ABSTRACT
The coronavirus outbreak has far-reaching ramifications for civilizations all around the world. People are worried and have a lot of requests. A research department from Covid19 Awareness was our recommendation. We supplemented it with AI-based chatbot models to aid hospitals, patients, medical facilities, and congested areas such as airports. We propose to develop this chatbot to support current scenarios and enable hospitals or governments to achieve more to solve the objective, given the two primary factors that inexpensive and fast production is now necessary. It is an immediate necessity in this epidemic circumstance. We built this bot from the ground up to be open source, so that anybody or any institution can use it to fight Corona, and commercialization is strictly prohibited. This bot isn't for sale;instead, we'd like to devote it to the country to help with current pandemic situations. The design of advanced artificial intelligence is presented in this paper (AI). If patients are exposed to COVID-19, the chatbot assesses the severity of the illness and consults with registered clinicians if the symptoms are severe, evaluating the diagnosis and recommending prompt action. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Now that the COVID-19 pandemic is serious, in order to maintain the quality of life and safety, it is very important to remind people of the outbreak. Because when the SARS pandemic occurred in the past, it was found that the pandemic situation had a certain relationship with the weather. Therefore, we tried to analyze the relationship between the pandemic situation and the weather by analyzing big data information, and we also tried to conclude the possible pandemic situation and climate-related prediction rules through big data. We hope to use the most popular instant messaging software - LINE in Taiwan to assist in the auxiliary reminders of the pandemic. When people use the weather pandemic robot, they can also find the correlation between the weather and the pandemic, and it also helps to remind the public to pay more attention to their own health. © 2022 IEEE.
ABSTRACT
The tried and tested way for effective Knowledge Retrieval is by posting questions and retrieving data from the huge information repository. In the recent past the prevalence of pandemics and the spread of COVID-19, has led people to rigorously question the various forms of epidemiology data available on different sources. In general, the amount of information gathered is proportionate to the questioning patterns by the knowledge seeker. Question answering (QA) system is useful during unexpected situations, especially during a pandemic. In this paper, we have proposed a Knowledge Retrieval Question Answering system (KRQA) for answering the queries of users related to COVID-19. The KRQA system is divided into two modules. The first module consists of preprocessing (tokenization, stemming, bag of words) of the question to produce a word vector. The second module involves building, training, and testing the data repository. Feedforward neural network is used to extract the most relevant answer from a repository of all possible answers. The volume and quality of information about the pandemic scenario around the world are increased at a tremendous rate. Hence our work focuses on effective knowledge retrieval using question and answering approach. Our experimental results are found to give better results based on Percentage closeness, precision, and recall parameters. KRQA has the novelty of retrieving more relevant answers with good quality. © 2022 IEEE.
ABSTRACT
The article discusses the development of an information system, the Main purpose of which is to help identify the symptoms of a new coronavirus infection COVID-19, predict the level of infection and mortality, using artificial intelligence technology, which is used for conducting a user survey and its subsequent analysis. Currently, predicting the rate of spread of COVID-19 is important for managing the situation and making decisions. Hospitals are overloaded and can’t accept all potential carriers, and the category of people who are prone to panic due to the slightest changes in their health status only complicates the work of medical institutions. The use of the developed technology will reduce the number of false calls to hospitals, help to make a forecast of the spread of infection, and also model the social graph of transmission from carriers to healthy people. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.