Multifunctional Resume Analyzer

Authors

  • Salil Liman Student, Department of Computer Science & Engineering, Pillai HOC College of Engineering and Technology, Rasayani, Maharashtra.
  • Sanika Rajurkar Student, Department of Computer Science & Engineering, Pillai HOC College of Engineering and Technology, Rasayani, Maharashtra.
  • Abhishek Raha Student, Department of Computer Science & Engineering, Pillai HOC College of Engineering and Technology, Rasayani, Maharashtra.

Keywords:

Organizations, resumes, artificial intelligence, MySQL, evaluator

Abstract

Agencies and numerous high-level organizations get agitated by an outsized range of awards, jobs or higher studies-seeking folks with numerous resumes. The challenge is of managing large volumes of textual data and selecting the most suitable candidate from a pool of applicants. It suggests that the process of choosing the best-fit candidate is difficult due to the vast amounts of information that must be processed. This project provides a wide range of solutions to the existing system that helps evaluators to find the best fit. It uses Artificial Intelligence to analyse information from resumes, find keywords and group them based on sectors and show the evaluator most relevant resumes based on keywords and ultimately matching the keywords. First, a candidate/nominee uploads a resume to the web platform that is stored in the MySQL database. A parser is used to extract necessary information from resumes and select a certain number of resumes to be presented to an evaluator.

References

Ciravegna F. Adaptive information extraction from text by rule induction and generalisation. In Proceedings of the 17th international joint conference on Artificial intelligence. 2001 Aug 4; 2: 1251–1256.

Chandel A, Nagesh PC, Sarawagi S. Efficient batch top-k search for dictionary-based entity recognition. In 22nd IEEE International Conference on Data Engineering (ICDE'06). 2006 Apr 3; 28–28.

Chakrabarti S. Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann; United States. 2002 Oct 9.

Cafarella MJ, Downey D, Soderland S, Etzioni O. Know it now: Fast, scalable information extraction from the web. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. 2005 Oct; 563–570.

Cafarella MJ, Etzioni O. A search engine for natural language applications. In Proceedings of the 14th international conference on World Wide Web. 2005 May 10; 442–452.

Sanyal S, Hazra S, Adhikary S, Ghosh N. Resume parser with natural language processing. International Journal of Engineering Science and Computing (IJESC). 2017 Feb; 7(2): 4484–4489.

Siddique A, Jan A, Majeed F, Qahmash AI, Quadri NN, Wahab MO. Predicting academic performance using an efficient model based on fusion of classifiers. Appl Sci. 2021 Dec 13; 11(24): 11845.

Fanani L, Priandani ND. Data Cleaning and Prototyping Using K-Means to Enhance Classification Accuracy. Int J Appl Eng Res. 2017; 12(15): 5242–5247.

Kumar, Vijaya & Guruprasad, H S. (2013). Clustering of Web Usage Data Using Fuzzy Tolerance Rough Set Similarity and Table Filling Algorithm. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR). 3. 143-152.

Yang XS, editor. Recent advances in swarm intelligence and evolutionary computation. Switzerland: Springer International Publishing; 2015 Apr.

Roy PK, Chowdhary SS, Bhatia R. A machine learning approach for automation of resume recommendation system. Procedia Comput Sci. 2020 Jan 1; 167: 2318–2327.

Ahirwar M, Behera BK. Prediction of Shrinkage Behavior of Stretch Fabrics Using Machine-Learning Based Artificial Neural Network. Textiles. 2023 Feb 2; 3(1): 88–97.

Downloads

Published

2023-05-20

Issue

Vol. 13 No. 1 (2023)

Section

Review Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.