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

Yıl 2025, Cilt: 13 Sayı: 2, 161 - 188, 12.06.2025

Mevlüt Kağan Balga , Fatih Basciftci

https://doi.org/10.30519/ahtr.1453400

Öz

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%.

Anahtar Kelimeler

holiday homes convolutional neural network deep learning imagenet resnet

Etik Beyan

This material is the author’s original work, which has not been previously published elsewhere.

Destekleyen Kurum

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Kaynakça

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  • 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
  • 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
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Yıl 2025, Cilt: 13 Sayı: 2, 161 - 188, 12.06.2025

Mevlüt Kağan Balga , Fatih Basciftci

https://doi.org/10.30519/ahtr.1453400

Öz

Kaynakça

  • 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
  • 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
  • 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.
  • Akılotu, B. N. (2019). Osas hastaları ile sağlıklı bireylerdeki nabız geçiş sürelerinin makina öğrenmesi teknikleriyle sınıflandırılması. Fırat University, Institute of Science, Master Thesis.
  • 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
  • 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
  • Balga, M. K. (2020). The classification and application of the rooms in holiday homes by deep learning. Selcuk University, Institute of Science, Master Thesis.
  • 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
  • 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
  • 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.
  • 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
  • Ezel, E. (2018). Derin öğrenme yöntemi kullanılarak görüntü-tabanlı türk işaret dili tanıma. Selcuk University, Institute of Science, Master Thesis.
  • 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
  • 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
  • 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
  • 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
  • Gürkaynak, C., D. (2018). Fine-tuning convolutional neural Networks for maritime vessel classification, verification, and recognition. Bahçeşehir University, Institute of Science, Master Thesis.
  • 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
  • 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
  • Kadiroğlu, Z. (2019). Histopatolojik meme kanseri görüntülerinin evrişimsel sinir ağları kullanılarak sınıflandırılması. Fırat University, Institute of Science, Master Thesis.
  • 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
  • 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/
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Mehr, H., D. (2017). Classification of lung CT images using deep convolutional neural network. Gazi University, Institute of Science, Master Thesis.
  • 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
  • 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
  • Örs, D. Z. D. (2018). Farklı dalga boylu görüntülerle buğday sınıflandırılması. Eskişehir Osmangazi University, Institute of Science, Master Thesis.
  • 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
  • Rasheed, S. M. (2019). Object detection from images using deep learning. Fırat University, Institute of Science, Master Thesis.
  • Raşo, H. (2019). Deep learning based stock market prediction using technical indicators. Gazi University, Institute of Science, Master Thesis.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • Statista, (2019). Vacation Rentals. Retrieved November 27, 2020, from https://www.statista.com/outlook/268/100/vacation-rentals/worldwide
  • 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/
  • 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
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  • 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.
  • 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
  • Toprak, T. (2018). Pedestrian detection for railway driver support systems. Dokuz Eylül University, Institute of Science, Master Thesis.
  • Turkish Statistical Institute, (2024). Turizm İstatistikleri. Retrieved August 01, 2024, from https://data.tuik.gov.tr/Bulten/DownloadIstatistikselTablo?p=R7g/EtmQKn964eY48QXFvkOGN/pQOTAdG7WyMsEoEYJsNbTXshXZecQZCHilnFnj
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  • 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
  • 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
  • Yaman, D. (2018). Age and Gender Image Classification from Ear Images. İstanbul Technical University, Institute of Science, Master Thesis.
  • 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
  • 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
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  • 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
  • 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
  • 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
  • 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
  • Zsarnoczky, M. (2017). How Does Artificial Intelligence Affect Tourism Industry?. Vadyba / Journal of Management, 31(2), 85-90.
Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Turizm (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Mevlüt Kağan Balga 0000-0003-1895-0744

Fatih Basciftci 0000-0003-1679-7416

Erken Görünüm Tarihi 6 Ocak 2025
Yayımlanma Tarihi 12 Haziran 2025
Gönderilme Tarihi 15 Mart 2024
Kabul Tarihi 2 Kasım 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 2

Kaynak Göster

APA 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), 13(2), 161-188. https://doi.org/10.30519/ahtr.1453400


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