A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment

Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It's become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.

A Supervised Learning-Based Framework for Predicting COVID-19 in Patients

The integration of ML and loT can provide insightful details for critical decision making, automated responses, etc. Predicting future trends and detecting anomalies are some of the areas where loT and ML are being used at a rapid rate. Machine learning can help decode the hidden patterns in IoT data. It may complement or replace manual processes in critical areas with automated systems that use statistically derived behavior. In healthcare, wearable sensors used for tracking patient activity have been continuously producing a staggering amount of data. This paper proposes an IoT-based scalable architecture for detecting COVID-19-positive patients and storing and processing such massive amount of data on the cloud. The proposed architecture also employs machine learning algorithms for correct classification of patients. The proposed architecture employs gradient boosting classifier method for early detection of COVID-19 in the patient's body. In order to make the architecture scalable and faster in terms of computational power, the architecture employs cloud computing for data storage. © 2023 IGI Global. All rights reserved.