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Narayanan, K. S.
- Predicting Student Performance Using Data Mining
Abstract Views :208 |
PDF Views:6
Authors
Affiliations
1 Department of Computer Science and Engineering, Kerala, IN
1 Department of Computer Science and Engineering, Kerala, IN
Source
Journal of Network and Information Security, Vol 5, No 2 (2017), Pagination: 8-11Abstract
The objective of the paper is to analyze the performance of a student by evaluating EQ and IQ level and also to check the correlation between EQ IQ and performance. Present system predicts the performance of student by comparing their EQ and IQ with some tests referred by the psychology department. Our study aims to establish and monitor the performance using EQ and IQ achievement among the undergraduate students by data mining procedures. Initially prepared the questionnaire of EQ and IQ. The data were collected from 150 students. Using WEKA tool, the data given as input. Using different classification algorithms predicted the individual performance of student by data mining concepts. And also compare the efficiency of classification algorithms and find the efficient one among them. EQ IQ is found to have a significant relation with the performance of students. Logistics function and random tree were used to explore the performance of student semester by semester. By means of applying these methods, the performance by graduation is predicted and accuracy is evaluated.Keywords
Data Mining, Emotional Quotient, Intelligent Quotient, Logistic Function, WEKA Tool.
References
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- Studies in Subdirectly irreducible Triple Systems and in a class of Lattice - Ordered Triple Systems
Abstract Views :142 |
PDF Views:0
Authors
Affiliations
1 Department of Mathematics, The M. D.T. Hindu College, Tirunelveli 627 010, IN
2 Department of Mathematics, Aijkgappa University, Karaikudi 623 003, IN
1 Department of Mathematics, The M. D.T. Hindu College, Tirunelveli 627 010, IN
2 Department of Mathematics, Aijkgappa University, Karaikudi 623 003, IN
Source
Journal of the Ramanujan Mathematical Society, Vol 10, No 2 (1995), Pagination: 175-194Abstract
In this paper we introduce the concepts of subdirectly irreducible triple systems, lattice - ordered triple systems, f-triple systems, l-ideals and the J-radical of an f-triple system. We study about idempotent elements in the above triple systems and show that an associative f-triple system with atleast one non-zero idempotent element is l-semi simple. Also we obtain some properties enjoyed by the idempotent heart in a subdirectly irreducible triple system.- Class Wise Linear Discriminant and Regression Based Binarized Nearest Learning in Digital Marketing
Abstract Views :202 |
PDF Views:1
Authors
Affiliations
1 Department of MCA, Rathnavel Subramaniam College of Arts and Science, IN
2 Department of Computer Science, Rathnavel Subramaniam College of Arts and Science, IN
1 Department of MCA, Rathnavel Subramaniam College of Arts and Science, IN
2 Department of Computer Science, Rathnavel Subramaniam College of Arts and Science, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 1 (2021), Pagination: 2485-2493Abstract
The employment of internet and social media has remodeled behavioral aspects of consumer or student communities and methods in which organizations or educational institutions perform their business pattern. Both social and digital marketing put forwards efficient scopes to educational institutions by way of reduced costs, enhanced brand perception and elevated sales. Nevertheless, notable disputes prevail from obstructive electronic word-of-mouth and invasive and annoying online brand existence. Nowadays, students use online promotions to know about best universities for education globally. This university choice and students’ feedback observed by student-experience shared across social media platforms. Several methods have been employed for selecting the university but not providing accurate information. This paper is motivated towards applying Machine Learning for learning, analyzing and classifying the student information based on the student experience by means of tweets in twitter. The twitter data with student tweets is collected from benchmark twitter dataset and applied to the proposed method, Class-wise Linear Discriminant and Regression-based Binarized Nearest Learning (CLD-RBNL). The CLD-RBNL method is split into two sections. First, preprocessing and relevant feature selection (i.e. tweets) are acquired by employing Class-wise Linear Discriminant-based Feature Selection (CLDFS) model to obtain dimensionality reduced tweets. To this result, Regression-based Binarized Nearest Neighbor model is applied for maximum lead generation. The CLD-RBNL method is compared with other state-of-the-art methods and found to outperform in terms of sensitivity, specificity, processing time, lead generation accuracy and error rate.Keywords
Class-Wise, Linear Discriminant, Feature Selection, Digital Marking, Educational Services, Regression, Binarized Nearest Neighbor.References
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