Showing papers by "Anupam Shukla published in 2023"

Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model

Abstract: Abstract In India, over 25,000 people have died from cardiovascular annually over the past 4 years , and over 28,000 in the previous 3 years. Most of the deaths nowadays are mainly due to cardiovascular diseases (CVD). Arrhythmia is the leading cause of cardiovascular mortality. Arrhythmia is a condition in which the heartbeat is abnormally fast or slow. The current detection method for diseases is analyzing by the electrocardiogram (ECG), a medical monitoring technique that records heart activity. Since actuations in ECG signals are so slight that they cannot be seen by the human eye, the identification of cardiac arrhythmias is one of the most difficult undertakings. Unfortunately, it takes a lot of medical time and money to find professionals to examine a large amount of ECG data . As a result, machine learning-based methods have become increasingly prevalent for recognizing ECG features. In this work, we classify five different heartbeats using the MIT-BIH arrhythmia database . Wavelet self-adaptive thresholding methods are used to first denoise the ECG signal. Then, an efficient 12-layer deep 1D Convolutional Neural Network (CNN) is introduced for better features extraction, and finally, SoftMax and machine learning classifiers are applied to classify the heartbeats. The proposed method achieved an average accuracy of 99.40%, precision of 98.78%, recall of 98.78%, and F1 score of 98.74%, which clearly show that it outperforms with the exiting model . Architecture of proposed work is simple but effective in remote cardiac diagnosis paradigm that can be implemented on e-health devices.

DDoS Attack Detection Through Digital Twin Technique in Metaverse

Abstract: A digital twin (DT) is an electronic replica of a real-world item. It is built on top of asset-specific data items and is often enhanced using semantic technologies and simulation environments. The DT lays the way for anything from routine monitoring to hands-off administration of a physical entity. With the development of the metaverse concept of DT gains importance. As it helps to manage the physical entity in the metaverse. Therefore, it is beneficial to use DT for the detection and mitigation of different types of cyber attacks. In this context, we use the concept of DT for the identification and detection of DDoS attacks in the IoT network. Our proposed approach uses the concept of support vector machine (SVM) learning technique for the identification and detection of DDoS attacks. In our proposed approach, the DT of physical routers is stored in the metaverse, which makes it secure against any type of physical attack. Our proposed approach detected the malicious packets with an accuracy of 93.25% accuracy.