2022

1-Aydın Temel F., Avcı E., Turan N. G. (2022). Investigation of Copper  (II)  Zinc  (II)  and Lead  (II)  Removal onto Expanded Perlite by Adsorption from the Wastes of Metal Casting Industry  Statistical Modeling and Optimization. Environmental Engineering and Management Journal, 21(5), 757–767. (SCI-E)

2-Avcı E. (2022). Cronbach Alfa Katsayısının Hipotez Testinde Bayesci Metaanalizi: Metodolojik Araştırma. Türkiye Klinikleri J Biostat.,14(3):180-9. DOI: 10.5336/biostatic.2022-88576. Alan indeksli (CIS, EBSCO)

3-Avcı E. (2022). The Difference Between Gender in Terms of Nomophobia in Turkey: A Meta-Analysis. The European Research Journal; 8(1):74-83. doi:10.18621/eurj.865153 (TR-Dizin)

4-Doğru, F. Z., & Arslan, O. (2022). Multivariate skew Laplace normal distribution for modeling skewness and heavy-tailedness in multivariate data sets. Statistics and Its Interface, 15(4), 475-485.

5-Bas, E. 2022,  Robust fuzzy regression functions approaches, Information Sciences, 613, pp. 419–434.

6-Bas, E., Egrioglu, E., Karahasan, O. 2022, A Pi-Sigma artificial neural network based on sine cosine optimization algorithm, Granular Computing, 2022, 7(4), pp. 813–820.

7-Bas, E., Egrioglu, E., Kolemen, E. 2022, A novel intuitionistic fuzzy time series method based on bootstrapped combined pi-sigma artificial neural network, Engineering Applications of Artificial Intelligence, 114, 105030.

8-Egrioglu, E., Baş, E., Chen, M.-Y., 2022, Recurrent dendritic neuron model artificial neural network for time series forecasting, Information Sciences, 2022, 607, pp. 572–584.

9-Bas, E., Egrioglu, E. 2022, A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm, Information Sciences, 2022, 592, pp. 206–214.

10-Yılmaz, O., Bas, E., Egrioglu, E., 2022, The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting, Computational Economics, 59(4), pp. 1699–1711.

11-Chen, M.-Y., Sangaiah, A.K., Chen, T.-H., Lughofer, E.D., Egrioglu, E. 2022Deep Learning for Financial Engineering, Computational Economics, 2022, 59(4), pp. 1277–1281.

12-Bas, E., Egrioglu, E., Kolemen, E. 2022, Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization, Granular Computing, 2022, 7(2), pp. 411–420.

13-Eğrioğlu, E., Fildes, R. 2022, A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting, Computational Economics, 59(4), pp. 1355–1383.

14-Chen, M.-Y., Liao, C.-H., Lughofer, E.D., Egrioglu, E. 2022, Editorial, Library Hi Tech, 2022, 40(1), pp. 1–2.

15-Yolcu, O.C., Egrioglu, E., Bas, E., Yolcu, U. 2022, Multivariate intuitionistic fuzzy inference system for stock market prediction: The cases of Istanbul and Taiwan, Applied Soft Computing, 116, 108363.

16-Egrioglu, E., Bas, E., A new automatic forecasting method based on a new input significancy test of a single multiplicative neuron model artificial neural network, Network: Computation in Neural Systems, 33(1-2), pp. 1–16.

17-Chen, M.-Y., Lughofer, E.D., Egrioglu, E., 2022, Deep learning and intelligent system towards smart manufacturing, Enterprise Information Systems, 2022, 16(2), pp. 189–192.

18- Egrioglu, E., Fildes, R., Baş, E. 2022, Recurrent fuzzy time series functions approaches for forecasting, Granular Computing, 7(1), pp. 163–170.