This study provides a thorough analysis of earlier DL techniques used to classify the ECG data. The large variability among individual patients and the high expense of labeling clinical ECG records are the main hurdles in automatically detecting arrhythmia by electrocardiogram (ECG). The classification of electrocardiogram (ECG) arrhythmias using a novel and more effective technique is presented in this research. A high-performance electrocardiogram (ECG)-based arrhythmic beats classification system is described in this research to develop a plan with an autonomous feature learning strategy and an effective optimization mechanism, based on the ECG heartbeat classification approach. We propose a method based on efficient 12-layer, the MIT-BIH Arrhythmia dataset's five micro-classes of heartbeat types and using the wavelet denoising technique. Compared to state-of-the-art approaches, the newly presented strategy enables considerable accuracy increase with quicker online retraining and less professional involvement.

Right from the beginning of the COVID-19 outbreak, everyone is aware of the havoc caused by the pandemic. To curb its spread, every healthcare agency and civic body around the globe has been advising to wear masks. However, this necessary practice has posed a significant challenge for the modern-day Facial Recognition technology. Face recognition finds significant application in the security domain that demands speed and accuracy both simultaneously. This requires the system to be highly optimized and efficient. Through this paper, we present a novel approach using Haar cascade classifier for face detection with Local Binary Patterns Histograms (LBPH) face recognizer. This work further goes on to address the various problems that occur when the user wears a mask that covers a different area and percentage coverage of the face resulting in inaccuracies as various tests come with false negatives or false positives. This problem is addressed by making use of a fuzzybased system that decides the “threshold confidence score” needed to pass the authentication dynamically. Our proposed model for masked Face recognition achieves an accuracy of 86% when a Haar-feature-based cascade classifier with LBPH face recognizer is used standalone which further improves to around 97% when used in conjunction with a fuzzy system