All Publications

S. Panigrahi and H. S. Behera,"A computationally efficient method for high order fuzzy time series forecasting", Journal of Theoretical and Applied Information Technology, vol.96, no.21, pp.7215-7226, Little Lion Scientific, November 2018, View Details       Article

S. Panigrahi and H. Behera,"A hybrid ETS–ANN model for time series forecasting", Engineering Applications of Artificial Intelligence, vol.66, pp.49-59, Elsevier 2017, 10.1016/j.engappai.2017.07.007       Article

S. Panigrahi,"A novel hybrid chemical reaction optimization algorithm with adaptive differential evolution mutation strategies for higher order neural network training", International Arab Journal of Information Technology, vol.14, no.1, pp.18-25, Zarqa University 2017       Article

S. Panigrahi, H., and A. Abraham,"A Fuzzy Filter Based Hybrid ARIMA-ANN Model For Time Series Forecasting", in Proceedings Of The Eighth International Conference On Soft Computing And Pattern Recognition (SoCPaR 2016), pp.592–601, Springer 2017, 10.1007/978-3-319-60618-7_58       Inproceedings

S. Panigrahi, B. Rath, and P. S. Kumar,"A hybrid CRO-K-means algorithm for data clustering", in Computational Intelligence in Data Mining, vol.3, pp.627-639, Springer 2014, 10.1007/978-81-322-2202-6_57       Inproceedings

Y. Karali, S. Panigrahi, and H. S. Behera,"A novel differential evolution based algorithm for higher order neural network training", Journal of Theoretical and Applied Information Technology, vol.56, no.2, pp.355-361, Little Lion Scientific, March 2013, View Details       Article

K. K. Sahu, S. Panigrahi, and H. S. Behera,"A novel chemical reaction optimization algorithm for higher order neural network training", Journal of Theoretical and Applied Information Technology, vol.53, no.3, pp.402-409, Little Lion Scientific, July 2013, View Details       Article