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Sheth, Jikitsha
- Best Practices for Adaptation of Data Mining Techniques in Education Sector
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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
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- 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.
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- 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.
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- Comparative Study of Web Search Engines and User-centric Search Engine
Abstract Views :501 |
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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
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- Internet Marketing: Comparative Analysis of Search Engine Optimization Applications on various Parameters
Abstract Views :384 |
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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 :328 |
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
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