scholarly journals Development of novel hybrid models for the prediction of COVID-19 in Kuwait

2021 ◽  
Author(s):  
Ahmad Al-Dousari ◽  
◽  
Maria Qurban ◽  
Ijaz Hussain ◽  
Maha Al-Hajeri ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Hybrid Approach ◽  
Absolute Error ◽  
The Novel ◽  
Time Data ◽  
Epidemic Size ◽  
First Case ◽  
Real Time Data ◽  
Better Than

The first case of COVID-19 in Kuwait was reported on February 24, 2020. There is a need to develop a prediction model for estimating this epidemic size. In this study, we aimed to develop and compare several prediction models using real-time data of COVID-19 from February 24 to June 12, 2020. We modeled the uncertainty and non-stationary real-time data of COVID-19 cases using a multilayer model with different decomposition techniques. We applied our proposed hybrid methodology to predict COVID-19 cases in Kuwait. We further evaluated the performance of the novel hybrid model with others using mean relative error, mean absolute error, and mean square error. We found that our proposed hybrid approach performed better than others for predicting COVID-19 cases.

scholarly journals Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7242
Author(s):  
Fabio Henrique Pereira ◽  
Francisco Elânio Bezerra ◽  
Diego Oliva ◽  
Gilberto Francisco Martha de Souza ◽  
Ivan Eduardo Chabu ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Forecast Model ◽  
Partial Discharges ◽  
Forecasting Model ◽  
Distributed Data ◽  
Time Data ◽  
Lambda Architecture ◽  
Real Time Data ◽  
Real Time Data Processing

The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors’ data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lambda architecture, which combines real-time and batch processing techniques. The results show that the proposed system enables real-time updates to be made to the forecasting model, allowing partial discharge forecasts to be improved with each update with increasing accuracy.

scholarly journals A hybrid approach to forecast the COVID-19 epidemic trend

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0256971
Author(s):  
Saqib Ali Nawaz ◽  
Jingbing Li ◽  
Uzair Aslam Bhatti ◽  
Sibghat Ullah Bazai ◽  
Asmat Zafar ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Prediction Models ◽  
Hybrid Approach ◽  
Peak Time ◽  
The Novel ◽  
Tuning Parameters ◽  
Epidemic Size ◽  
Proposed Model ◽  
Novel Coronavirus ◽  
Self Protection

Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model’s inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic.

A Wireless Sensors Network System for Local Multipoint Storm Surge Measurements

2018 ◽  
Vol 52 (4) ◽  
pp. 32-41 ◽  
Author(s):  
Sunil Chintalapati ◽  
Chelakara S. Subramanian
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Wireless Sensors ◽  
Prediction Models ◽  
Water Levels ◽  
Time Data ◽  
Network Systems ◽  
Efficient System ◽  
Real Time Data ◽  
Wireless Sensors Network

AbstractReal-time data of storm surge are much needed for developing effective prediction models and nowcasting of impending hazard potential. The crucial aspect of monitoring and transmitting information for extreme weather conditions in near real time is vitally important and would benefit from accurate, robust, and relatively inexpensive wireless sensing network systems. This article presents a detailed overview of the design, operation modes, system performance, and field testing of a prototype wireless sensors network (WSN) system for local multipoint storm surge measurements. Key differentiators for the prototype WSN system when compared to the existing infrastructure for monitoring water levels used by National Oceanic and Atmospheric Administration are (1) real-time data transmission, (2) ultra low cost, and (3) power-efficient system. The WSN system offers reliable field measurement employing single or multiple sensors and with features to upload data to either a local laptop or to the cloud for easy concurrent access to data for users located in any part of the world.

Research of Hybrid Scheduling Based on Network Calculus in Coal Mine Ethernet

2012 ◽  
Vol 546-547 ◽  
pp. 1003-1007
Author(s):  
Xiao Feng Kang ◽  
Kun Hu
Keyword(s):  
Real Time ◽  
Network Calculus ◽  
Ring Networks ◽  
Time Data ◽  
The Real ◽  
Hybrid Scheduling ◽  
Periodic Data ◽  
Real Time Data ◽  
Scheduling Method ◽  
Better Than

According to the characteristics of data in coalmine industrial Ethernet ring networks and in order to guarantee the demand of the real-time data in it, a hybrid scheduling method with non-preemptive priority and EDF scheduling method based on the IEEE802.1p protocol is proposed in the switch. We calculate the maximum transmitting delay of the real-time data using network calculus theory. The result shows that EDF decreases transmission delay for non-periodic data better than FCFS by theoretical computation and simulation.

scholarly journals Maintaining temporal validity of real-time data on non-continuously executing resources

2018 ◽  
Vol 160 ◽  
pp. 07008
Author(s):  
Tian Bai ◽  
Hong Lu ◽  
Juan Yang
Keyword(s):  
Response Time ◽  
Real Time ◽  
Time Data ◽  
Resource Model ◽  
Transaction Scheduling ◽  
Success Ratio ◽  
Real Time Data ◽  
Sensor Transaction ◽  
Time Bounds ◽  
Better Than

This paper studies the problem of temporal validity maintenance on non-continuously-executing resources. Response time bounds for sensor transaction scheduling are derived in the context of the hybrid extended multiprocessor periodic resource model. Then two deadline and period assignment schemes are proposed to maintain the temporal validity of real-time data. The calculation based scheme (DPA-C) uses the response time bounds to compute deadlines and periods. The check based scheme (DPA-A) assigns deadlines and periods directly. It then checks the feasibility of the assignment based on the response time bounds. Experiments are conducted to evaluate the performance of the proposed schemes. The results show that DPA-C performs better than DPA-A in terms of the scheduling success ratio and the imposed update workload.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data
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