Evolution of Machine Learning in Tourism: A Comprehensive Review of Seminal Research

Yıl 2023, Cilt: 3 Sayı: 2, 54 - 79, 15.12.2023

Ferhat Şeker

Öz

Machine learning is enabling transformative changes in the tourism industry. Various machine learning algorithms and models can detect patterns in huge amounts of data for the prediction process, recommendations, and decisions without any coding or programming. The tourism sector generates massive data through sources as such online reviews and ratings, social media activity, traffic information, and customer relationship management records. Machine learning is poised to unlock insights and opportunities from this data. This paper provides an overview of how machine learning is currently influencing and may shape the future of tourism. Techniques for predictive analytics, personalized recommendation systems, computer vision, natural language processing, and more are powering applications to improve customer experiences, optimize and automate operations, gain competitive advantage, and support sustainability. Current applications are discussed, including demand forecasting, personalized travel recommendations, automated photo filtering, sentiment analysis of tourism reviews, chatbots for customer service, and others. Emerging opportunities are explored, as machine learning may enhance smart tourism for destinations through intelligent transportation, customized experiences, optimized resource allocation, and improved accessibility. Challenges exist regarding data quality, privacy, bias, and job disruption. However, machine learning is expected to become an integral tool for data-driven, personalized, and sustainable tourism. Overall, this review paper aims to synthesize the state of machine learning in tourism by highlighting current applications, opportunities, considerations, and likely future trends. The conclusions point to machine learning as a catalyst for innovation in tourism that may significantly transform the visitor experience, business operations, and destination management in the years to come.

Anahtar Kelimeler

Artificial Intelligence, Machine Learning, Tourism, Literature Review

Kaynakça

• Zheng, Wenying, and Ye, Qiang (2009). Sentiment Classification of Chinese Traveler Reviews by Support Vector Machine Algorithm. 2009 Third International Symposium On Intelligent Information Technology Application, Vol 3, Proceedings, 335-338, ISSN 978-0-7695-3859-4, https://dx.doi.org/10.1109/IITA.2009.457.
• Wang, Yuanyuan, Chan, Stephen Chi-Fai, and Ngai, Grace (2012). Applicability of Demographic Recommender System to Tourist Attractions: A Case Study on TripAdvisor. 2012 Ieee/Wic/Acm International Conference On Web Intelligence And Intelligent Agent Technology Workshops (Wi-Iat Workshops 2012), Vol 3, 97-101, https://dx.doi.org/10.1109/WI-IAT.2012.133.
• Salman, Man Galih, Kanigoro, Bayu, Heryadi, Yaya, and IEEE (2015). Weather Forecasting using Deep Learning Techniques. 2015 International Conference On Advanced Computer Science And Information Systems (Icacsis), 281-285, ISSN 2330-4588
• Chang, Yu-Wei, and Tsai, Chih-Yung (2017). Apply deep learning neural network to forecast number of tourists. 2017 31st Ieee International Conference On Advanced Information Networking And Applications Workshops (Ieee Waina 2017), 259-264, https://dx.doi.org/10.1109/WAINA.2017.125
• Luong, Thai-Le, Cao, Minh-Son, Le, Duc-Thang, and Phan, Xuan-Hieu (2017). Intent Extraction from Social Media Texts Using Sequential Segmentation and Deep Learning Models. 2017 9th International Conference On Knowledge And Systems Engineering (Kse 2017), 215-220, ISSN 2164-2508
• Sun, Shaolong, Wang, Shouyang, Wei, Yunjie, Yang, Xianduan, and Tsui, Kwok-Leung (2017). Forecasting Tourist Arrivals with Machine Learning and Internet Search Index. 2017 Ieee International Conference On Big Data (Big Data), 4165-4169, ISSN 2639-1589
• Kbaier, Mohamed Elyes Ben Haj, Masri, Hela, Krichen, Saoussen, and IEEE (2017). A personalized hybrid tourism recommender system. 2017 Ieee/Acs 14th International Conference On Computer Systems And Applications (Aiccsa), 244-250, ISSN 2161-5322, https://dx.doi.org/10.1109/AICCSA.2017.12
• Son, Kyoungho, Byun, Yungcheol, Lee, Sangjoon, and IEEE (2018). Prediction of Visitors using Machine Learning. 2018 International Conference On Intelligent Informatics And Biomedical Sciences (Iciibms), 138-139, ISSN 2189-8723
• Ramanathan, Vallikannu, Meyyappan, T., and IEEE (2019). Twitter Text Mining for Sentiment Analysis on People's Feedback about Oman Tourism. 2019 4th Mec International Conference On Big Data And Smart City (Icbdsc), 30-34
• Yadav, Mukesh, and Bhojane, Varunakshi (2019). Semi-Supervised Mix-Hindi Sentiment Analysis using Neural Network. 2019 9th International Conference On Cloud Computing, Data Science and Engineering (Confluence 2019), 309-314
• Folgieri, Raffaella, Baldigara, Tea, and Mamula, Maja (2017). ARTIFICIAL NEURAL NETWORKS-BASED ECONOMETRIC MODELS FOR TOURISM DEMAND FORECASTING. 4th International Scientific Conference: Tosee - Tourism In Southern And Eastern Europe 2017, 4, 169-182, ISSN 1848-4050, https://dx.doi.org/10.20867/tosee.04.10
• Deldjoo, Yashar, Schedl, Markus, Cremonesi, Paolo, and Pasi, Gabriella (2020). Recommender Systems Leveraging Multimedia Content. Acm Computing Surveys, 53(5), ISSN 0360-0300, https://dx.doi.org/10.1145/3407190
• Siricharoen, Waralak V. (2008). Learning semantic web from e-tourism. Agent And Multi-Agent Systems: Technologies And Applications, Proceedings, 4953, 516-525, ISSN 0302-9743
• Birim, Sule, Kazancoglu, Ipek, Mangla, Sachin Kumar, Kahraman, Aysun, and Kazancoglu, Yigit (2022). The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods. Annals Of Operations Research, ISSN 0254-5330, https://dx.doi.org/10.1007/s10479-021-04429-x
• Rice, William L., Park, So Young, Pan, Bing, and Newman, Peter (2019). Forecasting campground demand in US national parks. Annals Of Tourism Research, 75, 424-438, ISSN 0160-7383, https://dx.doi.org/10.1016/j.annals.2019.01.013
• Yu, Joanne, and Egger, Roman (2021). Color and engagement in touristic Instagram pictures: A machine learning approach. Annals Of Tourism Research, 89, ISSN 0160-7383, https://dx.doi.org/10.1016/j.annals.2021.103204
• Zhang, Chen, and Huang, Zhenfang (2015). Mining Tourist Motive for Marketing Development via Twice-Learning. Applied Artificial Intelligence, 29(2), 119-133, ISSN 0883-9514, https://dx.doi.org/10.1080/08839514.2015.993554
• Starosta, Kejo, Budz, Sonia, and Krutwig, Michael (2019). The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans. Applied Economics, 51(14), 1558-1573, ISSN 0003-6846, https://dx.doi.org/10.1080/00036846.2018.1527463
• Claveria, Oscar, Monte, Enric, and Torra, Salvador (2016). Combination forecasts of tourism demand with machine learning models. Applied Economics Letters, 23(6), 428-431, ISSN 1350-4851, https://dx.doi.org/10.1080/13504851.2015.1078441
• Tsai, Yu Hsin, Stow, Douglas, An, Li, Chen, Hsiang Ling, Lewison, Rebecca, and Shi, Lei (2019). Monitoring land-cover and land-use dynamics in Fanjingshan National Nature Reserve. Applied Geography, 111, ISSN 0143-6228, https://dx.doi.org/10.1016/j.apgeog.2019.102077
• Li, Qin, Li, Shaobo, Zhang, Sen, Hu, Jie, and Hu, Jianjun (2019). A Review of Text Corpus-Based Tourism Big Data Mining. Applied Sciences-Basel, 9(16), https://dx.doi.org/10.3390/app9163300
• Chen, Wen, Xu, Zhiyun, Zheng, Xiaoyao, Yu, Qingying, and Luo, Yonglong (2020). Research on Sentiment Classification of Online Travel Review Text. Applied Sciences-Basel, 10(15), https://dx.doi.org/10.3390/app10155275
• Crivellari, Alessandro, and Beinat, Euro (2019). Identifying Foreign Tourists' Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings. Applied Sciences-Basel, 9(14), https://dx.doi.org/10.3390/app9142861
• Lv, Sheng-Xiang, Peng, Lu, and Wang, Lin (2018). Stacked autoencoder with echo-state regression for tourism demand forecasting using search query data. Applied Soft Computing, 73, 119-133, ISSN 1568-4946, https://dx.doi.org/10.1016/j.asoc.2018.08.024
• Yilmaz, Mustafa K., Kusakci, Ali Osman, Aksoy, Mine, and Hacioglu, Umit (2022). The evaluation of operational efficiencies of Turkish airports: An integrated spherical fuzzy AHP/DEA approach. Applied Soft Computing, 119, ISSN 1568-4946, https://dx.doi.org/10.1016/j.asoc.2022.108620
• Guerra-Montenegro, Juan, Sanchez-Medina, Javier, Lana, Ibai, Sanchez-Rodriguez, David, Alonso-Gonzalez, Itziar, and Ser, Javier Del (2021). Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges. Applied Soft Computing, 102, ISSN 1568-4946, https://dx.doi.org/10.1016/j.asoc.2021.107082
• Spiliotis, Evangelos, Abolghasemi, Mahdi, Hyndman, Rob J., Petropoulos, Fotios, and Assimakopoulos, Vassilios (2021). Hierarchical forecast reconciliation with machine learning. Applied Soft Computing, 112, ISSN 1568-4946, https://dx.doi.org/10.1016/j.asoc.2021.107756
• Bayliss, Christopher (2021). Machine learning based simulation optimisation for urban routing problems. Applied Soft Computing, 105, ISSN 1568-4946, https://dx.doi.org/10.1016/j.asoc.2021.107269
• Gajdosik, Tomas, and Marcis, Matus (2019). Artificial Intelligence Tools for Smart Tourism Development. Artificial Intelligence Methods In Intelligent Algorithms, 985, 392-402, ISSN 2194-5357, https://dx.doi.org/10.1007/978-3-030-19810-7_39
• Refanidis, Ioannis, Emmanouilidis, Christos, Sakellariou, Ilias, Alexiadis, Anastasios, Koutsiamanis, Remous-Aris, Agnantis, Konstantinos, Tasidou, Aimilia, Kokkoras, Fotios, and Efraimidis, Pavlos S. (2014). MYVISITPLANNER(GR): Personalized Itinerary Planning System for Tourism. Artificial Intelligence: Methods And Applications, 8445, 615-629, ISSN 0302-9743
• Deo, Ravinesh C., and Sahin, Mehmet (2015). Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia. Atmospheric Research, 161, 65-81, ISSN 0169-8095, https://dx.doi.org/10.1016/j.atmosres.2015.03.018
• Monkman, Graham G., Kaiser, Michel J., and Hyder, Kieran (2018). Text and data mining of social media to map wildlife recreation activity. Biological Conservation, 228, 89-99, ISSN 0006-3207, https://dx.doi.org/10.1016/j.biocon.2018.10.010
• Peng, Lu, Wang, Lin, Ai, Xue-Yi, and Zeng, Yu-Rong (2021). Forecasting Tourist Arrivals via Random Forest and Long Short-term Memory. Cognitive Computation, 13(1), 125-138, ISSN 1866-9956, https://dx.doi.org/10.1007/s12559-020-09747-z
• Altulyan, May, Yao, Lina, Wang, Xianzhi, Huang, Chaoran, Kanhere, Salil S., and Sheng, Quan Z. (2022). A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions. Computer Journal, 65(8), 2098-2132, ISSN 0010-4620, https://dx.doi.org/10.1093/comjnl/bxab049
• Jain, Praphula Kumar, Pamula, Rajendra, and Srivastava, Gautam (2021). A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Computer Science Review, 41, ISSN 1574-0137, https://dx.doi.org/10.1016/j.cosrev.2021.100413
• Gaur, Loveleen, Afaq, Anam, Solanki, Arun, Singh, Gurmeet, Sharma, Shavneet, Jhanjhi, N. Z., My, Hoang Thi, and Le, Dac-Nhuong (2021). Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels. Computers and Electrical Engineering, 95, ISSN 0045-7906, https://dx.doi.org/10.1016/j.compeleceng.2021.107374
• Nilashi, Mehrbakhsh, Bagherifard, Karamollah, Rahmani, Mohsen, and Rafe, Vahid (2017). A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Computers and Industrial Engineering, 109, 357-368, ISSN 0360-8352, https://dx.doi.org/10.1016/j.cie.2017.05.016
• Nilashi, Mehrbakhsh, Samad, Sarminah, Ahani, Ali, Ahmadi, Hossein, Alsolami, Eesa, Mahmoud, Marwan, Majeed, Hamsa D., and Alarood, Ala Abdulsalam (2021). Travellers decision making through preferences learning: A case on Malaysian spa hotels in TripAdvisor. Computers and Industrial Engineering, 158, ISSN 0360-8352, https://dx.doi.org/10.1016/j.cie.2021.107348
• Sun, Yeran, Fan, Hongchao, Bakillah, Mohamed, and Zipf, Alexander (2015). Road-based travel recommendation using geo-tagged images. Computers Environment And Urban Systems, 53, 110-122, ISSN 0198-9715, https://dx.doi.org/10.1016/j.compenvurbsys.2013.07.006
• Weerdenburg, Demi van, Scheider, Simon, Adams, Benjamin, Spierings, Bas, and Zee, Egbert van der (2019). Where to go and what to do: Extracting leisure activity potentials from Web data on urban space. Computers Environment And Urban Systems, 73, 143-156, ISSN 0198-9715, https://dx.doi.org/10.1016/j.compenvurbsys.2018.09.005
• Antonio, Nuno, Almeida, Ana de, and Nunes, Luis (2019). Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior. Cornell Hospitality Quarterly, 60(4), 298-319, ISSN 1938-9655, https://dx.doi.org/10.1177/1938965519851466
• Le, Truc H., Arcodia, Charles, Novais, Margarida Abreu, and Kralj, Anna (2021). Proposing a systematic approach for integrating traditional research methods into machine learning in text analytics in tourism and hospitality. Current Issues In Tourism, 24(12), 1640-1655, ISSN 1368-3500, https://dx.doi.org/10.1080/13683500.2020.1829568
• Zhao, Erlong, Du, Pei, Azaglo, Ernest Young, Wang, Shouyang, and Sun, Shaolong (2023). Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble. Current Issues In Tourism, 26(7), 1112-1131, ISSN 1368-3500, https://dx.doi.org/10.1080/13683500.2022.2048806
• Kim, Jina, Jang, Yeonju, Bae, Kunwoo, Oh, Soyoung, Jeong, Nam Jeong, Park, Eunil, Han, Jinyoung, and Pobil, Angel P. del (2021). Between comments and repeat visit: capturing repeat visitors with a hybrid approach. Data Technologies And Applications, 55(4), 542-557, ISSN 2514-9288, https://dx.doi.org/10.1108/DTA-06-2020-0123
• Mingotto, Erica, Montaguti, Federica, and Tamma, Michele (2021). Challenges in re-designing operations and jobs to embody AI and robotics in services. Findings from a case in the hospitality industry. Electronic Markets, 31(3), 493-510, ISSN 1019-6781, https://dx.doi.org/10.1007/s12525-020-00439-y
• Peng, Rongqun, Lou, Yingxi, Kadoch, Michel, and Cheriet, Mohamed (2020). A Human-Guided Machine Learning Approach for 5G Smart Tourism IoT. Electronics, 9(6), https://dx.doi.org/10.3390/electronics9060947
• Livieris, Ioannis E., Pintelas, Emmanuel, Kotsilieris, Theodore, Stavroyiannis, Stavros, and Pintelas, Panagiotis (2019). Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study. Electronics, 8(9), https://dx.doi.org/10.3390/electronics8091005
• Sidorov, Denis, Panasetsky, Daniil, Tomin, Nikita, Karamov, Dmitriy, Zhukov, Aleksei, Muftahov, Ildar, Dreglea, Aliona, Liu, Fang, and Li, Yong (2020). Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region. Energies, 13(5), https://dx.doi.org/10.3390/en13051226
• Brzustewicz, Pawel, and Singh, Anupam (2021). Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA. Energies, 14(18), https://dx.doi.org/10.3390/en14185787
• Chobar, Adel Pourghader, Adibi, Mohammad Amin, and Kazemi, Abolfazl (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment Development And Sustainability, ISSN 1387-585X, https://dx.doi.org/10.1007/s10668-022-02350-2
• Gomez, Diego, Salvador, Pablo, Sanz, Julia, and Casanova, Jose Luis (2021). A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods*. Environmental Pollution, 286, ISSN 0269-7491, https://dx.doi.org/10.1016/j.envpol.2021.117489
• Martin-Gutierrez, Jorge, Mora, Carlos Efren, Anorbe-Diaz, Beatriz, and Gonzalez-Marrero, Antonio (2017). Virtual Technologies Trends in Education. Eurasia Journal Of Mathematics Science And Technology Education, 13(2), 469-486, ISSN 1305-8215, https://dx.doi.org/10.12973/eurasia.2017.00626a
• Kirkos, Efstathios (2022). Airbnb listings' performance: determinants and predictive models. European Journal Of Tourism Research, 30, ISSN 1994-7658
• Ye, Qiang, Zhang, Ziqiong, and Law, Rob (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems With Applications, 36(3), 6527-6535, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2008.07.035
• Wu, Qi, Law, Rob, and Xu, Xin (2012). A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong. Expert Systems With Applications, 39(5), 4769-4774, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2011.09.159
• Belmonte-Fernandez, Oscar, Montoliu, Raul, Torres-Sospedra, Joaquin, Sansano-Sansano, Emilio, and Chia-Aguilar, Daniel (2018). A radiosity-based method to avoid calibration for indoor positioning systems. Expert Systems With Applications, 105, 89-101, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2018.03.054
• Li, Xixi, Kang, Yanfei, and Li, Feng (2020). Forecasting with time series imaging. Expert Systems With Applications, 160, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2020.113680
• King, Michael A., Abrahams, Alan S., and Ragsdale, Cliff T. (2014). Ensemble methods for advanced skier days prediction. Expert Systems With Applications, 41(4), 1176-1188, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2013.08.002
• Yao, Yuan, Cao, Yi, Ding, Xuemei, Zhai, Jia, Liu, Junxiu, Luo, Yuling, Ma, Shuai, and Zou, Kailin (2018). A paired neural network model for tourist arrival forecasting. Expert Systems With Applications, 114, 588-614, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2018.08.025
• Lee, Chung-Hong, and Wang, Shih-Hao (2012). An information fusion approach to integrate image annotation and text mining methods for geographic knowledge discovery. Expert Systems With Applications, 39(10), 8954-8967, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2012.02.028
• Nilashi, Mehrbakhsh, Minaei-Bidgoli, Behrouz, Alrizq, Mesfer, Alghamdi, Abdullah, Alsulami, Abdulaziz A., Samad, Sarminah, and Mohd, Saidatulakmal (2021). An analytical approach for big social data analysis for customer decision-making in eco-friendly hotels. Expert Systems With Applications, 186, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2021.115722
• Sun, Shaolong, Li, Mingchen, Wang, Shouyang, and Zhang, Chengyuan (2022). Multi-step ahead tourism demand forecasting: The perspective of the learning using privileged information paradigm. Expert Systems With Applications, 210, ISSN 0957-4174, https://dx.doi.org/10.1016/j.eswa.2022.118502
• Lennox, Robert J., Brownscombe, Jacob W., Cooke, Steven J., and Danylchuk, Andy J. (2018). Post-release behaviour and survival of recreationally-angled arapaima (Arapaima cf. arapaima) assessed with accelerometer biologgers. Fisheries Research, 207, 197-203, ISSN 0165-7836, https://dx.doi.org/10.1016/j.fishres.2018.05.007
• Lisi, Francesca A., and Straccia, Umberto (2013). A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms. Fundamenta Informaticae, 124(4), 503-519, ISSN 0169-2968, https://dx.doi.org/10.3233/FI-2013-846
• Santamaria-Granados, Luz, Mendoza-Moreno, Juan Francisco, and Ramirez-Gonzalez, Gustavo (2021). Tourist Recommender Systems Based on Emotion Recognition-A Scientometric Review. Future Internet, 13(1), https://dx.doi.org/10.3390/fi13010002
• Do, Hai-Ninh, Shih, Wurong, and Ha, Quang-An (2020). Effects of mobile augmented reality apps on impulse buying behavior: An investigation in the tourism field. Heliyon, 6(8), https://dx.doi.org/10.1016/j.heliyon.2020.e04667
• Buckley, S., Ettl, M., Jain, P., Luss, R., Petrik, M., Ravi, R. K., and Venkatramani, C. (2014). Social media and customer behavior analytics for personalized customer engagements. Ibm Journal Of Research And Development, 58(5), ISSN 0018-8646, https://dx.doi.org/10.1147/JRD.2014.2344515
• Bonin-Font, Francisco, Burguera, Antoni, and Lisani, Jose-Luis (2017). Visual Discrimination and Large Area Mapping of Posidonia Oceanica Using a Lightweight AUV. Ieee Access, 5, 24479-24494, ISSN 2169-3536, https://dx.doi.org/10.1109/ACCESS.2017.2764998
• Afzaal, Muhammad, Usman, Muhammad, and Fong, Alvis (2019). Tourism Mobile App With Aspect-Based Sentiment Classification Framework for Tourist Reviews. Ieee Transactions On Consumer Electronics, 65(2), 233-242, ISSN 0098-3063, https://dx.doi.org/10.1109/TCE.2019.2908944
• Navigli, R, and Velardi, P (2005). Structural semantic interconnections: A knowledge-based approach to word sense disambiguation. Ieee Transactions On Pattern Analysis And Machine Intelligence, 27(7), 1075-1086, ISSN 0162-8828
• Deglint, Jason L., Jin, Chao, and Wong, Alexander (2019). Investigating the Automatic Classification of Algae Using the Spectral and Morphological Characteristics via Deep Residual Learning. Image Analysis And Recognition (Iciar 2019), Pt Ii, 11663, 269-280, ISSN 0302-9743, https://dx.doi.org/10.1007/978-3-030-27272-2_23
• Shin, Seunghun, Du, Qianzhou, and Xiang, Zheng (2019). What's Vs. How's in Online Hotel Reviews: Comparing Information Value of Content and Writing Style with Machine Learning. Information And Communication Technologies In Tourism 2019, 321-332, ISSN 978-3-030-05939-2, https://dx.doi.org/10.1007/978-3-030-05940-8_25
• Kaya, Kiymet, Yilmaz, Yaren, Yaslan, Yusuf, Oguducu, Sule Gunduz, and Cingi, Furkan (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information Processing and Management, 59(1), ISSN 0306-4573, https://dx.doi.org/10.1016/j.ipm.2021.102816
• Huh, Jun-Ho, Kim, Han-Byul, and Kim, Jinmo (2017). A Method of Modeling of Basic Big Data Analysis for Korean Medical Tourism: A Machine Learning Approach Using Apriori Algorithm. Information Science And Applications 2017, Icisa 2017, 424, 784-790, ISSN 1876-1100, https://dx.doi.org/10.1007/978-981-10-4154-9_91
• Neidhardt, Julia, and Werthner, Hannes (2018). IT and tourism: still a hot topic, but do not forget IT. Information Technology and Tourism, 20(1), 1-7, ISSN 1098-3058, https://dx.doi.org/10.1007/s40558-018-0115-x
• H., V Phan, Truong, and Do, Phuc (2020). BERT+vnKG: Using Deep Learning and Knowledge Graph to Improve Vietnamese Question Answering System. International Journal Of Advanced Computer Science And Applications, 11(7), 480-487, ISSN 2158-107X
• Xu, Xin, Law, Rob, and Wu, Tao (2009). Support Vector Machines with Manifold Learning and Probabilistic Space Projection for Tourist Expenditure Analysis. International Journal Of Computational Intelligence Systems, 2(1), 17-26, ISSN 1875-6891
• Ali, Faizan, Park, Eunhye (Olivia), Kwon, Junehee, and Chae, Bongsug (Kevin) (2019). 30 years of contemporary hospitality management Uncovering the bibliometrics and topical trends. International Journal Of Contemporary Hospitality Management, 31(7), 2641-2665, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-10-2018-0832
• Gour, Alekh, Aggarwal, Shikha, and Erdem, Mehmet (2021). Reading between the lines: analyzing online reviews by using a multi-method Web-analytics approach. International Journal Of Contemporary Hospitality Management, 33(2), 490-512, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-07-2020-0760
• Lin, Michael S., Liang, Yun, Xue, Joanne X., Pan, Bing, and Schroeder, Ashley (2021). Destination image through social media analytics and survey method. International Journal Of Contemporary Hospitality Management, 33(6), 2219-2238, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-08-2020-0861
• Lee, Minwoo, Kwon, Wooseok, and Back, Ki-Joon (2021). Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making. International Journal Of Contemporary Hospitality Management, 33(6), 2117-2136, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-06-2020-0587
• Park, Eunhye (Olivia), Chae, Bongsug, and Kwon, Junehee (2018). Toward understanding the topical structure of hospitality literature: Applying machine learning and traditional statistics. International Journal Of Contemporary Hospitality Management, 30(11), 3386-3411, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-11-2017-0714
• Filieri, Raffaele, D'Amico, Elettra, Destefanis, Alessandro, Paolucci, Emilio, and Raguseo, Elisabetta (2021). Artificial intelligence (AI) for tourism: an European-based study on successful AI tourism start-ups. International Journal Of Contemporary Hospitality Management, 33(11), 4099-4125, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-02-2021-0220
• Ampountolas, Apostolos, and Legg, Mark P. (2021). A segmented machine learning modeling approach of social media for predicting occupancy. International Journal Of Contemporary Hospitality Management, 33(6), 2001-2021, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-06-2020-0611
• Liu, Yiran, and Beldona, Srikanth (2021). Extracting revisit intentions from social media big data: a rule-based classification model. International Journal Of Contemporary Hospitality Management, 33(6), 2176-2193, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-06-2020-0592
• Lee, Yejin, and Kim, Dae-Young (2021). The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance. International Journal Of Contemporary Hospitality Management, 33(6), 2264-2282, ISSN 0959-6119, https://dx.doi.org/10.1108/IJCHM-06-2020-0594
• Nilashi, Mehrbakhsh, Yadegaridehkordi, Elaheh, Ibrahim, Othman, Samad, Sarminah, Ahani, Ali, and Sanzogni, Louis (2019). Analysis of Travellers' Online Reviews in Social Networking Sites Using Fuzzy Logic Approach. International Journal Of Fuzzy Systems, 21(5), 1367-1378, ISSN 1562-2479, https://dx.doi.org/10.1007/s40815-019-00630-0
• Kang, Junfeng, Guo, Xingyu, Fang, Lei, Wang, Xiangrong, and Fan, Zhengqiu (2022). Integration of Internet search data to predict tourism trends using spatial-temporal XGBoost composite model. International Journal Of Geographical Information Science, 36(2), 236-252, ISSN 1365-8816, https://dx.doi.org/10.1080/13658816.2021.1934476
• Ma, Yufeng, Xiang, Zheng, Du, Qianzhou, and Fan, Weiguo (2018). Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning. International Journal Of Hospitality Management, 71, 120-131, ISSN 0278-4319, https://dx.doi.org/10.1016/j.ijhm.2017.12.008
• Kwok, Linchi, Tang, Yingying, and Yu, Bei (2020). Research Paper The 7 Ps marketing mix of home-sharing services: Mining travelers' online reviews on Airbnb. International Journal Of Hospitality Management, 90, ISSN 0278-4319, https://dx.doi.org/10.1016/j.ijhm.2020.102616
• Sanchez-Medina, Agustin J., and C-Sanchez, Eleazar (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal Of Hospitality Management, 89, ISSN 0278-4319, https://dx.doi.org/10.1016/j.ijhm.2020.102546
• Le, Truc H., Arcodia, Charles, Novais, Margarida Abreu, and Kralj, Anna (2022). How consumers perceive authenticity in restaurants: A study of online reviews. International Journal Of Hospitality Management, 100, ISSN 0278-4319, https://dx.doi.org/10.1016/j.ijhm.2021.103102
• Shi, Yong, Zhu, Luyao, Li, Wei, Gao, Kun, and Zheng, Yuanchun (2019). Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques. International Journal Of Information Technology and Decision Making, 18(4), 1243-1287, ISSN 0219-6220, https://dx.doi.org/10.1142/S0219622019300015
• Perles-Ribes, Jose Francisco, Ramon-Rodriguez, Ana Belen, Moreno-Izquierdo, Luis, and Such-Devesa, Maria Jesus (2020). Machine learning techniques as a tool for predictingovertourism: The case of Spain. International Journal Of Tourism Research, 22(6), 825-838, ISSN 1099-2340, https://dx.doi.org/10.1002/jtr.2383
• Zhu, Min, Wu, Jinran, and Wang, You-Gan (2021). Multi-horizon accommodation demand forecasting: A New Zealand case study. International Journal Of Tourism Research, 23(3), 442-453, ISSN 1099-2340, https://dx.doi.org/10.1002/jtr.2416
• Moreno-Izquierdo, Luis, Egorova, Galina, Pereto-Rovira, Alexandre, and Mas-Ferrando, Adrian (2018). Exploring the use of artificial intelligence in price maximisation in the tourism sector: its application in the case of Airbnb in the Valencian Community. Investigaciones Regionales-Journal Of Regional Research(42), 113-128, ISSN 1695-7253
• Gu, Qiushi, Zhang, Haiping, Chen, Min, and Chen, Chongcheng (2019). Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows. Isprs International Journal Of Geo-Information, 8(7), https://dx.doi.org/10.3390/ijgi8070314
• Sanchez-Franco, Manuel J., Navarro-Garcia, Antonio, and Rondan-Cataluna, Francisco Javier (2019). A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services. Journal Of Business Research, 101, 499-506, ISSN 0148-2963, https://dx.doi.org/10.1016/j.jbusres.2018.12.051
• Garner, Benjamin, Thornton, Corliss, Pawluk, Anita Luo, Cortez, Roberto Mora, Johnston, Wesley, and Ayala, Cesar (2022). Utilizing text-mining to explore consumer happiness within tourism destinations. Journal Of Business Research, 139, 1366-1377, ISSN 0148-2963, https://dx.doi.org/10.1016/j.jbusres.2021.08.025
• D'Amato, D., Droste, N., Allen, B., Kettunen, M., Laehtinen, K., Korhonen, J., Leskinen, P., Matthies, B. D., and Toppinen, A. (2017). Green, circular, bio economy: A comparative analysis of sustainability avenues. Journal Of Cleaner Production, 168, 716-734, ISSN 0959-6526, https://dx.doi.org/10.1016/j.jclepro.2017.09.053
• Nilashi, Mehrbakhsh, Ahani, Ali, Esfahani, Mohammad Dalvi, Yadegaridehkordi, Elaheh, Samad, Sarminah, Ibrahim, Othman, Sharef, Nurfadhlina Mohd, and Akbari, Elnaz (2019). Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal Of Cleaner Production, 215, 767-783, ISSN 0959-6526, https://dx.doi.org/10.1016/j.jclepro.2019.01.012
• Nilashi, Mehrbakhsh, Ibrahim, Othman, Yadegaridehkordi, Elaheh, Samad, Sarminah, Akbari, Elnaz, and Alizadeh, Azar (2018). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal Of Computational Science, 28, 168-179, ISSN 1877-7503, https://dx.doi.org/10.1016/j.jocs.2018.09.006
• Gabriel, I Penagos-Londono,, Rodriguez-Sanchez, Carla, Ruiz-Moreno, Felipe, and Torres, Eduardo (2021). A machine learning approach to segmentation of tourists based on perceived destination sustainability and trustworthiness. Journal Of Destination Marketing and Management, 19, ISSN 2212-571X, https://dx.doi.org/10.1016/j.jdmm.2020.100532
• Arabadzhyan, Anastasia, Figini, Paolo, and Vici, Laura (2021). Measuring destination image: a novel approach based on visual data mining. A methodological proposal and an application to European islands. Journal Of Destination Marketing and Management, 20, ISSN 2212-571X, https://dx.doi.org/10.1016/j.jdmm.2021.100611
• Tsang, Wai Kit, and Benoit, Dries F. (2020). Gaussian Processes for Daily Demand Prediction. Journal Of Forecasting, 39(3), 551-568, ISSN 0277-6693, https://dx.doi.org/10.1002/for.2644
• Huang, Arthur, Chao, Ying, Velasco, Efren de la Mora, Bilgihan, Anil, and Wei, Wei (2022). When artificial intelligence meets the hospitality and tourism industry: an assessment framework to inform theory and management. Journal Of Hospitality And Tourism Insights, 5(5), 1080-1100, ISSN 2514-9792, https://dx.doi.org/10.1108/JHTI-01-2021-0021
• Puh, Karlo, and Babac, Marina Bagic (2022). Predicting sentiment and rating of tourist reviews using machine learning. Journal Of Hospitality And Tourism Insights, ISSN 2514-9792, https://dx.doi.org/10.1108/JHTI-02-2022-0078
• Shehhi, Mohammed Al, and Karathanasopoulos, Andreas (2020). Forecasting hotel room prices in selected GCC cities using deep learning. Journal Of Hospitality And Tourism Management, 42, 40-50, ISSN 1447-6770, https://dx.doi.org/10.1016/j.jhtm.2019.11.003
• Aggarwal, Shikha, and Gour, Alekh (2020). Peeking inside the minds of tourists using a novel web analytics approach. Journal Of Hospitality And Tourism Management, 45, 580-591, ISSN 1447-6770, https://dx.doi.org/10.1016/j.jhtm.2020.10.009
• Ahani, Ali, Nilashi, Mehrbakhsh, Zogaan, Waleed Abdu, Samad, Sarminah, Aljehane, Nojood O., Alhargan, Ashwaq, Mohd, Saidatulakmal, Ahmadi, Hossein, and Sanzogni, Louis (2021). Evaluating medical travelers' satisfaction through online review analysis. Journal Of Hospitality And Tourism Management, 48, 519-537, ISSN 1447-6770, https://dx.doi.org/10.1016/j.jhtm.2021.08.005
• Moro, Sergio, Rita, Paulo, Ramos, Pedro, and Esmerado, Joaquim (2019). Analysing recent augmented and virtual reality developments in tourism. Journal Of Hospitality And Tourism Technology, 10(4), 571-586, ISSN 1757-9880, https://dx.doi.org/10.1108/JHTT-07-2018-0059
• Baggio, Rodolfo, Micera, Roberto, and Chiappa, Giacomo Del (2020). Smart tourism destinations: a critical reflection. Journal Of Hospitality And Tourism Technology, 11(3), 559-574, ISSN 1757-9880, https://dx.doi.org/10.1108/JHTT-01-2019-0011
• Bagherzadeh, Sayeh, Shokouhyar, Sajjad, Jahani, Hamed, and Sigala, Marianna (2021). A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews. Journal Of Hospitality And Tourism Technology, 12(2), 210-238, ISSN 1757-9880, https://dx.doi.org/10.1108/JHTT-02-2020-0034
• Bilotta, Eleonora, Bertacchini, Francesca, Gabriele, Lorella, Giglio, Simona, Pantano, Pietro Salvatore, and Romita, Tullio (2021). Industry 4.0 technologies in tourism education: Nurturing students to think with technology. Journal Of Hospitality Leisure Sport and Tourism Education, 29, ISSN 1473-8376, https://dx.doi.org/10.1016/j.jhlste.2020.100275
• Ren, Meng, Vu, Huy Quan, Li, Gang, and Law, Rob (2021). Large-scale comparative analyses of hotel photo content posted by managers and customers to review platforms based on deep learning: implications for hospitality marketers. Journal Of Hospitality Marketing and Management, 30(1), 96-119, ISSN 1936-8623, https://dx.doi.org/10.1080/19368623.2020.1765226
• Jaseena, K. U., and Kovoor, Binsu C. (2022). Deterministic weather forecasting models based on intelligent predictors: A survey. Journal Of King Saud University-Computer And Information Sciences, 34(6), 3393-3412, ISSN 1319-1578, https://dx.doi.org/10.1016/j.jksuci.2020.09.009
• Akman, Engin, Karaman, Abdullah S., and Kuzey, Cemil (2020). Visa trial of international trade: evidence from support vector machines and neural networks. Journal Of Management Analytics, 7(2), 231-252, ISSN 2327-0012, https://dx.doi.org/10.1080/23270012.2020.1731719
• Rios-Morales, Ruth, Gamberger, Dragan, Jenkins, Ian, and Smuc, Tom (2011). Modelling investment in the tourism industry using the World Bank's good governance indicators. Journal Of Modelling In Management, 6(3), 279-296, ISSN 1746-5664, https://dx.doi.org/10.1108/17465661111183694
• Sangkaew, Nichapat, and Zhu, Hongrui (2022). Understanding Tourists' Experiences at Local Markets in Phuket: An Analysis of TripAdvisor Reviews. Journal Of Quality Assurance In Hospitality and Tourism, 23(1), 89-114, ISSN 1528-008X, https://dx.doi.org/10.1080/1528008X.2020.1848747
• Ahani, Ali, Nilashi, Mehrbakhsh, Yadegaridehkordi, Elaheh, Sanzogni, Louis, Tarik, A. Rashid, Knox, Kathy, Samad, Sarminah, and Ibrahim, Othman (2019). Revealing customers' satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal Of Retailing And Consumer Services, 51, 331-343, ISSN 0969-6989, https://dx.doi.org/10.1016/j.jretconser.2019.06.014
• Nilashi, Mehrbakhsh, Abumalloh, Rabab Ali, Minaei-Bidgoli, Behrouz, Zogaan, Waleed Abdu, Alhargan, Ashwaq, Mohd, Saidatulakmal, Azhar, Sharifah Nurlaili Farhana Syed, Asadi, Shahla, and Samad, Sarminah (2022). Revealing travellers' satisfaction during COVID-19 outbreak: Moderating role of service quality. Journal Of Retailing And Consumer Services, 64, ISSN 0969-6989, https://dx.doi.org/10.1016/j.jretconser.2021.102783
• Pantano, Eleonora, and Dennis, Charles (2019). Store buildings as tourist attractions: Mining retail meaning of store building pictures through a machine learning approach. Journal Of Retailing And Consumer Services, 51, 304-310, ISSN 0969-6989, https://dx.doi.org/10.1016/j.jretconser.2019.06.018
• Arora, Anuja, Srivastava, Aman, and Bansal, Shivam (2020). Business competitive analysis using promoted post detection on social media. Journal Of Retailing And Consumer Services, 54, ISSN 0969-6989, https://dx.doi.org/10.1016/j.jretconser.2019.101941
• Agarwal, Pragati, Swami, Sanjeev, and Malhotra, Sunita Kumari (2022). Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review. Journal Of Science And Technology Policy Management, ISSN 2053-4620, https://dx.doi.org/10.1108/JSTPM-08-2021-0122
• Filieri, Raffaele, Lin, Zhibin, Li, Yulei, Lu, Xiaoqian, and Yang, Xingwei (2022). Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach. Journal Of Service Research, 25(4), 614-629, ISSN 1094-6705, https://dx.doi.org/10.1177/10946705221103937
• Jain, Praphula Kumar, Pamula, Rajendra, and Yekun, Ephrem Admasu (2022). A multi-label ensemble predicting model to service recommendation from social media contents. Journal Of Supercomputing, 78(4), 5203-5220, ISSN 0920-8542, https://dx.doi.org/10.1007/s11227-021-04087-7
• Rezapouraghdam, Hamed, Akhshik, Arash, and Ramkissoon, Haywantee (2021). Application of machine learning to predict visitors' green behavior in marine protected areas: evidence from Cyprus. Journal Of Sustainable Tourism, ISSN 0966-9582, https://dx.doi.org/10.1080/09669582.2021.1887878
• Hardy, Anne, and Aryal, Jagannath (2020). Using innovations to understand tourist mobility in national parks. Journal Of Sustainable Tourism, 28(2), 263-283, ISSN 0966-9582, https://dx.doi.org/10.1080/09669582.2019.1670186
• Darko, Adjei Peter, and Liang, Decui (2023). A heterogeneous opinion-driven decision-support model for tourists' selection with different travel needs in online reviews. Journal Of The Operational Research Society, 74(1), 272-289, ISSN 0160-5682, https://dx.doi.org/10.1080/01605682.2022.2035274
• Samala, Nagaraj, Katkam, Bharath Shashanka, Bellamkonda, Raja Shekhar, and Rodriguez, Raul Villamarin (2022). Impact of AI and robotics in the tourism sector: a critical insight. Journal Of Tourism Futures, 8(1), 73-87, ISSN 2055-5911, https://dx.doi.org/10.1108/JTF-07-2019-0065
• Parvez, M. Omar (2021). Use of machine learning technology for tourist and organizational services: high-tech innovation in the hospitality industry. Journal Of Tourism Futures, 7(2), 240-244, ISSN 2055-5911, https://dx.doi.org/10.1108/JTF-09-2019-0083
• Li, Gang, Law, Rob, Rong, Jia, and Vu, Huy Quan (2010). INCORPORATING BOTH POSITIVE AND NEGATIVE ASSOCIATION RULES INTO THE ANALYSIS OF OUTBOUND TOURISM IN HONG KONG. Journal Of Travel and Tourism Marketing, 27(8), 812-828, ISSN 1054-8408, https://dx.doi.org/10.1080/10548408.2010.527248
• Alaei, Ali Reza, Becken, Susanne, and Stantic, Bela (2019). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal Of Travel Research, 58(2), 175-191, ISSN 0047-2875, https://dx.doi.org/10.1177/0047287517747753
• Kirilenko, Andrei P., Stepchenkova, Svetlana O., Kim, Hany, and Li, Xiang (Robert) (2018). Automated Sentiment Analysis in Tourism: Comparison of Approaches. Journal Of Travel Research, 57(8), 1012-1025, ISSN 0047-2875, https://dx.doi.org/10.1177/0047287517729757
• Hoepken, Wolfram, Eberle, Tobias, Fuchs, Matthias, and Lexhagen, Maria (2021). Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach. Journal Of Travel Research, 60(5), 998-1017, ISSN 0047-2875, https://dx.doi.org/10.1177/0047287520921244
• Li, Xin, Li, Hengyun, Pan, Bing, and Law, Rob (2021). Machine Learning in Internet Search Query Selection for Tourism Forecasting. Journal Of Travel Research, 60(6), 1213-1231, ISSN 0047-2875, https://dx.doi.org/10.1177/0047287520934871
• Ma, Shihan (David), and Kirilenko, Andrei P. (2020). Climate Change and Tourism in English-Language Newspaper Publications. Journal Of Travel Research, 59(2), 352-366, ISSN 0047-2875, https://dx.doi.org/10.1177/0047287519839157
• Zhang, Chengyuan, Li, Mingchen, Sun, Shaolong, Tang, Ling, and Wang, Shouyang (2022). Decomposition Methods for Tourism Demand Forecasting: A Comparative Study. Journal Of Travel Research, 61(7), 1682-1699, ISSN 0047-2875, https://dx.doi.org/10.1177/00472875211036194
• Song, Yang, Wang, Runzi, Fernandez, Jessica, and Li, Dongying (2021). Investigating sense of place of the Las Vegas Strip using online reviews and machine learning approaches. Landscape And Urban Planning, 205, ISSN 0169-2046, https://dx.doi.org/10.1016/j.landurbplan.2020.103956
• Koranga, Manisha, Pant, Pushpa, Kumar, Tarun, Pant, Durgesh, Bhatt, Ashutosh Kumar, and Pant, R. P. (2022). Efficient water quality prediction models based on machine learning algorithms for Nainital Lake, Uttarakhand. Materials Today-Proceedings, 57, 1706-1712, ISSN 2214-7853, https://dx.doi.org/10.1016/j.matpr.2021.12.334
• Anglisano, Anna, Casas, Lluis, Anglisano, Marc, and Queralt, Ignasi (2020). Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry. Minerals, 10(1), https://dx.doi.org/10.3390/min10010008
• Thakker, Urvish, Patel, Ruhi, and Shah, Manan (2021). A comprehensive analysis on movie recommendation system employing collaborative filtering. Multimedia Tools And Applications, 80(19), 28647-28672, ISSN 1380-7501, https://dx.doi.org/10.1007/s11042-021-10965-2
• Haklidir, Fusun S. Tut, and Haklidir, Mehmet (2020). Prediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approach. Natural Resources Research, 29(4), 2333-2346, ISSN 1520-7439, https://dx.doi.org/10.1007/s11053-019-09596-0
• Hussain, Amir, and Cambria, Erik (2018). Semi-supervised learning for big social data analysis. Neurocomputing, 275, 1662-1673, ISSN 0925-2312, https://dx.doi.org/10.1016/j.neucom.2017.10.010
• Pan, Yaoru, Flindt, Mogens, Schneider-Kamp, Peter, and Holmer, Marianne (2021). Beach wrack mapping using unmanned aerial vehicles for coastal environmental management. Ocean and Coastal Management, 213, ISSN 0964-5691, https://dx.doi.org/10.1016/j.ocecoaman.2021.105843
• Aamer, Ammar Mohamed, Yani, Luh Putu Eka, and Priyatna, I. Made Alan (2021). Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting. Operations And Supply Chain Management-An International Journal, 14(1), 1-13, ISSN 1979-3561, https://dx.doi.org/10.31387/oscm0440281
• Haas, Andreas F., Guibert, Marine, Foerschner, Anja, Co, Tim, Calhoun, Sandi, George, Emma, Hatay, Mark, Dinsdale, Elizabeth, Sandin, Stuart A., Smith, Jennifer E., Vermeij, Mark J. A., Felts, Ben, Dustan, Phillip, Salamon, Peter, and Rohwer, Forest (2015). Can we measure beauty? Computational evaluation of coral reef aesthetics. Peerj, 3, ISSN 2167-8359, https://dx.doi.org/10.7717/peerj.1390
• Wang, Meng (2020). Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. Plos One, 15(12), ISSN 1932-6203, https://dx.doi.org/10.1371/journal.pone.0240656
• Xue, Mingqiang, Wu, Huayu, Chen, Wei, Ng, Wee Siong, Goh, Gin Howe, and ACM (2014). Identifying Tourists from Public Transport Commuters. Proceedings Of The 20th Acm Sigkdd International Conference On Knowledge Discovery And Data Mining (Kdd'14), 1779-1788, https://dx.doi.org/10.1145/2623330.2623352
• Tsai, Yu Hsin, Stow, Douglas, Chen, Hsiang Ling, Lewison, Rebecca, An, Li, and Shi, Lei (2018). Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine. Remote Sensing, 10(6), https://dx.doi.org/10.3390/rs10060927
• Sun, Yanwei, Gao, Chao, Li, Jialin, Wang, Run, and Liu, Jian (2019). Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning. Remote Sensing, 11(8), https://dx.doi.org/10.3390/rs11080959
• Moya, Luis, Perez, Luis R. Marval, Mas, Erick, Adriano, Bruno, Koshimura, Shunichi, and Yamazaki, Fumio (2018). Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions. Remote Sensing, 10(2), https://dx.doi.org/10.3390/rs10020296
• Claveria, Oscar, Torra, Salvador, and Monte, Enric (2016). MODELLING TOURISM DEMAND TO SPAIN WITH MACHINE LEARNING TECHNIQUES. THE IMPACT OF FORECAST HORIZON ON MODEL SELECTION. Revista De Economia Aplicada, 24(72), 109-132, ISSN 1133-455X
• Hakim, Noorkholis Luthfil, Shih, Timothy K., Arachchi, Sandeli Priyanwada Kasthuri, Aditya, Wisnu, Chen, Yi-Cheng, and Lin, Chih-Yang (2019). Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model. Sensors, 19(24), https://dx.doi.org/10.3390/s19245429
• Sagheer, Alaa, Hamdoun, Hala, and Youness, Hassan (2021). Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series. Sensors, 21(13), https://dx.doi.org/10.3390/s21134379
• Crivellari, Alessandro, and Beinat, Euro (2020). Trace2trace-A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories. Sensors, 20(12), https://dx.doi.org/10.3390/s20123503
• Claveria, Oscar, Monte, Enric, and Torra, Salvador (2016). Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model. Series-Journal Of The Spanish Economic Association, 7(3), 341-357, ISSN 1869-4187, https://dx.doi.org/10.1007/s13209-016-0144-7
• Kesgin, Muhammet, and Murthy, Rajendran S. (2019). Consumer engagement: the role of social currency in online reviews. Service Industries Journal, 39(7), 609-636, ISSN 0264-2069, https://dx.doi.org/10.1080/02642069.2018.1553237
• Saura, Jose Ramon, Reyes-Menendez, Ana, and Alvarez-Alonso, Cesar (2018). Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels. Sustainability, 10(9), https://dx.doi.org/10.3390/su10093016
• Nilashi, Mehrbakhsh, Mardani, Abbas, Liao, Huchang, Ahmadi, Hossein, Manaf, Azizah Abdul, and Almukadi, Wafa (2019). A Hybrid Method with TOPSIS and Machine Learning Techniques for Sustainable Development of Green Hotels Considering Online Reviews. Sustainability, 11(21), https://dx.doi.org/10.3390/su11216013
• Tao, Yuguo, Zhang, Feng, Shi, Chunyun, and Chen, Yun (2019). Social Media Data-Based Sentiment Analysis of Tourists' Air Quality Perceptions. Sustainability, 11(18), https://dx.doi.org/10.3390/su11185070
• Sun, Yao, Ma, Hang, and Chan, Edwin H. W. (2018). A Model to Measure Tourist Preference toward Scenic Spots Based on Social Media Data: A Case of Dapeng in China. Sustainability, 10(1), https://dx.doi.org/10.3390/su10010043
• Lee, Jee Hoon, Wood, Jacob, and Kim, Jungsuk (2021). Tracing the Trends in Sustainability and Social Media Research Using Topic Modeling. Sustainability, 13(3), https://dx.doi.org/10.3390/su13031269
• Pasvisheh, Roghayeh Sadeghi, Forio, Marie Anne Eurie, Ho, Long Tuan, and Goethals, Peter L. M. (2021). Evidence-Based Management of the Anzali Wetland System (Northern Iran) Based on Innovative Monitoring and Modeling Methods. Sustainability, 13(10), https://dx.doi.org/10.3390/su13105503
• Arreeras, Tosporn, Arimura, Mikiharu, Asada, Takumi, and Arreeras, Saharat (2019). Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data. Sustainability, 11(14), https://dx.doi.org/10.3390/su11143967
• Cubric, Marija (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology In Society, 62, ISSN 0160-791X, https://dx.doi.org/10.1016/j.techsoc.2020.101257
• Yadegaridehkordi, Elaheh, Nilashi, Mehrbakhsh, Nasir, Mohd Hairul Nizam Bin Md, Momtazi, Saeedeh, Samad, Sarminah, Supriyanto, Eko, and Ghabban, Fahad (2021). Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. Technology In Society, 65, ISSN 0160-791X, https://dx.doi.org/10.1016/j.techsoc.2021.101528
• Zibarzani, Masoumeh, Abumalloh, Rabab Ali, Nilashi, Mehrbakhsh, Samad, Sarminah, Alghamdi, O. A., Nayer, Fatima Khan, Ismail, Muhammed Yousoof, Mohd, Saidatulakmal, and Akib, Noor Adelyna Mohammed (2022). Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology. Technology In Society, 70, ISSN 0160-791X, https://dx.doi.org/10.1016/j.techsoc.2022.101977
• Nilashi, Mehrbakhsh, Abumalloh, Rabab Ali, Alghamdi, Abdullah, Minaei-Bidgoli, Behrouz, Alsulami, Abdulaziz A., Thanoon, Mohammed, Asadi, Shahla, and Samad, Sarminah (2021). What is the impact of service quality on customers' satisfaction during COVID-19 outbreak? New findings from online reviews analysis. Telematics And Informatics, 64, ISSN 0736-5853, https://dx.doi.org/10.1016/j.tele.2021.101693
• Nilashi, Mehrbakhsh, Asadi, Shahla, Minaei-Bidgoli, Behrouz, Abumalloh, Rabab Ali, Samad, Sarminah, Ghabban, Fahad, and Ahani, Ali (2021). Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics And Informatics, 61, ISSN 0736-5853, https://dx.doi.org/10.1016/j.tele.2021.101597
• Tang, Ling, Zhang, Chengyuan, Li, Tingfei, and Li, Ling (2021). A novel BEMD-based method for forecasting tourist volume with search engine data. Tourism Economics, 27(5), 1015-1038, ISSN 1354-8166, https://dx.doi.org/10.1177/1354816620912995
• Schwartz, Zvi, Webb, Timothy, Jean-Pierre, I van der Rest,, and Koupriouchina, Larissa (2021). Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizons. Tourism Economics, 27(2), 273-291, ISSN 1354-8166, https://dx.doi.org/10.1177/1354816619884800
• Bi, Jian-Wu, Han, Tian-Yu, and Li, Hui (2022). International tourism demand forecasting with machine learning models: The power of the number of lagged inputs. Tourism Economics, 28(3), 621-645, ISSN 1354-8166, https://dx.doi.org/10.1177/1354816620976954
• Sun, Shaolong, Li, Yanzhao, Guo, Ju-e, and Wang, Shouyang (2022). Tourism demand forecasting: An ensemble deep learning approach. Tourism Economics, 28(8), 2021-2049, ISSN 1354-8166, https://dx.doi.org/10.1177/13548166211025160
• Xiang, Zheng, Du, Qianzhou, Ma, Yufeng, and Fan, Weiguo (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2016.10.001
• Sun, Shaolong, Wei, Yunjie, Tsui, Kwok-Leung, and Wang, Shouyang (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1-10, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2018.07.010
• Taecharungroj, Viriya, and Mathayomchan, Boonyanit (2019). Analysing TripAdvisor reviews of tourist attractions in Phuket, Thailand. Tourism Management, 75, 550-568, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2019.06.020
• Giglio, Simona, Bertacchini, Francesca, Bilotta, Eleonora, and Pantano, Pietro (2019). Using social media to identify tourism attractiveness in six Italian cities. Tourism Management, 72, 306-312, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2018.12.007
• Rong, Jia, Vu, Huy Quan, Law, Rob, and Li, Gang (2012). A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining. Tourism Management, 33(4), 731-740, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2011.08.006
• Kaczmarek, Tomasz, Perez, Katarzyna, Demir, Ender, and Zaremba, Adam (2021). How to survive a pandemic: The corporate resiliency of travel and leisure companies to the COVID-19 outbreak. Tourism Management, 84, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2020.104281
• Xie, Gang, Qian, Yatong, and Wang, Shouyang (2021). Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tourism Management, 82, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2020.104208
• Arefieva, Veronika, Egger, Roman, and Yu, Joanne (2021). A machine learning approach to cluster destination image on Instagram. Tourism Management, 85, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2021.104318
• Law, Rob, Rong, Jia, Vu, Huy Quan, Li, Gang, and Lee, Hee Andy (2011). Identifying changes and trends in Hong Kong outbound tourism. Tourism Management, 32(5), 1106-1114, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2010.09.011
• Brida, Juan Gabriel, Lanzilotta, Bibiana, Moreno, Leonardo, and Santinaque, Florencia (2018). A non-linear approximation to the distribution of total expenditure distribution of cruise tourists in Uruguay. Tourism Management, 69, 62-68, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2018.05.006
• Zheng, Weimin, Liao, Zhixue, and Lin, Zhibin (2020). Navigating through the complex transport system: A heuristic approach for city tourism recommendation. Tourism Management, 81, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2020.104162
• Payntar, Nicole D., Hsiao, Wei-Lin, Covey, R. Alan, and Grauman, Kristen (2021). Learning patterns of tourist movement and photography from geotagged photos at archaeological heritage sites in Cuzco, Peru. Tourism Management, 82, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2020.104165
• Li, Hengyun, Ji, Haipeng, Liu, Hongbo, Cai, Danting, and Gao, Huicai (2022). Is a picture worth a thousand words? Understanding the role of review photo sentiment and text-photo sentiment disparity using deep learning algorithms. Tourism Management, 92, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2022.104559
• Chen, Shui-xia, Wang, Xiao-kang, Zhang, Hong-yu, Wang, Jian-qiang, and Peng, Juan-juan (2021). Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data. Tourism Management, 87, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2021.104357
• Li, Peizhe, Xiao, Xiao, and Seekamp, Erin (2022). Climate adaptation planning for cultural heritages in coastal tourism destinations: A multi-objective optimization approach. Tourism Management, 88, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2021.104380
• Ramos-Henriquez, Jose M., Gutierrez-Tano, Desiderio, and Diaz-Armas, Ricardo J. (2021). Value proposition operationalization in peer-to-peer platforms using machine learning. Tourism Management, 84, ISSN 0261-5177, https://dx.doi.org/10.1016/j.tourman.2021.104288
• Zhang, Chengyuan, Wang, Shouyang, Sun, Shaolong, and Wei, Yunjie (2020). Knowledge mapping of tourism demand forecasting research. Tourism Management Perspectives, 35, ISSN 2211-9736, https://dx.doi.org/10.1016/j.tmp.2020.100715
• Khorsand, Ramina, Rafiee, Majid, and Kayvanfar, Vahid (2020). Insights into TripAdvisor's online reviews: The case of Tehran's hotels. Tourism Management Perspectives, 34, ISSN 2211-9736, https://dx.doi.org/10.1016/j.tmp.2020.100673
• Luo, Yuyan, He, Jinjie, Mou, Yu, Wang, Jun, and Liu, Tao (2021). Exploring China's 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. Tourism Management Perspectives, 37, ISSN 2211-9736, https://dx.doi.org/10.1016/j.tmp.2020.100769
• Go, Hanyoung, Kang, Myunghwa, and Suh, SeungBeum Chris (2020). Machine learning of robots in tourism and hospitality: interactive technology acceptance model (iTAM) - cutting edge. Tourism Review, 75(4), 625-636, ISSN 1660-5373, https://dx.doi.org/10.1108/TR-02-2019-0062
• Egger, Roman, and Yu, Joanne (2022). Identifying hidden semantic structures in Instagram data: a topic modelling comparison. Tourism Review, 77(4), 1234-1246, ISSN 1660-5373, https://dx.doi.org/10.1108/TR-05-2021-0244
• Wang, Yuan, Zhang, Dongxiang, Liu, Ying, Dai, Bo, and Lee, Loo Hay (2019). Enhancing transportation systems via deep learning: A survey. Transportation Research Part C-Emerging Technologies, 99, 144-163, ISSN 0968-090X, https://dx.doi.org/10.1016/j.trc.2018.12.004
• Hasnat, Md Mehedi, and Hasan, Samiul (2018). Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data. Transportation Research Part C-Emerging Technologies, 96, 38-54, ISSN 0968-090X, https://dx.doi.org/10.1016/j.trc.2018.09.006
• Gungor, Osman Erman, and Al-Qadi, Imad L. (2018). Developing Machine-Learning Models to Predict Airfield Pavement Responses. Transportation Research Record, 2672(29), 23-34, ISSN 0361-1981, https://dx.doi.org/10.1177/0361198118780681
• Taecharungroj, Viriya (2022). An analysis of tripadvisor reviews of 127 urban rail transit networks worldwide. Travel Behaviour And Society, 26, 193-205, ISSN 2214-367X, https://dx.doi.org/10.1016/j.tbs.2021.10.007
• Vonk, Erwin, Cirkel, Dirk Gijsbert, and Blokker, Mirjam (2019). Estimating Peak Daily Water Demand under Different Climate Change and Vacation Scenarios. Water, 11(9), https://dx.doi.org/10.3390/w11091874
• Cankurt, S., and Subasi, A. (2015). Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components. Balkan Journal of Electrical and Computer Engineering, 3(1), 42-49.
• Kamel, N., Atiya, A. F., El Gayar, N., and El-Shishiny, H. (2008). Tourism demand foreacsting using machine learning methods. ICGST International Journal on Artificial Intelligence and Machine Learning, 8, 1-7.
• Egger, R. (2022). Machine Learning in Tourism: A Brief Overview: Generation of Knowledge from Experience. Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications, 85-107.
• Leelawat, N., Jariyapongpaiboon, S., Promjun, A., Boonyarak, S., Saengtabtim, K., Laosunthara, A., and Tang, J. (2022). Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning. Heliyon, 8(10), e10894.
• Das, S., Mondal, S., Puri, V., and Vrana, V. (2022). Structural review of relics tourism by text mining and machine learning. Marketing, 8(2), 25-34.
• Afsahhosseini, F., and Al-Mulla, Y. (2020, September). Machine learning in tourism. In 2020 The 3rd International Conference on Machine Learning and Machine Intelligence (pp. 53-57).
• Komninos, A., Simou, I., Gkorgkolis, N., and Garofalakis, J. (2019). Performance of raspberry pi microclusters for edge machine learning in tourism. Network (Mbps), 100(100), 100.
• Dewangan, A., and Chatterjee, R. (2018). Tourism recommendation using machine learning approach. In Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2016, Volume 2 (pp. 447-458). Springer Singapore.
• Karakitsiou, A., and Mavrommati, A. (2017). Machine learning methods in tourism demand forecasting: Some evidence from Greece. MIBES Transactions, 11(1), 92-105.
• Vijay, J., and Sridhar, R. (2016). A machine learning approach to named entity recognition for the travel and tourism domain. Asian Journal of Information Technology, 15(21), 4309-4317.
• Claveria, O., Monte, E., and Torra, S. (2017). Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting. Neural Network Models in a Multiple-Input Multiple-Output Setting.
• Buhalis, D., and Amaranggana, A. (2015). Smart tourism destinations enhancing tourism experience through personalisation of services. In Information and Communication Technologies in Tourism 2015: Proceedings of the International Conference in Lugano, Switzerland, February 3-6, 2015 (pp. 377-389). Springer International Publishing.
• Doan, A., Madhavan, J., Domingos, P., and Halevy, A. (2004). Ontology matching: A machine learning approach. Handbook on ontologies, 385-403.
• Schuckert, M., Liu, X., and Law, R. (2015). Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel and Tourism Marketing, 32(5), 608-621.
• Buhalis, D. (2020). Technology in tourism-from information communication technologies to eTourism and smart tourism towards ambient intelligence tourism: a perspective article. Tourism Review, 75(1), 267-272.
• Navigli, R., Velardi, P., and Gangemi, A. (2003). Ontology learning and its application to automated terminology translation. IEEE Intelligent systems, 18(1), 22-31.
• Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., and Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management.
• Buhalis, D., and Sinarta, Y. (2019). Real-time co-creation and nowness service: lessons from tourism and hospitality. Journal of Travel and Tourism Marketing, 36(5), 563-582.
• Chen, K. Y., and Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism management, 28(1), 215-226.
• Lu, W., and Stepchenkova, S. (2015). User-generated content as a research mode in tourism and hospitality applications: Topics, methods, and software. Journal of Hospitality Marketing and Management, 24(2), 119-154.
• Benckendorff, Pierre J., Zheng Xiang, and Pauline J. Sheldon. Tourism information technology. Cabi, 2019.
• Fotiadis, A., Polyzos, S., and Huan, T. C. T. (2021). The good, the bad and the ugly on COVID-19 tourism recovery. Annals of tourism research, 87, 103117.
• Fuchs, M., Höpken, W., and Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations–A case from Sweden. Journal of destination marketing and management, 3(4), 198-209.
• Tussyadiah, I. (2020). A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism. Annals of Tourism Research, 81, 102883.
• Li, X., Pan, B., Law, R., and Huang, X. (2017). Forecasting tourism demand with composite search index. Tourism management, 59, 57-66.
• Mariani, M., Baggio, R., Fuchs, M., and Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554.
• Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology and Tourism, 22(3), 455-476.
• Law, R., Li, G., Fong, D. K. C., and Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of tourism research, 75, 410-423.
• Sigala, M. (2018). New technologies in tourism: From multi-disciplinary to anti-disciplinary advances and trajectories. Tourism management perspectives, 25, 151-155.
• Xiang, Z. (2018). From digitization to the age of acceleration: On information technology and tourism. Tourism management perspectives, 25, 147-150.
• García-Crespo, A., Chamizo, J., Rivera, I., Mencke, M., Colomo-Palacios, R., and Gómez-Berbís, J. M. (2009). SPETA: Social pervasive e-Tourism advisor. Telematics and informatics, 26(3), 306-315.
• Aggarwal, C. C. (2018). Neural networks and deep learning. Springer, 10(978), 3.
• Ekman, M. (2021). Learning deep learning: theory and practice of neural networks, computer vision, NLP, and transformers using TensorFlow.
• Khan, M. A., Ali, M., and Khan, M. A. (2021). Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism. Sustainability, 13(15), 8141. https://doi.org/10.3390/su13158141.
• Putra, I. G. N. A. W., Kadyanan, I. G. A. G. A. (2021). Optimization Of Bali Tourism Recommendations Based On Personal Motivation Of Tourists Using the Naive Bayes Algorithm. JLK, 1(10), 83. https://doi.org/10.24843/jlk.2021.v10.i01.p11.
• J. Srisuan and A. Hanskunatai, "The ensemble of Naïve Bayes classifiers for hotel searching," 2014 International Computer Science and Engineering Conference (ICSEC), Khon Kaen, Thailand, 2014, pp. 168-173, doi: 10.1109