Türkiye’de yapay zekâ alanında yapılan çalışmaların bibliyometrik analizi

Öz

Bu çalışma, Türkiye'de yapay zekâ alanında yapılmış araştırmalara ilişkin bir bibliyometrik analiz sunmaktadır. Böylece, alandaki temel eğilimler ve temalar, literatüre katkıda bulunmuş etkili yayınlar, üretken kişi ve kurumlar ile iş birliği ağları belirlenerek Türkiye’deki araştırmaların odağı ve gelişimi değerlendirilebilecektir. Çalışmada, yayın üretkenliği, ortak yazarlık kalıpları, anahtar kelime birlikteliği, atıf ağları ve tematik harita gibi bibliyometrik göstergeleri belirleyebilmek amacıyla Scopus veri tabanındaki 4.049 makalenin bibliyometrik verisi VOSviewer ve R yazılımları aracılığıyla görselleştirilerek analiz edilmiştir. Bulgular, özellikle son beş yılda Türkiye'de yapay zekâ alanındaki araştırma çıktısında önemli bir artış olduğunu göstermektedir. Öne çıkan temel araştırma alanları arasında makine öğrenimi, derin öğrenme ve sinir ağları ile bunlara yönelik algoritma ve uygulamalar yer almakta olup araştırmalardaki küresel eğilimleri de yansıtmaktadır. Erciyes, Eskişehir Osmangazi, Fırat, Ankara ve Yakın Doğu üniversiteleri ve ilgili akademisyenlerinin çalışmalarıyla literatüre katkı anlamında öne çıktıkları görülmektedir. Hem Scopus hem de WoS veri tabanında endekslenen ve etki faktörü yüksek olan dergilerdeki SCIE nitelikli yayın sayısındaki artış da bunu doğrulamaktadır. Anahtar kavramlara ait bulgular, karar destek sistemleri ve optimizasyon teknikleri gibi temaların ivme kazanarak odak noktası haline geldiği ve uygulamalı yapay zekâ araştırmaları ile yapay zekâ pratik uygulamalarına doğru bir yönelimi işaret etmektedir. Fen bilimleri, tıp ve matematik sahasındaki gelişmelere rağmen, sosyal bilimlerde yapay zekânın kullanımı ile açıklanabilir yapay zekâ ve yapay zekâ etiği gibi alt alanlardaki dikkate değer boşluklar da dolaylı olarak vurgulanmıştır.

Anahtar Kelimeler

• Bibliyometrik analiz
• yapay zekâ
• yz
• üniversite
• akademisyen

The bibliometric analysis of studies in the field of artificial intelligence in Türkiye

Öz

This study presents a bibliometric analysis of research conducted in the field of artificial intelligence (AI) in Türkiye. By identifying key trends and themes, influential publications contributing to the literature, prolific individuals and institutions, and collaboration networks, the focus and development of research in Türkiye can be evaluated. The study has visualized and analyzed the bibliometric data of 4,049 articles from the Scopus database using VOSviewer and R software to determine bibliometric indicators such as publication productivity, co-authorship patterns, keyword co-occurrence, citation networks, and thematic maps. The findings have revealed a significant increase in research output in the field of artificial intelligence in Türkiye, particularly in the last five years. Prominent research areas include machine learning, deep learning, neural networks, and related algorithms and applications, reflecting global trends in research. Universities such as Erciyes, Eskişehir Osmangazi, Fırat, Ankara, and Near East, along with their respective academics, stand out in terms of their contributions to the literature. This is further supported by the increase in SCIE-quality publications in high-impact journals indexed in both the Scopus and WoS databases. Findings related to key concepts have indicated that themes such as decision support systems and optimization techniques are gaining momentum and becoming focal points, signaling a shift towards applied artificial intelligence research and practical applications of AI. Despite advancements in natural sciences, medicine, and mathematics, the study indirectly highlights notable gaps in subfields such as the use of AI in social sciences, explainable AI, and AI ethics.

Anahtar Kelimeler

• Bibliometric analysis
• artificial intelligence
• ai
• university
• academician

Kaynakça

1. Acedo, F. J., Barroso, C., Casanueva, C. ve Galán, J. L. (2006). Co‐authorship in management and organizational studies: An empirical and network analysis. Journal of Management Studies, 43(5), 957-983. https://doi.org/10.1111/j.1467-6486.2006.00625.x
2. Akay, B. ve Karaboğa, D. (2012). A modified artificial bee colony algorithm for real-parameter optimization. Swarm Intelligence and Its Applications, 192, 120-142. https://doi.org/10.1016/j.ins.2010.07.015
3. Akay, B. ve Karaboğa, D. (2015). A survey on the applications of artificial bee colony in signal, image, and video processing. Signal, Image and Video Processing, 9(4), 967-990. https://doi.org/10.1007/s11760-015-0758-4
4. Akpınar, S., Bayhan, G. M. ve Baykasoglu, A. (2013). Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Applied Soft Computing, 13(1), 574-589. https://doi.org/10.1016/j.asoc.2012.07.024
5. Aydın, N. (2024). Silahlı insansız hava araçlarına ilişkin bilimsel yayınların bibliyometrik analizi. Kütahya Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (80), 309-331. https://doi.org/10.51290/dpusbe.1455380
6. Baker, H. K., Kumar, S. ve Pandey, N. (2020). A bibliometric analysis of managerial finance: A retrospective. Managerial Finance, 46(11), 1495-1517. https://doi.org/10.1108/MF-06-2019-0277
7. Birkhoff, G. (1967). Lattice theory (3rd ed.). Providence: American mathematical society, Col Pub.
8. Bishop, C. M. (1995). Neural networks for pattern recognition. Clarendon Press; Oxford University Press.

9. Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies (First edition.). Oxford University Press.
10. Broadus, R. N. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5-6), 373-379. https://doi.org/10.1007/BF02016680
11. Chang, Y.-W., Huang, M.-H. ve Lin, C.-W. (2015). Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses. Scientometrics, 105(3), 2071-2087. https://doi.org/10.1007/s11192-015-1762-8
12. Cisneros, L., Ibanescu, M., Keen, C., Lobato-Calleros, O. ve Niebla-Zatarain, J. (2018). Bibliometric study of family business succession between 1939 and 2017: Mapping and analyzing authors’ networks. Scientometrics, 117(2), 919-951. https://doi.org/10.1007/s11192-018-2889-1
13. Çelik, Y., Talo, M., Yıldırım, O., Karabatak, M. ve Acharya, U. R. (2020). Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recognition Letters, 133, 232-239. https://doi.org/10.1016/j.patrec.2020.03.011
14. Çınar, A. C. (2020). Training feed-forward multi-layer perceptron artificial neural networks with a tree-seed algorithm. Arabian Journal for Science and Engineering, 45(12), 10915-10938. https://doi.org/10.1007/s13369-020-04872-1
15. Damar, M., Küme, T., Yüksel, İ., Çetinkol, A. E., K. Pal, J. ve Safa Erenay, F. (2024). Medical informatics as a concept and field-based medical informatics research: The case of Türkiye. Duzce Medical Journal, 26(1), 44-55. https://doi.org/10.18678/dtfd.1410276
16. Demirkol, D., Koçoğlu, F.Ö., Aktaş, Ş. ve Erol, Ç. (2022). A bibliometric analysis of the relationship between diabets and artificial intelligence. Journal of Istanbul Faculty of Medicine, 85(2), 249-257. http://dx.doi.org/10.26650/IUITFD.928111
17. Donthu, N., Kumar, S., Pandey, N. ve Lim, W. M. (2021). Research constituents, intellectual structure, and collaboration patterns in journal of international marketing: An analytical retrospective. Journal of International Marketing, 29(2), 1-25. https://doi.org/10.1177/1069031X211004234
18. Dorigo, M., Birattari, M., Blum, C., Gambardella, L. M., Mondada, F. ve Stützle, T. (Ed.). (2004). Ant colony optimization and swarm intelligence: 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004. Proceedings. Lecture Notes in Computer Science (C. 3172). Berlin, Heidelberg: Springer Berlin Heidelberg. http://dx.doi.org/10.1007/b99492
19. Dubois, D. ve Prade, H. M. (Ed.). (2000). Fundamentals of fuzzy sets. The handbooks of fuzzy sets series. Kluwer Academic.
20. Ekinci, G. ve Bilginer Özsaatcı, F. G. (2023). Yapay zekâ ve pazarlama alanındaki yayınların bibliyometrik analizi. Sosyoekonomi, 31(56), 369-388. https://doi.org/10.17233/sosyoekonomi.2023.02.17
21. Geem, W. Z., Kim, J. H. ve Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. SIMULATION, 76(2), 60-68. https://doi.org/10.1177/003754970107600201
22. Goodfellow, I., Bengio, Y. ve Courville, A. (2016). Deep learning. adaptive computation and machine learning. The MIT Press.
23. Haykin, S. S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.
24. He, K., Zhang, X., Ren, S. ve Sun, J. (2015). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778. https://api.semanticscholar.org/CorpusID:206594692
25. Hirani, R., Noruzi, K., Khuram, H., Hussaini, A. S., Aifuwa, E. I., Ely, K. E., … Etienne, M. (2024). Artificial intelligence and healthcare: A Journey through history, present innovations, and future possibilities. Life, 14(5). https://doi.org/10.3390/life14050557
26. Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press.
27. Huang, G.-B., Zhu, Q.-Y. ve Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126
28. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Technical Report-TR06). Department of Computer Engineering, Engineering Faculty, Erciyes University, Türkiye. https://abc.erciyes.edu.tr/pub/tr06_2005.pdf
29. Karaboğa, D. ve Baştürk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x
30. Karaboga, D. ve Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697. https://doi.org/10.1016/j.asoc.2007.05.007
31. Karaboğa, D. ve Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108-132. https://doi.org/10.1016/j.amc.2009.03.090
32. Karaboğa, D. ve Akay, B. (2011). A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing, 11(3), 3021-3031. https://doi.org/10.1016/j.asoc.2010.12.001
33. Karaboğa, D. ve Görkemli, B. (2014). A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Applied Soft Computing, 23, 227-238. https://doi.org/10.1016/j.asoc.2014.06.035
34. Karaboğa, D., Görkemli, B., Öztürk, C. ve Karaboğa, N. (2014). A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57. https://doi.org/10.1007/s10462-012-9328-0
35. Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10-25. https://doi.org/10.1002/asi.5090140103
36. Kıran, M. S. ve Fındık, O. (2015). A directed artificial bee colony algorithm. Applied Soft Computing Journal, 26, 454-462. https://doi.org/10.1016/j.asoc.2014.10.020
37. Kırtıl, İ. G. ve Aşkun, V. (2021). Artificial intelligence in tourism: A review and bibliometrics research. Advances in Hospitality and Tourism Research (AHTR), 9(1), 205-233. https://doi.org/10.30519/ahtr.801690
38. Kirkpatrick, S., Gelatt, C. D. ve Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671
39. Kişi, Ö., Dailr, A. H., Cimen, M. ve Shiri, J. (2012). Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology, 450-451, 48-58. https://doi.org/10.1016/j.jhydrol.2012.05.031
40. Kişi, O., Shiri, J. ve Tombul, M. (2013). Modeling rainfall-runoff process using soft computing techniques. Computers and Geosciences, 51, 108-117. https://doi.org/10.1016/j.cageo.2012.07.001
41. Klement, E. P., Mesiar, R. ve Pap, E. (2000). Triangular norms. Trends in logic. Kluwer Academic Publishers.
42. Koza, J. R. (1994). Genetic programming: On the programming of computers by means of natural selection. Complex adaptative systems. MIT press.
43. LeCun, Y., Bengio, Y. ve Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
44. Liu, Z., Yin, Y., Liu, W. ve Dunford, M. (2015). Visualizing the intellectual structure and evolution of innovation systems research: A bibliometric analysis. Scientometrics, 103(1), 135-158. https://doi.org/10.1007/s11192-014-1517-y
45. McCarthy, J., Minsky, M. L., Rochester, N. ve Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12. https://doi.org/10.1609/aimag.v27i4.1904
46. MIT Lincoln Laboratory. (2019). Artificial intelligence: Short history, present developments, and future outlook. MIT Lincoln Laboratory Report. https://www.ll.mit.edu/sites/default/files/publication/doc/2022-10/Artificial%20Intelligence%20Short%20History%2C%20Present%20Developments%2C%20and%20Future%20Outlook%20-%20Final%20Report%20-%20Martinez_.pdf (23.08.2024)
47. Nourani, V., Kişi, Ö. ve Komasi, M. (2011). Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 402(1), 41-59. https://doi.org/10.1016/j.jhydrol.2011.03.002
48. Nourani, V., Elkiran, G. ve Abba, S. I. (2018). Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach. Water Science and Technology, 78(10), 2064-2076. https://doi.org/10.2166/wst.2018.477
49. Oliveira, A. L. ve Figueiredo, M. A. T. (2024). Artificial intelligence: Historical context and state of the art. H. Sousa Antunes, P. M. Freitas, A. L. Oliveira, C. Martins Pereira, E. Vaz de Sequeira ve L. Barreto Xavier (Ed.), Multidisciplinary Perspectives on Artificial Intelligence and the Law içinde (ss. 3-24). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-41264-6_1
50. Olyaie, E., Zare Abyaneh, H. ve Danandeh Mehr, A. (2017). A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River. Geoscience Frontiers, 8(3), 517-527. https://doi.org/10.1016/j.gsf.2016.04.007
51. Orhan, K., Bayrakdar, I. S., Ezhov, M., Kravtsov, A. ve Özyürek, T. (2020). Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. International Endodontic Journal, 53(5), 680-689. https://doi.org/10.1111/iej.13265
52. Özesmi, U. ve Özesmi, S. L. (2004). Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1), 43-64. https://doi.org/10.1016/j.ecolmodel.2003.10.027
53. Öztürk, T., Talo, M., Yıldırım, E. A., Baloğlu, U. B., Yıldırım, O. ve Rajendra Acharya, U. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792. https://doi.org/10.1016/j.compbiomed.2020.103792
54. Ramos‐Rodríguez, A. ve Ruíz‐Navarro, J. (2004). Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal , 1980–2000. Strategic Management Journal, 25(10), 981-1004. https://doi.org/10.1002/smj.397
55. Russell, S. J. ve Norvig, P. (2021). Artificial intelligence: A modern approach. Pearson eries in artificial intelligence (Fourth edition.). Hoboken: Pearson.
56. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill International Book Co.
57. Sezer, Ö. B., Güdelek, M. U. ve Özbayoğlu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
58. Sharghi, E., Nourani, V. ve Behfar, N. (2018). Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. Journal of Hydroinformatics, 20(5), 1071-1084. https://doi.org/10.2166/hydro.2018.151
59. Sheikh, H., Prins, C. ve Schrijvers, E. (2023). Artificial intelligence: Definition and background. H. Sheikh, C. Prins ve E. Schrijvers (Ed.), Mission AI: The New System Technology içinde (ss. 15-41). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21448-6
60. Shiri, J., Kişi, Ö., Yoon, H., Lee, K.-K. ve Hossein Nazemi, A. (2013). Predicting groundwater level fluctuations with meteorological effect implications-A comparative study among soft computing techniques. Computers ve Geosciences, 56, 32-44. https://doi.org/10.1016/j.cageo.2013.01.007
61. Simonyan, K. ve Zisserman, A. (2015, May 7-9) Very deep convolutional networks for large-scale image recognition (Conference presentatition-Accepted, 2014). The 3rd International Conference on Learning Representations (ICLR2015), San Diego, USA. https://doi.org/10.48550/arXiv.1409.1556
62. Şengöz, A., Orhun, B. N. ve Konyalilar, N. (2024). A holistic approach to artificial intelligence-related research in the transportation system: Bibliometric analysis. Worldwide Hospitality and Tourism Themes, 16(2), 138-149. https://doi.org/10.1108/WHATT-03-2024-0059
63. Tahamtan, I., Safipour Afshar, A. ve Ahamdzadeh, K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 107(3), 1195-1225. https://doi.org/10.1007/s11192-016-1889-2
64. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. http://www.jstor.org/stable/2251299
65. Türkdoğan-Aydınol, F. I. ve Yetilmezsoy, K. (2010). A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. Journal of Hazardous Materials, 182(1), 460-471. https://doi.org/10.1016/j.jhazmat.2010.06.054
66. Uçar, F. ve Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140, 109761. https://doi.org/10.1016%2Fj.mehy.2020.109761
67. Uymaz, S. A., Tezel, G. ve Yel, E. (2015). Artificial algae algorithm (AAA) for nonlinear global optimization. Applied Soft Computing, 31, 153-171. https://doi.org/10.1016/j.asoc.2015.03.003
68. Vapnik, V. N. (1995). The nature of statistical learning theory. New York, NY: Springer New York. http://dx.doi.org/10.1007/978-1-4757-2440-0
69. Verma, S. ve Gustafsson, A. (2020). Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research, 118, 253-261. https://doi.org/10.1016%2Fj.jbusres.2020.06.057
70. Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10, 1-37. https://ui.adsabs.harvard.edu/link_gateway/2019JAGI...10....1W/doi:10.2478/jagi-2019-0002
71. Yağcı, O., Mercan, D. E., Cigizoglu, H. K. ve Kabdasli, M. S. (2005). Artificial intelligence methods in breakwater damage ratio estimation. Ocean Engineering, 32(17), 2088-2106. https://doi.org/10.1016/j.oceaneng.2005.03.004
72. Zupic, I. ve Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629
73. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X