1-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
2-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)
3-Chen M.Y., Chiang H.S., Lughofer E., Egrioglu E. (2020) Deep learning: emerging trends, applications and research challenges, Soft Computing (https://doi.org/10.1007/s00500-020-04939-z). (SCI)
4-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)
5-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.
6-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.
7-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.
8-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)
9-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). https://doi.org/10.1007/s00500-019-04432-2.
10-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)
11-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)
12-Avcı, E., (2020) Çapraz Tablolarda Multinomial-Dirichlet Dagılımının Uygulanması: Iki Şehir Ögrencilerinin Internet Kullanım Sıklığının Karşılaştırılması. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2020, 13(1), 190-200, DOI: 10.18185/erzifbed.620533. (TR-Dizin)
13-Genç Kurt, A., Avcı, E.,(2020) Binomial (Oran) Verilerin Meta-Analizi: Türkiye’deki Internet Bağımlılık Oranının Belirlenmesi. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2020, 13(3), 1050-1068, DOI: 10.18185/erzifbed.712013. (TR-Dizin)
14-Genç S., Uğur M., Uzunoğlu Karagöz E.,Avcı E. (2020) Giresun İli Hepatit C Hastalarında Genotip Dağılımının Araştırılması. Flora 2020;25(4):549-554, DOI: 10.5578/flora.69198. (E-SCI,TR-Dizin)
15-Yaman, I., & Dalkılıç, T. E., (2020), A STUDY ON PORTFOLIO OPTIMIZATION BASED ON FUZZY INFERENCE SYSTEM, ISPEC Publishing House/ ISBN: 978-625-7720-06-9.
