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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

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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.
Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods Lakshmana Kumar Ramasamy; Seifedine Kadry; Sangsoon Lim
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2098

Abstract

Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.
An efficient apriori algorithm for frequent pattern mining using mapreduce in healthcare data M. Sornalakshmi; S. Balamurali; M. Venkatesulu; M. Navaneetha Krishnan; Lakshmana Kumar Ramasamy; Seifedine Kadry; Sangsoon Lim
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2096

Abstract

The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names enhanced parallel and distributed apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values.
Co-Authors Firoz Khan Jinesh Ahamed M. Navaneetha Krishnan M. Sornalakshmi M. Venkatesulu Maytham N. Meqdad S. Balamurali Sangsoon Lim Seifedine Kadry Yunyoung Nam