Browsing by Author "Rozario, R."

Now showing 1 - 3 of 3

Fake News Detection Using Genetic Algorithm-Based Feature Selection and Ensemble Learning
(Springer Science and Business Media Deutschland GmbH, 2022) Nikitha, K.M.; Rozario, R.; Pradeep, C.; Ananthanarayana, V.S.
Since its conception roughly 40Â years ago, the Internet has always been an unpoliced area of human interaction. This lawlessness has since been curbed with legislation, making nefarious activities on the web constitutionally punishable. However, in the case of fake news and disinformation campaigns, the responsibility of verification is placed on the reader and the publisher, and there is no easily executable legal recourse for wrongdoers. This lack of policing combined with the power of controlling popular opinion for uses such as election manipulation, slander as a form of blackmail, stock manipulation for insider trading, shielding corporate wrong-doing makes it clear that this is a problem worth solving. Furthermore, we believe that automating the process is crucial as the task requires processing a massive amount of information whilst also being free of all biases, which is not possible by a human team. This paper explores different text properties that can indicate if a newspaper article is likely to be false or real. Our novel approach makes use of an ensemble learner created using weak learners. The weak learners are further trained on selective features to make them moderate learners. Our study shows that training individual models on different sets of features extracted using genetic algorithms performs better than models trained on all features. These become moderate learners and surpass the weak learners on performance. Further, when we ensemble these moderate learners, we achieve superior results than normal ensemble learners. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Performance analysis of secondary storage media through file systems benchmarking
(2019) Rakshith, G.; Rozario, R.; Rhevanth, M.; Nikitha, K.M.; Mohan, B.R.
Efficient performance of a disk I/O operation involves a multitude of factors such as the type of the disk, I/O scheduling, and the type of the file system used. Due to the various types of file systems available, with each having different structure and logic, properties of speed, flexibility, security, size and more, it becomes imperative to have an objective overview of the merits and demerits of each file system according to the needs of the users. In this work, we present a thorough performance evaluation of ext4, NTFS and Btrfs filesystems along with CFQ, NOOP and Deadline I/O schedulers tested on regular hard disk drives and SSDs. � 2019 IEEE.
Performance analysis of secondary storage media through file systems benchmarking
(Institute of Electrical and Electronics Engineers Inc., 2019) Rakshith, G.; Rozario, R.; Rhevanth, M.; Nikitha, K.M.; Mohan, B.R.
Efficient performance of a disk I/O operation involves a multitude of factors such as the type of the disk, I/O scheduling, and the type of the file system used. Due to the various types of file systems available, with each having different structure and logic, properties of speed, flexibility, security, size and more, it becomes imperative to have an objective overview of the merits and demerits of each file system according to the needs of the users. In this work, we present a thorough performance evaluation of ext4, NTFS and Btrfs filesystems along with CFQ, NOOP and Deadline I/O schedulers tested on regular hard disk drives and SSDs. Â© 2019 IEEE.