@article{(Open Science Index):https://publications.waset.org/pdf/10013623,
	  title     = {Enhancing Predictive Accuracy in Pharmaceutical Sales Through an Ensemble Kernel Gaussian Process Regression Approach},
	  author    = {Shahin Mirshekari and  Mohammadreza Moradi and  Hossein Jafari and  Mehdi Jafari and  Mohammad Ensaf},
	  country	= {},
	  institution	= {},
	  abstract     = {This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matérn, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matérn, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {18},
	  number    = {5},
	  year      = {2024},
	  pages     = {255 - 260},
	  ee        = {https://publications.waset.org/pdf/10013623},
	  url   	= {https://publications.waset.org/vol/209},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 209, 2024},
	}