Influencing models and determinants in big data analytics research: A bibliometric analysis

Aboelmaged, Mohamed Gamal; Mouakket, Samar

doi:10.1016/j.ipm.2020.102234

Cited by 41 publications

(23 citation statements)

References 212 publications

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“…The artificial neural network which simulates the connection between human neurons can process the relevant signals, obtain the data signal prediction model, and solve the nonlinear data prediction and other related problems. Therefore, this paper selects the BP neural network as the foundation of the tourism security early warning information system and extracts the implicit relationship of the static data that need to be analyzed and predicted [ 24 ]. The neurons of the BP neural network can connect multiple inputs but only have one output node, as shown in Figure 2 .…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short‐Term Memory (LSTM)

Feng

2021

Computational Intelligence and Neuroscience

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Tourism safety is the focus of the tourism industry. It is not only related to the safety of tourists’ lives and property, but also related to social stability and sustainable development of the tourism industry. However, the security early warning of many scenic spots focuses on the response measures and remedial plans after the occurrence of security incidents, and the staff of many scenic spots have limited security awareness and information analysis ability, which is prone to lag in information release, and do not pay attention to the information of potential security problems. Therefore, this paper studies the optimization algorithm of the tourism security early warning information system based on the LSTM model and uses the recurrent neural network and LSTM to improve the processing and prediction ability of time-series data. The experimental results show that the number of three hidden layers in the tourism security early warning information system based on the LSTM model can reduce the training time of the model and improve the performance. Compared with the tourism safety early warning information system based on the BP neural network, it has better accuracy and stability, has better processing and prediction ability for time series data, and can monitor and analyze data scientifically in real-time and dynamically analyze data.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short‐Term Memory (LSTM)

Feng

2021

Computational Intelligence and Neuroscience

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“…Text reader detects a given document and predicts the range of answers. In this study, we use match the LSTM attention mechanism to build a text reader's answer prediction model, which can represent the given document and question in the matrix [22][23][24]. e LSTM of the match LSTM reading comprehension model encodes the problem and the paragraph of the document, respectively, and the context information begins to fuse.…”

Section: Text Reader Taskmentioning

confidence: 99%

[Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Tang

2021

Computational Intelligence and Neuroscience

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In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition.

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“…There is little prior work employing actual airline customer behavior information. There is a scarcity of research on building machine learning models to predict whether the customer will accept any 'airline's upgrade offers (Aboelmaged & Mouakket, 2020;Chen et al, 2021;Huang et al, 2016;Ma et al, 2020;Renjith et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Will You Buy It Now?: Predicting Passengers that Purchase Premium Promotions Using the PAX Model

Emadi¹,

Thirumuruganathan²,

Robillos³

et al. 2021

Journal of Smart Tourism

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Upselling is often a critical factor in revenue generation for businesses in the tourism and travel industry. Utilizing passenger data from a major international airline company, we develop the PAX (Passenger, Airline, eXternal) model to predict passengers that are most likely to accept an upgrade offer from economy to premium. Formulating the problem as an extremely unbalanced, cost-sensitive, supervised binary classification, we predict if a customer will take an upgrade offer. We use a feature vector created from the historical data of 3 million passenger records from 2017 to 2019, in which passengers received approximately 635,000 upgrade offers worth more than $422,000,000 U.S. dollars. The model has an F1-score of 0.75, outperforming the airline's current rule-based approach. Findings have several practical applications, including identifying promising customers for upselling and minimizing the number of indiscriminate emails sent to customers. Accurately identifying the few customers who will react positively to upgrade offers is of paramount importance given the airline 'industry's razor-thin margins. Research results have significant real-world impacts because there is the potential to improve targeted upselling to customers in the airline and related industries.

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Influencing models and determinants in big data analytics research: A bibliometric analysis

Cited by 41 publications

References 212 publications

[Retracted] Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short‐Term Memory (LSTM)

[Retracted] Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short‐Term Memory (LSTM)

[Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Will You Buy It Now?: Predicting Passengers that Purchase Premium Promotions Using the PAX Model

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