A Novel Hybrid Based Method in Covid 19 Health System for Data Extraction with Blockchain Technology

Millions of people have been afflicted by the COVID-19 epidemic, which has resulted in hundreds of thousands of fatalities throughout the world. Extracting correct data on patients and facilities with and without COVID-19 with high confidence for medical specialists or the government is extremely difficult. As a result, utilizing blockchain technology, a reliable data extraction methodology for the COVID-19 database is constructed. In this accurate data extraction model development and validation study in blockchain technology for COVID analysis, here a novel Hybrid Deep Belief Lionized Optimization (HDBLO) approach is proposed. The weights of the deep model are optimized by the fitness of lion optimization. The implementation of this work is executed using MATLAB software. The simulation outcomes shows the effective performance of proposed model in blockchain technology in COVID paradigm in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), accuracy, F-measure, Processing time, precision and error. Consequently, the proposed approach is compared with the conventional strategies for significant validation

Performance Comparison of Wavelet and Multiwavelet Denoising Methods for an Electrocardiogram Signal

The increase in the occurrence of cardiovascular diseases in the world has made electrocardiogram an important tool to diagnose the various arrhythmias of the heart. But the recorded electrocardiogram often contains artefacts like power line noise, baseline noise, and muscle artefacts. Hence denoising of electrocardiogram signals is very important for accurate diagnosis of heart diseases. The properties of wavelets and multiwavelets have better denoising capability compared to conventional filtering techniques. The electrocardiogram signals have been taken from the MIT-BIH arrhythmia database. The simulation results prove that there is a 29.7% increase in the performance of multiwavelets over the performance of wavelets in terms of signal to noise ratio (SNR)