ABSTRACT
Modern years, the Internet of Things (IoT) is mechanizing in abundant real-world functions such as smart transportation, smart business to build an individual life more accessible. IoT is the mainly used method in the previous decade in different functions. Deadly diseases always had severe effects unless they were well controlled. The latest knowledge with COVID-19 explains that by using a neat and speedy approach to deal with deadly diseases, avoid devastating of healthcare structures, and reduce the loss of valuable life. The elegant things are associated with wireless or wired communication, processing, computing, and monitoring dissimilar real-time situations. These things are varied and have low remembrance, less processing control. This article explains a summary of the system and the field of its function. The recent technology has supplied to manage previous closest diseases. From years ago, scientists, investigators, physicians, and healthcare specialists are using novel computer methods to resolve the mysteries of disease. The major objective is to study dissimilar innovation-based methods and methods that support handling deadly disease challenges that are further appropriate developments that can probably be utilized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Coronavirus disease 2019 (COVID-19) is a highly infectious viral disease caused by the novel SARS-CoV-2 virus. Different prediction techniques have been developed to predict the coronavirus disease's existence in patients. However, the accurate prediction was not improved and time consumption was not minimized. In order to address these existing problems, a novel technique called Biserial Targeted Feature Projection-based Radial Kernel Regressive Deep Belief Neural Learning (BTFP-RKRDBNL) is introduced to perform accurate disease prediction with lesser time consumption. The BTFP-RKRDBNL techniques perform disease prediction with the help of different layers such as two visible layers namely input and layer and two hidden layers. Initially, the features and data are collected from the dataset and transmitted to the input layer. The Point Biserial Correlative Target feature projection is used to select relevant features and other irrelevant features are removed with minimizing the disease prediction time. Then the relevant features are sent to the hidden layer 2. Next, Radial Kernel Regression is applied to analyze the training features and testing disease features to identify the disease with higher accuracy and a lesser false positive rate. Experimental analysis is planned to measure the prediction accuracy, sensitivity, and specificity, and prediction time for different numbers of patients. The result illustrates that the method increases the prediction accuracy, sensitivity, and specificity by 10, 6, and 21% and reduces the prediction time by 10% as compared to state-of-the-art works.
ABSTRACT
BACKGROUND: All health care workers including nurses are working in the frontline against coronavirus disease 2019 (Covid-19), which keeps them at high risk of getting infected. This study was conducted to identify risk factors for Covid-19 infection and compliance to Covid appropriate behavior among nurses. MATERIAL AND METHODS: A cross-sectional study was conducted on 150 nurses in a tertiary care hospital attached to a medical college in Mumbai, from April 2020 to December 2020. Data were collected telephonically using an interviewer-administered pre-validated, semi-structured questionnaire. Data entry and analysis were performed using SPSS version 21.0. RESULTS: The mean age of the nurses was 38.19 ± 12.14 years. The majority (80.7%) were exposed to Covid-19 while taking active care of Covid patients;a total of 108 (72%) were symptomatic at the time of testing;dietary modifications because of fear of Covid were performed by 121 (80.2%);92.77% used the appropriate personal protective equipment (PPE) category according to the workplace;121 (80.77%) followed all steps of donning and doffing at all times, and 19 (12.77%) reported a breach in PPE. A greater proportion of nurses working in Covid duties opted for hospital isolation than home isolation (p = 0.003). Risk factors such as sleep, shift duty, shift pattern, food timing, mode of travel, and type of PPE during travel were also found to be significantly associated with work type – Covid versus non-Covid (p < 0.05). CONCLUSIONS: Use of workplace appropriate PPE, proper donning and doffing facilities, duty shifts with a fixed duration, adequate hand hygiene practices, and regular food intake with adequate sleep can prevent Covid-19 infection at the workplace among nurses.
ABSTRACT
Eleven years later, in 2017, #MeToo went viral in response to sexual assault allegations against director Harvey Weinstein.1 Across the world, individuals shared their stories of experiencing gender-based violence and harassment;as of December 2019, the hashtag had more than 24 million impressions.1 Sexual violence is not regularly framed as a social determinant of health, even though the literature linking sexual violence to mental health outcomes such as depression, anxiety, and eating disorders is abundant.2 The momentum resulting from #MeToo prompted a larger question: could a global social movement potentially play a role in improving mental health outcomes for victims of gender-based violence? In 2018, the South Korean government increased both maximum sentence time and the statute of limitations for sexual harassment and sex crimes involving abuse of power.4 However, some of the discourse surrounding #MeToo in South Korea has received pushback and spurred support for men's rights groups.5 #MeToo AS A FAVORABLE HEALTH EXPOSURE The authors' findings suggest that the #MeToo movement had a beneficial effect on depressive symptoms among female survivors of gender-based violence. Social stigma remains a key driver in normalizing sexual violence, potentially lowering reporting rates;it has also been shown to worsen mental health disorders that result from gender-based violence.8 Encouraging discourse and acknowledging the issue's prevalence could have an impact on both the isolation and lack of support survivors typically experience.
ABSTRACT
Modern years, the Internet of Things (IoT) is mechanizing in abundant real-world functions such as smart transportation, smart business to build an individual life more accessible. IoT is the mainly used method in the previous decade in different functions. Deadly diseases always had severe effects unless they were well controlled. The latest knowledge with COVID-19 explains that by using a neat and speedy approach to deal with deadly diseases, avoid devastating of healthcare structures, and reduce the loss of valuable life. The elegant things are associated with wireless or wired communication, processing, computing, and monitoring dissimilar real-time situations. These things are varied and have low remembrance, less processing control. This article explains a summary of the system and the field of its function. The recent technology has supplied to manage previous closest diseases. From years ago, scientists, investigators, physicians, and healthcare specialists are using novel computer methods to resolve the mysteries of disease. The major objective is to study dissimilar innovation-based methods and methods that support handling deadly disease challenges that are further appropriate developments that can probably be utilized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.