1-Egrioglu, E., Bas, E. A new deep neural network for forecasting: Deep dendritic artificial neural network. Artif Intell Rev 57, 171 (2024). https://doi.org/10.1007/s10462-024-10790-7
2-Cansu, T., Bas, E., Egrioglu, E. et al. Intuitionistic fuzzy time series forecasting method based on dendrite neuron model and exponential smoothing. Granul. Comput. 9, 49 (2024)
3-Egrioglu, E., Bas, E. & Chen, MY. A fuzzy Gaussian process regression function approach for forecasting problem. Granul. Comput. 9, 47 (2024).
4-Gul, H.H., Egrioglu, E. & Bas, E. A new statistical training algorithm for a single multiplicative neuron model artificial neural network. Granul. Comput. 9, 28 (2024).
5-Chen, C.-C., Hung, P.C.K., Egrioglu, E., Chiu, D.K.W. and Ho, K.K.W. (2024), "Guest editorial: Contemporary learning behaviors on mobile devices and social media – part II", Library Hi Tech, Vol. 42 No. 2, pp. 381-391.
6-Eren Bas, Erol Egrioglu, Turan Cansu,Robust training of median dendritic artificial neural networks for time series forecasting,Expert Systems with Applications, Volume 238, Part C,2024,122080.
7-Karahasan, O., Bas, E. & Egrioglu, E. New deep recurrent hybrid artificial neural network for forecasting seasonal time series. Granul. Comput. 9, 19 (2024).
8-Işık, H., Bas, E., Egrioglu, E. et al. A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis. Stoch Environ Res Risk Assess 38, 4259–4274 (2024).
9-Kolemen, E., Egrioglu, E., Bas, E. et al. A new deep recurrent hybrid artificial neural network of gated recurrent units and simple seasonal exponential smoothing. Granul. Comput. 9, 7 (2024).
10-Bas, E., Egrioglu, E. Robust Picture Fuzzy Regression Functions Approach Based on M-Estimators for the Forecasting Problem. Comput Econ (2024).
11-Eskin, E. N., & Doğru, F. Z. (2024). A heteroscedastic regression model with the generalized normal distribution. Sigma Journal of Engineering and Natural Sciences, 42(5), 1480-1489.
