K. S. Bhuvaneshwari ¹, D. Bhanu ², S. Sophia ³

Affiliations
1 Department of CSE, Karpagam College of Engineering, Coimbatore - 641032, Tamil Nadu, IN
2 Department of CSE, Karpagam Institute of Technology, Coimbatore - 641105, Tamil Nadu, IN
3 Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore - 641008,Tamil Nadu, IN

Abstract

Objectives: The objective of this research work is to analyse the learning styles of individual users in an e-learning system and to formulate a mathematical model to determine it. Methods: This research work proposes MNDBO, a rough set based clustering technique for elucidating learning styles by finding minimum normalized dissimilarity between objects in e-learning. The proposed clustering technique uses a normalized score value for estimating the deviation between data’s through the equivalence property of rough set theory. Findings: Further, the result predicts that the clusters produced by MNDBO algorithm perform better than MADO by 11%, 14% than SDR and 16% than MMR in terms of cohesion. Furthermore, MNDBO algorithm also produces better results than MADO by 15%, 23% than SDR and 27% than MMR in terms of coupling. In addition MNDBO algorithm maximizes the cohesion and simultaneously reduces the coupling rate based on varying number of cluster size on an average 15% and 19% respectively. Applications/Improvements: If this Rough set based clustering technique is used means we can able to discover successfully relations with inconsistent or incomplete data.

Keywords

Clustering, Dissimilarity between Objects, E-learning, Learning Styles, Rough Set Theory, Standard Deviation

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S. Kannimuthu ¹, C. K. Somesh ¹, P. D. Mahendhiran ², D. Bhanu ², K. S. Bhuvaneshwari ¹

Affiliations
1 Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore - 641032, Tamil Nadu, IN
2 Department of Information Technology, Karpagam Institute of Technology, Coimbatore - 641032, Tamil Nadu, IN

Abstract

Objective: The primary objective of this work is to review the vehicle tracking system, discuss the challenges in Internet of Things (IoT) based vehicle tracking system. IoT is used to get intellectual connecting the objects with each other via information sensing in the physical world and broadcasting devices linked with the internet. Analysis: This paper provides a comprehensive investigation on vehicle tracking system in association with the IoT and Big Data Analytics. This paper anlalyze the challenges pertaining to the IoT based Vehicle tracking system. Findings: This paper provides the novel methodology for assessing the quality of vehicle tracking system and identifies opportunity for future research in this area. Applications: The importance of QoE can be realized in vehicle tracking system for the improving the performance of the surveillance system.

Keywords

GPS, GSM, IoT, IST, QoE, Vehicle Tracking System.

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