JISE


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Journal of Information Science and Engineering, Vol. 39 No. 6, pp. 1383-1401


Discover the New Factor for Dengue Fever Outbreaks and Predicted using Bayes Network-PSO (BN-PSO)


RAVI KUMAR SUGGALA1,+, DR. M. VAMSI KRISHNA2 AND DR. SANGRAM KESHARI SWAIN3
1,+Research Scholar, Department of Computer Science and Engineering
Centurion University of Technology and Management
Paralakhemundi, Odisha, 761200 India
E-mail: ravi.suggala@gmail.com

2Department of Computer Science and Engineering
Aditya Engineering College
East Godavari District, Andhra Pradesh, 533437 India

3Department of Computer Science and Engineering
Centurion University of Technology and Management
Khurda, Odisha, 752050 India


Dengue fever is a mosquito-borne pathological infection that is the nation's most dan-gerous widespread human illness disorder, posing a critical threat to humankind. Moreover, accuracy is a major challenge during dengue epidemic prediction that must be addressed. A few research studies have looked into the factors influencing dengue outbreak prediction. Furthermore, only a tiny fraction of the infected population can be properly predicted using a forecasting approach for dengue infection disorders based solely on meteorological vari-ables. This limitation is caused by a low mosquito population below infection transmission thresholds. Therefore, an Improved Deep Learning Model for Predicting Dengue Outbreaks is proposed, in which novel climatic parameters such as the TempWind factor are evaluated. Then to estimate the dengue risk incidence level, the Bayes network model combined with Particle Swarm Optimization (PSO) is introduced. As a result, the proposed model has pro-ven that using the correct and relevant factor of putting aspects for epidemic forecasting yields improved and accurate findings.


Keywords: dengue outbreak prediction, deep learning, particle swarm optimization (PSO), Bayes network mode, TempWindFactor (TWF)

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