☰

Omer F. Beyca, Ph.D. - Publications

Affiliations: 
2013 Industrial Engineering & Management Oklahoma State University, Stillwater, OK, United States 
Area:
Industrial Engineering, Nanotechnology, Statistics
Tree Info Similar researchers PubMed Report error

12 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2020 Yazici I, Beyca OF, Gurcan OF, Zaim H, Delen D, Zaim S. A comparative analysis of machine learning techniques and fuzzy analytic hierarchy process to determine the tacit knowledge criteria Annals of Operations Research. 1-24. DOI: 10.1007/S10479-020-03697-3  0.32
2019 Beyca OF, Ervural BC, Tatoglu E, Ozuyar PG, Zaim S. Using machine learning tools for forecasting natural gas consumption in the province of Istanbul Energy Economics. 80: 937-949. DOI: 10.1016/J.Eneco.2019.03.006  0.31
2017 Liu JP, Beyca OF, Rao PK, Kong ZJ, Bukkapatnam STS. Dirichlet Process Gaussian Mixture Models for Real-Time Monitoring and Their Application to Chemical Mechanical Planarization Ieee Transactions On Automation Science and Engineering. 14: 208-221. DOI: 10.1109/Tase.2016.2599436  0.723
2016 Beyca OF, Rao PK, Kong Z, Bukkapatnam STS, Komanduri R. Heterogeneous Sensor Data Fusion Approach for Real-time Monitoring in Ultraprecision Machining (UPM) Process Using Non-Parametric Bayesian Clustering and Evidence Theory Ieee Transactions On Automation Science and Engineering. 13: 1033-1044. DOI: 10.1109/Tase.2015.2447454  0.739
2016 Çapraz AG, Özel P, Şevkli M, Beyca ÖF. Fuel Consumption Models Applied to Automobiles Using Real-time Data: A Comparison of Statistical Models Procedia Computer Science. 83: 774-781. DOI: 10.1016/J.Procs.2016.04.166  0.353
2015 Cheng C, Sa-Ngasoongsong A, Beyca O, Le T, Yang H, Kong Z(, Bukkapatnam ST. Time series forecasting for nonlinear and non-stationary processes: a review and comparative study Iie Transactions. 47: 1053-1071. DOI: 10.1080/0740817X.2014.999180  0.682
2015 Rao PK, Beyca OF, Kong Z(, Bukkapatnam STS, Case KE, Komanduri R. A graph-theoretic approach for quantification of surface morphology variation and its application to chemical mechanical planarization process Iie Transactions. 47: 1088-1111. DOI: 10.1080/0740817X.2014.1001927  0.642
2015 Rao P, Bukkapatnam S, Kong Z, Beyca O, Case K, Komanduri R. Quantification of Ultraprecision Surface Morphology using an Algebraic Graph Theoretic Approach Procedia Manufacturing. 1: 12-26. DOI: 10.1016/J.Promfg.2015.09.025  0.698
2014 Rao P, Bukkapatnam S, Beyca O, Kong Z(, Komanduri R. Real-Time Identification of Incipient Surface Morphology Variations in Ultraprecision Machining Process Journal of Manufacturing Science and Engineering. 136. DOI: 10.1115/1.4026210  0.771
2014 Rao PK, Bhushan MB, Bukkapatnam STS, Kong Z, Byalal S, Beyca OF, Fields A, Komanduri R. Process-machine interaction (PMI) modeling and monitoring of chemical mechanical planarization (CMP) process using wireless vibration sensors Ieee Transactions On Semiconductor Manufacturing. 27: 1-15. DOI: 10.1109/Tsm.2013.2293095  0.759
2011 Kong Z, Beyca O, Bukkapatnam ST, Komanduri R. Nonlinear Sequential Bayesian Analysis-Based Decision Making for End-Point Detection of Chemical Mechanical Planarization (CMP) Processes Ieee Transactions On Semiconductor Manufacturing. 24: 523-532. DOI: 10.1109/Tsm.2011.2164100  0.747
2010 Kong Z, Oztekin A, Beyca OF, Phatak U, Bukkapatnam STS, Komanduri R. Process performance prediction for chemical mechanical planarization (CMP) by integration of nonlinear bayesian analysis and statistical modeling Ieee Transactions On Semiconductor Manufacturing. 23: 316-327. DOI: 10.1109/Tsm.2010.2046110  0.74
Show low-probability matches.