Electrocardiogram (ECG) is an electrical signal that contains data about the state and functions of the heart and can be used to diagnose various types of arrhythmias effectively. The modeling and simulation of ECG under different conditions are significant to understand the function of the cardiovascular system and in the diagnosis of heart diseases. Arrhythmia is a severe peril to the patient recovering from acute myocardial infarction. The reliable detection of arrhythmia is a challenge for a cardiovascular diagnostic system. As a result, a considerable amount of research has focused on the development of algorithms for the accurate diagnosis of arrhythmias. In this paper, a system for the classification of arrhythmia is developed by employing the probabilistic principal component analysis (PPCA) model. Initially, the cluster head is selected for the effective transmission of ECG signals of patients using the adaptive fractional artificial bee colony algorithm, and multipath routing for transmission is selected using the fractional bee BAT algorithm. Features such as wavelet features, Gabor transform, empirical mode decomposition, and linear predictive coding features are extracted from the ECG signal with high dimension (which are reduced using PPCA) and finally given to the proposed classifier called adaptive genetic-bat (AGB) support vector neural network (which is trained using the AGB algorithm) for arrhythmia detection. The experimentation of the proposed system is done based on evaluation metrics, such as the number of alive nodes, normalized network energy, goodput, and accuracy. The proposed method obtained a classification accuracy of 0.9865 and a goodput of 0.0590 and provides a better classification of arrhythmia. The experimental results show that the proposed system is useful for the classification of arrhythmias, with a reasonably high accuracy of 0.9865 and a goodput of 0.0590. The validation of the proposed system offers acceptable results for clinical implementation.
Cloud storage is considered to be the most critical factor in decision making for users as it largely scales down the infrastructure in terms of size, cost and design. Considering factors such as local storage cost, maintenance a single server model can support multiple users on a needed basis. This raises concerns for integrity verification i.e., assuring the correctness of the data stored available in cloud. The proposed auditing algorithm suggests and investigates digital signature for integrity verification. A Modified Version of Elliptic curve digital Signature Algorithm is proposed for auditing the task. The main focus of this study is to address problems such as privacy preserving, public auditing. In addition, the performance of the auditing task is optimized. Data dynamics have been modeled through various data operations such as block insertion, deletion and block modification. Extensive theoretical and experimental analysis presented in the paper shows that security, performance of the proposed algorithm are improved in terms of verification time of the auditing process.
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