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Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data [article]

Julian Lemmel, Zahra Babaiee, Marvin Kleinlehner, Ivan Majic, Philipp Neubauer, Johannes Scholz, Radu Grosu, Sophie A. Neubauer
2022 arXiv   pre-print
In this paper, we empirically evaluate the performance of several state-of-the-art deep-learning methods in the field of visitor flow prediction with limited data by using available granular data supplied  ...  In this context, a proper and accurate prediction of tourism volume and tourism flow within a certain area is important and critical for visitor management tasks such as visitor flow control and prevention  ...  After data preprocessing and dataset preparation, we attempt to compare the performance of different deep-learning based methods for time-series prediction with ARIMA, a traditional statistics based method  ... 
arXiv:2206.13274v1 fatcat:pa267hhglzaeji2r3bpxq2p42q

A machine learning metasystem for robust probabilistic nonlinear regression-based forecasting of seasonal water availability in the US West

Sean W. Fleming, Angus G. Goodbody
2019 IEEE Access  
INDEX TERMS Machine learning, regression analysis, forecast uncertainty, hydroelectric power generation, water resources, environmental management, industry applications.  ...  Some methods satisfied some of these requirements but none met all, leading us to develop a novel, interdisciplinary, and pragmatic prediction metasystem through a carefully considered synthesis of well-established  ...  In fact, some major current trends in machine learning, such as mining big data for predictive patterns using deep learning-based massive neural networks, for example, seem to be moving further away from  ... 
doi:10.1109/access.2019.2936989 fatcat:qwwyufndvjeablhncoo4stozzi

Forecasting Liquidated Damages via Machine Learning-Based Modified Regression Models for Highway Construction Projects

Odey Alshboul, Mohammad A. Alzubaidi, Rabia Emhamed Al Mamlook, Ghassan Almasabha, Ali Saeed Almuflih, Ali Shehadeh
2022 Sustainability  
It represents an innovative use of Multiple Linear Regression (MLR) models hybridized with machine learning (ML).  ...  This paper proposes modified regression modeling using machine learning (ML) techniques to develop solutions to the problem of predicting LDs for construction projects.  ...  It provides the decision-maker with an insight into all the details and factors embedded in Figure 1 . 1 Figure 1. Flow chart of data modeling. Figure 2 . 2 Figure 2.  ... 
doi:10.3390/su14105835 fatcat:xh6gbdvobrgkdi2ja2rskdhpf4

Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Antonia Ivanda, Ljiljana Šerić, Marin Bugarić, Maja Braović
2021 Electronics  
In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in  ...  This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements.  ...  Conflicts of Interest: The authors declare no conflict of interest. Sample Availability: Code is available from the authors.  ... 
doi:10.3390/electronics10233004 fatcat:4chsabd7k5gy7la7b4qradktjm

Tourism Growth Prediction Based on Deep Learning Approach

Xiaoling Ren, Yanyan Li, JuanJuan Zhao, Yan Qiang, M. Irfan Uddin
2021 Complexity  
The main objective in this study is to predict the tourism growth via one of the deep learning algorithms of extracting new features.  ...  In this work, the framework for deep learning-based monthly prediction of the volumes of Macau tourist arrivals was presented.  ...  Taken features control the number of layers and nodes and control the system accordingly. Deep Learning vs.  ... 
doi:10.1155/2021/5531754 fatcat:niuezs27kzdxpnobr75it37z5e

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Juan Guerra-Montenegro, Javier Sanchez-Medina, Ibai Laña, David Sanchez-Rodriguez, Itziar Alonso-Gonzalez, Javier Del Ser
2021 Applied Soft Computing  
Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth.  ...  This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches.  ...  Combining keywords such as ''Hospitality Industry'', ''Data Stream Mining'', ''Tourism'', ''Online Learning'', ''Computational Intelligence'' or ''Deep Learning'', along with a filter by topic (title,  ... 
doi:10.1016/j.asoc.2021.107082 fatcat:b6oavdvehvaabcsd2vhypmgafu

RECENT TRENDS OF MACHINE LEARNING PREDICTIONS USING OPEN DATA: A SYSTEMATIC REVIEW

Norismiza Ismail, Umi Kalsom Yusof
2022 Journal of Information and Communication Technology  
Machine learning (ML) prediction determinants based on open data (OD) are investigated in this work, which is accomplished by examining current research trends over ten years.  ...  data (OGD).The purpose of this study was to examine if there was an increase in the usage of OD in ML prediction techniques by conducting a systematic literature review (SLR) of the results of the trends  ...  ACKNOWLEDGMENT The authors gratefully acknowledge Universiti Sains Malaysia (USM) and Universiti Malaysia Perlis (UniMAP) for the support they have extended in the completion of this research.  ... 
doi:10.32890/jict2022.21.3.3 fatcat:lzou2vul2ngfxdu5noggpmus64

A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared

Luca Casini, Marco Roccetti
2020 Sensors  
Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared  ...  in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country  ...  This cognitive model was developed using the Keras library, part of the Tensorflow framework for deep learning [28] .  ... 
doi:10.3390/s20247319 pmid:33352802 fatcat:4ekn4aw5evgvjbo6mx6cz5e4ta

Data Analytics Applications for Streaming Data From Social Media: What to Predict?

Frank Emmert-Streib, Olli P. Yli-Harja, Matthias Dehmer
2018 Frontiers in Big Data  
Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data?  ...  In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature.  ...  ., 2015) , e.g., deep neural networks, deep decision trees or deep belief networks, will change the type of questions addressed with social media data.  ... 
doi:10.3389/fdata.2018.00002 pmid:33693318 pmcid:PMC7931880 fatcat:drihkk45gba55kjn2ieru27ena

AI Powered Holistic Solution for Travelersduring Pandemic

2020 International Journal of Engineering and Advanced Technology  
A software-based approach is taken for providing a simple and engaging user experience to the user along with an AI approach to detect and predict the COVID trend in various cities.  ...  As the world is engulfed with COVID-19 pandemic and the glimpse of vaccine is still a distant dream, taking precautions and maintaining the norms suggested by WHO will keep us safe.  ...  Thus, deep learning models such as LSTM were used for forecasting time series data for getting better predictions.  ... 
doi:10.35940/ijeat.f1428.089620 fatcat:qitg6lkqmzgazoj7pc37rxqanm

Index [chapter]

2021 Cognitive Computing for Human-Robot Interaction  
See also Machine learning (ML) autonomous vehicle benefits, 157 classification, 153 comparison of accuracy of current study, 157t data collection, 151À152 deep learning-based architectures, 54 electroencephalogram  ...  , 45 CC and reinforcement learning, 44 in predicting units of solar energy based on past data, 14À16 in renewable energy, 14À16 and robot technology, 43 system components, 23À24, 23f technologies  ... 
doi:10.1016/b978-0-323-85769-7.00024-0 fatcat:elwoct6va5fxnn37abjdykp3ca

Comparative analysis of short-term demand predicting models using ARIMA and deep learning

Halima Bousqaoui, Ilham Slimani, Said Achchab
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends.  ...  model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning  ...  The present paper tackles the proposed solution which is using time series forecasting models and deep learning models for demand prediction.  ... 
doi:10.11591/ijece.v11i4.pp3319-3328 fatcat:mfjct7mecbajvnl2r7hbaawsp4

Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy

Varun Varghese, Makoto Chikaraishi, Junji Urata
2020 Journal of Big Data Analytics in Transportation  
We concluded this paper with a comprehensive summary of the existing findings on the applications of deep learning to transport studies.  ...  Moreover, the accuracy level also depends on other factors such as the type of data, sample size, region of data collection, and time of prediction.  ...  Acknowledgements The authors would like to acknowledge that a part of this research was conducted under the research project "Short-term  ... 
doi:10.1007/s42421-020-00030-z fatcat:dbsovivksvfkvonsd3252bz534

CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities

Gürkan Solmaz, Pankaj Baranwal, Flavio Cirillo
2022 Zenodo  
This paper presents a new adaptive machine learning system, called CountMeIn, to address the crowd estimation problem using polynomial regression and neural networks.  ...  The approach transfers the calibration task from cameras to machine learning after a short training with people counting from stereo- scopic cameras, Wi-Fi probe packets, and temporal features.  ...  The pilot study in Gold Coast is conducted with NEC Australia. The content of this paper does not reflect the official opinion of EU.  ... 
doi:10.5281/zenodo.6322752 fatcat:zv3qgoyvs5fofegnpvbvcwndpe

Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

Miguel Ángel Ruiz Reina
2021 Engineering Proceedings  
The results of our model present better indicators of the RMSE and Ratio Theil's for the predictive evaluation period of twelve months.  ...  The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case.  ...  Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/engproc2021005017 fatcat:prr44irbh5cbhlhbz3y3djuf3m
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