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Anuj Sharma
Lovely Professional University

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A detailed survey regarding the usage of different ICT technology modes adopted by higher education institutions Rakshit khajuria; Anuj Sharma; Ashok Sharma
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1634-1641

Abstract

Information and communication technologies (ICTs) in all aspects of life have become the tools that are present everywhere and in a ubiquitous manner. Over the last twenty to thirty years, it has been noticed that the application of ICT has significantly changed the procedures and courses of almost all the higher education institutions. The higher education field is a highly socially-focused practice and providing quality education is traditionally been linked with the efficient teacher and their capacity for a high level of one-on-one contact with students. The use of ICT in higher education allows for more student-centered learning settings. Due to the reason that the world is quickly developing into a digital place with a huge level of digital information, however, the role of ICT in higher education is becoming increasingly significant and will persistently to grow and evolve in the 21st century. The application of ICT in the teaching and learning process, as well as its effective usage in higher education, depends on a variety of tools and techniques. In this paper, a detailed analysis of various modes adopted by the institutions of higher education is made.
Performance analysis of frequent pattern mining algorithm on different real-life dataset Rakshit khajuria; Anuj Sharma; Sunny Sharma; Ashok Sharma; Jyoti Narayan Baliya; Parveen Singh
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1355-1363

Abstract

The efficient finding of common patterns: a group of items that appear frequently in a dataset is a critical task in data mining, especially in transaction datasets. The goal of this paper is to look into the efficiency of various algorithms for frequent pattern mining in terms of computing time and memory consumption, as well as the problem of how to apply the algorithms to different datasets. In this paper, the algorithms investigated for mining the frequent patterns are; Pre-post, Pre-post+, FIN, H-mine, R-Elim, and estDec+ algorithms. These algorithms have been implemented and tested on four real-life datasets that are: The retail dataset, the Accidents dataset, the Chess dataset, and the Mushrooms dataset. From the results, it has been observed that, for the Retail dataset, estDec+ algorithm is the fastest among all algorithms in terms of run time as well as consumes less memory for its execution. Pre-post+ algorithm performs better than all other algorithms in terms of run time and maximum memory for the Mushrooms dataset. Pre-Post outperforms other algorithms in terms of performance. And for Accident datasets, in terms of execution time and memory consumption, the FIN method outperforms other algorithms.
Co-Authors Ashok Sharma Jyoti Narayan Baliya Parveen Singh Rakshit khajuria Sunny Sharma