| ASSOCIATION RULE MINING WITH MOSTLY ASSOCIATED SEQUENTIAL PATTERNS OM Soysal Expert Systems With Applications 42 (5), 2582–2592, 2015 | 61 | 2015 |
| A model suggestion for the determination of the traffic accident hotspots on the Turkish highway road network: A pilot study S Erdogan, V Ilçi, OM Soysal, A Kormaz Boletim de Ciências Geodésicas 21 (1), 169-188, 2015 | 47 | 2015 |
| A Methodology for Comparing Classification Methods through the Assessment of Model stability and Validity in Variable Selection JAN Shreve, H Schneider, ÖM Soysal Decision Support Systems 52 (1), 247–257, 2011 | 41 | 2011 |
| Zonal statistics to identify hot-regions of traffic accidents ÖM Soysal, H Schneider, A Shrestha, CD Guempel, P Li, H Donepudi, ... Proceedings of the International Conference on Modeling, Simulation and …, 2012 | 21 | 2012 |
| Video mining for facial action unit classification using statistical spatial–temporal feature image and LoG deep convolutional neural network KS Masoud Z. Lifkooee, Ömer M. Soysal Machine Vision and Applications, 2018 | 17* | 2018 |
| A sparse memory allocation data structure for sequential and parallel association rule mining ÖM Soysal, E Gupta, H Donepudi The Journal of Supercomputing 72 (2), 347-370, 2016 | 12 | 2016 |
| Coğrafi Bilgi Sistemleri Destekli Trafik Kaza Kara Nokta Belirleme: Empirik Bayes Örneği MA Dereli, S Erdoğan, Ö Soysal, A Çabuk, M Uysal, İ Tiryakioğlu, ... | 11* | 2015 |
| Comparison of SIFT, Bi-SIFT, and Tri-SIFT and their frequency spectrum analysis K Şekeroğlu, ÖM Soysal Machine Vision and Applications 28, 875-902, 2017 | 10 | 2017 |
| Facial action unit recognition using data mining integrated deep learning ÖM Soysal, S Shirzad, K Sekeroglu 2017 International conference on computational science and computational …, 2017 | 8 | 2017 |
| An automated geo-spatial correction framework for transportation ÖM Soysal, K Şekeroglu, J Dickey Journal of Traffic and Transportation Engineering (English Edition) 6 (2 …, 2019 | 6 | 2019 |
| A New Spectral Feature for Shape Comparison. ÖM Soysal, J Chen, H Schneider IPCV, 23-27, 2008 | 6 | 2008 |
| Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification ÖMS Sekeroglu, Kazim sensors 22 (8949), 2022 | 5 | 2022 |
| Object recognition by spectral feature derived from canonical shape representation ÖM Soysal, J Chen Machine vision and applications 24, 855-868, 2013 | 5 | 2013 |
| Image retrieval using canonical cyclic string representation of polygons OM Soysal, B Gunturk, KL Matthews 2006 International Conference on Image Processing, 1493-1496, 2006 | 5 | 2006 |
| Quantifying brain activity state: EEG analysis of background music in a serious game on attention of children ÖM Soysal, F Kiran, J Chen 2020 4th international symposium on multidisciplinary studies and innovative …, 2020 | 4 | 2020 |
| An image processing tool for efficient feature extraction in computer-aided detection systems OM Soysal, J Chen, H Schneider 2010 IEEE International Conference on Granular Computing, 438-442, 2010 | 4 | 2010 |
| Inductively coupled transmission of neuro‐active signals: Analysis of optimal parameters M Tulgar, ÖM Soysal Medical Physics 30 (1), 79-87, 2003 | 4 | 2003 |
| Integration of Wireless Sensor Networks in Geographical Information Systems: A survey. M Salam, ÖM Soysal, H Schneider MSV, 307-314, 2010 | 3 | 2010 |
| A Hierarchical Decision Engine for Computer Aided Lung Nodule Detection from CT Images. ÖM Soysal, J Chen, S Bujenovic, H Schneider BIOCOMP, 387-393, 2010 | 3 | 2010 |
| Matching Polygons Using Canonical String Representation. ÖM Soysal, J Chen IPCV 7, 407-413, 2007 | 3 | 2007 |
Saffet Erdogan在 harran.edu.tr 的电子邮件经过验证
