Recent Publications

• Sakar, C.O., Kursun, O., "Discriminative Feature Extraction by a Neural Implementation of Canonical Correlation Analysis," IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2015.2504724, 2016.


• Aytekin, T., Karakaya, M. O., "Clustering-Based Diversity Improvement in Top-N Recommendation," Journal of Intelligent Information Systems, 42, pp. 1-18, 2014.


• Sakar, C.O., Kursun, O., Seker, H., Gurgen, F., Aydin, N., Favorov, O., "Combining Multiple Views: Case Studies on Protein and Arrhythmia Features", Engineering Applications of Artificial Intelligence, vol. 28, pp. 174–180, 2014.


• Sakar, C.O., Kursun, O., Seker, H., Gurgen, F., "Combining Multiple Clusterings for Protein Structure Prediction", International Journal of Data Mining and Bioinformatics, vol. 10(2), pp. 162- 174, 2014.


• Sakar, C.O., Kursun, O., Gurgen, F., "Ensemble Canonical Correlation Analysis", Applied Intelligence, vol. 40(2), pp. 291-304, 2014.


• Z. Kurt, N. Aydin, G. Altay, "A Comprehensive Comparison of Association Estimators for Gene Network Inference Algorithms", Bioinformatics, 2014.


• G. Altay, Z. Kurt, M. Dehmer, F. Emmert Streib, "Netmes: Assessing gene network inference algorithms by ensemble network-based measures", Evolutionary Bioinformatics, 2014:10 1-9.


• Erdogdu Sakar, B., Isenkul, M., Sakar, C.O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., Kursun, O., "Collection and analysis of a parkinson speech dataset with multiple types of sound recordings", IEEE Journal of Biomedical and Health Informatics, vol. 17(4), pp. 828-834, 2013.


• Serbes, G., Sakar, C.O., Kahya, Y., Aydin, N., "Pulmonary crackle detection using time–frequency and time–scale analysis", Digital Signal Processing, vol. 23(3), pp. 1012-1021, 2013.


• G. Altay, N. Altay, D.E. Neal,"Global assessment of network inference algorithms based on available literature of gene/protein interactions", Turk J Biol, 37:547-555, 2013.


• Sakar, C.O., Kursun, O., Gurgen F., “A Feature Selection Method Based on Kernel Canonical Correlation Analysis and the Minimum Redundancy Maximum Relevance Filter Method”, Expert Sytems with Applications, vol. 39(3), pp. 3333-3344, 2012.


• Sakar, C.O., Kursun, O., “A Method for Combining Mutual Information and Canonical Correlation Analysis: Predictive Mutual Information and Its Use in Feature Selection”, Expert Sytems with Applications, vol. 39(3), pp. 3432-3437, 2012.


• G. Altay, Empirically determining the sample size for large-scale gene network inference algorithms, IET Systems Biology, 6(2). p.35-63, 2012.


• Emmert-Streib F, Glazko GV, Altay G and de Matos Simoes R, Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Front. Gene. 3:8, 2012.


• Ozogur-Akyuz, S., Unay, D., Smola, A., Special Issue, “Guest Editorial: model selection and optimization in machine learning”, Machine Learning, 85 (1-2), 1-2, 2011.


• G. Üstünkar, S. Özögür-Akyüz, G.-W. Weber, Y. Aydin Son, C. M, Friedrich, Selection of to Representative SNP Sets for Genome-Wide Association Studies: A Metaheuristic Approach, Online First DOI: 10.1007/s11590-011-0419-7, Optimization Letters, 2011.


• Karahoca,D., Karahoca,A., Yavuz,Ö. An early warning system approach for the identification of currency crises with data mining techniques, Neural Computing & Applications, Springer, DOI: 10.1007/s00521-012-1206-9


• Ucar,T., Karahoca,A., Karahoca,D. Tuberculosis Disease Diagnosis by using Adaptive Neuro Fuzzy Inference System and Rough Sets, Neural Computing and Applications, Springer, DOI:10.1007/s00521-012- 0942-1


• Karahoca, A., Tunga, M.A., Dosage planning for type 2 diabetes mellitus patients using Indexing HDMR , Journal of Expert Systems with Applications, 39(8), 2012, 7207-7215


• M. Georgiopoulos, C. Li and T. Kocak “Learning in the feed-forward Random Neural Network: A Critical Review”, Performance Evaluation, accepted for publication, 2010.


• T. Kocak, “Two decades of random neural networks”, Computer Journal, vol. 53, no. 3, pp. 249-250, 2010