Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi

Ezgi Avcı

doi:10.35674/kent.1490925

EN TR

Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi

Öz

21. yüzyılın hızlı kentleşme ve teknolojik ilerlemeleri, dijital teknolojiler ve veri odaklı çözümlerle şehir yaşamını iyileştirmeyi amaçlayan akıllı şehirler kavramını ortaya çıkarmıştır. Üretken yapay zekâ, kentsel yaşamı değiştirebilecek yetenekler sunan yapay zekâ teknolojisinde önemli bir sıçramayı temsil etmektedir. Bu makale, üretken yapay zekânın akıllı şehirlere entegrasyonunu incelemekte ve etkili ve etik bir şekilde uygulanması için kavramsal bir çerçeve sunmaktadır. Çerçevenin ana bileşenleri; veri toplama ve entegrasyonu, üretken yapay zekâ tabanlı analizler, kişiselleştirme, iş birliği ve yönetişimini içermektedir. Çerçeve; veri gizliliği, adalet, şeffaflık ve sürekli iyileştirmenin önemini vurgulamaktadır. Üretken yapay zekâdan yararlanarak, şehirler karmaşık zorlukların üstesinden gelebilir ve gelecekteki kentsel yenilikler için bir yol haritası oluşturabilir.

Anahtar Kelimeler

Akıllı Şehir , Üretken Yapay Zeka , Kavramsal Çerçeve , Karar Destek Sistemi

Kaynakça

Adewopo, V., Elsayed, N., Elsayed, Z., Ozer, M., Zekios, C. L., Abdelgawad, A., & Bayoumi, M. (2024, March). Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems. In SoutheastCon 2024 (pp. 675-680). IEEE. https://doi.org/10.1109/southeastcon52093.2024.10500288
Ahmad, T., Chen, H., & Wang, J. (2014). A review on renewable energy and electricity requirement forecasting models for smart grid and buildings. Sustainable Cities and Society, 12, 94-105. https://doi.org/10.1016/j.scs.2014.04.009
Ai, J. (2024). Revolutionizing Urban Waste Management in San Francisco: The Role of Technology-Driven Solutions in Advancing Circular Economy Practices. International Journal of Business and Technology Management, 6(1), 489-502. https://doi.org/10.55057/ijbtm.2024.6.1.41
Allam, Z., & Dhunny, Z. A. (2019). From solid waste management to sustainable smart city: A case study of Port Louis, Mauritius. Journal of Environmental Management, 234, 34-44. https://doi.org/10.1016/j.jenvman.2018.12.083
Arango, M., Campbell, C., Plangger, K., & Sands, S. (2023). AI-generated marketing content: How to balance creativity and efficiency. Journal of Marketing, 87(3), 45-62. https://doi.org/10.1177/00222429231134798
Ayemowa, M. O., Ibrahim, R., & Khan, M. M. (2024). Analysis of Recommender System Using Generative Artificial Intelligence: A Systematic Literature Review. IEEE Access. https://doi.org/10.1109/access.2024.3416962
Bibri, S. E., Alexandre, A., Sharifi, A., & Krogstie, J. (2023). Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review. Energy Informatics, 6(1), 9. https://doi.org/10.1186/s42162-023-00259-2
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, 100330. https://doi.org/10.1016/j.ese.2023.100330
Bowers, K., & Johnson, S. (2024). Facing the Future of Crime: A Framework for Police Use of Technology. The Political Quarterly. https://doi.org/10.1111/1467-923x.13426
Bourhnane, R., Benhaddadi, F., & Essadiki, M. (2020). AI and Big Data for smart city applications: Challenges and opportunities. In Proceedings of the 2020 International Conference on Data Science and Its Applications (ICoDSA) (pp. 1-7). IEEE. https://doi.org/10.1109/ICoDSA50375.2020.9255637

Böcking, L., Michaelis, A., Schäfermeier, B., Baier, A., Kühl, N., Körner, M. F., & Nolting, L. (2024). Generative Artificial Intelligence in the energy sector. https://epub.uni-bayreuth.de/id/eprint/7674/1/GenAI-in-the-Energy-Sector.pdf
Campbell, C., Plangger, K., Sands, S., & Kietzmann, J. (2022). Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, 51(1), 22-38. https://doi.org/10.1080/00913367.2021.1909515
Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023). Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1), 1-5. https://doi.org/10.1007/s10916-023-01925-4
Cazzaniga, M., Jaumotte, M. F., Li, L., Melina, M. G., Panton, A. J., Pizzinelli, C., ... & Tavares, M. M. M. (2024). Gen-ai: Artificial intelligence and the future of work. International Monetary Fund.
Chandralingam, R. (2024). Role of AI in Enhancing Citizen Engagement in Finnish Municipal Services: Ethical Considerations in UX Design.
Chang, H., & Ross, A. R. (2024). Barcelona, Spain. In Climate Change, Urbanization, and Water Resources: Towards Resilient Urban Water Resource Management (pp. 67-82). Cham: Springer International Publishing.
Chauncey, S. A., & McKenna, H. P. (2024). Creativity and Innovation in Civic Spaces Supported by Cognitive Flexibility When Learning with AI Chatbots in Smart Cities. Urban Science, 8(1), 16. https://doi.org/10.3390/urbansci8010016
Chen, Q., Sun, H., Liu, H., Jiang, Y., Ran, T., Jin, X., ... & Niu, Z. (2023). An extensive benchmark study on biomedical text generation and mining with ChatGPT. Bioinformatics, 39(9). https://doi.org/10.1093/bioinformatics/btad557
Correia, P. M. A. R., Pedro, R. L. D., Mendes, I. D. O., & Serra, A. D. (2024). The Challenges of Artificial Intelligence in Public Administration in the Framework of Smart Cities: Reflections and Legal Issues. Social Sciences, 13(2), 75. https://doi.org/10.3390/socsci13020075
Dada, M. A., Majemite, M. T., Obaigbena, A., Daraojimba, O. H., Oliha, J. S., & Nwokediegwu, Z. Q. S. (2024). Review of smart water management: IoT and AI in water and wastewater treatment. World Journal of Advanced Research and Reviews, 21(1), 1373-1382. https://doi.org/10.30574/wjarr.2024.21.1.0171
Dasborough, M. T. (2023). Awe-inspiring advancements in AI: The impact of ChatGPT on the field of organizational behavior. Journal of Organizational Behavior, 44(2), 177-179. https://doi.org/10.1002/job.2695
Daut, I., Irwanto, M., Syafruddin, H., & Muda, N. (2017). Short-term electrical load forecasting using artificial neural network. Indonesian Journal of Electrical Engineering and Computer Science, 8(1), 33-40. https://doi.org/10.11591/ijeecs.v8.i1.pp33-40
Dureja, A., Dureja, A., Kumar, V., & Sabharwal, S. (2024). Combining Digital Twin Technology and Intelligent Transportation Systems for Smart Mobility. In Transforming Industry using Digital Twin Technology (pp. 281-296). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-58523-4_14
Frey, C. B., & Osborne, M. (2024). Generative AI and the future of work: a reappraisal. Brown Journal of World Affairs, 30(1).
Gasser, L., Le Gall, F., & Abily, M. (2024). Water efficiency in smart cities: optimising irrigation for public green spaces. LHB, 110(1), 2294076. https://doi.org/10.1080/27678490.2023.2294076
Gillotte, J. L. (2019). Copyright infringement in AI-generated artworks. UC Davis Law Review, 53, 2655. https://ssrn.com/abstract=3657423
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
Guevara, J. P., & Auat Cheein, F. A. (2020). Smart cities, the necessity of big data analytics, and the role of IoT. In P. C. Saroj & S. Saravanan (Eds.), Smart Cities: Big Data Prediction Methods and Applications (pp. 17-36). Elsevier. https://doi.org/10.1016/B978-0-12-816646-7.00002-2
Hamdan, A., Ibekwe, K. I., Ilojianya, V. I., Sonko, S., & Etukudoh, E. A. (2024). AI in renewable energy: A review of predictive maintenance and energy optimization. International Journal of Science and Research Archive, 11(1), 718-729. https://doi.org/10.30574/ijsra.2024.11.1.0112
Harris, A. (2024). Understanding Innovation in the Ontario Health System: A Scoping Review and Survey of Ontario Healthcare Providers (Doctoral dissertation, Université d'Ottawa| University of Ottawa).
Iyer, L. S. (2021). AI enabled applications towards intelligent transportation. Transportation Engineering, 5, Article 100083. https://doi.org/10.1016/j.treng.2021.100083
Kadayat, Y., Sharma, S., Agarwal, P., & Mohan, S. (2024). Internet-of-Things Enabled Smart Health Monitoring System Using AutoAI: A Graphical Tool of IBM Watson Studio. In Communication Technologies and Security Challenges in IoT: Present and Future (pp. 427-445). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0052-3_21
Katal, N. (2024). AI-Driven Healthcare Services and Infrastructure in Smart Cities. In Smart Cities (pp. 150-170). CRC Press. https://doi.org/10.1201/9781003442660-7
Khalil, R. A., Safelnasr, Z., Yemane, N., Kedir, M., Shafiqurrahman, A., & Saeed, N. (2024). Advanced learning technologies for intelligent transportation systems: Prospects and challenges. IEEE Open Journal of Vehicular Technology. https://doi.org/10.36227/techrxiv.170906004.46353480/v1
Kim, Y., Park, H., & Ko, H. (2018). An emotionally aware AI smart classroom paradigm. Journal of Education and Learning, 7(4), 29-37. https://doi.org/10.5539/jel.v7n4p29
Konya, A., & Nematzadeh, P. (2024). Recent applications of AI to environmental disciplines: A review. Science of The Total Environment, 906, 167705. https://doi.org/10.1016/j.scitotenv.2023.167705
Liu, J., Niu, M., & Xu, Z. (2021). AI-generated magnetograms of the Sun: Assessing the capabilities of AI in space research. Solar Physics, 296(6), 83. https://doi.org/10.1007/s11207-021-01845-2
Livieris, I. E., Alimpertis, N., Domalis, G., & Tsakalidis, D. (2024, June). An evaluation framework for synthetic data generation models. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 320-335). Cham: Springer Nature Switzerland.
Logani, M., & Makkar, S. (2024). Machine Learning for Smart City AI Systems. In Handbook of Artificial Intelligence for Smart City Development (pp. 1-26). CRC Press. https://doi.org/10.1201/9781003225317-1
Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542. https://doi.org/10.1016/j.jbusres.2024.114542
Marji, N., Kohout, M., Chen, L., Isik, G. E., & Kumar, A. R. (2024). AI-enabled transition to smart European cities. Acta Polytechnica CTU Proceedings, 46, 85-93. https://doi.org/10.14311/app.2024.46.0085
Nikolaeva, A. (2024). Smart Cities and (Smart) Cycling: Exploring the Synergies in Copenhagen and Amsterdam. Journal of Urban Technology, 1-21. https://doi.org/10.1080/10630732.2024.2322007
Okoli, N. J., & Kabaso, B. (2024). Building a smart water city: iot smart water technologies, applications, and future directions. Water, 16(4), 557. https://doi.org/10.3390/w16040557
Olatunde, T. M., Adelani, F. A., & Sikhakhane, Z. Q. (2024.A). A review of smart water management systems from Africa and the United States. Engineering Science & Technology Journal, 5(4), 1231-1242. https://doi.org/10.51594/estj.v5i4.1014
Olatunde, T. M., Okwandu, A. C., Akande, D. O., & Sikhakhane, Z. Q. (2024.B). Reviewing the role of artificial intelligence in energy efficiency optimization. Engineering Science & Technology Journal, 5(4), 1243-1256. https://doi.org/10.51594/estj.v5i4.1015
Pacheco, A., Cano, P., Flores, E., Trujillo, E., & Marquez, P. (2018). A smart classroom based on deep learning and osmotic IoT computing. In 2018 Congreso internacional de innovación y tendencias en ingeniería (CONIITI) (pp. 1-5). IEEE. https://doi.org/10.1109/coniiti.2018.8587095
Pachiappan, K., Anitha, K., Pitchai, R., Sangeetha, S., Satyanarayana, T. V. V., & Boopathi, S. (2024). Intelligent Machines, IoT, and AI in Revolutionizing Agriculture for Water Processing. In Handbook of Research on AI and ML for Intelligent Machines and Systems (pp. 374-399). IGI Global. https://doi.org/10.4018/978-1-6684-9999-3.ch015
Panduman, Y. Y. F., Funabiki, N., Fajrianti, E. D., Fang, S., & Sukaridhoto, S. (2024). A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform. Information, 15(3), 153. https://doi.org/10.3390/info15030153
Park, W., & Kwon, H. (2024). Implementing artificial intelligence education for middle school technology education in Republic of Korea. International journal of technology and design education, 34(1), 109-135. https://doi.org/10.1007/s10798-023-09812-2
Pasandi, F. B. (2024). Creative Organic Smart Spaces and Communities: Leveraging Technology to Fight Socio-Environmental Impacts. https://hal.science/hal-04527432/document
Pelaez, S., Verma, G., Ribeiro, B., & Shapira, P. (2024). Large-scale text analysis using generative language models: A case study in discovering public value expressions in AI patents. Quantitative Science Studies, 5(1), 153-169. https://doi.org/10.1162/qss_a_00285
Ray, S. (2023, February 22). JPMorgan Chase restricts staffers’ use of ChatGPT. Forbes. https://www.forbes.com/sites/siladityaray/2023/02/22/jpmorgan-chase-restricts-staffers-use-of-chatgpt/?sh=714e937d6bc7
Rissanen, T. (2024). The Use of Generative Artificial Intelligence in Public Procurement. https://www.theseus.fi/bitstream/handle/10024/855072/Rissanen_Toni.pdf?sequence=2&isAllowed=y
Rodriguez, D. V., Lawrence, K., Gonzalez, J., Brandfield-Harvey, B., Xu, L., Tasneem, S., ... & Mann, D. (2024). Leveraging generative AI tools to support the development of digital solutions in health care research: case study. JMIR Human Factors, 11(1), e52885. https://doi.org/10.2196/52885
Roy, P., Ghosh, S., Podder, A., & Paul, S. (2024). Green IoT for Eco-Friendly and Sustainable Smart Cities. In Convergence Strategies for Green Computing and Sustainable Development (pp. 124-137). IGI Global. https://doi.org/10.4018/979-8-3693-0338-2.ch007
Schwalt Chan, C. (2024). Exploring the Ethics of Generative AI within Humanitarian Organisations in Geneva. https://mau.diva-portal.org/smash/get/diva2:1866697/FULLTEXT02.pdf
Siau, K. (2018). Education in the age of artificial intelligence: How will technology shape learning? The Global Analyst, 7(3), 22-24. https://www.temjournal.com/content/131/TEMJournalFebruary2024_404_413.pdf
Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management, 31(2), 74-87. https://doi.org/10.4018/JDM.2020040105
Sipahi, E. B., & Saayi, Z. (2024). The world’s first “Smart Nation” vision: the case of Singapore. Smart Cities and Regional Development (SCRD) Journal, 8(1), 41-58. https://doi.org/10.25019/dvm98x09
Sonko, S., Adewusi, A. O., Obi, O. C., Onwusinkwue, S., & Atadoga, A. (2024). A critical review towards artificial general intelligence: Challenges, ethical considerations, and the path forward. World Journal of Advanced Research and Reviews, 21(3), 1262-1268. https://doi.org/10.30574/wjarr.2024.21.3.0817
Stancati, M., & Schechner, S. (2023, March 31). ChatGPT banned in Italy over data-privacy concerns. The Wall Street Journal. https://www.wsj.com/articles/chatgpt-banned-in-italy-over-data-privacy-concerns-4b984e75
Stephens, R. (2023). Green Cities Artificial Intelligence. https://scholarsbank.uoregon.edu/xmlui/bitstream/handle/1794/29247/PPPM_445_Green_Cities_AI_2023.pdf?sequence=1&isAllowed=y
University of Oxford. (2023). Four lessons from ChatGPT: Challenges and opportunities for educators. Available at https://www.ctl.ox.ac.uk/article/four-lessons-from-chatgpt-challenges-and-opportunities-for-educators#Lesson3
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Wan, Z., Wang, S., & Yuan, Y. (2022). The ethical considerations of AI in medical diagnosis: A review. Journal of Medical Ethics, 48(10), 806-814. https://doi.org/10.1136/medethics-2022-108104
Whittaker, J., Asri, L. E., & Shapiro, A. (2020). The ethics of AI-generated synthetic faces: Deepfakes in 2020. Journal of Artificial Intelligence Research, 69, 251-266. https://doi.org/10.1613/jair.1.12373
Wolniak, R. (2023). Smart mobility in smart city–Copenhagen and Barcelona comparision. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska.
Wong, Y., Fan, S., Guo, Y., Xu, Z., Stephen, K., Sheoran, R., & Kankanhalli, M. (2022, October). Compute to tell the tale: Goal-driven narrative generation. In Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal (pp. 6875-6882). https://doi.org/10.1145/3474085.3478327
Woolf, J. (2022). ChatGPT passed the Turing test. Available at https://mpost.io/chatgpt-passes-the-turing-test/
Yin, Y., Siau, K., & Wen, X. (2022). Smart health: Intelligent healthcare systems in the metaverse, artificial intelligence, and data science era. Journal of Organizational and End User Computing, 34(1), 1-14. https://doi.org/10.4018/JOEUC.308814
Yussuf, R. O., & Asfour, O. S. (2024). Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview. Energy and Buildings, 113903. https://doi.org/10.1016/j.enbuild.2024.113903

Ayrıntılar

Birincil Dil

Türkçe

Konular

Kentsel Bilişim

Bölüm

Araştırma Makalesi

Yazarlar

Ezgi Avcı ^*
0000-0002-9826-1027
Türkiye

Yayımlanma Tarihi

17 Eylül 2024

Gönderilme Tarihi

27 Mayıs 2024

Kabul Tarihi

16 Eylül 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 17 Sayı: 5

DOI

https://doi.org/10.35674/kent.1490925

IZ

https://izlik.org/JA65NR24HP

APA

Avcı, E. (2024). Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi. Kent Akademisi, 17(5), 1654-1675. https://doi.org/10.35674/kent.1490925

Cited By

Long-term monitoring (2022–2024) of solid waste pollution in inland water ecosystems of Türkiye: Spatial and temporal dynamics

Waste Management

https://doi.org/10.1016/j.wasman.2025.114807

Artificial Intelligence Research in Smart Cities: A Bibliometric Analysis

Kent Akademisi

https://doi.org/10.35674/kent.1669862

Akıllı Şehir Uygulamaları Farkındalık Ölçeği Geliştirme ve Uygulama Çalışması

Anadolu Üniversitesi Sosyal Bilimler Dergisi

https://doi.org/10.18037/ausbd.1712489

Artificial Intelligence and Landscape Sustainability: Comparative Insights from Urban Sports and Recreation Areas in Turkey and Lithuania

Land

https://doi.org/10.3390/land14122330

Examining the Impact of Digital Technologies on the Generative Artificial Intelligence Integration of Businesses in Türkiye

OPUS Toplum Araştırmaları Dergisi

https://doi.org/10.26466/opusjsr.1684795

International Refereed and Indexed Journal of Urban Culture and Management | Kent Kültürü ve Yönetimi Uluslararası Hakemli İndeksli Dergi

Bilgi, İletişim, Kültür, Sanat ve Medya Hizmetleri (ICAM Network) www.icamnetwork.net

Executive Office: Ahmet Emin Fidan Culture and Research Center, Evkaf Neigh. No: 34 Fatsa Ordu
Tel: +90452 310 20 30 Faks: +90452 310 20 30 | E-Mail: (int): info@icamnetwork.net | (TR) bilgi@icamnetwork.net

Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi

A generative AI conceptual framework for smart cities

Abstract

Keywords

Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Long-term monitoring (2022–2024) of solid waste pollution in inland water ecosystems of Türkiye: Spatial and temporal dynamics

Artificial Intelligence Research in Smart Cities: A Bibliometric Analysis

Akıllı Şehir Uygulamaları Farkındalık Ölçeği Geliştirme ve Uygulama Çalışması

Artificial Intelligence and Landscape Sustainability: Comparative Insights from Urban Sports and Recreation Areas in Turkey and Lithuania

Examining the Impact of Digital Technologies on the Generative Artificial Intelligence Integration of Businesses in Türkiye