Machine Learning
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Firefly Algorithm for Continuous Constrained Optimization Tasks
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Maximal Discrepancy for Support Vector Machines
Neurocomputing
Multitask multiclass support vector machines: Model and experiments
Pattern Recognition
Multitask twin support vector machines
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
| Hi-index | 0.01 |
Accurate and reliable forecasts of malarial incidences are necessary for the health authorities to ensure the appropriate action for the control of the outbreak. In this study, a novel method based on coupling the Firefly Algorithm (FFA) and Support Vector Machines (SVM) has been proposed to forecast the malaria incidences. The performance of SVM models depends upon the appropriate choice of SVM parameters. In this study FFA has been employed for determining the parameters of SVM. The proposed SVM-FFA model has been adopted in predicting the malarial incidences in Jodhpur and Bikaner area where the malaria transmission is unstable. Monthly averages of rainfall, temperature, relative humidity and malarial incidences have been considered as input variables. Time series of monthly notifications of malaria cases has been obtained from primary health centers and from other local health facilities for a period of January 1998 to December 2002 in the region of Bikaner and from January 1998 to December 2000 in Jodhpur region. Further, the rainfall, relative humidity and temperature data have been obtained from meteorological records. The performance of the proposed SVM-FFA model has been compared with Artificial Neural Networks (ANN), Auto-Regressive Moving Average method and also with Support Vector Machine. The results indicate that the proposed SVM-FFA model provides more accurate forecasts compared to the other traditional techniques. Further, it has been recommended to carry out additional strides to explore the utility and efficacy of SVM-FFA model. Thus SVM-FFA can be an alternate tool to facilitate the control of vector borne diseases like malaria.