Year 2019, Volume 22 , Issue 2, Pages 405 - 419 2019-11-30

Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi
Investigation of Machine Learning Applications in Commercial Air Transportation Industry

Eyüp Bayram ŞEKERLİ [1]


Karmaşıklığın oldukça fazla olduğu, dinamik bir çevrede operasyonlarını sürdüren havayolu işletmelerinin birçok kısıta rağmen doğru karar almaları oldukça önemlidir. Bugün çok çeşitli veri ve büyük miktarda veri üreten havayolu işletmelerinin bu verileri en doğru şekilde değerlendirebilme becerileri kararlarının etkinlik derecesini belirleyebilecektir. Bu nedenle, bu çalışma kapsamında Yapay Zeka (YZ) uygulaması olan Makine Öğrenmesinin (MÖ) havayolu işletmelerinin hangi süreçlerinde, hangi algoritmalar ile kullanılabileceği alanyazında yer alan çalışmalar incelenerek tespit edilmeye çalışılmıştır. Elde edilen sonuçlar, özellikle son yıllarda MÖ’nün “dispeç güvenilirliği”, “uçuş emniyeti”, “gelir yönetimi/fiyatlama” ve “müşteri davranışları” konularına uygulanmasında bir artış olduğunu ortaya koymaktadır.azarlarına aittir.

It is very important that the airlines that carry out operations in a dynamic and complex environment struggle to make the right decision despite many limitations. Today, a wide range of data and a large amount of data generated by airline companies and their ability of data evaluation will determine the effectiveness of the decisions. For this reason, in this study, it has been tried to determine the applications of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in airline processes of by examining previous literature. The results show that there has been an increase in the application ML algorithms in “dispatch reliability”, “flight safety”, “yield management/pricing” and “customer behavior” issues especially in recent years.

  • Abhijit, G. (2004). A Reinforcement Learning Algorithm Based on Policy Iteration for Average Reward: Empirical Results with Yield Management and Convergence Analysis. Machine Learning, 55(1), 5-29.
  • Achenbach, A., & Spinler, S. (2018). Prescriptive Analytics in Airline Operations: Arrival time prediction and cost index optimization for short-haul flights. Operations Research Perspectives, 5, 265-279.
  • Al-Tabbakh, S. M., Mohamed, H. M., & El-Zahed, H. (2018). Machine Learning Techniques For Analysis Of Egyptian Flight Delay. International Journal of Data Mining & Knowledge Management Process (IJDKP), 8(3), 1-14.
  • Amnur, H. (2017). Customer Relationship Management and Machine Learning. International Journal of Informatics Visualization, 1(1), 12-15.
  • Andronie, M. (2015). Airline Applications of Business Intelligence Systems. INCAS, 7(3), 153-160.
  • Aoun, O., Sarhani, M., & El Afia, A. (2016). Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems. IFAC PapersOnLine., 49, 347-352.
  • Atalay, M., & Çelik, E. (2017). Büyük Veri Analizinde Yapay Zekâ ve Makine Öğrenmesi Uygulamaları. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155-172.
  • Aytug, H., Bhattacharyya, S., Koehler, G., & Snowdon, J. (1994). A review of machine learning in scheduling. IEEE Transactions on Engineering Management, 41(2), 165-171.
  • Azoff, M. (2015). Machine Learning in Business Use Cases:Artificial intelligence solutions that can be applied today. London: OVUM.
  • Badea, L. M. (2014). Predicting Consumer Behavior with Artificial Neural Networks. Procedia Economics and Finance, 15, 238-246.
  • Bai, Y., Sun, Z., Deng, J., Li, L., Long, J., & Li, C. (2018). Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study,. Sustainability, 10(1), 1-15.
  • Balakrishna, P., Ganesan, R., & Sherry, L. A. (2010). Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-study of Tampa Bay departures. Transportation Research: Part C., 18(6), Balakrishna P, Ganesan R, Sherry L. Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-s950-962.
  • Barnhart, C., & Cohn., A. (2004). Airline Schedule Planning: Accomplishments and Opportunities. Manufacturing and Service Operations Management, 6(1), 3-22.
  • Barnhart, C., Farahat, A., & Lohatepanont, M. (2009). Airline Fleet Assignment with Enhanced Revenue Modeling. Barnhart, C.,
  • Farahat, A., & Lohatepanont, M. (2009). AiOperations Research, 57(1), 231–244.
  • Bartosz, B., Dariusz, M., & Krzysztof, A. C. (2018). A Machine Learning Approach to the Detection of Pilot’s Reaction to Unexpected Events Based on EEG Signals. Computational Intelligence and Neuroscience, 2, 1-9.
  • Bayoumi, A. E.-M., Saleh, M., Atiya, A., & Aziz, H. A. (2013). Dynamic Pricing for Hotel Revenue Management Using Price Multipliers. Journal of Revenue and Pricing Management, 12(3), 271-285.Belkin, V. A. (2017). On the Issue of Aircraft Maitenance Process Optimization on the Criterion of Minimum Fuel Consumption. Naučnyj Vestnik MGTU GA, 20(1), 61-68.
  • Belobaba, P. (2016). Airline Operating Costs and Measures of Productivity. P. Belobaba, A. Odoni, & C. Barnhart içinde, The Global Airline Industry (s. 146-158). Noida, India: John Wiley & Sons.
  • Belobaba, P. P. (1987). Airline Yield Management. An Overview of Seat Inventory Control. Transportation Science, 21(2), 63-73.
  • Belobaba, P. P., & Farkas, A. (1999). Yield management impacts on airline spill estimation. Transportation Science, 33(2), 217–232.
  • Belobaba, P., Odoni, A., & Barnhart, C. (2009). The Global Airline Industry. Wiltshire: John Wiley & Sons.
  • Bhatnagar, R. (2018). Machine Learning and Big Data Processing: A Technological Perspective and Review. M. F. Aboul Ella Hassanien içinde, The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (s. 468–478). Cham: Springer.
  • Boelaert, J., & Ollion, É. (2018). The Great Regression. Revue française de sociologie, 3(59), 216-246.
  • Bramer, L. M., Chatterjee, S., Holmes, A. E., Robinson, S. M., Bradley, S. F., & Webb-Robertson, B.-J. M. (2015). A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data. The 11th International Conference on Data Mining (DMIN 2015) (s. 162-167). Las Vegas, Nevada: Pacific Northwest National Lab. .
  • Buchanan, B. (2005). A (very) Brief History of Artificial Intelligence. AI Magazine, Winter, 53-60.
  • Bude, G., Hoogenboom, L., Kastrop, W., Reniers, N., & Frasincar, F. (2018). Predicting User Flight Preferences in an Airline E-Shop. Web Engineering, 2018, 245-260.
  • Caetano, D. J., Dionisio, N., & Gualda, F. (2017). Daniel JorgAn exact model for airline flight network optimization based on transport momentum and aircraft load factor. Daniel Jorge Caetano, Nicolau Dionisio Fares Gualda. An exact model for airline flight network optimizatioTransportes, 25(4), 14-26.
  • Chiu, C., Chiu, N. H., & Hsu, C. I. (2004). Intelligent aircraft maintenance support system using genetic algorithms and case-based reasoning. International Journal of Advanced Manufacturing Technology, 24(5), Chiu, C & Chiu, N.-H & Hsu, C.-I. (2004). Intelligent aircraft maintenance support system using genetic algorithms and case-ba440-446.
  • Cioca, M., Ghete, A. I., Cioca, L.-I., & Gîfu, D. (2013). Machine Learning and Creative Methods Used to Classify Customers in a CRM Systems. Applied Mechanics and Materials, 317, 769-773.
  • Collins, A., & Thomas, L. (2012). Comparing reinforcement learning approaches for solving game theoretic models: a dynamic airline pricing game example. A Collins, L Thomas. Comparing reinforcement learning approaches for solving game theoretic models: aThe Journal of the Operational Research Society, 63(8), 1165-1173.
  • Collins, A., & Thomas, L. (2013). Learning competitive dynamic airline pricing under different customer models. Journal of Revenue & Pricing Management, 12(5), 416-430.
  • Coughlan, J. (1999). Airline overbooking in the multi-class case. Journal of the Operational Research Society, 50(11), 1098-1103.
  • Dasgupta, A., & Nath, A. (2016). Classification of Machine Learning Algorithms. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 3(3), 6-11.
  • Dattaram, B. A., & Madhusudanan, N. (2016). Delay Prediction of Aircrafts Based on Health Monitoring Data. International Journal of Business Analytics & Intelligence, 4(1), Dattaram BA, Madhusudanan N. Delay Prediction of Aircrafts Based on Health Monito29-37.
  • Delahaye, T., Acuna-Agost, R., Bondoux, N., Nguyen, A., & Boudia, M. (2017). Data-driven models for itinerary preferences of air travelers and application for dynamic pricing optimization. Journal of Revenue and Pricing Management, 16(6), 621-639.
  • Deloiitte. (2017). Business impacts of machine learning. Sydney: Deloitte.
  • Dullaghan, C., & Rozaki, E. (2017). Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers. International Journal of Data Mining & Knowledge Management Process, 7(1), 13-24.
  • Emtiya, S., & Keyvanpour, M. R. (2011). Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management. Advances in Information Sciences and Service Sciences, 3(9), 229-236.
  • Escoba, C. A., & Morales-Menendez, R. (2018). Machine learning techniques for quality control in high conformance manufacturing environment. Advances in Mechanical Engineering, 10(2), 1-16.
  • Ferreira, K. J., Simchi-Levi, D., & Wang, H. (2018.). Online Network Revenue Management Using Thompson Sampling. Ferreira, Kris
  • Johnson, David Simchi-Levi, and He Wang. 2018. “Online NetOperations Research, 50(6), 1586-1602.Financial Stability Board. (2017). Artificial intelligence and machine learning in financial services:Market developments and financial stability implications. Basel: Financial Stability Board.
  • Finlay, S. (2017). Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies. Lancashire: Relativistic Books.
  • Florez-Lopez, R., & Ramon-Jeronimo, J. M. (2009). Marketing Segmentation Through Machine Learning Models: An Approach Based on Customer Relationship Management and Customer Profitability Accounting. Social Science Computer Review, 27(1), 96–117.
  • Gan, X.-S., Yang, C., & Duanmu, J.-S. (2014). Information-Applied Technology in Neural Network Prediction Model of Aviation Unsafe Event Based on PSO Algorithm with Gradient Acceleration. Advanced Materials Research, 952, 303-306.
  • Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn:Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol, CA: O’Reilly Media.
  • Gittinger, J. M., Suknot, A. N., Jimenez, E. S., Spaulding, T. W., & Wenrich, S. A. (2018). Passenger baggage object database. AIP Conference Proceedings, 1949, 1-6.
  • Gosavi, A. (2004). A Reinforcement Learning Algorithm Based on Policy Iteration for Average Reward: Empirical Results with Yield Management and Convergence Analysis. Machine Learning, 55(1), 5-29.
  • Gosavi, A., Bandla, N., & Das, T. K. (2002). A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking. IIE Transactions, 34(9), 729–742.
  • Grau, M. M., Tajtakova, M., Tajtakova, M., & D.Arias-Aranda. (2009). Machine learning methods for the market segmentation of the performing arts audiences. International Journal of Business Environment, 2(3), 356 - 375.
  • Gyulai, D., Kádár, B., & Monostori, L. (2014). Capacity Planning and Resource Allocation in Assembly Systems Consisting of Dedicated and Reconfigurable Lines. Procedia CIRP, 25, 185-191.
  • Homaie-Shandizi, A.-H., Nia, V. P., Gamache, M., & Agard, B. (2016). Flight deck crew reserve: From data to forecasting. Engineering Applications of Artificial Intelligence, 50, 106-114.
  • Hurwitz, J., & Kirsch, D. (2018). Machine Learning. Hoboken, N: John Wiley & Sons.
  • Kamthania, D., Pahwa, A., & Madhavan, S. S. (2018). Market Segmentation Analysis and Visualization Using K-Mode Clustering Algorithm for E-Commerce Business. Journal of Computing and Information Technology, 26(1), 57-68.
  • Khoo, H. L., & Teoh, L. E. (2014). An optimal aircraft fleet management decision model under uncertainty. Khoo, H. L., & Teoh, L. E. (2014). An optimal aircraft fleJournal of Advanced Transportation, 48(7), 798–820.
  • Kocak, B. B., & Polat, I. K. (2016). Twi̇tter Kullanicilarinin Havayolu Pazarina Yöneli̇k Duygu Kutuplarinin Beli̇rlenmesi̇: Bi̇r Fi̇ki̇r Madenci̇li̇ği̇ Örneği̇. PressAcademia, 2(1), 684-691.
  • Lautenbacher, C. J., & Stidham, S. (1999). Underlying Markov decision process in the single-leg airline yield-management problem. Transportation Science, 33(2), 136–146.
  • Legrand, K., Puechmorel, S., & Daniel Delahaye, Y. Z. (2018). Robust Aircraft Optimal Trajectory in the Presence of Wind. IEEE Aerospace and Electronic Systems Magazine, Aerospace and Electronic Systems Magazine, IEEE, IEEE Aerosp. Electron. Syst. Mag, 11, 30-39.
  • Lheritier, A., Bocamazo, M., Delahaye, T., & Acuna-Agost, R. (2018). Airline itinerary choice modeling using machine learning. Journal of Choice Modelling, DOI: 10.1016/j.jocm.2018.02.002. ´.
  • Lohatepanont, M., & Barnhart, C. (2004). Airline schedule planning: integrated models and algorithms for schedule design and fleet assignment. Transportation Science, 38(1), Lohatepanont M, Barnhart C (2004) Airline schedule planning: integrated19–32.
  • Lu, Z., Liang, X. Z., & Zhou, J. (2017). Markov process based time limited dispatch analysis with constraints of both dispatch reliability and average safety levels. Reliability Engineering & System Safety, 167, 84-94.
  • M., S., Deokar A., V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52-63.
  • Mei, A. v., & Doomernik, J.-P. (2017). Artificial intelligence potential in power distribution system planning. CIRED - Open Access Proceedings Journal, 2017, 2115-2117.
  • Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics, 13(1), 13-39.
  • Ming, W., Bao, Y., Hu, Z., & Xiong, T. (2014). Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models. The Scientific World Journal, 2014, 1-14.
  • Müller, H., Bosse, S., & Turowski, K. (2018). Capacity Management as a Service for Enterprise Standard Software. Complex Systems Informatics and Modeling Quarterly, 74(13), 1–21.
  • Narahari, Y., Raju, C., Ravikumar, K., & Shah, S. (2005). Dynamic pricing models for electronic business. Sadhana, 30(2), 231-256.
  • Nash, B. (1981). A Simplified Alternative to Current Airline Fuel Allocation Models. Interfaces, 11(1), 1-9.
  • Naumann, M., & Suhl, L. (2013). How does fuel price uncertainty affect strategic airline planning? Operational Research, 13(3), 343–362.
  • Oza, N., & Castle, J. S. (2009). Classification of Aeronautics System Health and Safety Documents. Classification of Aeronautics System Health and Safety Documents, 39(6), 670-680.
  • Prabakaran, N., & Kannadasan, R. (2018). Airline Delay Predictions using Supervised Machine Learning. Prabakaran. N; Rajendran International Journal of Pure and Applied Mathematics, 119(7), 329-337.
  • Punnoose, R., & Ajit, P. (2016). Prediction of Employee Turnover in Organizations using Machine Learning Algorithms. International Journal of Advanced Resea, 5(9), 22-26.
  • Rana, R., & Oliveira F., S. (2014). Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning. Omega, 47, 116-126.
  • Raschka, S., & Mirjalili, V. (2017). Python Machine Learning:Machine Learning and Deep Learning. Birmingham: Packt Publishing.
  • Rauf, K., Nyor, N., Kanu, R. U., & Omolehin, J. O. (2016). An Airline Crew Scheduling for Optimality. International. Journal of Mathematics & Computer Science, 11(2), 187-198.
  • Ravnik, R., Solina, F., & Zabkar, e. (2014). Modelling In-Store Consumer Behaviour Using Machine Learning and Digital Signage Audience Measurement Data. VAAM(8811), 123-133.
  • Rubin, J. A. (1973). Technique for the Solution of Massive Set Covering Problems, with Application to Airline Crew Scheduling. Transportation Science, 7(1), 34-48.
  • Sabbeh, S. F. (2018). Machine-Learning Techniques for Customer Retention: A Comparative Study. International Journal of Advanced Computer Science and Applications, 9(2), 273-281.
  • Schultz, M., & Reitmann, S. (2018). Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time. Aerospace (Basel), 5(4), 1-14.
  • Serengil, S. I., & Ozpinar, A. (2017). Workforce Optimization for Bank Operation Centers: A Machine Learning Approach. International Journal of Interactive Multimedia and Artificial Intelligence, 4(6), 81-87.
  • Shaw, S. (2007). Airline marketing and management. Burlington: Ashgate Publishing Company.
  • Sherali, H., & Zhu, X. (2008). Two-stage fleet assignment model considering stochastic demands. Operation Research, 56(2), 383–399.
  • Shin, C. K., & Park, S. C. (2000). A machine learning approach to yield management in semiconductor manufacturing. International Journal of Production Research, 38(17), 4261-4271.
  • Smart, E., Brown, D., & Denman, J. (2012). A two-phase method of detecting abnormalities in aircraft flight data and ranking their impact on individual flights. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1253-1265.
  • Song, C., Guan, X., Zhao, Q., & Ho, Y.-C. (2005). Machine Learning Approach for Determining Feasible Plans of a Remanufacturing System. IEEE Transactions on Automation Science and Engineering, 2(3), 262-275.
  • Spedicat, G. A., Dutang, C., & Petrini, L. (2018). Machine Learning Methods to Perform Pricing Optimization. A Comparison with Standard GLMs. Variance Advancing the Science Risk, 12(1), 69-89.
  • Srisaeng, P., Baxter, G. S., & Wild, G. (2015). An adaptive neuro-fuzzy inference system for forecasting Australia’s domestic low cost carrier passenger demand. Aviation, 19(3), 150-163.
  • Subramanian, J., Stidham, S., & Lautenbacher, C. J. (1999). Airline Yield Management with Overbooking, Cancellations, and No-Shows. Transportation Science, 33(2), 147-168.
  • Thiagarajan, B., Srinivasan, L., Sharma, A. V., Sreekanthan, D., & Vijayaraghavan, V. (2017). A machine learning approach for prediction of on-time performance of flights. 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (s. 1-6). St. Petersburg: IEEE.
  • Thomas, L., Gast, C., Grube, R., & Craig, K. (2015). Fatigue Detection in Commercial Flight Operations: Results Using Physiological Measures. Procedia Manufacturing, 3, 2357-2364.
  • Tulabandhula, T., & Rudin, C. (2013). Machine Learning with Operational Costs. Journal of Machine Learning Research, 14, 1989-2028.
  • Vance, P. H., Barnhart, C., Johnson, E. L., & Nemhauser, G. L. (1997). Airline Crew Scheduling: A New Formulation and Decomposition Algorithm. Pamela H. Vance, Cynthia Barnhart, Ellis L. Johnson, & George L. Nemhauser. (1Operations Research, 45(2), 188-200.
  • Wei, K., & Vikrant, V. (2018). Modeling Crew Itineraries and Delays in the National Air Transportation System. Transportation Science, 52(5), 1276–1296.
  • Williams, J. K. (2014). Using random forests to diagnose aviation turbulence. Machine Learning, 95(1), 51-70.
  • Yan, S., & Wang, C.‐R. (2010). The planning of aircraft routes and flight frequencies in an airline network operations. Journal of Advanced Transportation, 35(1), 33-46.
  • Yan, S., Tang, C.-H., & Fu, T.-C. (2008). An airline scheduling model and solution algorithms under stochastic demands. European Journal of Operational Research, 190(1), 22-29.
  • Yang, H., Lu, W. F., & Lin, A. C. (1992). Intelligent Process Planning Using a Machine Learning Approach. IFAC Proceedings Volumes, 25(28), 147-151.
  • Yanto, J., & Liem, R. P. (2018). Aircraft fuel burn performance study: A data-enhanced modeling approach. Transportation Research Part D Transport and Environment, 65, 574-595.
  • Yen, J. W., & Birge, J. R. (2006). A Stochastic Programming Approach to the Airline Crew Scheduling Problem. Transportation Science, 40(1), 3–14.
  • Yu, G., & Yang, J. (1998). Optimization Applications in the Airline Industry. P. Pardalos içinde, Handbook of Combinatorial Optimization (s. 1381-1472). Boston: Springer.
  • Zheng, Y., Sheng, W., Sun, X., & Chen, S. (2017). Airline Passenger Profiling Based on Fuzzy Deep Machine Learning. IEEE Transactions on Neural Networks and Learning Systems, 28(12), "Airline Passenger Profiling Based on Fuzzy Deep Machine Learning," in IEEE Tra2911-2923.
  • Zouein, P. P., Abillama, W. R., & Tohme, E. (2002). A Multiple Period Capacitated Inventory Model for Airline Fuel Management: A Case Study. The Journal of the Operational Research Society, 53(4), 379-386.
Primary Language tr
Subjects Social
Published Date Kasım
Journal Section Original Research Articles
Authors

Orcid: 0000-0003-1562-4716
Author: Eyüp Bayram ŞEKERLİ (Primary Author)
Country: Turkey


Dates

Publication Date : November 30, 2019

Bibtex @research article { selcuksbmyd537142, journal = {Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi}, issn = {}, eissn = {2564-7458}, address = {}, publisher = {Selcuk University}, year = {2019}, volume = {22}, pages = {405 - 419}, doi = {10.29249/selcuksbmyd.537142}, title = {Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi}, key = {cite}, author = {ŞEKERLİ, Eyüp Bayram} }
APA ŞEKERLİ, E . (2019). Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi , 22 (2) , 405-419 . DOI: 10.29249/selcuksbmyd.537142
MLA ŞEKERLİ, E . "Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi". Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi 22 (2019 ): 405-419 <https://dergipark.org.tr/en/pub/selcuksbmyd/issue/49087/537142>
Chicago ŞEKERLİ, E . "Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi". Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi 22 (2019 ): 405-419
RIS TY - JOUR T1 - Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi AU - Eyüp Bayram ŞEKERLİ Y1 - 2019 PY - 2019 N1 - doi: 10.29249/selcuksbmyd.537142 DO - 10.29249/selcuksbmyd.537142 T2 - Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi JF - Journal JO - JOR SP - 405 EP - 419 VL - 22 IS - 2 SN - -2564-7458 M3 - doi: 10.29249/selcuksbmyd.537142 UR - https://doi.org/10.29249/selcuksbmyd.537142 Y2 - 2019 ER -
EndNote %0 Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi %A Eyüp Bayram ŞEKERLİ %T Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi %D 2019 %J Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi %P -2564-7458 %V 22 %N 2 %R doi: 10.29249/selcuksbmyd.537142 %U 10.29249/selcuksbmyd.537142
ISNAD ŞEKERLİ, Eyüp Bayram . "Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi". Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi 22 / 2 (November 2019): 405-419 . https://doi.org/10.29249/selcuksbmyd.537142
AMA ŞEKERLİ E . Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi. 2019; 22(2): 405-419.
Vancouver ŞEKERLİ E . Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi. 2019; 22(2): 419-405.