ABSTRACT
Maintaining the purported Social Separating is one of the essential and greatest ways to stop the new popular episode. Legislators are enacting restrictions on the standard of private distance between people in order to concur with this restriction. In light of this real-life occurrence, it is crucial to evaluate how consistent with realistic imperatives in our lives this is, in order to ascertain the causes of any prospective cracks in such distance obstacles and determine whether this portends an anticipated risk. In order to do this, we offer the Visual Social Removing (VSD) problem, which is defined as the automatic evaluation of the difference between the depiction of connected person aggregations and the private separation from an image.When this requirement is violated, it is vital for VSD to conduct painless research to determine whether people agree to the social distance restriction and to provide assessments of the degree of wellbeing of particular places. We first draw attention to the fact that measuring VSD involves more than simply math;it also suggests a deeper comprehension of the social behavior in the setting. The goal is to genuinely identify potentially dangerous circumstances while avoiding false alerts (such as a family with children or other family members, an elderly person with their guardians), all while adhering to current security protocols. Then, at that point, we discuss how VSD links to earlier research in social sign handling and demonstrate how to investigate fresh PC vision techniques that might be able to address this issue. Future issues about the viability of VSD systems, ethical repercussions, and potential application scenarios are the result. © 2023 IEEE.
ABSTRACT
COVID-19 presents severely in persons with immunocompromised states such as diabetes mellitus. Steroids are used in the management of COVID-19. The use of steroids in immunocompromised persons increases the chances of opportunistic infections. Mucormycosis infects immunocompromised individuals. The aim of this systemic review was to study the clinico-epidemiological features of mucormycosis in patients of COVID-19. PubMed, Medline, Embase, and Google Scholar were searched for 'mucormycosis', 'COVID-19', and 'SARS-Cov 2'. A total of 30 studies were included in this study, which included 22 case reports and 8 original articles. The median age of patients was 54.80 years;79.56% (74/93) were diabetic. Steroid administration history was present in 74.19% (69/93) of patients. Rhino-orbital mucormycosis constituted 46.98% (39/83) of the cases, rhino-orbital-cerebral type in 37.35% (31/83) cases, pulmonary mucormycosis in 10.84% (9/83) cases, 1 case of gastric mucormycosis, 1 case of cutaneous mucormycosis, 1 case of palatal mucormycosis, and 1 case of disseminated mucormycosis. The median duration of presentation of mucormycosis was 14 days after hospital admission. The total number of deaths was reported in 32.26% (30/93) of the cases. The most common species found was Rhizopus, and amphotericin B was the most common antifungal administered. The COVID-19 pandemic is creating a massive pool of susceptible patients having poorly controlled diabetes mellitus and receiving steroids for COVID-19 management, thus creating an unholy alliance with mucormycosis. Mucormycosis started as a new epidemic in India, which is an otherwise rare opportunistic fungal infection. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.
ABSTRACT
Machine learning algorithms immediately became critical in the battle against the COVID-19 outbreak. Diagnoses, medicine research, an illness spread predictions, and population surveillance all required the use of artificial intelligence (AI) methods as the epidemic grew in scope. To combat COVID-19, screening procedures that are both effective and rapid are required. At COVID-19, AI developers took a chance to show how AI can benefit all mankind. It was only after the employment of AI in the battle against COVID-19. AI's various and diverse applications in the epidemic are documented in this study. It is the purpose of this study to help shape the future development and usage of these technologies, whether in the present or future health crises. © 2023, Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
This chapter deals with in depth analysis and design of a multi-agent system supported by a thorough knowledge base, Covid19_ONT, developed and enriched in Protégé 5.2.0. NLP is used to extract the information regarding COVID-19 from valuable sources like mygov.in, who.int, nhs.uk and others. India introduced the concept of zones according to the degree and magnitude of the disease. The red, orange and green zones were demarcated through the districts at the lowest level. A district at any time can be in one of the zones and it is also possible that the district might wave between the zones according to the number of active cases. Each of the zones has its own rules to be imposed, called policies. This chapter allows for systematic allotment of zones to the districts and automated policy formulation for the districts by using ontological inference done through the Drool's reasoner. © 2021 Nova Science Publishers, Inc.
ABSTRACT
Introduction: Media is considered the most powerful platform to create awareness in human society. It plays an important role in spreading public awareness and disseminating information about pandemic diseases such as coronavirus. A survey through a questionnaire was used and the descriptive analysis of questions as asked to the people on COVID 19 published by the leading newspapers of Rajasthan, western India was done to analyze the role print media played in spreading awareness about Coronavirus Disease (COVID-19). Materials and Methods: The objectives used both quantitative and qualitative methodology. In the quantitative method, a self-structured questionnaire was circulated among the various professionals. A descriptive method of secondary sources was also used to measure the effect of articles and advisements on the people published in the newspapers. Results: A total of 106 various professionals participated in the study. The research reveals that 56 percent of males and 43 percent of females are part of this research;more than 80 percent of respondents belong to the age group of fewer than 40 years. Among the active respondents, 91 percent are said to read the newspaper regularly. The study also shows that 96 percent of respondents are read articles, news, columns related to COVID-19 and particular about Coronavirus. Nearly 91 percent of respondents are in favor that media coverage of COVID-19 has improved their knowledge of Coronavirus. Around 93 percent of respondents agree that media spreading awareness among the people on Coronavirus through newspaper published articles, news, and advisements. The study exhibits that 95 percent of respondents expressed their views that print media has played an active role in disseminating the government directives on Coronavirus and almost 97 percent of the respondents are opined that print media will play a more effective role in spreading awareness on coronavirus. Conclusion: The respondents expressed that print media has played a vital role in spreading awareness on the pandemic of coronavirus disease. The newspaper spread awareness among the people on coronavirus through newspaper published articles, news and advisements. The newspaper also published a variety of queries and doubts as asked by the people to Doctors on coronvirus. © 2022, Indian Association of Preventive and Social Medicine. All rights reserved.
ABSTRACT
Today, the general situation worldwide is that the hospitals, sanatoriums and medical colleges are running out of beds, oxygen, medical staff, ventilators and other required paraphernalia that is mandatory for the treatment of the vicious pandemic [1]. The requirement is for a system that takes in some input parameters like Oxygen level of the patient, pulse rate and respiration rate and in turn predicts the Life Risk Rate of that patient [2]. The model used here is a fuzzy regression model that gives the prediction of Life Risk Rate between 1 and 10 units. The lower the predicted Life Risk Rate, the better the chances of survival of the Covid patient. But if the predicted Life Risk Rate is more than the mean of the observations of the Risk in the dataset, then immediate emergency is needed. The benefit of this system is that the patients requiring immediate admission and treatment can be filtered and medical aid in hospital be thereby provided for critical patients. Rest may be home quarantined and domestic medical aid may be given to them until in some unfortunate situation their Risk Rate is near alarming. This paper aims to provide some help in this crucial situation. © 2021 Gnedenko Forum. All right reserved.