Optimizing Broad Learning System Hyper-parameters through Particle Swarm Optimization for Predicting COVID-19 in 184 Countries

Daily PM2.5 Forecasting Using Graph Convolutional Networks Based on Human Migration

Empirical Mode Decomposition Based Deep Neural Networks for AQI Forecasting

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

COVID-19’s Impact on the Box office: Machine Learning and Difference-in-Difference

Detection Capability Prediction Based on Broad Learning System during the COVID-19 Pandemic

Predicting Video Game Sales Based on Machine Learning and Hybrid Feature Selection Method

Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding

Modeling Human Activity With Seasonality Bursty Dynamics

Identifying epidemic spreading dynamics of COVID-19 by pseudocoevolutionary simulated annealing optimizers

Analysis of collective action propagation with multiple recurrences

Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data

General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic Progression in Japan and the United States

Background The coronavirus disease (COVID-19) began to spread in mid-December 2019 from Wuhan, China, to most provinces in China and over 200 other countries through an active travel network. Limited by the ability of the country or city to perform tests, the officially reported number of confirmed cases is expected to be much smaller than the true number of infected cases. Objective This study aims to develop a new susceptible-exposed-infected-confirmed-removed (SEICR) model for predicting the spreading progression of COVID-19 with consideration of intercity travel and the difference between the number of confirmed cases and actual infected cases, and to apply the model to provide a realistic prediction for the United States and Japan under different scenarios of active intervention. Methods The model introduces a new state variable corresponding to the actual number of infected cases, integrates intercity travel data to track the movement of exposed and infected individuals among cities, and allows different levels of active intervention to be considered so that a realistic prediction of the number of infected individuals can be performed. Moreover, the model generates future progression profiles for different levels of intervention by setting the parameters relative to the values found from the data fitting. Results By fitting the model with the data of the COVID-19 infection cases and the intercity travel data for Japan (January 15 to March 20, 2020) and the United States (February 20 to March 20, 2020), model parameters were found and then used to predict the pandemic progression in 47 regions of Japan and 50 states (plus a federal district) in the United States. The model revealed that, as of March 19, 2020, the number of infected individuals in Japan and the United States could be 20-fold and 5-fold as many as the number of confirmed cases, respectively. The results showed that, without tightening the implementation of active intervention, Japan and the United States will see about 6.55% and 18.2% of the population eventually infected, respectively, and with a drastic 10-fold elevated active intervention, the number of people eventually infected can be reduced by up to 95% in Japan and 70% in the United States. Conclusions The new SEICR model has revealed the effectiveness of active intervention for controlling the spread of COVID-19. Stepping up active intervention would be more effective for Japan, and raising the level of public vigilance in maintaining personal hygiene and social distancing is comparatively more important for the United States.

Short-term PM2.5 Forecasting with a Hybrid Model Based on Ensemble GRU Neural Network

E-commerce Sales Forecast Based on Ensemble Learning

Impact of COVID-19 Lockdown on Human Activity and Air Quality in China

Application of big data to cities in the United States

A Study of The Growth of The Amount of Meteorological Observation Sites over 200 Years

Modelling for Dynamic Growth of User Population of Products and Services

Prediction of Daily Precipitation Based on Deep Learning and Broad Learning Techniques

A Novel Algorithm for Estimating Purchase Incentive of the Public Based on Mobile Cloud Computing

Movie box office prediction based on ensemble learning

Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening

Daily Rainfall Data Construction and Application to Weather Prediction

A Model for Growth of Markets of Products or Services Having Hierarchical Dependence

An Empirical Investigation on Movie Industry from 1980 to 2018

A Novel Emergency Healthcare System for Elderly Community in Outdoor Environment

By exploiting the advanced information and communication technologies, the current community healthcare systems provide digital healthcare services. However, the current healthcare framework for senior citizen in outdoor environment faces new challenges. The traditional healthcare systems are not efficient, and they do not comprise user-friendly devices and interfaces suitable for the elderly in outdoor environment. Hence, in this work, we develop an outdoor healthcare system for community senior citizens based on unmanned aerial vehicle (UAV) and Internet of things (IoT). Our system includes physical devices, wireless and wired networks, cloud and data center, and smart terminals. Further, the analysis of the proposed healthcare architecture is presented from the perspective of different layers, and an algorithm on UAV for creating high-speed communication channel and delivering medicine is provided. In addition, the healthcare UAV, related devices, and friendly APPs are designed. The proposed framework is evaluated by comparison with the current healthcare architecture. The emulating and experimental results show that our proposed system can provide a high-quality wireless communication link even at large communication distances, and in real testbed our proposal can reduce the response time by about 20% in comparison with the current methods.

Robust Hybrid Beamforming in Millimeter Wave Relay Networks With Imperfect CSI

A model for collective behaviour propagation: a case study of video game industry

The evolution of keywords co-occurrence network based on MAG dataset, 1800–2017

Impulse Weibull distribution for daily precipitation and climate change in China during 1961–2011