SavedAdd your journal

Journals / Advances in Hospitality and Tourism Research (AHTR) / 2026 / Volume: 13 Issue: 2

Case Study: The Classification of the Rooms in Holiday Homes with Deep Learning

Mevlüt Kağan Balga(SELCUK UNIVERSITY)0000-0003-1895-0744
Fatih Basciftci(SELCUK UNIVERSITY, FACULTY OF TECHNOLOGY)0000-0003-1679-7416
Pages
—
Publication Date
2025-06-12
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The following accuracies were observed based on 20 epochs of training; ResNet 97.4%, DenseNet 98.69%, VGGNet 97.31%, Inception v3 97.33%, and NASNet 97.21%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:From reservation to the accommodation process, the effects of technology are increasing day by day in the field of tourism.
📊
Key Findings & Data:In the second step, 5 different deep learning models were trained to compare the accuracy and loss with the dataset.
💡
Conclusion & Impact:The following accuracies were observed based on 20 epochs of training; ResNet 97.4%, DenseNet 98.69%, VGGNet 97.31%, Inception v3 97.33%, and NASNet 97.21%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:holiday homes · convolutional neural network · deep learning

Abstract

From reservation to the accommodation process, the effects of technology are increasing day by day in the field of tourism. Online booking platforms, virtual support assistants, mobile applications, and artificial intelligence tools can be given as examples. In the focus on artificial intelligence for tourism, different tools can be presented as examples, especially price analysis regression/recommendations, room, house & amenity classifications from images, and occupancy estimations. Our case study consists of two different steps. First, a dataset was created from a German-based tourism reservation company. In the second step, 5 different deep learning models were trained to compare the accuracy and loss with the dataset. We trained ResNet, DenseNet, VGGNet, Inception v3, and NASNet models. The following accuracies were observed based on 20 epochs of training; ResNet 97.4%, DenseNet 98.69%, VGGNet 97.31%, Inception v3 97.33%, and NASNet 97.21%.

🔖

Cite This Article

Balga, M. K., Basciftci, F. (2025). Case Study: The Classification of the Rooms in Holiday Homes with Deep Learning. Advances in Hospitality and Tourism Research (AHTR), Volume: 13 Issue: 2. https://doi.org/10.30519/ahtr.1453400

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (64)

  1. Abhisheka, B., Biswas, S. K., & Purkayastha, B. (2023). A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning. Archives of Computational Methods in Engineering, 30(8), 5023-5052. https://doi.org/10.1007/s11831-023-09968-z
  2. Alshmrani, G. M. M., Ni, Q., Jiang, R., Pervaiz, H., & Elshennawy, N. M. (2023). A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images. Alexandria Engineering Journal, 64, 923-935. https://doi.org/10.1016/j.aej.2022.10.053
  3. Algani, Y. M. A., Caro, O. J. M., Bravo, L. M. R., Kaur, C., Ansari, M. S. A., & Bala, B. K. (2023). Leaf disease identification and classification using optimized deep learning. Measurement: Sensors, 25, 100643.
  4. Akılotu, B. N. (2019). Osas hastaları ile sağlıklı bireylerdeki nabız geçiş sürelerinin makina öğrenmesi teknikleriyle sınıflandırılması. Fırat University, Institute of Science, Master Thesis.
  5. Almisreb, A., A., Jamil, N., & Din, N. M. (2018). Utilizing AlexNet Deep Transfer Learning for Ear Recognition. Fourth International Conference on Information Retrieval and Knowledge Management, 8-12. https://doi.org/10.1109/INFRKM.2018.8464769
  6. Assa Abloy. (2017). Europe’s holiday rentals market. Retrieved November 27, 2020, from https://www.assaabloy.de/Other/Testlab/EMEA%20Marketing/SDL/holiday-rental-2017-09/WEB_BRO%20SDL%E2%80%89%E2%80%89HOLIDAY%20RENTAL%2009%202017%20ENG%20UK-white-paper.pdf
  7. Balga, M. K. (2020). The classification and application of the rooms in holiday homes by deep learning. Selcuk University, Institute of Science, Master Thesis.
  8. Bozyiğit, F., Taşkın, A., Akar, K., & Kılınç, D. (2021). A Deep Learning-Based Hotel Image Classifier for Online Travel Agencies. DEUFMD, 23(67), 257-264. https://doi.org/10.21205/deufmd.2021236722
  9. Chang, M., Xing, Y. Y., Zhang, Q. Y., Han, S., & Kim, M. (2020). A CNN Image Classification Analysis for ‘Clean-Coast Detector’ as Tourism Service Distribution. Journal of Distribution Science, 18(1), 15-26. https://doi.org/10.15722/jds.18.1.202001.15
  10. Crnojevac, I. H., Gugić, J., & Karlovčan, S. (2010). eTourism: A comparison of Online and Offline Bookings and the Importance of Hotel Attributes. Journal of Information and Organizational Sciences, 34(1), 41-54.
  11. Deng, L., & Yu, D. (2013). Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, 7(3-4), 197-387. http://dx.doi.org/10.1561/2000000039
  12. Ezel, E. (2018). Derin öğrenme yöntemi kullanılarak görüntü-tabanlı türk işaret dili tanıma. Selcuk University, Institute of Science, Master Thesis.
  13. Firdaus, N. M., Chahyati, D., & Fanany, M. I. (2018). Tourist Attractions Classification using ResNet. International Conference on Advanced Computer Science and Information Systems (ICACSIS), 429-433. https://doi.org/10.1109/ICACSIS.2018.8618235
  14. Future of Privacy Forum. (2018). The Privacy Expert’s Guide to Artificial Intelligence and Machine Learning. Future of Privacy Forum. Retrieved November 27, 2020, from https://fpf.org/wp-content/uploads/2018/10/FPF_Artificial-Intelligence_Digital.pdf
  15. Gupta, A., Chug, A., & Singh, A. P. (2024). Potato disease prediction using machine learning, image processing and IoT – a systematic literature survey. Journal of Crop Improvement, 38(2), 95-137. https://doi.org/10.1080/15427528.2023.2285827
  16. Gupta, K., & Bajaj, V. (2023). Deep learning models-based CT-scan image classification for automated screening of COVID-19. Biomed Signal Process Control, 80, 104268. https://doi.org/10.1016/j.bspc.2022.104268
  17. Gürkaynak, C., D. (2018). Fine-tuning convolutional neural Networks for maritime vessel classification, verification, and recognition. Bahçeşehir University, Institute of Science, Master Thesis.
  18. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90
  19. Huang, G., Liu, Z., Maaten, L., & Weinberger, K., Q. (2018). Densely Connected Convolutional Networks. arXiv preprint arXiv:1608.06993. https://doi.org/10.48550/arXiv.1608.06993
  20. Kadiroğlu, Z. (2019). Histopatolojik meme kanseri görüntülerinin evrişimsel sinir ağları kullanılarak sınıflandırılması. Fırat University, Institute of Science, Master Thesis.
  21. Kang, Y., Cho, N., Park, S., & Kim, J. (2021). Exploring Tourism Activities of Tourists and Residents through Convolutional Neural Network-based SNS Photo Classification. Journal of the Korean Geographical Society, 56(3), 247-264. https://doi.org/10.22776/kgs.2021.56.3.247
  22. Kayaalp, K., & Süzen, A. A. (2018). Derin Öğrenme ve Türkiye’deki Uygulamaları. İKSAD Publishing House. Keras. (2019). Retrieved November 27, 2020, from https://keras.io/
  23. Kılınç, H. (2018). Derin öğrenmeli konvolüsyonel sinir ağları (deep learning convolutional neural network) kullanarak fotoğraftan trizomi 21 (down sendromu) tespiti. Çukurova University, Institute of Science, Master Thesis.
  24. Kim, J., Kang, Y., Cho, N., & Park, S. (2021). Classifying Tourists’ Photos and Exploring Tourism Destination Image Using a Deep Learning Model. Abstr. Int. Cartogr. Assoc., 3, 150. https://doi.org/10.5194/ica-abs-3-150-2021
  25. Kim, J., & Kang, Y. (2022). Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering. International Journal of Geo-Information, 11(4), 245. https://doi.org/10.3390/ijgi11040245
  26. Law, R., Li, G., Fong, D. K. C., Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75(C), 410-423. https://doi.org/10.1016/j.annals.2019.01.014
  27. Lévy, D., & Jain, A. (2016). Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks. arXiv preprint arXiv: 1612.00542. https://doi.org/10.48550/arXiv.1612.00542
  28. Marigliano, P. (2024). Analyzing tourism reviews using Deep Learning and AI to predict sentiments. Quality & Quantity. https://doi.org/10.1007/s11135-024-01840-x
  29. Martín, J. M. M., Martín, J. A. R., Mejía, K. A. Z., & Fernández, J. A. S. (2018). Effects of Vacation Rental Websites on the Concentration of Tourists —Potential Environmental Impacts. An Application to the Balearic Islands in Spain. International Journal of Environmental Research and Public Health, 15(2), 347. https://doi.org/10.3390/ijerph15020347
  30. Mehr, H., D. (2017). Classification of lung CT images using deep convolutional neural network. Gazi University, Institute of Science, Master Thesis.
  31. Mira, E. S., Sapri, A. M. S., Aljehani, R. F., Jambi, B. S., Bashir, T., El-Kenawy, E. M., & Saber, M. (2024). Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications (FPA), 14(1), 293-308. https://doi.org/10.54216/FPA.140122
  32. Oskam, J., & Boswijk, A. (2016). Airbnb: the future of networked hospitality businesses. Journal of Tourism Futures, 2(1), 22-42. https://doi.org/10.1108/JTF-11-2015-0048
  33. Örs, D. Z. D. (2018). Farklı dalga boylu görüntülerle buğday sınıflandırılması. Eskişehir Osmangazi University, Institute of Science, Master Thesis.
  34. Pliakos, K., & Kontropoulos, C. (2015). Building an image annotation and tourism recommender system. International Journal on Artificial Intelligence Tools, 24(5), 1540021. https://doi.org/10.1142/S0218213015400217
  35. Rasheed, S. M. (2019). Object detection from images using deep learning. Fırat University, Institute of Science, Master Thesis.
  36. Raşo, H. (2019). Deep learning based stock market prediction using technical indicators. Gazi University, Institute of Science, Master Thesis.
  37. Razali, M. N., Tony, E. O. N., Ibrahim, A. A. A., Hanapi, R., & Iswandono, Z. (2023). Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis. International Journal of Advanced Computer Science and Applications, 14(2). https://dx.doi.org/10.14569/IJACSA.2023.0140225
  38. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. arXiv preprint arXiv:1409.0575v3. https://doi.org/10.48550/arXiv.1409.0575
  39. Scanlon, K., Sagor, E., Whitehead, C. (2014). The economic impact of holiday rentals in the UK. HomeAway Final Report London School of Economics. Retrieved September 15, 2019, from https://www.homeaway.co.uk/files/live/sites/hr/files/shared/partnerships/lse/HomeAwayEconomicImpact-Report_November2014-FinalReport.pdf
  40. Seyfioğlu, M., S. (2017). Radar mikro-doppler imza sınıflandırma problemi için derin sinir ağı başlatma ve eğitim metodolojileri. TOBB University of Echonomics & Technology, Institute of Science, Master Thesis.
  41. Shi, S., Wang, Q., Xu, P., & Chu, X. (2017). Benchmarking State-of-the-Art Deep Learning Software Tools. arXiv preprint arXiv:1608.07249v7. https://doi.org/10.48550/arXiv.1608.07249
  42. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
  43. Singla, A., Yuan, L., & Ebrahimi, T. (2016). Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model. Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, 3-11. https://doi.org/10.1145/2986035.2986039
  44. Statista, (2019). Vacation Rentals. Retrieved November 27, 2020, from https://www.statista.com/outlook/268/100/vacation-rentals/worldwide
  45. Statista. (2023). Preferred summer holiday accommodation among Europeans as of April 2023. Retrieved January 12, 2024, from https://www.statista.com/statistics/868047/summer-holiday-accommodation-preference-europe/
  46. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition, 1-9. https://doi.org/10.1109/CVPR.2015.7298594
  47. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. IEEE Conference on Computer Vision and Pattern Recognition, 2818-2826. https://doi.org/10.1109/CVPR.2016.308
  48. Şeker, A., Diri, B., Balık, H. H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Journal of Engineering Sciences, 3(3), 47-64.
  49. Tahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R. A., & Lee, S. (2023). DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images. Cancers, 15(7), 2179. https://doi.org/10.3390/cancers15072179
  50. Toprak, T. (2018). Pedestrian detection for railway driver support systems. Dokuz Eylül University, Institute of Science, Master Thesis.
  51. Turkish Statistical Institute, (2024). Turizm İstatistikleri. Retrieved August 01, 2024, from https://data.tuik.gov.tr/Bulten/DownloadIstatistikselTablo?p=R7g/EtmQKn964eY48QXFvkOGN/pQOTAdG7WyMsEoEYJsNbTXshXZecQZCHilnFnj
  52. Wang, S., Xie, S., Chen, X., Guttery, D., S., Tang, C., Sun, J., & Zhang, Y. (2019). Alcoholism Identification Based on an AlexNet Transfer Learning Model. Frontiers in Psychiatry, 10(205), 1-13. https://doi.org/10.3389/fpsyt.2019.00205
  53. Xiao, X., Fang, C., & Lin, H. (2020). Characterizing Tourism Destination Image Using Photos’ Visual Content. International Journal of Geo-Information, 9(12), 730. https://doi.org/10.3390/ijgi9120730
  54. Xie, O. (2022). Image Retrieval of Tourism Landscape in Rural Revitalization Based on Wireless Communication Network. Wireless Communications and Mobile Computing, 7167611. https://doi.org/10.1155/2022/7167611
  55. Xu, L., Chen, X., & Yang, X. (2024). Tourism image classification based on convolutional neural network SqueezeNet-Taking Slender West Lake as an example. PloS One, 19(1), e0295439. https://doi.org/10.1371/journal.pone.0295439
  56. Yaman, D. (2018). Age and Gender Image Classification from Ear Images. İstanbul Technical University, Institute of Science, Master Thesis.
  57. Yeong-gyu, K., & Eui-Young, C. (2016). Streamlined GoogLeNet Algorithm Based on CNN for Korean Character Recognition. Journal of the Korea Institute of Information and Communication Engineering, 20(9), 1657-1665. http://dx.doi.org/10.6109/jkiice.2016.20.9.1657
  58. Yu, H. (2024). Application of the artificial intelligence system based on graphics and vision in ethnic tourism of subtropical grasslands. Heliyon, 10(11), e31442. https://doi.org/10.1016/j.heliyon.2024.e31442
  59. Yuan, Y., Tseng, Y., Ho, C. (2019). Tourism information technology research trends: 1990-2016. Tourism Review, 74(1), 5-19. http://dx.doi.org/10.1108/TR-08-2017-0128
  60. Zhao, Y., & Agyeiwaah, E. (2024). How Do Tourism Stakeholders Co-Create Destination Images with Photos on Social Media?. Journal of Travel Research, https://doi.org/10.1177/00472875241253006
  61. Zhou, B., Chen, D., & Wang, X. (2017). Seat Belt Detection Using Convolutional Neural Network BN-AlexNet. ICIC 2017: Intelligent Computing Theories and Application. Lecture Notes in Computer Science, 10361, 384-395. http://dx.doi.org/10.1007/978-3-319-63309-1_36
  62. Zhu, X., Vondrick, C., Ramanan, D., & Fowlkes, C. C. (2015). Do We Need More Training Data or Better Models for Object Detection?. International Journal of Computer Vision, 119, 76-92. https://doi.org/10.1007/s11263-015-0812-2
  63. Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning Transferable Architectures for Scalable Image Recognition. arXiv preprint arXiv: 1707.07012v4. https://doi.org/10.48550/arXiv.1707.07012
  64. Zsarnoczky, M. (2017). How Does Artificial Intelligence Affect Tourism Industry?. Vadyba / Journal of Management, 31(2), 85-90.