2021 :
  • Kocak, C; Egrioglu, EBas, E, A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory, JOURNAL OF SUPERCOMPUTING, Early Access, 10.1007/s11227-020-03503-8. (SCI)
  • Egrioglu, E; Fildes, R, A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting, COMPUTATIONAL ECONOMICS, Early Access, 10.1007/s10614-020-10073-7. (SCI)
  • Bas, EEgrioglu, E; Yolcu, U, A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks, JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, Early Access, 10.1007/s12652-020-01950-y. (SCI)
  • Yolcu, U; Egrioglu, EBas, E; Yolcu, OC; Dalar, AZ, Probabilistic forecasting, linearity and nonlinearity hypothesis tests with bootstrapped linear and nonlinear artificial neural network, JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, Early Access, 10.1080/0952813X.2019.1595167. (SCI)
  • Yilmaz O. Bas E.Egrioglu E., The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting, Computational Economics, Early Access, (SCI)
  • Egrioglu, E; Fildes, R, Bas E., Recurrent fuzzy time series functions approaches for forecasting, Granular Computing, Early Access,
  • Bas E., Yolcu U., Egrioglu E., Intuitionistic fuzzy time series functions approach for time series forecasting, Granular Computing, Early Access,
  • Tak N., Egrioglu E.Bas E., Yolcu U., An adaptive forecast combination approach based on meta intuitionistic fuzzy functions, DOI: 10.3233/JIFS-202021, Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021.
  • Egrioglu E., Fildes R.,  A Note on the Robustness of Performance of Methods and Rankings for M4 Competition, Turkish Journal of Forecasting,  2020, Volume 04 , Issue 2, Pages 26 - 32
  • Chen M.Y., Chiang H.S., Lughofer E., Egrioglu E. (2020) Informetrics on Social Network Mining: Research, Policy and Practice challenges, Library Hi Tech Journal. (DOI: 10.1108/LHT-05-2020-0123). (SCI)
  • Chen M.Y., Chiang H.S., Lughofer E., Egrioglu E. (2020) Deep learning: emerging trends, applications and research challenges, Soft Computing ( (SCI)
  • Corba, B.S., Egrioglu, E.Dalar, A.Z., 2020, AR–ARCH Type Artificial Neural Network for Forecasting, Neural Processing Letters, 51, 819-836, doi: 10.1007/s11063-019-10117-6. (SCI-E)
  • Kizilaslan, B., Egrioglu, E., Evren, A.A. (2020). Intuitionistic fuzzy ridge regression functions, Communications in Statistics: Simulation and Computation, DOI: 10.1080/03610918.2019.1626887.
  • Bas E., Yolcu U., Egrioglu E., (2020). Picture Fuzzy Regression Functions Approach for Financial Time Series based on Ridge Regression and Genetic Algorithm, Journal of Computational and Applied Mathematics, Vol 370, 15 May 2020, 112656.
  • Egrioglu E.Bas E., Yolcu U., Chen M.Y., (2020). Picture Fuzzy Time Series: Defining, Modeling and Creating a New Forecasting Method, Engineering Applications of Artificial Intelligence, Volume 88, February 2020, 103367.
  • Kocak C., Dalar A.Z., Yolcu O.C., Bas E.Egrioglu E., (2020). A New Fuzzy Time Series Method Based on an ARMA Type Recurrent Pi-Sigma Artificial Neural Network, Soft Computing, 24 (11), 8243-8252, DOI: 10.1007/s00500-019-04506-1. (SCI-E)
  • Cagcag Yolcu, O., Bas, E.Egrioglu, E. et al. A new intuitionistic fuzzy functions approach based on hesitation margin for time-series prediction. Soft Comput 24, 8211–8222 (2020).
  • Sengul I., Sengul D., Egrioglu E., Ozturk T., Laterality of the thyroid nodules, anatomic and sonographic, as an estimator of thyroid malignancy and its neoplastic nature by comparing the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) and histopathology, JBUON 2020; 25(2): 1116-1121. (SCIE)
  • Sengul D., Sengul I., Egrioglu E., et al. (2020) Can cut-off points of 10 and 15 mm of thyroid nodule predict malignancy on the basis of three diagnostic tools: i) strain elastography, ii) the Bethesda System for Reporting Thyroid Cytopathology with 27-gauge fine-needle, and iii) histopathology?, JBUON 2020; 25(2): 1122-1129. (SCIE)
  • Erol EgriogluEren Bas, Ozge Cagcag Yolcu, Ufuk Yolcu, 2019, Intuitionistic time series fuzzy inference system, Engineering Applications of Artificial Intelligence 82, 175-183. (SCI)
  • Erol Eğrioğlu, Ufuk Yolcu, Eren BasAli Zafer Dalar, 2019, Median-Pi Artificial Neural Network for Forecasting, 31(1), 307-316, Neural computing & Applications. (SCI)
  • Egrioglu E., Yolcu U., Bas E., 2019, Intuitionistic high order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony, Granular Computing, 4,4,639-654.
  • Bas E.Egrioglu E., Yolcu U., Grosan C., 2019, Type 1 fuzzy function approach based on ridge regression for forecasting, Granular Computing, 4, 4, 629-637.
  • Ufuk Yolcu, Eren BasErol Egrioglu, A new fuzzy inference system for time series forecasting and obtaining the probabilistic forecasts via subsampling block bootstrap, Journal of Intelligent and Fuzzy System, 35(2), 2349-2358. (SCIE)
  • Sarica B., Egrioglu E., Asikgil B., A New Hybrid Method for Time Series Forecasting: AR-ANFIS,Neural computing & Applications, 29(3), 749-760., (SCIE)
  • Eren Bas, Crina Grosan, Erol Egrioglu, Ufuk Yolcu, High order fuzzy time series method based on pi sigma neural network, Journal Engineering Applications of Artificial Intelligence,72, 350-356., (SCI)
  • Nihat Tak, A. Atıf Evren, Müjgan Tez, Erol Eğrioğlu, Recurrent Type-1 Fuzzy Functions Approach for Time Series Forecasting, Applied Intelligence,48, 68-77., (SCI)
  • Ozge Cagcag Yolcu, Eren BasErol Egrioglu, Ufuk Yolcu, Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling, Neural Processing Letters, 47(3), pp. 1133-1147. (SCIE)
  • Akdeniz E., Egrioglu E.Bas E., Yolcu U., An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting, Journal of Artificial Intelligence and Soft Computing Research, 8(2),121-132. (ESCI).
  • Bas E.Egrioglu E., Uslu V.R., (2017) Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression, Peertechz J Comput Sci Eng 2(1): 012-020.
  • Eygi Erdoğan B., Egrioglu E., Akdeniz E. (2017), Support Vector Machines vs Multiplicative Neuron Model Neural Network in Prediction of Bank Failures. American Journal of Intelligent Systems, 7(5), 125-131.
  • Inan D., Egrioglu E., Sarica B., Askın Ö.E., Tez M., 2017, Particle Swarm Optimization Based Liu-Type Estimator, Communication in Statistics: Theory and Methods, 2017, Vol.46, Issue 22, 11358-11369. (SCI)