Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sheth, Jikitsha
- Best Practices for Adaptation of Data Mining Techniques in Education Sector
Abstract Views :351 |
PDF Views:2
Authors
Source
National Journal of System and Information Technology, Vol 3, No 2 (2010), Pagination: 186-192Abstract
Best practices help to make the business processes smooth. As best practices are always recommended and not forced, the authors have recommended few best practices to be followed in Educational institutes so that the activities related to educational data mining becomes easy to implement. The best practices suggested are with the objective to gain and maintain data quality; as quality data leads to correct analysis.Keywords
Educational Data Mining, Best Practices, Data Quality
References
- Anjewierden, A., Kolloffel B., and Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes, In Proceeding of International Workshop on Applying Data Mining in e-Learning (ADML’07), pp.23-32.
- Baker, R. and Carvalho, D. (2008). Labeling Student Behavior Faster and More Precisely with Text Replays, In Proceedings of the 1st International Conference on Educational Data Mining, pp.38-47.
- Ben-Zadoki, G., et. al. (2009). Examining online learning processes based on log files analysis: A case study, In Research, Reflections and Innovations in Integrating ICT in Education (Ed. A. Méndez-Vilas,et.al.), FORMATEX, pp.55-59.
- Chapman, P., et. al. CRISP-DM 1.0, Step-by-step data mining guide, SPSS, CRISPDM Consortium, http://www.crisp-dm.org/download.htm, accessed on Oct 2009.
- Eckerson, W., Data Quality and the Bottom Line, TDWI Report Series, 2002.
- Eckerson, W., Excerpt from TDWI’s Research Report - Data Quality and the Bottom Line, Business Intelligence Journal, Dec 2001,
- http://www.tdwi.org/research/display.aspx?ID=6589 accessed on Dec 2009. 7. Examination Reforms and Continuous and Comprehensive Evaluation (CCE) in CBSE, http://www.cbse.nic.in/cce/index.html, accessed on Dec 2009.
- Han, J. and Kamber M. (2001). Data Mining: Concepts and Techniques, San Francisco, Morgan Kaufmann.
- Jeong, H., and Biswas, G.(2008). Mining Student Behavior Models in Learning by- Teaching Environments, In Proceedings of the 1st International Conference on Educational Data Mining, pp.127-136.
- Lloyd, N., Heffernan, N. and Ruiz C. (2007). Predicting student engagement in intelligent tutoring systems using teacher expert knowledge, Supplementary Proceedings of the 13th International Conference of Artificial Intelligence in Education.pp.40-49.
- Mavrikis, M. (2008). Data-driven modelling of students' interactions in an ILE, In Proceedings of the 1st International Conference on Educational Data Mining, pp.87- 96.
- Sacin, C., Agapito J., et.al.(2009). Recommendation in Higher Education Using Data Mining Techniques, Proceedings of 2nd International Conference on Educational Data Mining, Spain
- Sheth, J., Patel B., and Bhatti, D. (2010). Improper Internet Usage: Controlling through Policy Model and Identifying through Data Mining, National Journal of Computer Science & Technology, Vol. 02(1), pp.16-21
- Srivastava, J., Cooleyz, R., et. al. (2000). Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, ACM SIGKDD, Vol. 01(2), pp.12-22. 15. Tang, Z., Maclennan, J. (2005). Data mining with SQL Server 2005, Wiley Publications.
- Comparative Study of Web Search Engines and User-centric Search Engine
Abstract Views :466 |
PDF Views:2
Authors
Source
National Journal of System and Information Technology, Vol 4, No 2 (2011), Pagination: 149-161Abstract
Searches of the entire World Wide Web using search engines such as Google, Yahoo!, Bing, and Ask have become an extremely common way of locating information. Search engines are providing great facilities to the Internet users to search intended information from hundreds of millions of Web pages within a part of second. Today search engines are become more powerful and efficient by using various algorithms and technologies to provide a best result which demanded by user. Search engines now provide various added services too. One more area where search engine can also improve by keeping the track of user activity and history of visited sites which help user to carry their previous visited sites among different Web browsers, now a day it is done by Web browser only. This paper presents critical comparison of various popular search engines based on added features. A detailed analysis is presented and results are provided.Keywords
Search Engine Evaluation, Search Engine Statistics, Feature Comparison, Search Engine Evaluation, World Wide WebReferences
- "Internet History - Search Engines" (from Search Engine Watch), University Leiden, Netherlands, September 2001, web: LeidenU-Archie.
- "Yahoo! And Netscape Ink International Distribution Deal"
- Goodrum, A. Spink, “Image Searching on the Exite Web search engine”, Information Processing and Management 37, 2001, pp. 295-311.
- Howe and D. Dreilinger, “SavvySearch: A Metasearch Engine That Learns Which Search Engines to Query”, AI Magazine, Vol. 18 No. 2, 1997.
- Spink, “A User Centered Approach to Evaluating Human Interaction with Web Search Engines: An Exploratory Study”, Information Processing and Management, 38 (2002) 401-426.
- Spink, D. Wolfram, B. Jansen and T. Saracevic, “Searching the Web: The Public and Their Queries”. Journal of the American Society for Information Science and Technology, Vol. 52 Issue 3, Feb. 1, 2001.
- Archive of NCSA what's new in December 1993 page
- Art of SEO Mastering Search Engine Optimization [Book- First Edition] (Chapter 1, Page 1)
- Browser Deals Push Netscape Stock Up 7.8%. Los Angeles Times. 1 April 1996
- Middleton and R. Baeza-yates, “A comparison of open source search engines,” Net Marketshare – World
- http://computer.howstuffworks.com/internet/basics/search-engine.htm [Access on Nov 2011]
- http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html
- http://news.netcraft.com/archives/2007/01/05/january_2007_web_server_ survey.html
- http://news.netcraft.com/archives/2011/01/12/january-2011-web-serversurvey- 4.html
- http://wrg.upf.edu/WRG/dctos/Middleton- Baeza.pdf. Dec. 03 2008. [Online]. Available
- http://www.budde.com.au/Research/Global-Internet-Search-Engines- Websites-Internet-User-Statistics.html
- http://www.chandlernguyen.com/2011/03/search-engine-market-shareby- country-mar-2011.html
- http://www.comscore.com/Press_Events/Press_Releases/2009/1/Global_In ternet_Audience_1_Billion
- http://www.comscore.com/Press_Events/Press_Releases/2011/5/Google_Si tes_Accounts_for_9_of_10_Searches_Conducted_in_Latin_America -- ChandlerNguyen Sun Jul 10 2011 05:07:44 GMT+0700 (SE Asia Standard Time)
- http://www.ebizmba.com/articles/search-engines(Last Updated: September 12, 2011)
- http://www.karmasnack.com/about/search-engine-market-share/
- Internet Search Engine Statistics, pwebs.net/news/wp-content /plugins /...pdf.../ kalins_pdf_create.php?
- J. Bernard and A. Spink, “A Comparison of Nine Search Engine Transaction Logs”, Information Processing and Management 42(1), pp. 248-263.
- M. Kaur, N. Bhatia and S. Singh “Web Search Engines Evaluation based on Features and end-user Experience”, International Journal of Enterprise Computing and Business Systems ISSN (Online) : 2230-8849
- P. Panigrahi “A language to create web page”, Vidyasagar University Journal of Library and Information Science; 1997; 2; p 61-70.
- S. Lawrence and C. Lee Giles, “Searching the World Wide Web”, Vol. 280 no. 5360 pp. 98-100, 3 April 1998.
- The Anatomy of a Large-Scale Hypertextual Web Search Engine, http://infolab.stanford.edu/~backrub/google.html
- Y. Rui, S. Thomas, Huang and Shih-Fu Chang, “Image Retrieval – Past, Present and Future”, Journal of Visual Communication and Image Representation, 1997, pp: 1-23.
- Internet Marketing: Comparative Analysis of Search Engine Optimization Applications on various Parameters
Abstract Views :342 |
PDF Views:0
Authors
Source
National Journal of System and Information Technology, Vol 6, No 1 (2013), Pagination: 79-100Abstract
There are presently 2 billion users on the internet which is approximately 28% of the global human population. According to Pew Research, people are depending on more and more on Internet search. Subsequently search engines are the main sources of traffic.[1] More traffic to a commercial website means more visitors, more clients, more deals and so great profit in industry. One ought to consider using SEO (Search Engine Optimization) to raise the website traffic. Search Engine Optimization (SEO) is the practice of designing or updating websites with the objective of gaining top rankings in search engines for a preferred set of keywords applicable to the website‘s target audience.[3]SEO is done by SEO specialists who use software tools to mechanise repetitive tasks. SEO tools are an intrinsic part of performing SEO work. Therefore opportunities exists to improve SEO tools and to offer an improved experience to SEO specialists. Here, the research of comparative study of features of several chosen SEO tools helps to reveal opportunities to improve SEO tools and to keep the SEO specialist parallel with the growing technologies and areas in the field of SEO.Keywords
Search Engine Optimization, Internet Marketing, WWW, Internet, Search Engine- Comparison of String Similarity Algorithms to Measure Lexical Similarity
Abstract Views :284 |
PDF Views:6
Authors
Affiliations
1 Shrimad Rajchandra Institute of Management and Computer Applications, UTU, Bardoli, IN
2 Shrimad Rajchandra Inst. of Management & Comp. Appl., UTU, Bardoli, IN
1 Shrimad Rajchandra Institute of Management and Computer Applications, UTU, Bardoli, IN
2 Shrimad Rajchandra Inst. of Management & Comp. Appl., UTU, Bardoli, IN
Source
National Journal of System and Information Technology, Vol 10, No 2 (2017), Pagination: 139-154Abstract
A string similarity represents the lexical similarity between two words. This can be further exploited to identify similarity between questions. Several string similarity algorithm exists in literature. In this paper the authors have implemented five string similarity algorithms viz. Dice coefficient, Jaccard similarity, Levenshtein distance, Jaro distance and Cosine similarity. The results of these algorithms are further compared with human judges to determine, which of them resembles the human way to dissimilarize the given strings. The experimentation is done over 1000 English word pairs.References
- Yogish, Deepa, T. N. Manjunath, and Ravindra S. Hegadi, "A Survey of Intelligent Question Answering System Using NLP and Information Retrieval Techniques."
- Hermjakob, U.; Hovy, E. H.; and Lin, C. 2000. "Knowledge-Based Question Answering." InProceedings of theSixth World Multiconference on Systems, Cybernetics, and Informatics (SCI-2002).Winter Garden, FL: International Institute of Informatics and Systemics.
- Feng, D., Shaw, E., Kim, J., & Hovy, E. (2006, January). "An intelligent discussion-bot for answering student queries in threaded discussions." In Proceedings of the 11th international conference on Intelligent user interfaces (pp.171-177).IEEE.
- Andrenucci, A., & Sneiders, E. (2005, July). "Automated question answering: Review of the main approaches". In Information Technology and Applications, 2005. ICITA 2005. Third International Conference on (pp.514-519). IEEE.
- Hovy, Eduard H., Ulf Hermjakob, and Chin-Yew Lin. "The Use of External Knowledge of Factoid QA."TREC. 2001.
- Brill, Eric, Susan Dumais, and Michele Banko. "An analysis of the AskMSR question-answering system." Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002.
- Radev, Dragomir R., et al. "Mining the web for answers to natural language questions." Proceedings of the tenth international conference on Information and knowledge management. ACM, 2001.
- START Natural Language QA system: http://start.csail.mit.edu/index.php
- Wikipedia :https://en.wikipedia.org/wiki/Question_answering
- W. Ahmed and B. P, "Question Analysis for Arabic Question Answering Systems", International Journal on Natural Language Computing, vol. 5, no. 6, pp. 21-30, 2016.
- V. Thada, and V. Jaglan, "Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm." International Journal of Innovations in Engineering and Technology 2.4, 2013.
- L. Cai, G. Zhou, K. Liu, and J. Zhao. "Learning the Latent Topics for Question Retrieval in Community QA.", IJCNLP, vol. 11, pp. 273-281. 2011.
- H. Duan, C. Yunb, C. Y. Lin, and Y. Yong, "Searching Questions by Identifying Question Topic and Question Focus." In ACL, pp. 156-164. 2008.
- S. P. Abney, "The English noun phrase in its sentential aspect." PhD diss., Massachusetts Institute of Technology, 1987.
- V. Shwartz, O. Levy, I. Dagan, and J. Goldberger, "Learning to Exploit Structured Resources for Lexical Inference." In CoNLL, pp. 175-184. 2015.
- W. Song, M. Feng, N. Gu, and L. Wenyin. "Question similarity calculation for FAQ answering." In Semantics, Knowledge and Grid, Third International Conference on, pp. 298-301. IEEE, 2007.
- Stop words list: http://xpo6.com/list-of-englishstopwords/
- Part of Speech Information: https://en.wikipedia.org/wiki/Partofspeech_tagging/
- Stop words Information: https://en.wikipedia.org/wiki/Stop_words
- NLP Information: https://en.wikipedia.org/wiki/Natural_language_processing/
- QA System Information: https://en.wikipedia.org/wiki/Question_answering/
- G.Bathla, R.Jindal "Similarity Measures of Research Papers and Patents using Adaptive and Parameter Free Threshold," In International Journal of Computer Applications (0975 - 8887), vol.33- No.5,November 2011.
- Maheshkumar B.Landge, Ramesh R.Naik, C. Namrata Mahender,"Measuring Author Impression Using Cosine Similarity Algorithm," In IOSR-JCE, e-ISSN: 2278-0661,p- ISSN: 2278-8727, PP 24-28.
- Yufei Sun , Liangli Ma, Shuang Wang, "A Comparative Evaluation of String Similarity Metrics for Ontology Alignment", In Journal of Information and Computational Science 12:3 (2015) 957-964, February 10, 2015.
- Maria del Pilar Angeles, Adrian Espino-Gamez, "Comparison of methods Hamming Distance, Jaro, and Monge-Elkan" In The Seventh International Conference on Advances in Databases, Knowledge, and Data Applications, DBKDA 2015.
- Vikas Thada, Dr Vivek Jaglan, "Comparison of Jaccard, Dice, Cosine Similarity Coefficient To Find Best Fitness Value for Web Retrieved Documents Using Genetic Algorithm", In the International Journal of Innovations in Engineering and Technology, SSN: 2319-1058, Vol. 2, 4 August 2013.
- https://en.wikipedia.org/wiki/Levenshtein_distance
- https://en.wikipedia.org/wiki/Jaro-Winkler_distance
- https://en.wikipedia.org/wiki/Cosine_similarity
- Hakan Sundblad, "Question Classification in Question Answering Systems", In Diss. Institutionen for datavetenskap, 2007.
- Poonam Gupta, Vishal Gupta,"A Survey of Text Question Answering Techniques," International Journal of Computer Applications (0975-8887),Volume 53- No.4, September, 2012.