Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences
Hegde, Gayatri
Unknown Affiliation
Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Contraction control factor-based gorilla troop optimizer for features in intrusion detection systems Sharma, Shalini; Khaitan, Supriya; Hegde, Gayatri; Rohatgi, Divya; Rafique, Nusrat Parveen Mohammad; Lawand, Suhas Janardan
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp373-383

Abstract

Internet of things (IoT) has evolved into a large-scale network due to the increasing number of connected devices and massive amount of data they generate. IoT networks produce massive amounts of heterogeneous data from various devices, making it difficult to identify relevant features for intrusion detection. Hence, this research proposes the contraction control factor-based gorilla troop optimizer (CCF-GTO) for feature selection and multiple parametric exponential linear units based long short-term memory (MPELU-LSTM) approach for classification of intrusion detection system (IDS) in IoT. CCF-GTO. It uses adjustable parameters to prioritize relevant information while eliminating unnecessary features, making the model more efficient and resulting in better classification accuracy. The experimental results demonstrate that the MPELU-LSTM approach achieves better accuracy of 99.56% on the UNSW-NB15 dataset as compared to the earlier approaches like convolutional neural network with LSTM (CNN-LSTM) and optimized deep residual convolutional neural networks (DCRNN). These findings suggest that the MPELU-LSTM method significantly enhances the accuracy and robustness of IDS in IoT environments by addressing issues like the identification of relevant features and feature redundancy, contributing to more effective and secure systems. This research has valuable implications for enhancing the security bearing of IoT infrastructure.
Early prediction of myocardial infarction using proposed score tree algorithm Parveen, Nusrat; Pacharaney, Utkarsha; Hegde, Gayatri; Rafique, Mohammad; Firoj Nalband, Sana; Akhtar, Shamim; Devane, Satish
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp813-822

Abstract

Early detection and diagnosis of a diseases will have a big impact on the medical field and help to prevent loss of life. This study begins by gathering information on myocardial infraction patients from hospitals and focuses on earlier diagnostics. In fact, the pre-processed, confirmed data from a qualified doctor is used for this research. Early prediction of myocardial infarction (MI) is proposed by many researchers. They have used Kaggle datasets that is not recent, and they work on post MI. We have proposed early myocardial infraction detection works on unsupervised datasets. To identify myocardial infraction, numerous machines learning supervised algorithms, including decision tree (DT), random forest (RF), are employed in the literature. In this study, we use the score tree algorithm (STA), which operates on an unsupervised dataset, to present a unique early MI prediction method.
Co-Authors Akhtar, Shamim Devane, Satish Firoj Nalband, Sana Khaitan, Supriya Lawand, Suhas Janardan Pacharaney, Utkarsha Parveen, Nusrat Rafique, Mohammad Rafique, Nusrat Parveen Mohammad Rohatgi, Divya Sharma, Shalini