ABSTRACT
Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations. © 2013 IEEE.
ABSTRACT
In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of 'Internet plus medical and health' has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected. © 2022 IEEE.
ABSTRACT
In this paper, Covid Explorer model's aim is to provide information on Covid 19 like-how you can prevent the Corona Virus, symptoms of Corona Virus or how to book slots for the Corona Virus. You can add the current data related to Covid 19 like a graph indicated the increase or decrease of death rate from this virus, a report is given to show the death rate, the number of vaccines doses given on a particular day, state wise cases are also shown through Application Programming Interface (API) in Covid Explorer model. The model is analyzing and tracking Corona Virus. As per the data analysis this pandemic creates mental health issues but if a model gave up-to date data of the current scenario then stress can be overcome and society can fight against this pandemic. © 2022 IEEE.
ABSTRACT
The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of our lives. While healthcare workers fight the virus in the front line, we do our part by creating an Electronic Health Records system that tracks state and federal prisoner's data through the countries in order to monitor COVID-19 cases in the U.S. The main objective of our system is to visualize the relationship between current data of deaths per day in both, state and federal prisons in U.S. State. In order to accomplish this process, we combine COVID-19 case and death rates into a single data collection which will show that state prisons have consistently reported greater rates of COVID-19 than federal prisons. The obtained results from the process satisfied our expectations and provide the efficiency of implementing Electronic Health Records Systems. © 2022 IEEE.
ABSTRACT
A new Coronavirus has caused panic wave among the public all over the world. It is being discussed extensively in various news channels and papers each day. The most affected countries are China, Italy, Spain, and USA. In India, more than 5000 cases have been reported and the number is increasing day by day. This paper has undertaken good use of Google Trends to analyse the public interest in COVID-19 outbreak. Google Trends has been used to collect data pertaining to Indian public interest in Corona Virus.Methods: Current data pertaining to public interest in Corona virus is extracted from Google Trends website by entering the search topic: COVID-19 with location set as India. The reported period is 10th March 2020 to 8th April 2020. The second data regarding mental health query of Indians is also extracted from the same tool.Results: As per the Google Trends observed for Indian public interest in COVID-19, the interest started rising from 10th March, 2020 and was gradually moving up till 21stMarch 2020 while number of reported corona cases in India had started emerging and lockdown was enforced on the public movement. The interest in COVID-19 doubled in just a time of one week from 21st march, 2020.Similar trend has been found with Indian mental hearth search queries showing first peak on 13th March, second on 19th and third on 24th March 2020. The last peak which is highest one involves almost triple population than the first peak. Hence Google trends can be used to predict the mental health and sensitivity of the people towards disease. © 2021 IEEE.
ABSTRACT
In the current scenario, the pandemic COVID-19 spread globally starting from the end of 2019, in Wuhan, a city of China. As per the current data taken up to 26th of May 2020, globally there are a huge number of people are affected (Approximately 3 billions) by the pandemic. Though the entire data varies depending upon the several parameters like, population size, congestion of area, climate condition, awareness of peoples etc. we have only analyzes on the data of the country USA. The entire data is partitioned into various categories such as: infected rate, mortality rate. A statistical analysis is prepared to analyze or predict the future strategies of the infected rate as well as the removal (Death/cured) rate. The growth of both the infected and the removed can be predicted with the same observed data taken on daily basis from 15th February 2020. We retrieved these data from an authenticate source provided by Worldometer (http://www.worldometers.info). However, Prophet Forecasting Model (PMF) is used to simulate and discussed for the prediction of the mortality rate, active rate due to pandemic COVID-19. The proposed method is also tested for accuracy of model via cross validation method. © 2021 IEEE.
ABSTRACT
COVID-19 takes an increasing share of everyday life and imposes the need for an exploratory data analysis executed by both, professionals and the general public. The primary focus of this paper is designing and implementing a system for processing the vast amount of case data available to obtain overall statistics for symptoms and rank them in real-time. Processing the current data and providing a mechanism to process new data generated in real-time from diverse and many sources is one of the current challenges. Our solution to tackle the challenge is to execute the processing in a massively parallel way enabled by CUDA along with principles and constructs for efficient parallel programming, which are eminent due to the volume and velocity of data, thus, checking the validity of a research question is it possible to process Covid-19 big data challenges more efficiently with GPU-based parallel constructs. © 2021 IEEE.
ABSTRACT
The article identifies the theoretical aspects of e-commerce as an economic category, the problems of the current state of the e-commerce market related to the COVID-19 pandemic, consumer distrust and less functionality in the choice of clothing units. The aim of the article is the generalization, development and implementation of an expert system that will increase the functionality and efficiency of e- business. Research methods are mathematical apparatus for solving classification problems, regression, discrete methods, artificial neural networks, decision trees and other machine learning algorithms for solving classification problems, statistical methods and tools for data processing and analysis, data visualization tools. The problems of developing an expert system to increase the functionality of e-commerce were solved using R and Python programming languages, data processing and analysis. Practical significance of the research is to identify and use an effective algorithm for emotional analysis of the text, improve the method of collaborative filtering and develop an information system to provide recommendations to customers. As a result, the functionality and efficiency of e-commerce will increase. The obtained research results can be used as a methodological reference by software developers during the design and development of recommendation systems. © 2021 IEEE.
ABSTRACT
A brushless motor and propeller test stand is used to test brushless motors and propellers. This testing instrument is still only available in research laboratories. Students and researchers are unable to use laboratory facilities because of the Covid-19 epidemic, thus students must be able to do tests independently from home. Purchasing this testing instrument would be too expensive for students. It is essential to construct a brushless motor and propeller testing instrument at home using simple components that are easy to get on the marketplace. The design concept reads force data using a loadcell sensor and an HX711 driver, and current and voltage data with an INA 219 sensor. The brushless motor's rotational speed is controlled by a potentiometer. Force, current, voltage, and power are all examples of test results data. A 16x2 LCD is used to show data immediately. Data is also transmitted via a USB connection to a computer device for storage or additional analysis. This study proposes a simple brushless motor and propeller test stand that can measure forces from 0 gf to 1000 gf with an error rate of 0.72 %. The power that can be read ranges from 0 mW to 18960 mW, with a 0.59 % error rate. © 2021 Institute of Physics Publishing. All rights reserved.