Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and diagnosis of these diseases can greatly reduce complications and improve outcomes for high-risk individuals. One method for detecting CVDs is through the use of electrocardiogram (ECG) monitoring systems, which use various technologies such as the Internet of Things (IoT), mobile applications, wireless sensor networks (WSN), and wearable devices to acquire and analyze ECG data for early diagnosis. However, despite the prevalence of these systems in the literature, there is a need for further optimization and improvement of their classification accuracy. In an effort to address this challenge, a novel heterogeneous unsupervised learning model for real-time ECG classification was proposed. The main goal of this work was to reduce the error rate and improve the classification accuracy of the system. This study presents a framework for the classification of multi-class abnormalities in electrocardiograms (ECGs) using an ensemble feature extraction technique and unsupervised learning. The framework utilizes a real-time electrocardiogramcardiotocography (ECG-CTG) system to extract features from the ECG signal, and then employs an ensemble of feature extraction techniques to enhance the discrimination of the extracted features. The extracted features are then used in an unsupervised learning-based classification algorithm to classify the ECG signals into different classes of abnormalities. The proposed framework is evaluated on a dataset of ECG signals and the results show that it can effectively classify ECG signals with high accuracy and low computational complexity.
To save money on maintenance and administrative costs, cloud computing aims to move high-end computer equipment to the internet and put it online. Both victims and attackers may reap the advantages of cloud computing. On the other side, attacks on cloud components might lead to massive losses for cloud service providers and users. Numerous cyber-attacks have been launched as a consequence of this readily available resource. One of the most significant hazards to communication networks and applications has long been DoS and DDoS attacks. Operations, availability, and security for companies are becoming a nightmare because of these attacks. Since cloud computing resources are scalable, these resources may be dynamically scaled to recognise the attack components and immediately withstand the attack. For this cyber-attack against cloud computing, fast exploitation of the attack data is necessary. This article addresses the majority of the previously published strategies for DDoS attack avoidance, early identification, and remediation.
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