Refine your search
Collections
Co-Authors
Journals
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
Jaya, A.
- Extraction of Actionable Knowledge to Predict Students' Academic Performance Using Data Mining Technique-an Experimental Study
Abstract Views :541 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Applications, BSA University, Chennai, Tamil Nadu., IN
2 B.S. Abdur Rahman University, Chennai, Tamil Nadu., IN
1 Department of Computer Applications, BSA University, Chennai, Tamil Nadu., IN
2 B.S. Abdur Rahman University, Chennai, Tamil Nadu., IN
Source
International Journal of Knowledge Based Computer System, Vol 1, No 1 (2013), Pagination: 28-32Abstract
Knowledge discovery in academic institution becomes more critical and crucial in terms of identifying the student's performance. In the extraction of actionable knowledge from a large database the data mining plays a vital role. The actionable knowledge extraction provides a interestingness and meaning to the mined data. This paper focuses on the prediction of the student's academic performance from the large student database. The mining algorithm like clustering and classification algorithm is revisited to predict the performance after initial mining of raw data. The main scope of this paper is to reveal the outcome of the performance analysis of a student .This work will help the university to reach betterment in providing the quality input to the student community and impart the knowledge effectively.Keywords
Actionable Knowledge, Classification, Clustering, Prediction and Analysis
References
- Pandey, U. K. & Pal, S. (2011). Data Mining: A prediction of performer or underperformer using classification. International Journal of Computer Science and Information Technologies, 2(2), 686-690.
- Lakshmi, T. M., Martin, A., Begum, R. M. & Venkatesan, V. P. (2013). An analysis on performance of decision tree algorithms using student’s Qualitative data. International Journal of Modern Education and Computer Science, June, 5(5), 18-27.
- Singh, C., Gopal, A. & Mishra, S. (2011). Management faculty performance evaluation with signed and unsigned student feedback using linear regression technique. International Journal of Information Technology and Knowledge Management, 4(2), 591-594.
- Thai-Nghe, N. Busche, A. & Schmidt-Thieme, L. (2009). Improving Academic Performance Prediction by Dealing with Class Imbalance. International Swaps and Derivatives Association, (pp. 878-883).
- Kumutha, S. & Sathick, K. J. (2013). Performance Prediction and Analysis of a University using Data Mining Technique. National Conference on Advanced Computing Technology.
- Thai-Nghe, N., Drumond, L. & Horv'ath, T. (2011). Matrix and Tensor Factorization for Predicting Student Performance. 3rd International Conference on Computer Supported Education.
- Sinhgad, C. S., Gopal, A. & Mishra, S. (2012). Faculty performance prediction from student feedback using linear regression technique. International Journal on Computational Sciences and Applications, 2(4), 1-4.
- Shreenath, A. & Madhu, N. (2012). Discovery of students’ academic patterns using data mining techniques. International Journal on Computer Science and Engineering, 4(6), 1054-1062.
- Mashat, A. F. S. & Khedra, A. M. (2012). Decision Support System Based Markov Model for Performance Evaluation of Students Flow in FCITKAU. International Conference on Communication and Information Technology.
- Bhardwaj, B. K. & Pal, S. (2011). Data Mining: A prediction for performance improvement using classification, International Journal of Computer Science and Information Security, 9(4), 136-140.
- Basha, S. K. A. H. & Kumar, Y. R. R. (2012). Predicting student academic performance using temporal association mining. International Journal of Information Science and Education, 2(1), 21-41.
- Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61-72.
- Osmanbegović, E. & Suljić, M. (2012). Data mining approach for predicting student performance. Economic Review - Journal of Economics and Business, 10(1), 3-12.
- Tair, M. M. T. & El-Halees, A. M. (2012). Mining educational data to Improve student’s performance: A case study. International Journal of Information and Communication Technology Research, 2(2).
- Natural Language to SQL Generation for Semantic Knowledge Extraction in Social Web Sources
Abstract Views :190 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Applications, B.S. Abdur Rahman University, Chennai, IN
1 Department of Computer Applications, B.S. Abdur Rahman University, Chennai, IN
Source
Indian Journal of Science and Technology, Vol 8, No 1 (2015), Pagination: 1-10Abstract
Enormous evolution of web data creates a peculiar myth in the field of computer and information technology for extracting the meaningful content from the web. Many organizations and social networks use databases for storing information and the data will be fetched from the specified data store. Data can be retrieved or accessed by SQL queries whereas the query is in the form of natural lingual statement which has to be processed. So, the primary objective of this research article is to find the suitable way to convert natural language query to SQL and make the data apt for semantic extraction. This Research paper also aims to derive an automatic query translator for Natural Language based questions into their associated SQL queries and provides an user friendly interface between end user and the database for easy access of social web data from different web sources such as facebook, twitter and linkedIn etc.,. This paper is implemented using java as the front end, SQL server as the back end and R-tool is used to collect the data from social web sources. This research article provides an optimized SQL query generation for the Natural Language question provided by the end user.Keywords
Natural Language Interface For Databases (NLIDB), Natural Language Processing (NLP), R-Tool, Semantic Knowledge Extraction (SKE), Structured Query Language (SQL), Social Web Data.Full Text
- Construction of Ontology for Software Requirements Elicitation
Abstract Views :178 |
PDF Views:0
Authors
S. Murugesh
1,
A. Jaya
2
Affiliations
1 B. S. Abdur Rahman University, Chennai – 600048, IN
2 Department of Computer Applications, B. S. Abdur Rahman University, Chennai – 600048, IN
1 B. S. Abdur Rahman University, Chennai – 600048, IN
2 Department of Computer Applications, B. S. Abdur Rahman University, Chennai – 600048, IN
Source
Indian Journal of Science and Technology, Vol 8, No 29 (2015), Pagination:Abstract
Background/Objective: Elicitation of requirements from informal descriptions remains a major challenge to be accomplished in software industry. Methods: An important task in order to accomplish this goal is to construct an ontology consisting of set of concepts i.e. entities, attributes and relations based on the application domain of interest. The ontology constructed here represents the domain knowledge and requirements are the specialized subset of it. As standard description formalism the ontology is encoded using OWL DL, supported by Pellet reasoned to check the consistency of the components of the ontology. The populated ontology can be queried for matching words using SPARQL. Findings: In software development projects, voluminous unstructured text documents from different stakeholders are to be analysed and to be converted into structured requirements. This process of elicitation will be time consuming if it is to be performed manually. Domain specific ontology helps in automating the process of requirements elicitation, this article intends to construct such domain specific background ontology. The findings are elaborated in Section 3. Improvements: This article portrays the construction and use of domain specific background ontology containing the concepts and their relationships in the Automated Teller Machine (ATM) operations domain to guide the process of automation of elicitation of requirements from informal descriptions or unstructured text, which otherwise would be time consuming if carried out manually.Keywords
ATM, Domain Ontology, Natural Language Processing, Software Requirements Elicitation, Unstructured DocumentsFull Text
- A Novel Approach of Collaborative KBQA System using Ontology
Abstract Views :164 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, B.S. Abdur Rahman University, Vandalur, Chennai - 600048, Tamil Nadu, IN
1 Department of Computer Science and Engineering, B.S. Abdur Rahman University, Vandalur, Chennai - 600048, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 48 (2016), Pagination:Abstract
The objective of the research is to provide relevant answers for the questions posted by the learners using Collaborative Knowledge Based Question Answering System (CKBQA) of Knowledge Extraction which provide the most suitable answers by sharing the ideas of various learners, when the user needs information. In the modern era numerous information available in the World Wide Web which is too difficult to get the required information. CKBQA system aims at retrieving precise information from a large collection of documents. It provides the most relevant answers rather than the irrelevant information by applying Latent Semantic Analysis. In collaborative learning the users benefit when the learner exposed to diverse viewpoints from the other learners with varied backgrounds and provides the best interactive teaching learning method where the interaction taken place between the learners and also the learners with the exports. The CKBQA system consists of four phases such as Question Preprocessing, Answer Evaluation, Concept Mapping of question with their answer and voting. Ontology plays the vital role for checking the semantic nature of the question and the answer. Sample of 100 questions with their Answers were taken and tested for their various question types and also checked with resoner which gave 89% of correctness.Keywords
Collaborative Learning, Concept Mapping, Knowledge based Question Answering System, Latent Semantic Analysis, Ontology and Voting.Full Text