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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Reddy Karna, Viswavardhan
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Computer Science & IT Engineering

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1-dimensional convolutional neural networks for predicting sudden cardiac Reddy Karna, Viswavardhan; Vishnu Vardhana Reddy, Karna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp984-993

Abstract

Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
Deep learning-based evaluation for distributed denial of service attacks detection S., Neethu; Aradhya, H. V. Ravish; Reddy Karna, Viswavardhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4982-4992

Abstract

Software-defined network (SDN) introduces a programmable and centralized control mechanism for managing network infrastructure, enhancing flexibility and efficiency. However, this architecture is prone to security threats, particularly distributed denial of service (DDoS) attacks that exploit centralized control. This study presents a comparative analysis of several deep learning (DL) models—namely, multilayer perceptron (MLP), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM)—for detecting DDoS threats within SDN environments. The research incorporates key preprocessing techniques such as feature selection and synthetic minority oversampling technique (SMOTE) to handle class imbalance. The results indicate that sequence-aware models like LSTM and RNN are highly effective in interpreting temporal network behavior, with LSTM achieving the highest performance (accuracy: 91%, precision: 86%, recall: 94%, and F1-score: 90%). These findings underscore the potential of advanced DL methods in fortifying SDN infrastructures against complex cyber threats.
Co-Authors Aradhya, H. V. Ravish S., Neethu Vishnu Vardhana Reddy, Karna