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Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province

Yıl 2025, Cilt: 37 Sayı: 2, 723 - 735, 30.09.2025
https://doi.org/10.35234/fumbd.1692094

Öz

The integration of artificial intelligence (AI) in the tourism sector has emerged as a transformative approach to enhance the travel experiences and promote regional attractions. This study explores the application of AI in smart tourism by developing a convolutional neural network (CNN)-based landmark classification system for Diyarbakır Province, a culturally rich region in South-East Turkey. The method specifically focuses on classifying images of five popular landmarks in Diyarbakır, leveraging state-of-the-art AI techniques to enhance regional visibility and tourist engagement. To achieve this, a pre-trained CNN model, namely AlexNet, was fine-tuned for the landmark classification task. By adapting the parameters of AlexNet to the specific dataset, the model was optimized for improved feature extraction and classification accuracy for totally 5 classes (places). The proposed framework was evaluated using a carefully curated dataset, yielding a remarkable 97.5% classification accuracy on the test set. The performance of the proposed model highlights the reliability and effectiveness of the methodology in accurately identifying landmarks, even with complex architectural and environmental features. These results can concretely contribute to both academic research and practical applications by providing a lightweight and accurate model that can be embedded into mobile travel applications or digital tourism platforms for real-time landmark recognition, enhancing tourist engagement and regional visibility.

Kaynakça

  • Knani M, Echchakoui S, Ladhari R. Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda. Int J Hosp Manag, 2022; 107, 103317.
  • Hott Correa SC, Gosling MdS. Travelers' perception of smart tourism experiences in smart tourism destinations. Tour Plan Dev, 2020; 18, 415–434.
  • Bhuiyan KH, Jahan I, Zayed NM, Islam KMA, Suyaiya S, Tkachenko O, Nitsenko V. Smart Tourism Ecosystem: A New Dimension toward Sustainable Value Co-Creation. Sustainability, 2022; 14(22): 15043.
  • Gretzel U, Sigala M, Xiang Z, Koo C. Smart tourism: Foundations and developments. Electron Mark, 2015; 25(3):179-188.
  • Gössling S. Technology, ICT and tourism: from big data to the big picture. J Sustain Tour, 2020; 29(5): 849–858.
  • Cui L, Xie G, Qu Y, Gao L, Yang Y. Security and Privacy in Smart Cities: Challenges and Opportunities. IEEE Access, 2018; 6: 46134-46145.
  • Liu X, Mehraliyev F, Liu C, Schuckert M. The roles of social media in tourists choices of travel components. Tourist Stud, 2020;20(1): 27-48.
  • Li Y, Hu C, Huang C, Duan L. The concept of smart tourism in the context of tourism information services. Tourism Manag, 2017; 58: 293-300.
  • Houssein EH, Othman MA, Mohamed WM, Younan M. Internet of Things in Smart Cities: Comprehensive Review, Open Issues, and Challenges. IEEE Internet Things J, 2024; 11(21): 34941-34952.
  • Cardoso L, Fraga C. Shaping the Future of Destinations: New Clues to Smart Tourism Research from a Neuroscience Methods Approach. Adm Sci, 2024; 14(6):106.
  • Huang XK, Yuan JZ, Shi MY. Condition and key issues analysis on the smarter tourism construction in China. In International Conference on Multimedia and Signal Processing. 2012, Springer, Berlin, 444-450.
  • Yoo CW, Goo J, Huang CD, Nam K, Woo M. Improving travel decision support satisfaction with smart tourism technologies: A framework of tourist elaboration likelihood and self-efficacy. Technol Forecast Soc Change. 2017; 123: 330–341.
  • Shen Y, Wu Y, Song J, Kong X, Pau G. Enabling personalized smart tourism with location-based social networks. PeerJ Comput Sci, 2024; 10:e2375.
  • Akdu U. Smart Tourism: Issues, Challenges and Opportunities. Emerald Handbook ICT Tourism Hosp, Emerald Publishing Limited, 2020, Leeds, 291-308.
  • Wu W, Xu C, Zhao M, Li X, Law R. Digital Tourism and Smart Development: State-of-the-Art Review. Sustainability, 2024;16(23):10382.
  • Rosário AT, Dias JC. Exploring the Landscape of Smart Tourism: A Systematic Bibliometric Review of the Literature of the Internet of Things. Adm Sci, 2024; 14(2): 22.
  • Zhu W, Zhang L, Li N. Challenges, function changing of government and enterprises in Chinese smart tourism. Inform Commun Technol Tour, 2014; 10;553-564.
  • Neuhofer B, Buhalis D, Ladkin A. Smart technologies for personalized experiences: a case study in the hospitality domain. Electron Mark, 2015; 25: 243–254.
  • Gretzel U, Werthner H, Koo C, Lamsfus C. Conceptual foundations for understanding smart tourism ecosystems. Comput Human Behav, 2015; 50:558– 563.
  • Kontogianni A, Alepis E. Smart tourism: State of the art and literature review for last six years. Array, 2020, 6(100020), 61–69.
  • Barbierato E, Gatti A. The Challenges of Machine Learning: A Critical Review. Electronics, 2024, 13(2), 416.
  • Gerlich M. Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study. Soc Sci, 2023; 12(9): 502.
  • Korkmaz Y, Boyaci A. Hybrid voice activity detection system based on LSTM and auditory speech features. Biomed Signal Process Control, 2023, Vol:80, 104408.
  • Korkmaz Y, Boyaci A. Analysis of speaker's gender effects in voice onset time of Turkish stop consonants. 6th International Symposium on Digital Forensic and Security (ISDFS), 2018, Antalya, Turkey.
  • Korkmaz Y, Boyaci A. Unsupervised and supervised VAD systems using combination of time and frequency domain features. Biomed Signal Process Control, 2020, Vol:61, 102044.
  • Okwor IA, Hitch G, Hakkim S, Akbar S, Sookhoo D, Kainesie J. Digital Technologies Impact on Healthcare Delivery: A Systematic Review of Artificial Intelligence (AI) and Machine-Learning (ML) Adoption, Challenges, and Opportunities. AI, 2024; 5(4): 1918-1941.
  • Samara D, Mahnisalis I, Peristeras V. Artificial intelligence and big data in tourism: a systematic literature review. J Hosp Tour Technol, 2020; 11(2): 343-367.
  • Buhalis D, Sinarta Y. Real-time co-creation and nowness service: lessons from tourism and hospitality. J Travel Tour Mark, 2019; 36(5): 563-582.
  • Khan N, Khan W, Humayun M, Naz A. Unlocking the Potential: Artificial Intelligence Applications in Sustainable Tourism. The Role of Artificial Intelligence in Regenerative Tourism and Green Destinations, New Perspect Tour Hosp Manag, Emerald Publishing Limited Leeds, 2024, 303-316.
  • Yersüren S, Özel ÇH. The effect of virtual reality experience quality on destination visit intention and virtual reality travel intention. J Hosp Tour Technol, 2024; 15(1): 70-103.
  • Han H, Kim SS, Hailu TB, Al-Ansi A, Lee J, Kim JJ. Effects of cognitive, affective and normative drivers of artificial intelligence ChatGPT on continuous use intention. J Hosp Tour Technol, 2024; 15(4): 629-647.
  • Zhu Y, Zhang RR, Zou Y, Jin D. Investigating customers’ responses to artificial intelligence chatbots in online travel agencies: the moderating role of product familiarity. J Hosp Tour Technol, 2023; 14(2): 208-224.
  • Goodfellow I, Bengio Y, Courville A. Deep Learning, MIT Press, 2016.
  • Alex K, Sutskever I, Geoffrey EH. ImageNet Classification with Deep Convolutional Neural Networks. Adv Neural Inf Process Syst, 2012, pp.1097-1105, 2012.
  • Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A Large-Scale Hierarchical Image Database. IEEE Conference on Computer Vision and Pattern Recognition, Miami/USA, 2009, pp.248-255.
  • Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data, 2016, 3(9).
  • Binrong W, Lin W, Yu-Rong Z. Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19. Appl Intell, 2023; 53:14493-14514.
  • Kaibo L, Huwei L, Man S, Junhui Z, Xiaolan L, Li Z. Enhancing scenic recommendation and tour route personalization in tourism using UGC text mining. Appl Intell, 2024; 54:1063-1098.
  • Jian W, Qing H, Mingshuo C, Yujia L, Hamido F. A group consensus-based travel destination evaluation method with online reviews. Appl Intell, 2022;52:1306-1324.

Akıllı Turizmde Yapay Zeka Kullanımı: Diyarbakır İli için Evrişimsel Sinir Ağı Tabanlı Bir Turistik Yer Sınıflandırması

Yıl 2025, Cilt: 37 Sayı: 2, 723 - 735, 30.09.2025
https://doi.org/10.35234/fumbd.1692094

Öz

Yapay zekanın turizm sektörüne entegrasyonu, seyahat deneyimlerini geliştirmek ve bölgesel cazibe merkezlerini tanıtmak için dönüştürücü bir yaklaşım olarak ortaya çıkmıştır. Bu çalışma, Türkiye'nin Güneydoğusunda yer alan kültürel açıdan zengin bir bölge olan Diyarbakır ili için Evrişimli Sinir Ağı (CNN) tabanlı bir turistik yer noktası sınıflandırma sistemi geliştirerek akıllı turizmde yapay zeka uygulamasını araştırmaktadır. Yöntem, özellikle bölgesel görünürlüğü ve turist katılımını artırmak için en son yapay zeka tekniklerinden yararlanarak, Diyarbakır'daki beş popüler turistik yer görüntülerini sınıflandırmaya odaklanmaktadır. Bunu başarmak için, AlexNet adlı önceden eğitilmiş bir CNN modeli, turistik yer sınıflandırma görevi için ince ayarlanmıştır. AlexNet'in parametrelerini belirli veri setine uyarlayarak, model toplam 5 sınıf (yer) için iyileştirilmiş özellik çıkarma ve sınıflandırma doğruluğu için optimize edilmiştir. Önerilen model, dikkatlice düzenlenmiş bir veri seti kullanılarak değerlendirilmiş ve test setinde %97,5’lik dikkate değer bir sınıflandırma doğruluğu elde edilmiştir. Önerilen modelin performansı, karmaşık mimari ve çevresel özelliklere sahip olsa bile turistik yer noktalarını doğru bir şekilde tanımlamada metodolojinin güvenilirliğini ve etkinliğini vurgulamaktadır. Bu çalışmanın sonuçları, mobil seyahat uygulamalarına entegre edilebilecek hafif ve doğru bir model sunarak, hem akademik çalışmalara hem de turistik etkileşimi arttırabilecek pratik uygulamalara katkı sağlamaktadır.

Kaynakça

  • Knani M, Echchakoui S, Ladhari R. Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda. Int J Hosp Manag, 2022; 107, 103317.
  • Hott Correa SC, Gosling MdS. Travelers' perception of smart tourism experiences in smart tourism destinations. Tour Plan Dev, 2020; 18, 415–434.
  • Bhuiyan KH, Jahan I, Zayed NM, Islam KMA, Suyaiya S, Tkachenko O, Nitsenko V. Smart Tourism Ecosystem: A New Dimension toward Sustainable Value Co-Creation. Sustainability, 2022; 14(22): 15043.
  • Gretzel U, Sigala M, Xiang Z, Koo C. Smart tourism: Foundations and developments. Electron Mark, 2015; 25(3):179-188.
  • Gössling S. Technology, ICT and tourism: from big data to the big picture. J Sustain Tour, 2020; 29(5): 849–858.
  • Cui L, Xie G, Qu Y, Gao L, Yang Y. Security and Privacy in Smart Cities: Challenges and Opportunities. IEEE Access, 2018; 6: 46134-46145.
  • Liu X, Mehraliyev F, Liu C, Schuckert M. The roles of social media in tourists choices of travel components. Tourist Stud, 2020;20(1): 27-48.
  • Li Y, Hu C, Huang C, Duan L. The concept of smart tourism in the context of tourism information services. Tourism Manag, 2017; 58: 293-300.
  • Houssein EH, Othman MA, Mohamed WM, Younan M. Internet of Things in Smart Cities: Comprehensive Review, Open Issues, and Challenges. IEEE Internet Things J, 2024; 11(21): 34941-34952.
  • Cardoso L, Fraga C. Shaping the Future of Destinations: New Clues to Smart Tourism Research from a Neuroscience Methods Approach. Adm Sci, 2024; 14(6):106.
  • Huang XK, Yuan JZ, Shi MY. Condition and key issues analysis on the smarter tourism construction in China. In International Conference on Multimedia and Signal Processing. 2012, Springer, Berlin, 444-450.
  • Yoo CW, Goo J, Huang CD, Nam K, Woo M. Improving travel decision support satisfaction with smart tourism technologies: A framework of tourist elaboration likelihood and self-efficacy. Technol Forecast Soc Change. 2017; 123: 330–341.
  • Shen Y, Wu Y, Song J, Kong X, Pau G. Enabling personalized smart tourism with location-based social networks. PeerJ Comput Sci, 2024; 10:e2375.
  • Akdu U. Smart Tourism: Issues, Challenges and Opportunities. Emerald Handbook ICT Tourism Hosp, Emerald Publishing Limited, 2020, Leeds, 291-308.
  • Wu W, Xu C, Zhao M, Li X, Law R. Digital Tourism and Smart Development: State-of-the-Art Review. Sustainability, 2024;16(23):10382.
  • Rosário AT, Dias JC. Exploring the Landscape of Smart Tourism: A Systematic Bibliometric Review of the Literature of the Internet of Things. Adm Sci, 2024; 14(2): 22.
  • Zhu W, Zhang L, Li N. Challenges, function changing of government and enterprises in Chinese smart tourism. Inform Commun Technol Tour, 2014; 10;553-564.
  • Neuhofer B, Buhalis D, Ladkin A. Smart technologies for personalized experiences: a case study in the hospitality domain. Electron Mark, 2015; 25: 243–254.
  • Gretzel U, Werthner H, Koo C, Lamsfus C. Conceptual foundations for understanding smart tourism ecosystems. Comput Human Behav, 2015; 50:558– 563.
  • Kontogianni A, Alepis E. Smart tourism: State of the art and literature review for last six years. Array, 2020, 6(100020), 61–69.
  • Barbierato E, Gatti A. The Challenges of Machine Learning: A Critical Review. Electronics, 2024, 13(2), 416.
  • Gerlich M. Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study. Soc Sci, 2023; 12(9): 502.
  • Korkmaz Y, Boyaci A. Hybrid voice activity detection system based on LSTM and auditory speech features. Biomed Signal Process Control, 2023, Vol:80, 104408.
  • Korkmaz Y, Boyaci A. Analysis of speaker's gender effects in voice onset time of Turkish stop consonants. 6th International Symposium on Digital Forensic and Security (ISDFS), 2018, Antalya, Turkey.
  • Korkmaz Y, Boyaci A. Unsupervised and supervised VAD systems using combination of time and frequency domain features. Biomed Signal Process Control, 2020, Vol:61, 102044.
  • Okwor IA, Hitch G, Hakkim S, Akbar S, Sookhoo D, Kainesie J. Digital Technologies Impact on Healthcare Delivery: A Systematic Review of Artificial Intelligence (AI) and Machine-Learning (ML) Adoption, Challenges, and Opportunities. AI, 2024; 5(4): 1918-1941.
  • Samara D, Mahnisalis I, Peristeras V. Artificial intelligence and big data in tourism: a systematic literature review. J Hosp Tour Technol, 2020; 11(2): 343-367.
  • Buhalis D, Sinarta Y. Real-time co-creation and nowness service: lessons from tourism and hospitality. J Travel Tour Mark, 2019; 36(5): 563-582.
  • Khan N, Khan W, Humayun M, Naz A. Unlocking the Potential: Artificial Intelligence Applications in Sustainable Tourism. The Role of Artificial Intelligence in Regenerative Tourism and Green Destinations, New Perspect Tour Hosp Manag, Emerald Publishing Limited Leeds, 2024, 303-316.
  • Yersüren S, Özel ÇH. The effect of virtual reality experience quality on destination visit intention and virtual reality travel intention. J Hosp Tour Technol, 2024; 15(1): 70-103.
  • Han H, Kim SS, Hailu TB, Al-Ansi A, Lee J, Kim JJ. Effects of cognitive, affective and normative drivers of artificial intelligence ChatGPT on continuous use intention. J Hosp Tour Technol, 2024; 15(4): 629-647.
  • Zhu Y, Zhang RR, Zou Y, Jin D. Investigating customers’ responses to artificial intelligence chatbots in online travel agencies: the moderating role of product familiarity. J Hosp Tour Technol, 2023; 14(2): 208-224.
  • Goodfellow I, Bengio Y, Courville A. Deep Learning, MIT Press, 2016.
  • Alex K, Sutskever I, Geoffrey EH. ImageNet Classification with Deep Convolutional Neural Networks. Adv Neural Inf Process Syst, 2012, pp.1097-1105, 2012.
  • Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A Large-Scale Hierarchical Image Database. IEEE Conference on Computer Vision and Pattern Recognition, Miami/USA, 2009, pp.248-255.
  • Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data, 2016, 3(9).
  • Binrong W, Lin W, Yu-Rong Z. Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19. Appl Intell, 2023; 53:14493-14514.
  • Kaibo L, Huwei L, Man S, Junhui Z, Xiaolan L, Li Z. Enhancing scenic recommendation and tour route personalization in tourism using UGC text mining. Appl Intell, 2024; 54:1063-1098.
  • Jian W, Qing H, Mingshuo C, Yujia L, Hamido F. A group consensus-based travel destination evaluation method with online reviews. Appl Intell, 2022;52:1306-1324.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme, Yapay Zeka (Diğer)
Bölüm MBD
Yazarlar

Yunus Korkmaz 0000-0002-6315-5750

Yayımlanma Tarihi 30 Eylül 2025
Gönderilme Tarihi 5 Mayıs 2025
Kabul Tarihi 10 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 37 Sayı: 2

Kaynak Göster

APA Korkmaz, Y. (2025). Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 37(2), 723-735. https://doi.org/10.35234/fumbd.1692094
AMA Korkmaz Y. Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. Eylül 2025;37(2):723-735. doi:10.35234/fumbd.1692094
Chicago Korkmaz, Yunus. “Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 37, sy. 2 (Eylül 2025): 723-35. https://doi.org/10.35234/fumbd.1692094.
EndNote Korkmaz Y (01 Eylül 2025) Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 37 2 723–735.
IEEE Y. Korkmaz, “Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 37, sy. 2, ss. 723–735, 2025, doi: 10.35234/fumbd.1692094.
ISNAD Korkmaz, Yunus. “Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 37/2 (Eylül2025), 723-735. https://doi.org/10.35234/fumbd.1692094.
JAMA Korkmaz Y. Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2025;37:723–735.
MLA Korkmaz, Yunus. “Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 37, sy. 2, 2025, ss. 723-35, doi:10.35234/fumbd.1692094.
Vancouver Korkmaz Y. Usage of Artificial Intelligence in Smart Tourism: A CNN-based Landmark Classification for Diyarbakir Province. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2025;37(2):723-35.