Artificial Intelligence (AI) is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Logic
Commonsense
Understanding
Learning
Self-awareness
Problem Solving
Planning
Creativity
People
Prof. Dr. Emin Erkan Korkmaz
Artificial Intelligence, Machine Learning, Deep Learning, Robotics
ekorkmaz@cse.yeditepe.edu.tr
+90-216-578-0426
Assist. Prof. Dr. Dionysis Goularas
Computer Vision, Image Processing, Deep Learning
goularas@cse.yeditepe.edu.tr
+90-216-578-0423
R.A. Çağrı Yeşil
Machine Learning, Graph Theory, Deep Learning, Optimization
cyesil@cse.yeditepe.edu.tr
+90-216-578-0747
R.A. Yusuf Can Semerci
Machine Learning, Deep Learning, Image Processing, Robotics
ysemerci@cse.yeditepe.edu.tr
+90-216-578-0747
Publications, Theses and Projects
Publications
| Year | Authors | Title | Info |
|---|---|---|---|
| 2019 | Tuğba Usta, Enes Burak Dündar, Emin Erkan Korkmaz | A Cellular Automata Based Classification Algorithm | In Proceedings of the International Conference on Pattern Recognition Applications and Methods (ICPRAM 2019), 22-24 February 2019, Prague, Czech Republic, Pages:155-162. |
| 2018 | Orhun Uzun, Tuğba Usta, Enes Burak Dündar, Emin Erkan Korkmaz | A solution to the classification problem with cellular automata | Pattern Recognition Letters, Volume: 116, Pages: 114-120, published: December 2018. |
| 2018 | Murat Kalender, M. Tolga Eren, Zonghuan Wu, Ozgun Cirakman, Sezer Kutluk, Gunay Gultekin, Emin Erkan Korkmaz | Videolization: knowledge graph based automated video generation from web content | Multimedia Tools and Applications, Volume: 77, Issue: 1, Pages: 567-595, Published: JAN 2018. |
| 2018 | Murat Kalender, Emin Erkan Korkmaz | THINKER-Entity Linking System for Turkish Language | IEEE Transactions on Knowledge and Data Engineering, Volume: 30, Issue: 2, Pages: 367-380, Published: FEB 1 2018. |
| 2018 | Enes Burak Dündar, Emin Erkan Korkmaz | Data Clustering with Stochastic Cellular Automata | Intelligent Data Analysis, Volume: 22, Issue: 4, Pages: 735-750, Published: June 2018. |
| 2017 | F.Er, D.Goularas, B.Ormeci | A novel Convolutional Neural Network Model Based on Voxel-based Morphometry of Imaging Data in Predicting the Prognosis of Patients with Mild Cognitive Impairment. | Journal of Neurological Sciences (Turkish) 34.1. |
| 2017 | F.Er, P.Iscen, S.Sahin, N.Çinar, S.Karsidag, D.Goularas | Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms | Journal of Clinical Neuroscience. |
| 2017 | Murat Kalender, Emin Erkan Korkmaz | Turkish Entity Discovery with Word Embeddings | Turk J Elec Eng & Comp Sci, 25, (2017), 2388-2398. |
| 2012 | Can Yalkin, Emin Erkan Korkmaz | A Neural Network Based Selection Method for Genetic Algorithms | Neural Network World - International Journal on Neural and Mass-Parallel Computing and Information Systems: Volume 22, Issue 6 (2012), Pages 495-510 |
| 2012 | Cagri Yesil, Hasan Turkyilmaz, Emin Erkan Korkmaz | A New Hybrid Local Search Algorithm on Bin Packing Problem | 12th International Conference on Hybrid Intelligent Systems (HIS 2012), Dec 4-7, 2012, Pune, India. |
| 2012 | Ismail Ugur Bayindir, Engin Mercan, Emin Erkan Korkmaz | A Hybrid Multi-Objective Genetic Algorithm for Bandwidth Multi-Coloring Problem | 12th International Conference on Hybrid Intelligent Systems (HIS 2012), Dec 4-7, 2012, Pune, India. |
| 2011 | Cagri Yesil, Buse Yilmaz, Emin Erkan Korkmaz | Hybrid Local Search Algorithms on Graph Coloring Problem | In 11th International Conference on Hybrid Intelligent Systems (HIS 2011), Dec 5-8, 2011, Malacca, Malaysia. |
| 2010 | Emin Erkan Korkmaz | Multi-Objective Genetic Algorithms for Grouping Problems | Applied Intelligence: Volume 33, Issue 2 (2010), Page 179 |
| 2010 | Buse Yilmaz, Emin Erkan Korkmaz | Representation Issue In Graph Coloring | 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010), Nov 29 – Dec 1, 2010, Cairo, Egypt |
| 2009 | Ender Özcan, Can Basaran | A Case Study of Memetic Algorithms for Constraint Optimization | Soft Comput (2009) 13:871–882. DOI 10.1007/s00500-008-0354-4 |
| 2009 | Ender Özcan, Yuri Bykov, Murat Birben and Edmund Burke | Examination Timetabling Using Late Acceptance Hyper-heuristics | 2009 IEEE Congress on Evolutionary Computation (CEC2009), pp. 997-1004 |
| 2009 | Ö. B. Asik, E. Özcan | Bidirectional Best-fit Approach for Orthogonal Rectangular Strip Packing | Annals of Operations Research, 172:405-427, 2009 |
| 2008 | Ender Özcan, Murat Kalender, Edmund K. Burke | A Greedy Gradient-Simulated Annealing Hyperheuristic | Workshop on Hyperheuristics – Automating the Heuristic Design Process in PPSN X, 2008 |
| 2008 | Ozgur Ülker, Emin Erkan Korkmaz, and Ender Özcan | A Genetic Algorithm Using Linear Linkage Encoding for Bin Packing | 10th International Conference on Parallel Problem Solving from Nature, September 2008 |
| 2008 | E.K. Burke, G. Kendall, M. Misir, E. Özcan | A Study of Simulated Annealing Hyperheuristics | International Conference on the Practice and Theory of Automated Timetabling, 2008. |
| 2008 | E.K. Burke, M. Misir, G. Ochoa, E. Özcan | Learning Heuristic Selection in Hyperheuristics for Examination Timetabling | International Conference on the Practice and Theory of Automated Timetabling, 2008. |
| 2008 | Ender Özcan, Türker Erçal | A Genetic Algorithm for Generating Improvised Music | LNCS 4926, EA 2007, pp. 266 – 277, 2008. |
| 2008 | Ender Özcan, Burak Bilgin, Emin Erkan Korkmaz | A Comprehensive Analysis of Hyper-heuristics | Intelligent Data Analysis, 12:1, pp. 3-23, 2008. |
| 2008 | Ender Özcan, Alpay Alkan | A Memetic Algorithm for Solving a Timetabling Problem: An Incremental Strategy | Proc. of the 3rd Multidisciplinary Int. Conf. On Scheduling: Theory and Applications, P. Bapti |
| 2007 | Ender Özcan, Murat Yilmaz | Particle Swarms for Multimodal Optimization | B. Beliczynski et al. (Eds.): ICANNGA07, Springer-Verlag, Lecture Notes in Computer Science, vol. 4431, 366-375. |
| 2007 | Ersan Ersoy, Ender Özcan, Sima Uyar | Memetic Algorithms and Hyperhill-climbers | Proc. of the 3rd Multidisciplinary Int. Conf. On Scheduling: Theory and Applications, P. Baptiste, G. Kendall, A. M. Kor |
| 2007 | Burak Bilgin, Ender Ozcan, Emin Erkan Korkmaz | An Experimental Study on Hyper-Heuristics and Final Exam Scheduling | The 2006 International Conference on the Practice and Theory of Automated Timetabling, August 2006, Brno/Czech Repulic. Lecture Notes in Computer Science, Volume 3867/2007, pp. 394-412,2007 |
| 2006 | Ozgur Ulker, Ender Ozcan, Emin Erkan Korkmaz | Linear Linkage Encoding in Grouping Problems: Applications on Graph Coloring and Timetabling | The 2006 International Conference on the Practice and Theory of Automated Timetabling, August 2006, Brno/Czech Repulic. Lecture Notes in Computer Science, Volume 3867/2007, pp. 347-363, 2007 |
| 2006 | Emin Erkan Korkmaz | A Two-Level Clustering Method Using Linear Linkage Encoding | 2006 International Conference on Parallel Problem Solving From Nature, Lecture Notes in Computer Science, Volume 4193/2006, Springer-Verlag, 2006, pp. 681 – 690. |
| 2006 | Ender Özcan, Burak Bilgin, Emin Erkan Korkmaz | Hill Climbers and Mutational Heuristics in Hyperheuristics | Lecture Notes in Computer Science, Springer-Verlag, The 9th International Conference on Parallel Problem Solving From Nature, 2006, pp. 202-211 |
| 2006 | Erkan Korkmaz, Reda Alhajj, Ken Barker | Efficient Clustering of High Dimensional Data | Proc. of 6th Industrial Conference on Data Mining ICDM’2006 July 14-15, 2006, Leipzig/Germany. |
| 2006 | Emin Erkan Korkmaz, Jun Du, Reda Alhajj, Ken Barker | Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clustering Intelligent Data Analysis. | May 2006, Vol.10, Number 2, Page(s): 163- 182. |
| 2005 | Jun Du, Erkan Korkmaz, Reda Alhajj, Ken Barker | Alternative Clustering by Utilizing Multi-Objective Genetic Algorithm with Linked-List Based Chromosome Encoding | Lecture Notes in Computer Science, Volume 3587/2005, Springer-Verlag, pp. 346 – 355. |
| 2005 | E. Yavuz, E. Ozcan, E. E. Korkmaz | Türkiye Haritasi Üzerinde Gezgin Satici Probleminin Karinca Sistemleri ile Çözülmesi | The 14th Turkish Symposium on Artificial Intelligence and Neural Networks, pp. 193-200, 16-17 June 2005. |
| 2005 | Janaki Gaplan, Erkan Korkmaz, Reda Alhajj, Ken Barker | Effective Data Mining by Integrating Genetic Algorithm into the Data Preprocessing Phase | Proc. of icmla, pp. 331-336, Fourth International Conference on Machine Learning and Applications (ICMLA05), 2005. |
Theses
| Year | Author | Supervisor | Title | Thesis Type |
|---|---|---|---|---|
| 2016 | Murat Kalender | Emin Erkan Korkmaz | Automated Video Generation from Internet Content Using Semantic Web Technologies | Ph.D. Thesis |
| 2015 | Yusuf Can Semerci | Emin Erkan Korkmaz, Dionysis Goularas | Self-Localization by using Artificial Neural Networks for the Humanoid Robot NAO | M.Sc. Thesis |
| 2014 | Uğur Bayındır | Emin Erkan Korkmaz | A Hybrid Multi-Objective Genetic Algorithm for Bandwidth Multi-Coloring Problem | M.Sc. Thesis |
| 2014 | Cagri Yesil | Emin Erkan Korkmaz | A Novel Meta-Heuristic for Graph and Graph Set-T Coloring Problem | M.Sc. Thesis |
| 2012 | Buse Yılmaz | Emin Erkan Korkmaz | A Novel Meta-Heuristic for Graph Coloring Problem: Simulated Annealing with Backtracking (SABT) | M.Sc. Thesis |
| 2010 | Ahmet Ulak | Emin Erkan Korkmaz | University Timetabling Using Multi-Objective Genetic Algorithms | M.Sc. Thesis |
| 2010 | Turker Ercal | Soft Morphological Filter Optimization Using a Genetic Algorithm for Noise Elimination | M.Sc. Thesis | |
| 2008 | Misir M. | Group Decision Making During Move Acceptance in Hyperheuristics | M.Sc. Thesis | |
| 2006 | Özgür Ülker | Emin Erkan Korkmaz | Linear Linkage Encoding in Genetic Algorithms | M.Sc. Thesis |
| 2006 | Bilgin, B. | Performance Analysis of Hyperheuristics and Their Use With Hill Climbers | M.Sc. Thesis | |
| 2006 | Yilmaz, M. | Particle Swarm Systems for Multimodal Optimization | M.Sc. Thesis |
Projects
| Year | Researchers | Title | Info |
|---|---|---|---|
| September 2017 - August 2019 | Dionysis Goularas, Yusuf Can Semerci | Timeline Travel: An Alternative Tool for Architectural History Learning and Teaching | Erasmus+ Project, 2017-1-TR01-KA203-046818 |
| July 2016 - July 2018 | Emin Erkan Korkmaz | Entity Based Semantic Search Tool for Corporate Companies | TÜBİTAK-TEYDEB, 31507055, Huawei & Yeditepe Üniversitesi |
| September 2014 - September 2015 | Emin Erkan Korkmaz | Videolization Platform for Internet Content | TÜBİTAK-TEYDEB Project, 3140025, Huawei & Yeditepe University |
| January 2014 - December 2014 | Emin Erkan Korkmaz | Vibration Analysis by Using MEMS Sensors | TÜBİTAK-1512 Project, 2130116, Rasteda Elektronics & Yeditepe Üniversitesi |
| 2011 - 2012 | Emin Erkan Korkmaz, Cağrı Yeşil, Uğur Bayındır | Solving Graph Coloring and Graph Set Coloring Problems by Using Multi-Objective Genetic Algorithms and Stochastic Local Algorithms | TUBITAK-1001 Project, 110E187 |
| 2005 - 2008 | Işıl Kurnaz, Cem Ünsalan, Ender Özcan | A Study of Transcriptional Profiling of Gene Experession in Metastatic Tumors Using Integrated Bioinformatics Approach and Drug Target Identification | DPT Project |
| 2005 - 2006 | Ender Özcan, Emin Erkan Korkmaz, Özgür Ülker, Burak Bilgin | Hyper-Heuristics for Scheduling Problems | TUBITAK-1001 Project |
Courses
CSE462
Introduction to Artificial Intelligence
CSE 462 Introduction to Artificial Intelligence
Basic concepts and techniques of machine learning. Supervised learning techniques. Concept and Decision Tree Learning. Bayesian approach in machine learning. Evolutionary approach and genetic programming. Neural Networks, Support Vector Machines and reinforcement learning. Unsupervised machine learning and clustering.
CSE585
Machine Learning
CSE 585 Machine Learning
Basic concepts and techniques of machine learning. Supervised learning techniques. Concept and Decision Tree Learning. Bayesian approach in machine learning. Evolutionary approach and genetic programming. Neural Networks, Support Vector Machines and reinforcement learning. Unsupervised machine learning and clustering.
CSE588
Deep Learning
CSE 588 Deep Learning
The aim of this course is to provide students the knowledege about the basic techniques and methodologies of Deep Learning and abilities to apply Deep Learning methods on practical problems. There will be a term project and the students are expected to present their work at the end of the semester.