A Machine Learning-Based 10 Years Ahead Prediction of Departing Foreign Visitors by Reasons: A Case on Türkiye

Tutsoy, Önder; Tanrıkulu, Ceyda

doi:10.3390/app122111163

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…The robustness and scalability of SFAO in more extensive and diverse datasets remain areas for further exploration. Additionally, integrating adaptive learning mechanisms, which modulate hyperparameters based on input data [41], could further enhance the efficacy of SFAO.…”

Section: Discussionmentioning

confidence: 99%

SFAO: Sign-Flipping-Aware Optimization for Early-Stopping of Binarized Neural Networks

Kang,

Ryu,

Kang

et al. 2023

IEEE Access

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One of the vital challenges for the binary neural networks (BNNs) is improving their inference performance by expanding their data representation capabilities for figuring out delicate patterns and nuances in the data. Addressing the explosive computational demands on neural network training is essential to guarantee sustainable development and scalable deployment. However, mitigating the increase in the computational cost during the training phase is critical for ensuring sustainability and scalability during deployment. In this study, an advanced sign-flipping-aware optimizer (SFAO) that focuses on BNNs was introduced to diminish the computational burden. SFAO balanced the model performance and computational cost through sign-flipping-aware updating rules throughout the training of BNNs. SFAO optimizer, tailored for BNNs with binary weight-specific updating rules, considerably reduced the computing resources needed for training on the CIFAR-10 dataset. Specifically, it surpassed the conventional full-precision updating rule by reducing the total instruction count by 21.89%. In contrast, SFAO showed a marginal 0.44% decline in the image classification accuracy relative to the updating rules for the full-precision parameters. Furthermore, the implementation of early stopping using the sign flip rate led to a notable reduction of 9.37% in the average computation time per network for the ImageNet dataset.INDEX TERMS artificial intelligence, model compression, optimizer, efficient machine learning, binarized neural networks, layer freezing.

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Section: Discussionmentioning

confidence: 99%

SFAO: Sign-Flipping-Aware Optimization for Early-Stopping of Binarized Neural Networks

Kang,

Ryu,

Kang

et al. 2023

IEEE Access

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“…The present study included the development of predictive models for several tourist places that comprise the Moche Route; however, the reviewed studies differentiated in terms of the results, because the focus was on a single place of analysis or the one with the greatest influx, as in [20], which focused on Turkey. This difference in the analysis in the present investigation and the previous studies that focused on a single place could have contributed to the models not having the expected performance with high predictions.…”

Section: Discussionmentioning

confidence: 99%

“…In other works, deep learning algorithms and neural networks have been applied, such as in [20], where a machine learning model was developed to estimate the number of foreign visitors leaving Turkey for certain reasons. In addition, algorithms have been applied to batch type genetics to learn unknown model parameters when considering disruptions, for example, because of COVID-19.…”

Section: Digital Transformation Applied To Tourismmentioning

confidence: 99%

“…Learning models are widely used tools in forecasting research. As can be seen in [20], a machine learning model was developed to estimate the number of foreign visitors leaving Turkey, and it also identified 10 reasons for foreign visitor departures over the next 10 years in order to gain a deeper understanding of their future behaviors.…”

Section: Machine Learning Model and Deep Learningmentioning

confidence: 99%

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Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

et al. 2023

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Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.

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“…Huang et al [10] researched potential combinations of traditional Automated Passenger Counters (APC) and a novel source capable of collecting detailed mobile demand data but did not include a reputation management module to prevent malicious data uploads. Tutsoy et al [25] proposed an AI based long-term policy making algorithm aiming to maximize the number of the students attending the schools while minimizing the number of the casualties. Tutsoy et al [26] proposed a study that can contribute to marketing science by presenting a strong estimation of future consumer behavior in tourism through machine-learningbased predictions.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Wang,

Chen

2024

CMES

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Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking a dedicated reputation update module. This paper introduces a reputation update scheme tailored for crowdsourcing, with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system. Here, the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method (ECOD). Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance. Computation of user reputation scores relies on their behavioral history, actively discouraging undesirable conduct. Additionally, we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation, enabling the system to allocate incentives based on user behavior and reputation. Our scheme undergoes evaluation on 11 datasets, revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior. This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications, holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

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A Machine Learning-Based 10 Years Ahead Prediction of Departing Foreign Visitors by Reasons: A Case on Türkiye

Cited by 10 publications

References 28 publications

SFAO: Sign-Flipping-Aware Optimization for Early-Stopping of Binarized Neural Networks

SFAO: Sign-Flipping-Aware Optimization for Early-Stopping of Binarized Neural Networks

Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

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