Article Per Year (5 Year)
p-Index From 2019 - 2024
0.751
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Lakshmana Kumar Ramasamy
Hindusthan College of Engineering and Technology
Computer Science & IT Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Detecting malicious URLs using binary classification through adaboost algorithm Firoz Khan; Jinesh Ahamed; Seifedine Kadry; Lakshmana Kumar Ramasamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.843 KB) | DOI: 10.11591/ijece.v10i1.pp997-1005

Abstract

Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm.
Performance analysis of sentiments in Twitter dataset using SVM models Lakshmana Kumar Ramasamy; Seifedine Kadry; Yunyoung Nam; Maytham N. Meqdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2275-2284

Abstract

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.
Co-Authors Firoz Khan Jinesh Ahamed M. Navaneetha Krishnan M. Sornalakshmi M. Venkatesulu Maytham N. Meqdad S. Balamurali Sangsoon Lim Seifedine Kadry Yunyoung Nam