Predicting overnights in smart villages: the importance of context information

  1. Bolaños-Martinez, Daniel
  2. Garrido, Jose Luis 1
  3. Bermudez-Edo, Maria
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

Journal:
International Journal of Machine Learning and Cybernetics

ISSN: 1868-8071 1868-808X

Year of publication: 2024

Type: Article

DOI: 10.1007/S13042-024-02337-7 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: International Journal of Machine Learning and Cybernetics

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Bibliographic References

  • Laaroussi H, Guerouate F (2020) Deep learning framework for forecasting tourism demand. In: 2020 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), 1–4 . IEEE
  • Sáenz FT, Arcas-Tunez F, Muñoz A (2023) Nation-wide touristic flow prediction with graph neural networks and heterogeneous open data. Inf Fus 91:582–597
  • Zhai Z, Liu P, Zhao L, Qian J, Cheng B (2021) An efficiency-enhanced deep learning model for citywide crowd flows prediction. Int J Mach Learn Cybern 12:1879–1891
  • Lin M, Zhao X (2019) Application research of neural network in vehicle target recognition and classification. In: 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 5–8 . IEEE
  • Ning Z, Huang J (2019) Wang X Vehicular fog computing: Enabling real-time traffic management for smart cities. IEEE Wirel Commun 26(1):87–93
  • Yao W, Chen C, Su H, Chen N, Jin S, Bai C (2022) Analysis of key commuting routes based on spatiotemporal trip chain. J Adv Transp 2022:25
  • Liu Z, Liu Y, Meng Q, Cheng Q (2019) A tailored machine learning approach for urban transport network flow estimation. Transp Res Part C Emerg Technol 108:130–150
  • Cats O, Ferranti F (2022) Unravelling individual mobility temporal patterns using longitudinal smart card data. Res Transport Bus Manag 43:100816
  • Mondal MA, Rehena Z (2019) Identifying traffic congestion pattern using k-means clustering technique. In: 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 1–5. IEEE
  • Peixoto MLM, Maia AH, Mota E, Rangel E, Costa DG, Turgut D, Villas LAA (2021) Traffic data clustering framework based on fog computing for vanets. Veh Commun 31:100370
  • Buhalis D (2020) Technology in tourism-from information communication technologies to etourism and smart tourism towards ambient intelligence tourism: a perspective article. Tour Rev 75(1):267–272
  • Tang J, Zeng J, Wang Y, Yuan H, Liu F, Huang H (2021) Traffic flow prediction on urban road network based on license plate recognition data: combining attention-lstm with genetic algorithm. Transportmetrica A: Transp Sci 17(4):1217–1243
  • Tang J (2022) Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data. Comput-Aided Civ Infrastruct Eng 37(1):3–23
  • Yang G, Coble D, Vaughan C, Peele C, Morsali A, List GF, Findley DJ (2022) Waiting time estimation at ferry terminals based on license plate recognition. J Transp Eng Part A Syst 148(9):04022064
  • Yao W, Yu J, Yang Y, Chen N, Jin S, Hu Y, Bai C (2022) Understanding travel behavior adjustment under covid-19. Commun Transp Res 2:100068
  • Wang P, Lai J, Huang Z, Tan Q (2020) Estimating traffic flow in large road networks based on multi-source traffic data. IEEE Trans Intell Transp Syst 22(9):5672–5683
  • Liu Q, Zhang J, Liu J, Yang Z (2022) Feature extraction and classification algorithm, which one is more essential? an experimental study on a specific task of vibration signal diagnosis. Int J Mach Learn Cybern 2:1–12
  • Meyes R, Lu M, Puiseau CW, Meisen T (2019) Ablation studies in artificial neural networks. arXiv preprint arXiv:1901.08644
  • Gómez-Pulido JA, Romero-Muelas JM, Gómez-Pulido JM, Castillo Sequera JL, Sanz Moreno J, Polo-Luque M-L (2020) Predicting infectious diseases by using machine learning classifiers. In: Rojas I, Valenzuela O, Rojas F, Herrera LJ, Ortuño F (eds) Bioinf Biomed Eng. Springer, Cham, pp 590–599
  • Liu B, Pei J, Yu Z (2023) Stock price prediction through gra-wd-bilstm model with air quality and weather factors. Int J Mach Learn Cybern 2:1–18
  • Maiti A, Shi S, Vucetic S (2023) An ablation study on the use of publication venue quality to rank computer science departments: Publication quality is strongly correlated with the subjective perception of research strength. Scientometrics 128(8):4197–4218
  • Saraswathi N, Rooba TS, Chakaravarthi S (2023) Improving the accuracy of sentiment analysis using a linguistic rule-based feature selection method in tourism reviews. Measurement: Sensors 29, 100888
  • Anamisa DR, Mufarroha FA, Jauhari A (2023) Feature selection to increase the attractiveness of visitors in bangkalan tourism, madura based on chi-square method. In: AIP Conference Proceedings, vol. 2679. AIP Publishing
  • Sun S, Li M, Wang S, Zhang C (2022) Multi-step ahead tourism demand forecasting: the perspective of the learning using privileged information paradigm. Expert Syst Appl 210:118502
  • Zhan X, Li R, Ukkusuri SV (2020) Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data. Transp Res Part C Emerg Technol 117:102660
  • Song H, Liu H (2017) Predicting tourist demand using big data. Analytics in smart tourism design: Concepts and methods, 13–29
  • Peters S, Keller P (2022) Applications and issues of big data in tourism research
  • Madzík P, Falát L, Copuš L, Valeri M (2023) Digital transformation in tourism: bibliometric literature review based on machine learning approach. Eur J Innov Manag 26(7):177–205
  • Peng T, Chen J, Wang C (2021) Cao Y A forecast model of tourism demand driven by social network data. IEEE Access 9:109488–109496
  • Bi J-W (2020) Liu Y, Li H Daily tourism volume forecasting for tourist attractions. Ann Tour Res 83:102923
  • Lau BPL, Marakkalage SH, Zhou Y, Hassan NU, Yuen C, Zhang M, Tan U-XA (2019) survey of data fusion in smart city applications. Inf Fusion 52:357–374
  • Bolaños-Martinez D, Bermudez-Edo M, Garrido JL (2023) Clustering pipeline for vehicle behavior in smart villages. Inf Fusion 10:2164
  • Bolaños-Martinez D, Bermudez-Edo M, Garrido JL (2022) Clustering study of vehicle behaviors using license plate recognition. In: Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022), 784–795. Springer
  • Zheng L, Wang H (2018) Gao S Sentimental feature selection for sentiment analysis of chinese online reviews. Int J Mach Learn Cybern 9:75–84
  • Sun C, Li H, Song M, Cai D, Zhang B, Hong S (2023) Adaptive model training strategy for continuous classification of time series. Appl Intell 2:1–19
  • Swaminathan B, Palani S (2023) Feature fusion based deep neural collaborative filtering model for fertilizer prediction. Expert Syst Appl 216:119441
  • Abu-Mostafa YS, Magdon-Ismail M, Lin H-T (2012) Learning from data. AMLBook
  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R. Springer, Berlin
  • Liu FT, Ting KM, Zhou Z-H (2008) Isolation forest. In: 2008 Eighth Ieee International Conference on Data Mining, pp. 413–422. IEEE
  • Henderi H, Wahyuningsih T, Rahwanto E (2021) Comparison of min–max normalization and z-score normalization in the k-nearest neighbor (knn) algorithm to test the accuracy of types of breast cancer. Int J Inf Inf Syst 4(1):13–20
  • Patro S, Sahu KK (2015) Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462
  • Eesa AS, Arabo WK (2017) A normalization methods for backpropagation: a comparative study. Sci J Univ Zakho 5(4):319–323
  • Bektaş S (2010) Şişman Y The comparison of l1 and l2-norm minimization methods. Int J Phys Sci 5(11):1721–1727
  • Mendoza-Pittí L, Gómez-Pulido JM, Vargas-Lombardo M, Gómez-Pulido JA, Polo-Luque M-L (2022) Rodréguez-Puyol D Machine-learning model to predict the intradialytic hypotension based on clinical-analytical data. IEEE Access 10:72065–72079
  • Gutiérrez O, Sancho Núñez J.C, Homaei M, Díaz J (2022) Aplicación de técnicas de reducción de dimensionalidad y balanceo en ciberseguridad
  • Misengo EE, Prastyo DD, Kuswanto H (2023) Modeling and forecasting monthly tourist arrivals to the united states and indonesia using arima hybrids of multilayer perceptron models. In: AIP Conference Proceedings, vol. 2540. AIP Publishing
  • Jatmika S, Patmanthara S, Wibawa AP (2024) The model of local wisdom for smart wellness tourism with optimization multilayer perceptron. J Theor Appl Inf Technol 102:2
  • Ali J, Khan R, Ahmad N, Maqsood I (2012) Random forests and decision trees. Int J Comput Sci Issues (IJCSI) 9(5):272
  • Ariyani N, Fauzi A, Umar F (2023) Predicting and determining antecedent factors of tourist village development using naive bayes and tree algorithm. Int J Appl Sci Tour Events 7(1):1–15
  • Peng L, Wang L, Ai X-Y, Zeng Y-R (2021) Forecasting tourist arrivals via random forest and long short-term memory. Cogn Comput 13:125–138
  • Celiker N, Guzeller CO (2024) Predicting organizational citizenship behaviour in hospitality businesses with decision tree method. Int J Hosp Tour Admin 25(2):436–474
  • Peterson LE (2009) K-nearest neighbor. Scholarpedia 4(2):1883
  • Rachmawanto EH, Sari CA, Pramono H, Sari WS (2022) Visitor prediction decision support system at dieng tourism objects using the k-nearest neighbor method. J Appl Intell Syst 7(2):183–192
  • Anamisa DR, Jauhari A, Mufarroha FA (2023) K-nearest neighbors method for recommendation system in bangkalan’s tourism. ComTech Comput Math Eng Appl 14(1):33–44
  • Tsangaratos P (2016) Ilia I Comparison of a logistic regression and naïve bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. CATENA 145:164–179
  • Siroosi H, Heshmati G (2020) Salmanmahiny A Can empirically based model results be fed into mathematical models? mce for neural network and logistic regression in tourism landscape planning. Environ Dev Sustain 22(4):3701–3722
  • Devianto D, Maryati S, Rahman H (2021) Logistic regression model for entrepreneurial capability factors in tourism development of the rural areas with bayesian inference approach. J Phys Conf Ser 1940:012022
  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30:2
  • Zhao D, Hu Z, Yang Y (2023) Tourist trajectory prediction based on improved lightgbm. In: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022),12510 pp. 54–59 . SPIE
  • Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining pp. 785–794
  • Kang J, Guo X, Fang L, Wang X, Fan Z (2022) Integration of internet search data to predict tourism trends using spatial-temporal xgboost composite model. Int J Geogr Inf Sci 36(2):236–252
  • Hu Y, Shao L, La L, Hua H (2021) Using investor and news sentiment in tourism stock price prediction based on xgboost model. In: 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD), 20–24. IEEE
  • Li H, Gao H (2023) Song H Tourism forecasting with granular sentiment analysis. Ann Tour Res 103:103667
  • Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) Catboost: unbiased boosting with categorical features. Adv Neural Inf Process Syst 31:2
  • Chen Y, Ding C, Ye H, Zhou Y (2022) Comparison and analysis of machine learning models to predict hotel booking cancellation. In: 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022), pp. 1363–1370 . Atlantis Press
  • Tang J, Cheng J, Zhang M (2024) Forecasting airbnb prices through machine learning. Manag Decis Econ 45(1):148–160
  • Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001
  • Arik SÖ, Pfister T (2021) Tabnet: Attentive interpretable tabular learning. Proc AAAI Conf Artif Intell 35:6679–6687
  • Kim S, Shin W, Kim H-W (2024) Predicting online customer purchase: the integration of customer characteristics and browsing patterns. Decis Support Syst 177:114105
  • Hermanto D, Ziaurrahman M, Bianto M, Setyanto A (2018) Twitter social media sentiment analysis in tourist destinations using algorithms naive bayes classifier. In: Journal of Physics: Conference Series, vol. 1140, p. 012037. IOP Publishing
  • Joachims T (2006) Training linear svms in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–226
  • Purnaningrum E, Athoillah M (2021) Svm approach for forecasting international tourism arrival in east java. In: Journal of Physics: Conference Series, vol. 1863, p. 012060. IOP Publishing
  • Otchere DA, Gholami Ganat TOA, Ridha S (2021) Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ann and svm models. J Petrol Sci Eng 200:108182
  • Bonaccorso G (2018) Machine learning algorithms: popular algorithms for data science and machine learning. Packt Publishing Ltd, Singapore
  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
  • Sigletos G, Paliouras G, Spyropoulos CD, Hatzopoulos M, Cohen W (2005) Combining information extraction systems using voting and stacked generalization. J Mach Learn Res 6:11
  • Dietterich TG (2000) Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems. Springer, Berlin, pp 1–15
  • Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1):29–36
  • Gupta A, Tatbul N, Marcus R, Zhou S, Lee I, Gottschlich J (2020) Class-weighted evaluation metrics for imbalanced data classification. arXiv preprint arXiv:2010.05995
  • Jeni LA, Cohn JF, De La Torre F (2013) Facing imbalanced data–recommendations for the use of performance metrics. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 245–251. IEEE