RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights.

Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis.

A Survey on IoT-based Hand Hygiene Dispenser with Temperature and Level Monitoring Systems

Coronavirus flare-up chances human existence and it challenges the general wellbeing. Hand cleanliness, an extremely basic activity, is very much acknowledged to be one of the essential methods of decreasing medical care-related disease and of improving patient security. These days wearing masks, utilizing hand sanitizers, checking temperatures are being done in all places, and so forth yet at the same time in many spots individuals utilize conventional sanitizer gadgets which build the spread of microorganisms. Normally an individual is in charge of observing the body temperature and sanitizer level by taking a chance with his life. This survey Internet of Things (IoT) based hand sanitizer dispenser with temperature and level monitoring distinguishes the presence of the people and gives out alerts in form of the human voice to sanitize their hands if the dispenser goes unrecognized. Furthermore, we have a contactless sanitizer dispenser to stay away from contact among humans and dispensers which will keep us away from the further spread of infection and microorganisms. © 2022 IEEE.