Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-416X
Online ISSN:
0976-4178


  About IJCLR
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
Journal of Multimedia Processing and Technologies (JMPT)
International Journal of Web Application (IJWA)

 

 
International Journal of Computational Linguistics Research
 

 

A Novel PSO Methodology for Web Documents Retrieval
Ramya C
Department of Computer Science & Engineering U. B. D. T College of Engineering, Davangere University Karnataka, India
Abstract: This paper focuses on retrieval of web documents with improved response time and similarity using particle swarm optimization (PSO) technique. Since the nature of the web data is distributed, volatile and uncertain, an accurate and speedy access is required. Hence a novel approach on evolutionary bio-inspired Swarm Intelligence techniques to optimize search process in Web Information Retrieval systems is proposed and developed. Here, we propose a novel algorithm using basic PSO technique which works on both small CACM and huge RCV1 collections. We apply this on the pre-processed documents to retrieve most similar documents with a very less response time. This paper also reveals a comparative study with the existing method.
Keywords: Particle Swarm Optimization, Web Information Retrieval (WIR), Pre-processing of Documents, Indexing A Novel PSO Methodology for Web Documents Retrieval
DOI:https://doi.org/10.6025/jcl/2019/10/3/67-75
Full_Text   PDF 1.2 MB   Download:   101  times
References:
[1] Drias, Habiba. (2011). Parallel Swarm Optimization for Web Information Retrieval, In: Proceedings of Third World Congress on Nature and Biologically Inspired computing, 249-254.
[2] Anna Bou Ezzeddine. (2011). Web information retrieval inspired by social insect behavior. Information Sciences and Technologies Bulletin of the ACM Slovakia, 3(1) 93-100.
[3] Liu, Peiyu., Zhu, Zhenfang., Zhao, Lina. (2009). Research on Information Retrieval System Based on Ant Clustering Algorithm. Journal of Software, 4(9) 1032-1036.
[4] Hasanen, S., Abdullah., Mustafa, J., Hadi. (2014). Artificial Bee Colony based Approach for Web Information Retrieval. Engineering and Technology Journal, 32(5) 899-909.
[5] Drias, Habiba., Mosteghanemi, Hadia. (2010). Bees Swarm Optimization based Approach for Web Information Retrieval, In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 6-13.
[6] Priya, I., Borkar, Leena, H., Patil. (2013). Web Information Retrieval Using Genetic Algorithm-Particle Swarm Optimization, International Journal of Future Computer and Communication, 2(6).
[7] Cui, Xiaohui., Thomas, E., Potok, Palathingal, Paul. (2005). Document Clustering using Particle Swarm Optimization, In: Proceedings of IEEE Swarm Intelligence Symposium, 185-191.
[8] Navrat, Pavol., Anna Bou Ezzeddine. (2010). Bee Hive at Work: Following a Developing Story on the Web, Artificial Intelligence in Theory and Practice III, 331, Springer, 187-196.
[9] Kohli, Shruti., Gupta, Ankit. (2014). A Survey on Web Information Retrieval Inside Fuzzy Framework, In: Proceedings of Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, 433-445.
[10] Yates, Baeza., R., Neto, Ribiero. B. (1999). Modern Information Retrieval, Addison Wesley Longman Publishing Co. Inc.
[11] Manning, C. D., Raghavan, P., Schutze, H. (2008). Introduction to Information Retrieval, Cambridge University Press.
[12] Kennedy, J., Eberhart, R. C. (1995). Particle Swarm Optimization, In: Proceedings of the IEEE International Conference On Neural Networks, Piscataway, NJ, 1942-1948.
[13] Pathak, P., Gordon, M., Fan, W. (2000). Effective Information Retrieval using Genetic Algorithms based Matching Functions Adaptation, 33rd IEEE HICSS.
[14] Hsinchun, C. (1995). Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning and Genetic Algorithm, Journal of the American Society for Information Science, 194-216.
[15] Abraham, Ajith., Guo, He., Liu, Hongbo. (2006). Swarm Intelligence: Foundations, Perspectives and Applications, Studies in Computational Intelligence (SCI) 26, 3–25.
[16] Van Ast, J., Babuska, R., De Schutter, B. (2008). Particle swarms in optimization and control, In: Proceedings of the 17th IFAC World Congress, Seoul, Korea, 5131–5136, (July).
[17] Teodorovic, Dusan. (2006). Bee Colony Optimization: Principle and Applications, Eighth IEEE Seminar on Neural Network Applications in Electrical Engineering, NEUREL 2006, (September).
[18] Dorigo, Marco., Birattari, Mauro., Stutzle, Thomas. (2006). Ant Colony Optimization: Artificial Ants as a Computation Intelligence Technique, IEEE Computational Intelligence Magazine, 28-39, (November).
[19] Salton, G., Buckley, C. (1988). Term weighting approaches in automatic text retrieval, Information Processing and Management, 513-523.
[20] Bratton, Dan., Blackwell, Tim. (2008). A Simplified Recombinant PSO, Journal of Artificial Evolution and Applications, Hindawi Publishing Corporation, 1-10.
[21] Karol, Stuti., Mangat, Veenu. (2012). Survey on Particle Swarm Optimization based Web Mining, Journal of Information and Operations Management, 3(1) 273-276.
[22] Anuradha, G., Devi, Lavanya. G. (2014). Artificial Bee Colony (ABC) Approach for Ranking Web Pages, International Journal of Computer Applications (0975 – 8887) 99(1), (August).
[23] Rocchio, J. (1971). Relevance Feedback in Information Retrieval, In G. Salton, editor, The Smart Retrieval System: Experiments in Automatic Document Processing, Prentice-Hall, Englewood Cliffs, NJ, 313–323.
[24] Christopher, D. (2009). Manning, Prabhakar Raghavan and Hinrich Schütze, An Introduction to Information Retrieval, Cambridge University Press.
[25] Grosan, Crina., Abraham, Ajith., Chis, Monica. (2006). Swarm Intelligence in Data Mining, Studies in Computational Intelligence (SCI) 34, 1–20.
[26] Kennedy, J., Eberhart, R. C. (1995). Particle Swarm Optimization, In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1942–1948.

Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info