ABSTRACT
A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.
ABSTRACT
The study proposes a novel deep learning-based model for early and accurate detection of the Tomato Flu virus, also known as tomato fever, which has recently emerged in children under the age of five in the Indian state of Kerala. The model utilizes a deep learning method to classify skin pictures and check whether a person is suffering from the virus or not, with an accuracy of 100% and a validation loss of 0.2463. Additionally, an API is developed for easy integration into various web/app frameworks. The authors highlight the importance of careful management of rare viral diseases, especially in the context of the ongoing COVID-19 pandemic. © 2023 Bharati Vidyapeeth, New Delhi.
ABSTRACT
An elderly squire indicated the importance of medication intake in everyday life. Some individuals forget to take medicine on time. Also, monitor using the scheduled medicine to help people with an illness. In addition, technology has increased for utilization in healthcare. According to Harvard Medical School by Stephanie Watson about the technology advancement is flourishing in the Philippines, and telehealth stays with innovation to improve digital health platforms. In addition, healthcare services are facing challenges with Covid-19. On the other hand, telehealth gives good service, and about 91% of consumers use digital healthcare services. Also, A reminder system should be simple, familiar, flexible, and recognizable.Technology can positively impact the lives of older people, including their physical and mental health and daily activities. Technology can help people become active. It also increases awareness and motivation to increase physical activity. © 2022 IEEE.
ABSTRACT
In this pandemic period, social distancing is essential to avoid the spreading of the Covid-19 disease. Also, vaccination is one of the critical aspects. According to that, there is aninnovate idea of a project which fulfills all the above aspects such as Social Distancing, Physical touch, Vaccination details, and also Temperature detection. During this Covid-19, people were suffered a lot, so on a social and noble cause, it's our responsibility to think and develop products that will help us to prevent this disease as much as possible, which is going to be our primary goal and also by using advanced technologies like face recognition and temperature measuring device, so there is need to make our model much more efficient and trustworthy. In today's era, security has played a pivotal role in many of our places, like offices, institutions, libraries, laboratories, etc., to keep our data confidential so that no other unauthorized person could access them. This project presents a face-recognition-based door opening system that provides security and can be used for many banks, institutes, organizations, etc. For verifying the authentification, there are various methods like passwords and RFID, but this method is the most efficient and reliable. Temperature is sensed by the sensor and is validated for security purposes. If the temperature matches, then a camera is open, and after face-Recognition, the door will be get opened automatically.
ABSTRACT
The discrete-event system specification (DEVS) formalism has been recognized to be able to enable a formal and complete description of the components and subsystems of hybrid models. What is missing for accelerated adoption of DEVS-based methodology is to offer a way to design web apps to interact with a simulation model and to automatically deploy it on an online server which is remotely accessible from web app. The deployment of DEVS simulation models is the process of making models available in production where web applications, enterprise software, and APIs can consume the simulation by providing new inputs and generating outputs. This paper proposes a framework allowing one to simplify the DEVS simulation model building and deployment on the web by the modeling and simulation engineers with minimal web development knowledge. A case study on the management of COVID-19 epidemic surveillance is presented. © 2022 IEEE.
ABSTRACT
BACKGROUND: Early detection and response to influenza and COVID-19 outbreaks in aged care facilities (ACFs) are critical to minimizing health impacts. The Sydney Local Health District (SLHD) Public Health Unit (PHU) has developed and implemented a novel web-based app with integrated functions for online line listings, detection algorithms, and automatic notifications to responders, to assist ACFs in outbreak response. The goal of the Influenza Outbreak Communication, Advice and Reporting (FluCARE) app is to reduce time delays to notifications, which we hope will reduce the spread, duration, and health impacts of an influenza or COVID-19 outbreak, as well as ease workload burdens on ACF staff. OBJECTIVE: The specific aims of the study were to (1) evaluate the acceptability and user satisfaction of the implementation and use of FluCARE in helping ACFs recognize, notify, and manage influenza and COVID-19 outbreaks in their facility; (2) identify the safety of FluCARE and any potential adverse outcomes of using the app; and (3) identify any perceived barriers or facilitators to the implementation and use of FluCARE from the ACF user perspective. METHODS: The FluCARE app was piloted from September 2019 to December 2020 in the SLHD. Associated implementation included promotion and engagement, user training, and operational policies. Participating ACF staff were invited to complete a posttraining survey. Staff were also invited to complete a postpilot evaluation survey that included the user Mobile Application Rating Scale (uMARS) measuring app acceptance, utility, and barriers and facilitators to use. An issues log was also prospectively maintained to assess safety. Survey data were analyzed descriptively or via content analysis where appropriate. RESULTS: Surveys were completed by 31 consenting users from 27 ACFs. FluCARE was rated 3.91 of 5 overall on the uMARS. Of the 31 users, 25 (80%) would definitely use FluCARE for future outbreaks, and all users agreed that the app was useful for identifying influenza and COVID-19 outbreaks at their facilities. There were no reported critical issues with incorrect or missed outbreak detection. User training, particularly online training modules, and technical support were identified as key facilitators to FluCARE use. CONCLUSIONS: FluCARE is an acceptable, useful, and safe app to assist ACF staff with early detection and response to influenza and COVID-19 outbreaks. This study supports feasibility for ongoing implementation and efficacy evaluation, followed by scale-up into other health districts in New South Wales.
ABSTRACT
Corona is one of the most destructive viruses that has ever produced a pandemic in human life, not only in terms of direct victims but also in terms of the socio-economic consequences of the virus' transmission. The 2nd anniversary of the global coronavirus pandemic passed away in 2021. However, it's still impossible to say how long the epidemic will last. After reviewing a study by the World Health Organization on COVID-19, the country's national government urged residents to use facemask in order to reduce the incidence of COVID-19 transmission. As a result of COVID-19, there are presently no facemask detection app that are in great demand for ensuring safety in public area. In the context of the outbreak of COVID-19, A facemask detection model based on deep learning approach of state-of-the-art YOLOv5 may be useful in real-time applications. In this paper, we propose a web app for detecting if the people wears facemask or not in real-time via webcam or public camera. In the app, we deployed and persisted many different YOLOv5-based models that the users can switch between them to guarantee the performance and timing trade-off. Furthermore, our system is able to detect if an individual person captured by surveillance cameras is wearing facemask in acceptable counting time at staging level. In our opinion, this kind of system is extremely efficient for use in airports, train stations, offices, and other public areas, as well as in military. © 2022 IEEE.
ABSTRACT
Covid-19 pandemic hit a giant blow on in-person education. This was especially difficult when teaching laboratory related exercises. In this paper we show that we can treat this hardship as an opportunity, and engage students into creating tools that help their colleagues. In this particular case we present diploma projects that constituted equipment rental apps. We show that such form of diploma increases motivation and leads to nice results. © 2022 IEEE.
ABSTRACT
BACKGROUND: COVID-19 has led to over 500 million cases and 6.2 million deaths around the world. Low- and middle-income countries (LMICs) like Armenia face unique infrastructure, financial, and capacity challenges that in many cases result in worse outcomes. Health care facilities across Armenia experienced a shortage of resources, including hospital beds and oxygen, which was further exacerbated by the war with neighboring Azerbaijan. Without a framework for home-based care, health care facilities were severely strained by COVID-19 patients who had prolonged oxygen requirements but were otherwise clinically stable. OBJECTIVE: This paper describes our approach to establishing an international collaboration to develop a web app to support home monitoring of patients with COVID-19 with persistent oxygen requirements. METHODS: The app was developed using a rapid, coordinated, and collaborative approach involving an international group of clinicians, developers, and collaborators. Health screening, monitoring, and discharge forms were developed into a lightweight OpenMRS web app and customized for the local Armenian context. RESULTS: The software was designed and developed over 2 months using human-centered design and agile sprints. Once live, 5087 patient records were created for 439 unique patients. CONCLUSIONS: This project suggests a promising framework for designing and implementing remote monitoring programs in LMICs, despite pandemic and geopolitical challenges.
ABSTRACT
The early diagnosis and treatment of lung diseases is a very critical procedure and it requires the use of Computed Tomography (CT) imaging for the segmentation of lungs. Segmentation of the lung helps in the analysis of the lesions. The project proposes a CT lung and vessel segmentation model without any labels which is based on medical image processing using Python. This would assist the medical practitioners and scientists who are working in the field of CT intensity segmentation of lungs. It would make the diagnosis process easier and more convenient for patients, especially in pandemic situations like COVID. © 2022 IEEE.
ABSTRACT
Nowadays, more than 2,1 million confirmed cases had been reported in Peru of people infected by COVID-19. According to the Ministry of Health data, an increase in the occupancy of Intensive Care Unit beds is observed. This high number of cases added to the capacity of few hospital beds causes the increase in patients whom medical centres in Peru cannot treat. We present MoniCovid as an application to monitor health parameters periodically and ensure the treatment and stability for patients with suspicions or mild COVID-19 symptoms and who belong to the low socioeconomic levels that represent more than half of the Peruvian population. In addition, it allows physicians to review follow-up reports for each patient and determine if they need emergency care. We use the methodology of Building Progressive Web Apps book to incorporate native functionality into the web application. In this way, MoniCovid becomes a multiplatform capable application that allows it to be acquired by many patients. A case study in Lima showed that MoniCovid application is functional and allows reducing the time of patient monitoring and medical diagnosis by 70.89% and 56.56% respectively. In addition, a user acceptance of 4.74 in usability and 4.71 in user experience. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
In this article, a mobile robot for item delivery with tele-operation capability is developed and used in the field hospital. The user is able to control the robot and communicate with the patients via an web-App on a cloud server. The robots are deployed and tested on-site in a large size field hospital, while the workload of the robot is studied and planned by using simulation approach. However, the difficulties on actual implementation have arise due to the working condition and risk of infection. These take into account in the development and deployment phases of the system. © 2021 IEEE.
ABSTRACT
This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 s even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.
ABSTRACT
Most deaf people use Sign Language (SL) to communicate. This usually requires the presence of an SL interpreter to mediate and decode the communication with a non-deaf person. However, the presence of an SL interpreter to support a deaf person can be very difficult, expensive and not always possible, for example during the COVID-19 pandemic which requires limiting contact between people in presence. This work proposes a Progressive Web Application (PWA), called LISA, as a solution to facilitate communication between a deaf citizen and a non-deaf person, thanks to a remote Sign Language Interpreting Service (SLIS). The LISA prototype is designed to promote the communication of deaf citizens with the Public Administrations (PA). This real-time SLIS can be used flexibly on different types of devices (i.e. mobile and desk). This allows PA operators to easily respond to the needs of deaf citizens. Furthermore, to facilitate written communication and to overcome the difficulties encountered by deaf people in writing text messages, the LISA system integrates a text/SL gateway. The user selects items from a gallery of GIF images that represent simple pre-set phrases and words in SL, and the system can also convert them into text. This improves accessibility by offering a more suitable messaging tool than a text chat for the needs of the target population. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
International R&D activities are strategical to provide solutions that can solve global problems by creating interdisciplinary and multicultural teams. One of the current challenges that international collaboration could solve is related to outbreaks of infectious diseases, which have a devastating impact on society. Influenza in Spain was responsible of 4000 deaths during 2019, despite having a vaccine. Tropical infectious diseases, such as Dengue or Zika, are transmitted through mosquitoes and proliferate in hot and humid conditions. Brazil reported more than 3 million cases and more than a thousand deaths during the dengue fever epidemic that started in 2019. New coronavirus have once again shown the consequences that infectious diseases have both for the society and for the economy, claiming thousands of lives and forcing many countries to take restrictive lockdowns. Preventive measures can mitigate the devastating consequences of outbreaks of infectious diseases. In this sense, infodemiological systems compile data regarding public-health concerns to forecast new outbreaks. In this manuscript we describe the CollaborativeHealth tool and we report three case-studies carried out with this tool: Influenza in Spain, Zika and Dengue in Ecuador, and COVID-2019 in Spanish-speaking countries. © 2021, Springer Nature Switzerland AG.
ABSTRACT
BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has sparked attention in many countries, especially those that have experienced a steep spike in the number of identified cases. The continued spread of the coronavirus suggests that this situation may be here to stay for a while. Contact tracing is a time-consuming and resource-intensive process, which taps on the already lean healthcare resource in certain countries. Furthermore, the massive infodemic on COVID-19 on the Internet has also resulted in widespread circulation of misinformation online. This outbreak has evoked irrational fear and anxiety from the public, which has resulted in destabilizing of societal norms, such as panic buying and hoarding of daily necessities, and can potentially pose serious health risks to the public. The aim of this paper is to present a COVID-19 Symptom Monitoring and Contact Tracking Record (CoV-SCR) web-app as a bottom-up, proactive approach to supplement the current management strategies for COVID-19. MAIN TEXT: The CoV-SCR web-app ( http://bit.ly/covscrapp ) enables individuals to keep a personal record of their close contacts and monitor their symptoms on a daily basis, so that they can provide relevant and accurate details when they see the doctor and during the contact tracing process. Individuals can record their temperature and rate their symptoms on a 5-point severity scale, as well as record details of their travel and contact history for the last 14 days. The recorded information will be sent to their e-mail address for potential symptom monitoring and contact tracing purposes. In addition, this web-app consolidates evidence-based information on the coronavirus from credible sources, such as the World Health Organization, countries' health authorities, and PubMed literature. CONCLUSIONS: A COVID-19 Symptom Monitoring and Contact Tracking web-app has been developed to facilitate contact tracing efforts through public engagement. This app serves an additional purpose of providing information about COVID-19 from reliable resources.