanas, Pallavi S. Patil, " An Algorithm Search Engine for Extracting Algorithm From PDF Document , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 5, pp.10-16, September-October-2019. Available at doi : https://doi.org/10.32628/CSEIT195454