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Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi

Yıl 2024, Cilt: 17 Sayı: 5, 1654 - 1675, 17.09.2024
https://doi.org/10.35674/kent.1490925

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.

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

A generative AI conceptual framework for smart cities

Yıl 2024, Cilt: 17 Sayı: 5, 1654 - 1675, 17.09.2024
https://doi.org/10.35674/kent.1490925

Öz

The rapid urbanization and technological advancements of the 21st century have given rise to the concept of smart cities, which aim to improve urban life through digital technologies and data-driven solutions. Generative artificial intelligence represents a significant leap in AI technology, offering capabilities that can transform urban living. This paper investigates the integration of generative artificial intelligence into smart cities and presents a conceptual framework for its effective and ethical implementation. The main components of the framework include data collection and integration, generative artificial intelligence-based analyses, personalization, collaboration, and governance. The framework emphasizes the importance of data privacy, fairness, transparency, and continuous improvement. By leveraging generative AI, cities can overcome complex challenges and create a roadmap for future urban innovations.

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
Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Kentsel Bilişim
Bölüm Tüm Makaleler
Yazarlar

Ezgi Avcı 0000-0002-9826-1027

Yayımlanma Tarihi 17 Eylül 2024
Gönderilme Tarihi 27 Mayıs 2024
Kabul Tarihi 16 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 17 Sayı: 5

Kaynak Göster

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

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

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