Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 14 de 14
Filter
1.
Acm Transactions on Asian and Low-Resource Language Information Processing ; 21(5), 2022.
Article in English | Web of Science | ID: covidwho-2307148

ABSTRACT

Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet interaction. With such increased interaction because of the COVID-19 pandemic, more online tools have been widely used to provide evidence-based mental health services. This increase helps cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help solve mental health issues quickly. In this research, we propose a fuzzy contrast-based model that uses an attention network for positional weighted words and classifies mental patient authored text into distinct symptoms. After that, the trained embedding is used to label mental data. Then the attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes. Our method is compared with non-embedding and traditional techniques to demonstrate the proposed model. From the experiments, the feature vector can achieve a high ROC curve of 0.82 with problems associated with nine symptoms.

2.
4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 ; : 2221-2225, 2022.
Article in English | Scopus | ID: covidwho-2300154

ABSTRACT

Automation has been into existence since the mid Fifties but had simplest began to gain attention lately. The RPA software program makes use of existing generation's interface to automate the human detail in the technique. So, essentially, there's no want for human intervention. web scraping is a software of robot system Automation that is used in almost all of the industries. either or not it's a e-trade internet site, commodities buying and selling web sites, or any internet site and so forth. you can scrape the information from any of them based on your hobby. Now, the problem with guide scraping by hand is that it's miles at risk of mistakes and takes numerous times. also, the facts available on websites does now not change in any respect. up to date regularly, for this reason facts saved domestically might not usually be terrible. So, industries can actually automate this mission. The main objective of the project is to save time and send the updated information to the person using RPA technology. As this COVID-19 Global Pandemic going on, we thought of creating a project around COVID-19. So, in the project we will use Data Scrapping to extract Web Table (which contains COVID-19 data such as number of affected people, recovered people etc.) from web page. And, write the extracted data into Excel then we will send that excel over the email as attachment. In this project we researched how we can send data through email using RPA and extract the data from live covid_19 website. For software automation, there are many software's that are available in market. The main RPA vendors are UiPath, Automation Anywhere, and Blue Prism. So, to complete our RPA project, we have chosen UiPath which the best in the field of automation. You should be familiar with at least one of these tools before working on the following projects. This paper aims to provide RPA reviews as technical, as well as its implementation applications. © 2022 IEEE.

3.
Science of the Total Environment ; Part 2. 858 (no pagination), 2023.
Article in English | EMBASE | ID: covidwho-2277905

ABSTRACT

Students spend nearly one third of their typical day in the school environment, where they may be exposed to harmful air pollutants. A consolidated knowledge base of interventions to reduce this exposure is required for making informed decisions on their implementation and wider uptake. We attempt to fill this knowledge gap by synthesising the existing scientific literature on different school-based air pollution exposure interventions, their efficiency, suitability, and limitations. We assessed technological (air purifiers, HVAC - Heating Ventilation and Air Conditioning etc.), behavioural, physical barriers, structural, school-commute and policy and regulatory interventions. Studies suggest that the removal efficiency of air purifiers for PM2.5, PM10, PM1 and BC can be up to 57 %, 34 %, 70 % and 58 %, respectively, depending on the air purification technology compared with control levels in classroom. The HVAC system combined with high efficiency filters has BC, PM10 and PM2.5 removal efficiency up to 97 %, 34 % and 30 %, respectively. Citizen science campaigns are effective in reducing the indoor air pollutants' exposure up to 94 %. The concentration of PM10, NO2, O3, BC and PNC can be reduced by up to 60 %, 59 %, 16 %, 63 % and 77 %, respectively as compared to control conditions, by installing green infrastructure (GI) as a physical barrier. School commute interventions can reduce NO2 concentration by up to 23 %. The in-cabin concentration reduction of up to 77 % for PM2.5, 43 % for PNC, 89 % for BC, 74 % for PM10 and 75 % for NO2, along with 94 % reduction in tailpipe emission of total particles, can be achieved using clean fuels and retrofits. No stand-alone method is found as the absolute solution for controlling pollutants exposure, their combined application can be effective in most of the scenarios. More research is needed on assessing combined interventions, and their operational synchronisation for getting the optimum results.Copyright © 2022 The Authors

4.
Clinical Practice and Epidemiology in Mental Health ; 19 (no pagination), 2023.
Article in English | EMBASE | ID: covidwho-2274922

ABSTRACT

Background: The COVID-19 pandemic and its related consequences caused a higher risk of mental health problems for nurses. Hence, this study aims to reduce the level of fear and stress related to the COVID-19 pandemic and promote active coping among Egyptian nurses. Method(s): This quasi-intervention study was conducted on 125 nurses working at Benha's University hospitals, who were selected by a systematic random sampling technique within the time interval of March 2021 to July 2021. The study was conducted using the fear of COVID-19 scale, the stress scale of depression, anxiety and stress scales, and the Brief (COPE) inventory scale. Result(s): The mean ages of the studied nurses were 36.70 +/- 9.50. Almost half of the studied nurses were males and married. Before the intervention, 47.2% of nurses had severe stress levels while 82.4% had a high level of fear of COVID-19. Experience years, type of department, and worries about vaccine side effects were the predictors of the fear of COVID-19. A significant difference (p =.000) was found between both mean stress and fear scores pre-intervention (15.27 +/- 5.47 and 25.56 +/- 6.13) and post-intervention (4.87 +/- 2.14 and 11.92 +/- 2.43). The most prevalent coping strategies among nurses before the intervention were self-distraction (5.03 +/- 1.53), followed by behavioral disengagement and self-blaming. However, after the intervention, religion was found to be the utmost coping mechanism (6.12 +/- 1.17), followed by positive reframing and acceptance. Conclusion(s): The majority of the nurses in the study reported a significant fear of COVID-19, and around half of the nurses had severe stress as a result. After the intervention, the stress and fear scores were reduced by half or even less. Age, longer work experience, and worries about the vaccine were the predictors of fear of COVID-19. The coping strategies used after the intervention shifted toward active coping strategies.Copyright © 2023 Omar et al.

5.
4th International Conference on Circuits, Control, Communication and Computing, I4C 2022 ; : 511-514, 2022.
Article in English | Scopus | ID: covidwho-2274225

ABSTRACT

The study's goal is to create a detector that detects and analyses whether pedestrians or individuals in public gatherings are maintaining social distancing. Drone-shot videos, live webcam feeds, and photographs are all kinds of input for the detector. With no human intervention, Dynamic Detection through live stream provides safety and simplifies monitoring of social distance. The webcam input can be integrated with an external webcam or a drone's camera. Furthermore, the YOLOv4 algorithm is used for the data set for the initial phase ofobject detection, identifying various items in each frame. The recognized objects are narrowed down to humans, and the Euclidian distance between one data point and every other data point is determined The Euclidian distance determines if they are maintaining the minimal distance between them or not by depicting them with a colored border box. Euclidian distance assists in detecting if they are keeping the minimal distance between them or not, as shown by a coloredboundary box, red for unsafe and green for safe, with an indication reflecting the number of people in danger. © 2022 IEEE.

6.
Clinical Trials ; 20(Supplement 1):89, 2023.
Article in English | EMBASE | ID: covidwho-2271471

ABSTRACT

Background: Important lapses in the research enterprise, notably low-quality studies, amount to research waste. Close to 50% of this research waste comes from research on low-priority research questions, omitting important outcomes, not involving stakeholders in research design and poor methodology. With the COVID-19 pandemic, the urge to generate evidence to address important questions regarding optimal management strategies has further aggravated this problem. Most COVID-related trials are of low quality. This is in part due to deficiencies in designing high-quality trials at short notice. Consequently, results from these trials do not reliably inform clinical practice for the treatment or management of patients with COVID-19. Innovative approaches to trial design that incorporate existing tools are required to ensure that trials can be designed rapidly, efficiently, and consistently. Learning objectives: (1) To understand the key features of trial design. (2) To apply the use of existing trial resources in trial design. (3) To learn about how to match the research question with the appropriate design features. (4) To be able to use an electronic application to design a trial. Outline: In the first part, participants will review core concepts in trial design (equipoise, research question formulation, knowledge gaps, hypotheses etc.) and a collection of tools/frameworks meant to enhance trial design. These tools/frameworks include the PRagmatic Explanatory Continuum Indicator Summary-2 (PRECIS-2), Template for Intervention Description and Replication (TIDieR), Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT), Core Outcome Measures in Effectiveness Trials (COMET;https://comet-initiative.org/), TrialForge (Tools aimed at improving trial efficiency;https:// www.trialforge.org/), Support for statistical analyses plans (SAP), PROGRESS-Plus (a framework of sociodemographic factors-Place of residence, Race/ethnicity/ culture/language, Occupation, Gender/Sex, Religion, Education, Socioeconomic status, and Social capital-'Plus' refers to other personal, time-dependent or relationship-dependent factors, such as pregnancy, age, disability, and sexual orientation). The second part will be a hands-on session in trial design using the TrialTree application (https://trialtree.logicnets.net/ lmc/TT) and the production of a design report. TrialTree is organized into eight modules that cover the main design features. It includes tips, prompts, and feedback on trial design. Evaluation: (1) Completion of a post-workshop quiz. (2) Production of a complete design report in TrialTree. Materials required: (1) A laptop (access to the TrialTree application will be provided free of charge). (2) Pre-workshop readings will be provided. Goals of Session: The goal of this session is to build capacity in novice and experienced trialists on the use of an electronic application for interactive trial design.

7.
Pulse ; 9(Supplement 1):3-4, 2021.
Article in English | EMBASE | ID: covidwho-2252814

ABSTRACT

Arterial stiffness is the best surrogate for vascular aging. We have put forward the notion of early vascular aging (EVA), in which subjects have arteries older than chronological age. On the opposite, subjects can be resilient to the effect of age and risk factors, and present arteries younger than chronological age, called supernormal vascular aging (SUPERNOVA). In the present lecture, I will discuss the definition and clinical utility of EVA and SUPERNOVA, how we can practically measure EVA and SUPERNOVA in clinics, and what measures can be applied to correct for EVA, and promote SUPERNOVA, especially in the context of COVID pandemic. I will also present the first results from the SPARTE interventional trial, and what the next steps should be.

8.
Science, Technology & Human Values ; 48(2):343-373, 2023.
Article in English | ProQuest Central | ID: covidwho-2281198

ABSTRACT

Prediction plays a vital role in every branch of our contemporary lives. While the credibility of quantitative simulations through mathematical modeling may seem to be universal, how they are perceived and embedded in policy processes may vary by society. Investigating the ecology of quantitative prediction tools, this article articulates the cultural specificity of Japanese society through the concept of Jasanoff's "civic epistemology.” Taking COVID-19 and nuclear disasters as examples, this article examines how predictive simulations are mobilized, contested, and abandoned. In both cases, current empirical observation eventually replaces predictive future simulations, and mechanical application of preset criteria substitutes political judgment. These analyses suggest that the preferred register of objectivity in Japan—one of the constitutive dimensions of civic epistemology—consists not in producing numerical results, but in precluding human judgment. Such public calls to eliminate human agency both in knowledge and in policy-making can be a distinct character of Japanese civic epistemology, which may explain why Japan repeatedly withdraws from predictive simulations. It implies the possibility that Western societies' faith in human judgment should not be taken for granted, but explained.

9.
25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; 13438 LNCS:3-12, 2022.
Article in English | Scopus | ID: covidwho-2059730

ABSTRACT

The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found that these pre-labeling based methods can induce hallucinating artifacts, which might mislead the downstream clinical tasks, while manual adjustment could be onerous and subjective. To avoid manual adjustment and pre-labeling, we propose a novel controllable and simultaneous synthesizer (dubbed CS$$

10.
International Conference on Smart Technologies for Sustainable Development 2021, ICSTSD 2021 ; 2286, 2022.
Article in English | Scopus | ID: covidwho-1991986

ABSTRACT

Machine learning contributes into gamut of domains starting from industry automation to healthcare services. It is a field of Artificial Intelligence using which machine can make decision without human intervention. There are many predominant machine learning algorithms which have proven their excellence in the field of regression and classification problem. Machine learning now a day is used in large scale in field of disease prediction. The acceptability of a machine learning based model depends on dataset used for training the model. Analysis of dataset is very important to identify the importance of individual attributes contribute to make decision. In this paper a cardiovascular disease dataset collected from UCI has been analyzed in detail to identify the distribution and impact of them in decision making. © Published under licence by IOP Publishing Ltd.

11.
5th International Conference on Software Engineering and Information Management, ICSIM 2022 ; : 193-198, 2022.
Article in English | Scopus | ID: covidwho-1840645

ABSTRACT

Due to the global pandemic of Coronavirus 2019 disease (COVID-19), which began in late 2019, the world has changed and forced all human life to adapt by living in new forms, isolated from the community for both works and learning from their residences. COVID-19 disease undoubtedly has had an impact on the transportation ecosystem. Food, groceries, and even medications are delivered directly from restaurants to customers by deliverers, who can cause exposure and easily infect. Delivery solutions have been proposed and solved through the application of Internet of Things (IoT) knowledge to reduce customer-deliverer exposure. The Internet of Things concept can assist us in developing a mobility object or simulated car that operates without human intervention. The vehicle is programmed to run parallel to the surface of a continuous line, which is referred to as line following. Calibration of the line follower sensor is required to achieve a higher effective tracking accuracy under various conditions, such as line width or rough surface. This research examines and compares how the simulated car behaves when run on various linewidths in preparation for future application and development. © 2022 ACM.

12.
3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021 ; : 38-42, 2021.
Article in English | Scopus | ID: covidwho-1774598

ABSTRACT

The tremendous advancements in nanotechnology have given life to the technology called Lab-on-Chip (LoC). The Nanoscale impression on semiconductors and the metals are achieved by the Lithography processes. Many of the experiments and analysis which are to be done in the laboratory have been done on this miniaturized module. The LoC technology helps to perform many laboratory functions on a single few centimeters chip size. This helps achieve high-throughput screening and automation. LoC technology requires a very less sample in drops for the analysis of the sample provided and also helps in cost effectiveness and speed response. It has great control over the concentration of samples as well as interactions to reduce the huge chemical waste. LoC through mass production aids to the development of highly compact design of systems. This paper overviews the development in the field of LoC. © 2021 IEEE.

13.
Journal of Clinical and Diagnostic Research ; 16(2):ZC35-ZC40, 2022.
Article in English | EMBASE | ID: covidwho-1709699

ABSTRACT

Introduction: Oral Health (OH) is essential to general health and quality of life. It is affected by the individual's experiences and perceptions. Aim: To evaluate the effect of online Oral Health Education (OHE) programme on OH knowledge level on school students in Riyadh, Saudi Arabia. Materials and Methods: This interventional cross-sectional study was conducted virtually on school students in Riyadh city, Saudi Arabia, between February 2021 and May 2021. The sample was based on non probability convenience sampling technique in which 489 students participated in the study. The electronic survey consisted of questions about demographics, school characteristics, and OH knowledge. Online OHE was conducted by dental students of Vision colleges via Zoom and Microsoft teams. Collected data were analysed using using IBM, Statistical Package for the Social Sciences (SPSS) version 20.0, IL, USA. Comparison of differences in the mean knowledge scores across different variables was done using independent t-test for two means and one-way Analysis of Variance (ANOVA) for more than two means. Linear regression analysis was used to analyse the association between knowledge and other variables in a multivariate environment, and presented by β coefficients and 95% Confidence Interval (95% CI). Significance level was set at p-value <0.05. Results: Online education had significantly increased the level of knowledge about OH compared to no education (β: 0.46, 95%CI: 0.01, 0.89, p-value=0.04). Students in public schools had significantly higher level of knowledge about OH compared to private schools (β: 0.60, 95%CI: 0.10, 1.11, p-value=0.02). Compared to '1st to 3rd grade', students in 'middle to high grades' had significantly lower knowledge about OH (β:-1.17, 95% CI:-1.87,-0.47, p-value=0.001). Conclusion: It was concluded that the online health education programme increased the OH knowledge of school students. Students in public schools had higher level about OH compared to private schools' students. Additionally, primary schools' children had higher knowledge than middle and higher schools' children.

14.
2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1672763

ABSTRACT

Chatbots or virtual assistants are being used by industries all over the world, they can reduce human intervention and improve efficiency. These days smart-Assistants such as Amazon Alexa and Google Assistant help users get quick access to most generic queries within seconds, but when it comes to students and their everyday queries, these assistants fall short in answering the queries they have related to their academic schedules i.e., timetable queries, online classes links, syllabus queries, test dates, etc. The motive behind building this chatbot is to help students get quick and accurate responses to their schedule and syllabus-related queries, this is especially beneficial for students who are taking online classes due to the COVID-19 pandemic and cannot talk to their peers face to face. This chatbot was developed with the Rasa, it is a framework for developing contextual AI assistants and chatbots. Rasa enables the use of components in the NLU pipeline to customize the intent classification, entity extraction, and response selection. This paper goes through the pipeline customizations that were necessary to process the schedule-specific queries from students. It also goes over the stories and custom actions used to generate responses once the intent and entities are extracted. Telegram was used to deploy the chatbot onto the real world to enable students to talk to this chatbot from the comfort of their smartphones. © 2021 IEEE.

SELECTION OF CITATIONS
SEARCH DETAIL