Showing papers presented at "International Conference on Computer and Information Science in 2020"

Fighting COVID-19 with Fever Screening, Face Recognition and Tracing

Abstract: Since the outbreak of COVID-19 corona virus in late 2019, it has become a tremendous threat to the whole world. Driven by the mission to save lives, we develop a fever screening and tracing system which can detect patients with fever symptom, and identify the patients using face recognition. In addition, our big data AI platform enables the tracing of the patients possible. A real-time alert sent to the personnel on duty on a web or mobile app activates the action to trace the patient and the close contacts providing an effective means to control the spread of the virus.

An Algorithm Combining the Branch-Cut Method and Rhombus Phase Unwrapping Algorithm

Abstract: In order to solve the problem that the wrapped phase cannot be unwrapped effectively, this paper proposes an algorithm combining the branch-cut method and rhombus phase unwrapping algorithm for unwrap phase. The algorithm combines the advantages of the two methods, at first, the Goldstein's branch-cut method is used to find the residual points in the wrapping phase, then the rhombus phase unwrapping algorithm is used to unwrap the non-residual area phase, finally the phase of the residual area is obtained by using the cubic spline interpolation method, so as to ensure that the phase of each pixel can be unwrapped smoothly. The simulation results verify the feasibility and effectiveness of the algorithm, the combined phase unwrapping algorithm can effectively reduce the line and island phenomenon.

A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

Abstract: With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

The Application of Logistic Model in COVID-19

Abstract: COVID-19 began to break out in China in early 2020, characterized by rapid transmission and a high fatality rate In the face of the outbreak, it is particularly important and urgent to predict the number of infections in each region Even now, some countries are still in the early stages of an outbreak The existing traditional mechanistic models such as SIR have a high demand for data For example, parameters such as exit rate need a large number of data to ensure the accuracy of the model;otherwise, the prediction effect is poor, while the models or algorithms related to artificial intelligence have a higher demand for data Moreover, both of them have a large amount of work to solve, which has poor effect on the prediction of early epidemic prevention and control The problem of epidemic situation prediction in the absence of data urgently needs to be solved After data preprocessing, this paper used the Logistic model (the growth retardation model) to predict the number of infections of China Meanwhile, error analysis and stability analysis were carried out, and the data of Italy were used for checking the universality of the model The results show that the Logistic model is easy to be solved, has good stability and accuracy, and is suitable for early prediction of COVID-19 © Published under licence by IOP Publishing Ltd

Design of a Web-based Personalized E-learning Platform

Abstract: It is important to carry out the distant learning especially in the COVID-19 period when most of schools have to close down Therefore, a Web-based personalized e-learning platform is presented to satisfy the requests of the middle school student Firstly, the features of the learner's needs for e-learning are analysed as the base of the platform Secondly a novel architecture is proposed that describes the components necessary for the distribution of courses and knowledge in Internet Thirdly the design of the platform is enabled by the real time collaboration system, multimedia transmission and knowledge repository to help the middle school student study in their own natural ways such as gaming and blogging with friends from interests The experiment results show that the designed e-learning platform can improve the performance of network throughput and the learner's satisfactory level © Published under licence by IOP Publishing Ltd

A risk assessment system of COVID-19 based on Bayesian inference

Abstract: The novel coronavirus disease (COVID-19) has now spread to most countries in the world Preventing and controlling the risk of the coronavirus disease has rapidly become a major concern A risk assessment system of novel coronavirus disease is proposed based on Bayesian inference in this paper The system includes multiple handheld terminals and a cloud processing centre The handheld terminal measures, records, and uploads the individual's physical information (e g , body temperature, cough) and GPS information of the terminal We establish a Bayesian diagnosis network to deduce the risk probability related to the individual's detection information The cloud obtains the individual's detection information and positions in last 14 days, and estimates the epidemic risk probability using Bayesian inference This probability can be helpful for relevant institutions to judge the individual's risk levels and corresponding measures This risk assessment system, which assesses the COVID-19 risk of subjects dynamically, can not only assist and guide the normalization of epidemic prevention and control in relevant institutions, but also assist in epidemiological case tracing © Published under licence by IOP Publishing Ltd