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p-Index From 2021 - 2026
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All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Reconfigurable and Embedded Systems (IJRES)
Arun Kumar Ramamoorthy
University of South Wales
Automotive Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Heart disease prediction using hybrid deep learning and medical imaging with wavelet-based feature extraction Chairmadurai Palanisamy; Kavitha Pachamuthu; Arun Kumar Ramamoorthy
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp183-193

Abstract

The process of heart disease prediction is based on patient medical information, which can be addressed in terms of medical image as well as the results of an electrocardiogram (ECG) conducted to determine the risk of developing heart disease. The hybrid deep learning (DL) algorithms are developed using past data that can identify trends related to cardiovascular disease (CVDs). In the current paper, it is possible to offer a new method of heart disease prediction that would combine high-quality image processing and hybrid DL to enhance the effectiveness of predictions and avoid the shortcomings of the modern approaches. First, medical images like ECG images are pre-processed with butterworth adaptive 2D wavelet filter, which ensures maximal noise reduction, followed by maintenance of spatial and frequency information. The Gabor Wavelet-based feature extraction technique is applied to extract meaningful patterns, including both spatial and frequency domain information, which is essential for detecting heart-related anomalies. The resultant features are then categorized, along with both convolutional neural networks (CNN) and long short-term memory (LSTM), to make reliable and precise predictions of heart disease. The performance indicators, including accuracy (92.4%), precision (91.2%), recall (93.5%), and F1-score (91.0%), are utilized. Applying the model yields significant levels of reliability and generalization compared to traditional applications.
Graph-guided contrastive transformer architecture for robust and explainable network intrusion detection Archana Jayapal; Kamalakkannan Somasundaram; Arun Kumar Ramamoorthy
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp698-708

Abstract

Intrusion detection systems (IDS) are very instrumental in protecting contemporary network infrastructures against the ever-advancing cyberattacks. Conventional signature-based and machine learning-enabled IDS solutions frequently have difficulty when it comes to high false-positive rates, inability to flexibly adapt to novel attacks, and the lack of support for complex traffic dynamics. New deep learning architectures have better detection properties, yet are limited by feature overlap, temporality, and lack of extensiveness to generalization in changing network conditions. To overcome these issues, this paper presents a new graph-guided contrastive transformer-based intrusion detection system (GCT-IDS) which aims at improving detection accuracy and robustness and preserving real-time feasibility. The framework combines feature interaction by graph modeling, contrastive representation learning, and a sparse self-attention transformer to effectively learn global traffic relationships and behavioral variations. The CSE-CIC-IDS2018 data is used to test the proposed method in real network conditions.
Co-Authors Archana Jayapal Chairmadurai Palanisamy Kamalakkannan Somasundaram Kavitha Pachamuthu