2026

  1. Egrioglu, E., Bas, E., & Albayrak, G. (2026). Forecasting Nasdaq stock exchange time series using an improved recurrent spiking Pi-Sigma artificial neural network. Scientific Reports, 16(1), Article 18593. https://doi.org/10.1038/s41598-026-49954-6
  2. Egrioglu, E., Bas, E., & Tatlı, S. D. (2026). A robust Sigma–pi neural network trained by crow search algorithm for financial time series forecasting under outlier contamination. Expert Systems with Applications, 330, Article 132996. https://doi.org/10.1016/j.eswa.2026.132996
  3. Egrioglu, E., Bas, E., Yıldırım, A. N., Oguz, U., & Sarı, I. F. (2026). A Sugeno–multiplicative neuron hybrid system trained by differential evolution algorithm for time series forecasting. Scientific Reports, 16(1), Article 21174. https://doi.org/10.1038/s41598-026-51980-3
  4. Gundogdu, O., Egrioglu, E., & Bas, E. (2026). Pythagorean fuzzy time series approach based on hybrid high-order artificial neural network for nonlinear time series forecasting. Scientific Reports, 16(1), Article 27440. https://doi.org/10.1038/s41598-026-58132-7
  5. Isık, H., Egrioglu, E., Bas, E., Akkan, B. E., & Akkan, T. (2026). A novel approach for forecasting algal bloom: Long short-term memory artificial neural network with exponential smoothing feedback optimized by the arithmetic mean algorithm. Scientific Reports, 16(1), Article 27320. https://doi.org/10.1038/s41598-026-57110-3
  6. Kara, M. A., Keskin, A., & Egrioglu, E. (2026). Maximum temperature forecasting with an automatic forecasting method based on deep dendritic artificial neural network (AutoDeepdenT). Journal of Science and Mathematics Letters, 14(1), 114–123. https://doi.org/10.37134/jsml.vol14.1.10.2026
  7. Oğuz, U., Sönmez, M. G., Tok, B., Oltulu, P., Adanur, Ş., Demirtaş, R., Boğa, M. S., Pınarcı, T., Teoman, G., Mungan, S. A., Kazaz, İ. O., Sönmez, G., Akgün, H., Altun, E., Göksoy, İ., Demirelli, E., Öğreden, E., Tok, D. S., Al, S., Baş, E., Eğrioğlu, E., Ateş, M., & Güven, S. (2026). Does margin length predict recurrence after partial nephrectomy, or is presence alone sufficient? Journal of Kidney Cancer, 13(1), 27–32. https://doi.org/10.15586/jkcvhl.v13i1.421
  8. Onder, R. O., Aslan, S., Bekci, T., Tosun, A., & Egrioglu, E. (2026). Machine learning–based integration and comparison of ADC map radiomics with conventional imaging markers for cholesteatoma diagnosis. Neuroradiology. Baskıdaki Makale. https://doi.org/10.1007/s00234-026-04066-w
  9. Yucesoy, E., Egrioglu, E., & Bas, E. (2026). An intuitionistic fuzzy Gaussian process regression function approach for forecasting problem. Computational Economics, 68(1), 157–174. https://doi.org/10.1007/s10614-025-11058-0
  10. Doğru, F. Z., & Arslan, O. (2026). Robust parameter estimation in PLMs with skewed and heavy-tailed error distributions. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 75(2), 211–227. https://doi.org/10.31801/cfsuasmas.1677165
  11. Dalar, A. Z. (2026). A hybrid fuzzy–ensemble method for time series forecasting. Scientific Reports, 16(1), Article 20705. https://doi.org/10.1038/s41598-026-51585-w
  12. Aytekin, F. Y., Takir, S., Gürbüz, P., Ağraz, M., & Aytekin, K. (2026). Results of percutaneous needle culture sampling and synchronous irrigation with Dakins’ solution in Wagner Grade 3 diabetic foot osteomyelitis. Scientific Reports, 16(1), Article 4703. https://doi.org/10.1038/s41598-025-34785-8
  13. Şahin, H., Eğrioğlu, E., & Baş, E. (2026). Determination of optimal extraction conditions of pine honey using a Pi-Sigma artificial neural network–genetic algorithm approach. Journal of Apitherapy and Nature, 9(1), 48–56.
  14. Demirel Tatlı, Ş., & Gül, H. H. (2026). Maximum likelihood estimation of the unit Gompertz distribution using median ranked set sampling. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 15(1), 123–136.
  15. Gül, H. H. (2026). Parameter estimation for the inverted Nadarajah-Haghighi distribution based on median ranked set sampling. Sinop Üniversitesi Fen Bilimleri Dergisi, 11(1), 63–75.
  16. Cimsit, İ., Dalar, A. Z., & Yolcu, U. (2026). Beyond Euclidean: A metric-optimized type-1 fuzzy SVR functions architecture for time series forecasting. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 27(2), 639–672.
  17. Cansu, T., Bas, E., & Egrioglu, E. (2026). Mixed order single variable intuitionistic fuzzy time series forecasting method based on a new artificial neural network and grey wolf optimization algorithm. Scientific Reports, 16(1), Article 15682. https://doi.org/10.1038/s41598-026-45059-2
  18. Ö. Kaymaz, D. Vionanda, F. Z. Dogru, Y. Lee, H. M. Wood & A. Gusnanto (2026). Sparse Logistic Regression on Genomic Data for Prediction of Tumour Pathological Subtype. IEEE Transactions on Computational Biology and Bioinformatics,  23(5), 2334-2345.  doi: 10.1109/TCBBIO.2026.3732668.