Dijital Çağda Liminal Bir Alan Olarak Sanat Tarihinin Epistemolojik Dönüşümü

Öz

Bu makale, dijital teknolojiler ve yapay zekanın (YZ) sanat tarihi üzerindeki epistemolojik dönüşümlerini incelemektedir. Geleneksel metodolojiler ve teorik çerçeveler, dijital araçların sunduğu yeni olanaklar karşısında yeniden değerlendirilmek zorundadır. Çalışma, sanat tarihinin dijitalleşme sürecinde karşılaştığı zorlukları ve fırsatları analiz ederek, disiplinin geleceğine yönelik öneriler sunmaktadır. Dijital teknolojilerin sanat tarihi üzerindeki etkileri üzerine yapılan araştırmalar, genellikle teknolojinin sunduğu yeni olanaklar ve metodolojik yeniliklere odaklanmaktadır. Ancak, bu çalışmaların büyük bir kısmı, dijitalleşmenin sanat tarihinin epistemolojik temelleri üzerindeki etkilerini yeterince incelememektedir. Çalışmamız, dijital teknolojilerin sanat tarihi disiplinindeki epistemolojik dönüşümlerini ele alarak, literatürdeki bu boşluğu doldurmayı hedeflemektedir. “Liminal bir mekân olarak sanat tarihi” kavramsal önermesi ise araştırmanın temelini oluşturmaktadır. Liminalite, geleneksel olarak antropoloji ve kültürel çalışmalar gibi alanlarda geçiş aşamalarını, belirsizlikleri ve sınır durumları imâ etmektedir. Liminal bir mekân olarak sanat tarihi kavramı disiplinin beşerî ve doğa bilimleri perspektifleri arasında bir geçiş alanı olarak işlev görmesini tanımlar. Böylece, sanat tarihi, dijital teknolojiler ve yapay zekanın sunduğu yeni epistemolojik durumlar sayesinde, yenilikçi analiz yöntemlerini teşvik eden dinamik bir alan haline gelir. Ayrıca, sanat tarihinin liminal karakteri, disiplinin dijital çağın getirdiği yeniliklere uyum sağlama sürecinde ortaya çıkan belirsizlik ve dönüşüm süreçlerine ilişkin sorunları aşma potansiyelini ifade etmektedir. Araştırma, nitel yöntemlere dayalı olarak gerçekleştirilmiştir. Literatür taraması, vaka çalışmaları ve karşılaştırmalı analizler gibi veri toplama yöntemleri kullanılmıştır. Elde edilen bulgular, dijital araçların sanat tarihçilerine sunduğu yeni fırsatları ve karşılaştıkları zorlukları ortaya koymakta; ayrıca dijital çağda sanat tarihinin epistemolojik temellerinin yeniden tanımlanması gerektiğini vurgulamaktadır. Çalışma, disiplinin geleneksel metodolojilerinin dijital çağın gereksinimlerine ne ölçüde yanıt verebildiğini sorgularken, sanat tarihinin dijital paradigmaya adaptasyonu için gerekli olan dönüşümleri analiz etmektedir. Bu bağlamda, araştırma, sanat tarihinin dijitalleşme sürecinde karşılaştığı zorlukları ve fırsatları kapsamlı bir şekilde ele alarak, disiplinin geleceği için yeni teorik çerçeveler ve yöntemler geliştirilmesine zemin hazırlamaktadır.

Anahtar Kelimeler

• sanat tarihi
• dijitalleşme
• yapay zeka
• büyük veri
• epistemoloji
• liminalite
• metodoloji

Epistemological Transformation of Art History as a Liminal Field in the Digital Age

Abstract

This article examines the epistemological transformations of digital technologies and artificial intelligence (AI) on art history. Traditional methodologies and theoretical frameworks have to be re-evaluated in the face of new possibilities offered by digital tools. The study analyses the challenges and opportunities that art history faces in the process of digitalisation and offers suggestions for the future of the discipline. Research on the impact of digital technologies on art history generally focuses on the new possibilities and methodological innovations offered by technology. However, most of these studies do not sufficiently examine the effects of digitalisation on the epistemological foundations of art history. Our study aims to fill this gap in the literature by addressing the epistemological transformations of digital technologies in the discipline of art history. The conceptual proposition of "art history as a liminal space" forms the basis of the research. Liminality traditionally implies transitional stages, uncertainties and borderline situations in fields such as anthropology and cultural studies. The concept of art history as a liminal space defines the discipline's function as a transitional space between humanities and natural sciences perspectives. Thus, art history becomes a dynamic field that encourages innovative methods of analysis, thanks to the new epistemological situations offered by digital technologies and artificial intelligence. Moreover, the liminal character of art history refers to the discipline's potential to overcome the problems of uncertainty and transformation processes that arise in the process of adapting to the innovations brought about by the digital age. The research is based on qualitative methods. Data collection methods such as literature review, case studies and comparative analyses were used. The findings reveal the new opportunities that digital tools offer to art historians and the challenges they face; they also emphasise the need to redefine the epistemological foundations of art history in the digital age. The study questions the extent to which the discipline's traditional methodologies are able to respond to the requirements of the digital age and analyses the transformations necessary for the adaptation of art history to the digital paradigm. In this context, the research comprehensively addresses the challenges and opportunities that art history faces in the process of digitalisation, paving the way for the development of new theoretical frameworks and methods for the future of the discipline.

Keywords

• art history
• artificial intelligence
• big data
• digitalisation
• epistemology

Kaynakça

1. Alpers, S. (1983). The art of describing: Dutch art in the seventeenth century. University of Chicago Press.
2. Agamben, G. (2017). Çıplaklıklar. Alef Yayınları.
3. Anderson, C. (2008, 23 Haziran). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/ (Erişim Tarihi: 19.09.2024)
4. Artstor. (2024). Artstor: Try the new experience on JSTOR. JSTOR. https://www.jstor.org/artstor(Erişim Tarihi: 22.08.2024)
5. Ascott, R. (2003). Telematic embrace: Visionary theories of art, technology, and consciousness. University of California Press.
6. Assmann, A. (2011). Cultural memory and Western civilization: Functions, media, archives. Cambridge University Press.
7. Bachelard, G. (1938). La formation de l’esprit scientifique. Vrin.
8. Baxandall, M. (1985). Patterns of intention: On the historical explanation of pictures. Yale University Press. Belting, H. (2020). Sanat tarihinin sonu. İletişim.

9. Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. 58th Annual Meeting of the Association for Computational Linguistics, 5185-5198. doi: 10.18653/v1/2020.acl-main.463.
10. Berry, D. M., & Fagerjord, A. (2017). Digital humanities: Knowledge and critique in a digital age. Polity Press.
11. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679.
12. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., & Neelakantan, A., … & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
13. Browne, J. (2023). Synthetic data and aiapplications in art history. Harvard Art History and Digital Humanities Review, 31(4), 78-95.
14. Bryson, N. (1983). Vision and painting: The logic of the gaze. Macmillan.
15. Cao, X., Johnson, T., & Zuo, M. (2023). Neural networks in artistic style analysis: A case study in CNN applications. Art & Technology Journal, 12(3), 150-167.
16. Carr, N. (2020). The shallows: What the internet is doing to our brains. W. W. Norton & Company.
17. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. International Conference on Neural Information Processing Systems (NIPS'16), 2180-2188. https://papers.nips.cc/paper_files/paper/2016/hash/7c9d0b1f96aebd7b5eca8c3edaa19ebb-Abstract.html(Erişim Tarihi: 13.09.2024)
18. Chomsky, N. (1965). Aspects of the theory of syntax. MIT Press.
19. Christakis, D. A., & Garrison, M. M. (2020). The impact of digital media on children’s development: Lessons learned from 15 years of research. Pediatrics, 145(2), 256.
20. Crary, J. (1990). Techniques of the observer: On vision and modernity in the nineteenth century. MIT Press.
21. Crary, J. (1999). Suspensions of perception: Attention, spectacle, and modern culture. MIT Press.
22. Cross, E. S., Turgeon, M., & Atherton, G. (2019). The evolution of embodied social cognition: Insights from the arts. Trends in Cognitive Sciences, 23(9), 811-823.
23. Cross, I., Turgeon, M., & Atherton, M. (2019). Musicality and the human capacity for culture. Behavioral and Brain Sciences, 42, 407-421.
24. Cucerzan, S., & Whiting, T. (2016). Large scale image matching for a digital art library. Proceedings of the IEEE International Conference on Image Processing (ICIP), 532-536. https://doi.org/10.1109/ICIP.2016.7532435
25. Damasio, A. R. (1999). The feeling of what happens: Body and emotion in the making of consciousness. Harcourt Brace.
26. De Smedt, J., & De Cruz, H. (2011). A cognitive approach to the earliest art. Journal of Aesthetics and Art Criticism, 69(4), 357-370.
27. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113. https://doi.org/10.1145/1327452.1327492
28. Descartes, R., & Williams, B. (1996). Descartes: Meditations on first philosophy: With selections from the objections and replies (J. Cottingham, Ed.). Cambridge University Press.
29. Didi-Huberman, G. (2005). Confronting images: Questioning the ends of a certain history of art. Penn State University Press.
30. Dilthey, W. (Ed.). (2010). Selected works: Volume IV: Hermeneutics and the study of history (R. A. Makkreel & F. Rodi, Ed.). Princeton University Press.
31. Doersch, C. (2016). Tutorial on Variational Autoencoders. arXiv. https://arxiv.org/abs/1606.05908(Erişim Tarihi: 19.09.2024)
32. Domingos, P. (2023). The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books.
33. Drucker, J. (2014). Graphesis: Visual forms of knowledge production. Harvard University Press.
34. Dunbar, R. I. M. (1996). Grooming, gossip, and the evolution of language. Harvard University Press.
35. Dunkerton, J. (2022). Advanced imaging and paint analysis in Van Gogh's works. Conservation Science Journal, 45(1), 89-103.
36. Dutton, D. (2009). The art instinct: Beauty, pleasure, and human evolution. Bloomsbury Press.
37. Dyche, J. (2012) ‘Big data “eurekas!” don’t just happen’, Harvard Business Review Blog. http://blogs.hbr.org/cs/2012/11/eureka_doesnt_just_happen.html(Erişim Tarihi: 16.09.2024)
38. Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. ArXiv, abs/1706.07068.
39. Elkins, J. (2000). How to use your eyes. Routledge.
40. Elkins, J. (Ed.). (2008). Visual literacy. Routledge.
41. European Commission. (2021a). Creative Europe Programme 2021-2027. https://ec.europa.eu/programmes/creative-europe/(Erişim Tarihi: 17.09.2024) European Commission. (2021b). Digital Europe Programme. https://digital-strategy.ec.europa.eu/en/activities/digital-europe-programme (Erişim Tarihi: 17.09.2024) European Commission. (2021c). Horizon Europe Programme Guide 2021-2027. https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/how-to-participate/reference-documents;programCode=HORIZON (Erişim Tarihi: 17.09.2024)
42. Europeana. (2023). Europeana Collections. https://www.europeana.eu/ (Erişim Tarihi: 17.09.2024)
43. Floridi, L. (2014). The fourth revolution: How the infosphere is reshaping human reality. Oxford University Press. Gadamer, H.-G. (2004). Truth and method. Continuum.
44. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
45. Gettier, E. L. (1963). Is justified true belief knowledge?. Analysis, 23(6), 121-123.
46. Gombrich, E. H. (2015). Sanat ve yanılsama: Resim yoluyla betimlemenin psikolojisi. Remzi Kitabevi.
47. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.
48. Grba, D. (2024). Art notions in the age of (mis)anthropic AI. Arts, 13(5), 137. https://doi.org/10.3390/arts13050137
49. Greenfield, A. (2016). Radical technologies: The design of everyday life. Verso Books.
50. Heidegger, M. (1954). Introduction to metaphysics. Yale University Press.
51. Hermens, E. (Ed.). (2012). Technical art history: A symposium on the interpretation of technical information for art history. Archetype Publications.
52. Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., ... & Lerchner, A. (2017). beta-VAE: Learning basic visual concepts with a constrained variational framework. 5th International Conference on Learning Representations (ICLR 2017). https://arxiv.org/abs/1611.02731 (Erişim Tarihi: 02.08.2024)
53. Hilbert, M., & López, P. (2011). The World’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60-65. https://doi.org/10.1126/science.1200970
54. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33. https://arxiv.org/abs/2006.11239(Erişim Tarihi: 06.08.2024)
55. Husserl, E. (1970). The crisis of european sciences and transcendental phenomenology: An introduction to phenomenological philosophy. Northwestern University Press.
56. Husserl, E. (1982). Ideas pertaining to a pure phenomenology and to a phenomenological philosophy: First book. Martinus Nijhoff Publishers.
57. Illes, J., & Bird, S. J. (2006). Neuroethics: a modern context for ethics in neuroscience. Trends in Neurosciences, 29(9), 511-517.
58. Jacobsen T. (2010).Beauty and the brain: culture, history and individual differences in aesthetic appreciation. Journal of Anatomy. 216(2), 184-191. https://doi.org/10.1111/j.1469-7580.2009.01164.x
59. Jockers, M. L. (2013). Macroanalysis: Digital methods and literary history. University of Illinois Press.
60. Johnson, C. D. (2024). Memory, metaphor, and Aby Warburg’s atlas of images. Cornell University Press.
61. Johnson, C. R., Hendriks, E., & Stromberg, R. (2016). Image processing for artist identification: Computer vision and computational art history. IEEE Signal Processing Magazine, 33(4), 73-83. https://doi.org/10.1109/MSP.2016.2559618
62. Kahana, M. J., Ranganath, C., & Wagner, A. D. (2019). Neural mechanisms supporting long-term memory retrieval: Effects of artificial intelligence-based memory aids. Nature Neuroscience, 22(1), 104-113.
63. Kaplan, R., & Kaplan, S. (1989). The experience of nature: A psychological perspective. Cambridge University Press.
64. Karras, T., Laine, S., & Aila, T. (2018). A style-based generator architecture for generative adversarial networks. arXiv. https://arxiv.org/abs/1812.04948 (Erişim Tarihi: 12.09.2024)
65. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. Proceedings of the International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1312.6114(Erişim Tarihi: 10.09.2024)
66. Koselleck, R. (2004). Futures past: On the semantics of historical time. Columbia University Press.
67. Krämer, S. (2022). Should we really ‘hermeneutise’ the Digital Humanities? A plea for the epistemic productivity of a ‘cultural technique of flattening’. Journal of Cultural Analytics, 7(4). https://doi.org/10.22148/001c.55592
68. Krauss, R. E. (1985). The originality of the avant-garde and other modernist myths. MIT Press. Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety. META Group Research Note, 6(70), 1-4.
69. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
70. Lyotard, J.F. (2013). Postmodern durum. BilgeSu Yayıncılık.
71. Manning, C. D., Raghavan, P., & Schütze, H. (2020). Introduction to information retrieval. Cambridge University Press.
72. Manovich, L. (2001). The language of new media. MIT Press.
73. Manovich, L. (2020). Cultural analytics. MIT Press.
74. Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Pantheon.
75. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. John Murray.
76. McCosker, A. (2019). Artificial intelligence and the arts: Possibilities, promises, and perils. Springer.
77. Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. Proceedings of the European Conference on Computer Vision (ECCV), 33. https://arxiv.org/abs/2003.08934(Erişim Tarihi: 20.08.2024)
78. Miller, G. (2000). The mating mind: How sexual choice shaped the evolution of human nature. Anchor Books.
79. Min, B., Ross, H., Sulem, E., Veyseh, A.P., Nguyen, T.H., Sainz, O., Agirre, E., Heinz, I., & Roth, D. (2021). Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56, 1-40.
80. Mitchell, W. J. T. (1995). Picture theory: Essays on verbal and visual representation. University of Chicago Press.
81. Mitchell, W. J. T. (2005). What do pictures want? The lives and loves of images. University of Chicago Press.
82. Mordvintsev, A., Olah, C., & Tyka, M. (2015). Inceptionism: Going deeper into neural networks. Google Research Blog. https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html(Erişim Tarihi: 17.09.2024)
83. Moxey, K. (2013). Visual time: The image in history. Duke University Press.
84. Mutlugün, M. A., & Topuz, Y. (2020). Dijital anlatı bağlamında hikâyeciliğin yeni konumu. Uluslararası İnsan ve Sanat Araştırmaları Dergisi, 3(3), 37-45.
85. Nietzsche, F. (1966). Beyond good and evil: Prelude to a philosophy of the future. Vintage Books.
86. O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
87. Orians, G. H., & Heerwagen, J. H. (2016). Evolved responses to landscapes. J. H. Barkow, L. Cosmides, & J. Tooby (Eds.), Adapted mind: Evolutionary Psychology and the Generation of Culture (555-579), Oxford University Press.
88. Panofsky, E. (1955). Meaning in the visual arts. Doubleday Anchor Books.
89. Posner, M. I., Rothbart, M. K., & Tang, Y.-Y. (2021). The evolution of attention systems: From the brain to the classroom. Annual Review of Psychology, 72, 31-53.
90. Prensky, M. (2009). H. Sapiens Digital: From Digital Immigrants and Digital Natives to Digital Wisdom. Innovate: Journal of Online Education, 5, 1.
91. Preziosi, D. (1998). The art of art history: A critical anthology. Oxford University Press.
92. Preziosi, D. (2009). Rethinking art history: Meditations on a coy science. Yale University Press.
93. Quine, W. V. O. (1951). Two dogmas of empiricism. The Philosophical Review, 60(1), 20-43.
94. Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models FromNatural Language Supervision. International Conference on Machine Learning. https://proceedings.mlr.press/v139/radford21a.html(Erişim Tarihi: 26.08.2024) Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv. https://arxiv.org/abs/1511.06434(Erişim Tarihi: 11.09.2024)
95. Ramachandran, V. S., & Blakeslee, S. (1998). Phantoms in the brain: probing the mysteries of the human mind. William Morrow.
96. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., ... & Sutskever, I. (2021). Zero-shot text-to-image generation. Proceedings of the International Conference on Machine Learning (ICML), 139. https://proceedings.mlr.press/v139/ramesh21a.html(Erişim Tarihi: 16.09.2024)
97. Reiss, H. (1994). “The ‘naturalizatoon’ of the term ‘asthetik’ in eighteenth-century German: Alexander Gottlieb Baumgarten and his impact”, The Modern Language Review, 89/3, 335-352.
98. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. https://arxiv.org/abs/2112.10752(Erişim Tarihi: 13.08.2024)
99. Rosen, D. (2013). The life and times of a digital nomad: Reimagining contemporary urbanism. Routledge.
100. Rosen, J. (2013). The uncanny valley: The digital age and its discontents. Farrar, Straus and Giroux.
101. Rovatti, P. A. (1983). Il Pensiero Debole. Feltrinelli.
102. Sadeghi, F., Srinivas, A., Levine, S., & Abbeel, P. (2020). Variational autoencoders for visual creativity. IEEE Transactions on Neural Networks and Learning Systems, 31(10), 3876-3885.
103. Saleh, B., Abe, K., Arora, R. S., & Elgammal, A. (2015). Toward automated discovery of artistic influence. Multimedia Tools and Applications, 75(7), 3565–3591. https://doi.org/10.1007/s11042-015-2557-0
104. Shanken, E. A. (2015). Contemporary art and new media: Digital divide or hybrid discourse?. C. Paul (Ed.), A Companion to Digital Art (63-81). Wiley-Blackwell. Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. Wiley.
105. Sparrow, B., Liu, J., & Wegner, D. M. (2017). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776-778.
106. Steadman, I. (2013) ‘Big data and the death of the theorist’, Wired. https://www.wired.com/story/big-data-end-of-theory/(Erişim Tarihi: 17.09.2024)
107. Stork, D. G. (2023). Mapping Titian: GIS and big data in art history. Digital Humanities Quarterly, 17(2), 102-115.
108. Turner, V. (1967). The forest of symbols: Aspects of Ndembu ritual. Cornell University Press.
109. Van Gennep, A. (1909). Rites de passage. University of Chicago Press.
110. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762(Erişim Tarihi: 11.09.2024)
111. Vattimo, G. (1983). Il pensiero debole. Feltrinelli Editore.
112. Vattimo, G. (1989). The End of modernity: Nihilism and hermeneutics in postmodern culture. Polity Press.
113. Voshmgir, S. (2020). Token economy: How the web3 reinvents the internet. BlockchainHub.
114. Wang, H., Qiu, X. & Tan, X. (2024). Multivariate graph neural networks on enhancing syntactic and semantic for aspect-based sentiment analysis. Appl Intell. https://doi.org/10.1007/s10489-024-05802-6
115. White, H. (1973). Metahistory: The historical imagination in nineteenth-century Europe. Johns Hopkins University Press.
116. Wiener, N. (1948). Cybernetics: Or control and communication in the animal and the machine. MIT Press.
117. Winckelmann, J. J. (1857). “History of Art”, (Çev: G. Henry Lodge), The Crayon, 4(7), 212-213.
118. Wölfflin, H. (2015). Sanat tarihinin temel kavramları (Çev: Ahmet Cemal). Hayalperest Yayınları.
119. Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 10). https://www.usenix.org/conference/hotcloud-10/spark-cluster-computing-working-sets(Erişim Tarihi: 21.08.2024)
120. Zhang, X., Li, G., & Zhao, H. (2020). Full-duplex cognitive radio networks: A survey. IEEE Communications Surveys & Tutorials, 22(1), 15-37.
121. Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., & Sun, M. (2018). Graph neural networks: A review of methods and applications. AI open, 1(1), 57-81.