ABSTRACT
Coronavirus pandemic started spreading in 2019 and is still spreading until now in 2021 all over the world. Due to this the healthcare sectors are going on crisis all over the world. One basic protective measure that we can implement in our daily life is wearing a face mask. Wearing a mask properly can control the spread of this virus to a great extent. Various regions have made wearing face mask mandatory to prevent spread of this virus. In this paper we have proposed a deep learning-based model to detect face mask using python, OpenCV, TensorFlow and it can be used in our health care sectors. © 2022 IEEE.
ABSTRACT
Rural areas face a lack of medical facilities in many countries, with a significant crisis in the deployment and retention of medical professionals. Also, in the present scenario of the COVID-19 pandemic, social distancing is the critical factor in breaking the chain of transmission of the deadly virus. This has restricted the in-person visits in medical practices. The advancement in research and development of newer technology has paved the way for telemedicine via augmented/virtual reality (AR/VR). Telemedicine is a technique to remotely treat and monitor the patient and is also utilized to give procedural training to eligible medical professionals. While it was earlier publicized as a medium for the extension of healthcare services to rural people, it has become a safer way of treatment during novel coronavirus outbreaks. This approach has unprecedented benefits, including saving upon travel time and cost of the patient, proving to be environmentally, economically feasible, and a safer treatment method for both the patient and the medical officer. In this chapter, we review the various telepresence technologies developed using AR/VR platforms for remote procedural training. This is compared with the conventional methods available. The chapter covers the brief history of telecare, challenges in medical treatments, medical visualization, and diagnosis, basic requirements for telehealth such as management of patients records, real-time monitoring of patients with 3D representations and other telepresence technologies, and AR/VR technology in telemedicine. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
One of the hot topics of discussion today is coronavirus disease 2019 (COVID-19). The disease is easily transmitted from one person to another person. However, there are no specific drugs that can alleviate the disease thus non-pharmaceutical intervention strategies is a good option. This paper aims to apply the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method to outrank the intervention strategies. A case study is presented where five experts were invited to rate ten alternatives and ten criteria using linguistic scales. Spreadsheet software and PROMETHEE-GAIA software were employed to establish outranking results and to provide evidence on the vigorousness of the outranking results. The final outranking indicates that the most and the least preferred intervention strategies are alternative A1 (lockdown/quarantine) and alternative A10 (Practice of hand hygiene) respectively. The outranking results are further analyzed with distribution analysis and weights sensitivity analysis where these analyses provide evidence on the vigorous of the outranking results. It is found that these analyses confirm the position of A1 as the most preferred intervention strategy to curtail the COVID-19 transmissions. The findings would be beneficial for public health authorities to deal with multiple challenges to curb the spread of COVID-19. © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ABSTRACT
These days, the appraisal of the COVID-19 vulnerability has become a difficult errand for the whole world. The COVID-19 administration dynamic issue frequently includes numerous elective arrangements clashing standards. In this paper, we present a multi-criteria decision-making (MCDM) procedure based on the fuzzy VIKOR method to survey the COVID-19 vulnerability in the state of Assam, India. The trapezoidal fuzzy number is utilized to evaluate the rating of the loads for the set-up models. We have observed environment, social, and Medical factors after observing the spread of COVID-19. To study and to have comments, a committee of five experts has been formed from a different region of Assam to observe and comment to identify Coronavirus's weakest factors. For a better survey, we have divided the state into four areas namely Rural Area, Urban Area, Market Area in Rural Area, and Market Area in Urban Area. The current research looked at how the fuzzy VIKOR selects provinces for urgent adaptation needs differently than a traditional MCDM technique. © 2022 - IOS Press. All rights reserved.
ABSTRACT
In this article, we investigate the multi-criteria decision-making complications under Pythagorean fuzzy soft information. The Pythagorean fuzzy soft set (PFSS) is a proper extension of the Pythagorean fuzzy set (PFS) which discusses the parametrization of the attributes of alternatives. It is also a generalization of the intuitionistic fuzzy soft set (IFSS). The PFSS is used to precisely evaluate the deficiencies, anxiety, and hesitation in decision-making (DM). The most essential determination of the current study is to advance some operational laws along with aggregation operators (AOs) within the Pythagorean fuzzy soft environs such as Pythagorean fuzzy soft interaction weighted average (PFSIWA) and Pythagorean fuzzy soft interaction weighted geometric (PFSIWG) operators with their desirable features. Furthermore, a DM technique has been established based on the developed operators to solve multi-criteria decision-making (MCDM) problems. Moreover, an application of the projected method is presented for the selection of an effective hand sanitizer during the COVID-19 pandemic. A comparative analysis with the merits, effectivity, tractability, along with some available research deduces the effectiveness of this approach.
ABSTRACT
Coronavirus is a major threat being faced by the whole world. The uncertain information related to its symptoms as well as vaccines have widened the aspect of related confusion. In order to contemplate a vast amount of uncertain information, probabilistic dual hesitant fuzzy sets (PDHFSs) is an efficient tool as it can capture the hesitant membership and non-membership values along with their associated probabilities. Moreover, to control the screening of travelers at the terminal travel points, there is need of efficient algorithms to handle the uncertain circumstances. For it, a series of similarity and distance measures are proposed on PDHFSs and their relatable mathematical properties are proved. Based on them, advanced decision-making algorithms on Weighted Distance Based Approximation (WDBA) and Combinative Distance based Assessment (CODAS) are devised. To bridge the gap between the PDHFS formulation and available hesitant information, the concept of bipartite graphs has been introduced and a suitable approach is formulated to accomplish the task of successful hustle-free screening of the travelers. Finally, comparative studies of the proposed approach are done with the existing theories and significance of the proposed methodology is listed.