ABSTRACT
Digital data objects on viruses have played a pivotal role in the fight against COVID-19, leading to healthcare innovation such as new diagnostics, vaccines, and societal intervention strategies. To effectively achieve this, scientists access viral data from online communities (OCs). The social-interactionist view on generativity, however, has put little emphasis on data. We argue that generativity on data depends on the number of data instances, data timeliness, and completeness of data classes. We integrated and analyzed eight OCs containing SARS-CoV-2 nucleotide sequences to explore how community structures influence generativity, revealing considerable differences between OCs. By assessing provided data classes from user perspectives, we found that generativity was limited in two important ways: When required data classes were either insufficiently collected or not made available by OC providers. Our findings highlight that OC providers control generativity of data objects and provide guidance for scientists selecting OCs for their research. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
At present, one of the effective ways to deal with the widespread of COVID-19 is to control the source of infection. As the gate of population flow in various places, the security inspection department plays a vital role in screening positive patients in the population;To solve the problems of credibility and lack of human resources, this paper establishes a security check framework based on the blockchain and combines machine learning with the blockchain: the blockchain records the abnormal results of COVID-19 nucleic acid detection and the abnormal conditions detected by the security inspection system (such as no mask, high temperature);Use machine learning to realize mask recognition and other functions. The architecture, data flow, and key elements are presented and discussed. The study findings could solve the security problem under the epidemic and provide relevant enlightenment for the effective combination and application of machine learning and blockchain. © 2022 IEEE.
ABSTRACT
In the current uncertain world, data are kept growing bigger. Big data refer to the data flow of huge volume, high velocity, wide variety, and different levels of veracity (e.g., precise data, imprecise/uncertain data). Embedded in these big data are implicit, previously unknown, but valuable information and knowledge. With huge volumes of information and knowledge that can be discovered by techniques like data mining, a challenge is to validate and visualize the data mining results. To validate data for better data aggregation in estimation and prediction and for establishing trustworthy artificial intelligence, the synergy of visualization models and data mining strategies are needed. Hence, in this paper, we present a solution for visualization and visual knowledge discovery from big uncertain data. Our solution aims to discover knowledge in the form of frequently co-occurring patterns from big uncertain data and visualize the discovered knowledge. In particular, the solution shows the upper and lower bounds on frequency of these patterns. Evaluation with real-life Coronavirus disease 2019 (COVID-19) data demonstrates the effectiveness and practicality of our solution in visualization and visual knowledge discovery from big health informatics data collected from the current uncertain world. © 2022 IEEE.
ABSTRACT
Misinformation is always a serious problem for the general public, especially during pandemic. People constantly receive text messages of related coronavirus news and its cures from their smartphones. These health text messages help people update their coronavirus knowledge repeatedly and better manage their health, but some of the messages may mislead people and may even cause a fatal result. This research tries to identify mobile health text misinformation by proposing a self-reconfigurable system, which includes the preprocessing functions (involving lexical analysis, stopword removal, and stemming), a dataflow graph from TensorFlow, and a reconfiguration method for self-improvement. Experiment results show the proposed method significantly improves the accuracy of the mobile health text misinformation detection compared to the one without using self-reconfiguration. However, the results also show the accuracy still has room for improvement. More refinements need to be done before the method could be put into an effective use. © 2022 IEEE.
ABSTRACT
One of the most significant and rapidly developing works in the field of data analysis is information flow management. Within the analysis targeted and stochastic dissemination patterns are studied. The solving of such problems is relevant due to the global growth in the amount of information and its availability for a wide range of users. The paper presents a study of dissemination of information messages in open networks on the example of COVID-19. The study was conducted with the use of visual analytics. Informational messages from the largest world and Russian information services, social networks and instant messengers were used as sources of information. Due to the large amount of information on the topic, the authors proposed a pattern of the wave-like dissemination of information on the example of topic clusters on the connection of COVID-19, hydroxychloroquine and 5G. The developed methods can be scaled up to analyze information events of various topics. © 2021 Copyright for this paper by its authors.