Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

Purpose Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience. Design/methodology/approach The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”. Findings The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns. Practical implications This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries. Originality/value This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

The Use of Corpora in the Teaching of ESP (the Sphere of Pedagogy and Psychology)

Specialized language from the spheres of pedagogy and psychology constitutes a fundamental aspect within the teacher – student communication. The submitted paper handles lexical and morphological features of compound nouns containing the nouns amnesia, memory and recall. The primary research interest focuses on collocations and concordances in which they appear on a regular basis; we also deal with the grammatical relations and elements of meaning that have an impact on the discourse characteristics. The studied lexical units are elaborated within English Web 2015 utilizing Sketch Engine. In the research, both quantitative (statistical methods) and qualitative (observation, comparison, generalization, data-driven research data modelling) methods were employed. To begin with, we provide the frequency list of the studied words within the text corpus, and we categorize them from the morphological perspective as the two aspects influence their lexical behaviour and employment for teaching purposes. The research outcomes indicate that the analysed lexical units exist in a broad discourse scope; moreover, they appreciably grant profitable acts of communication in the pedagogical settings. Eventually, we outline feasible pedagogical inferences of the inquiry outcomes in the teaching of English and we suggest a set of corpus-driven exercises on the professionally-oriented language from the fields of pedagogy and psychology. To sum up, the outcomes confirm that the lexical and semantic characteristics of specialized language underline social aspects of communication between experts (and public too). Further, we intend to elaborate words belonging to other spheres of professional communication (medicine, sociology, etc.) in the framework of the corpus examination.

Panel Data Modelling for Indian Food Grain Production

The present investigation was carried out to study the food grain production trends in different states in India based on Panel Regression Model for the period 2001-02 to 2020-2021. The results reveal that between state-to-state food grain production is highly significant the highest food grain production was registered in Uttar Pradesh followed by Punjab and Madhya Pradesh. Very lowest was registered in Kerala and Himachal Pradesh. The findings reveal that the highly significant fixed effect model was found to be suitable to study the trend and this model explains the 82% of variations in food grain production. Over all increasing in food grain production is noted.

Hausman and Taylor Estimator Analysis on The Linear Data Panel Model

Panel data modelling in the field of econometrics applies two main approaches, namely fixed effect estimators and random effects. The application of the Hausman and Taylor estimator to real data is used to test for fixed effects or random effects based on the idea that the set of estimated coefficients obtained from the fixed effect estimates is taken as a group. A good estimator is an estimator that is as close as possible to represent the characteristics of the population. The characteristics of a good estimator include unbiasedness, efficiency, and consistency. The purpose of this study is to identify the properties of the Hausman and Taylor estimator in the linear model of panel data. Based on the analysis using panel data, it is found that the Hausman and Taylor estimator on the random effects panel data is an estimator that is consistent and efficient even though it is not unbiased.

Publishing Scholarly Editions

Publishing Scholarly Editions offers new intellectual tools for publishing digital editions that bring readers closer to the experimental practices of literature, editing, and reading. After the Introduction (Section 1), Sections 2 and 3 frame intentionality and data analysis as intersubjective, interrelated, and illustrative of experience-as-experimentation. These ideas are demonstrated in two editorial exhibitions of nineteenth-century works: Herman Melville's Billy Budd, Sailor, and the anti-slavery anthology The Bow in the Cloud, edited by Mary Anne Rawson. Section 4 uses pragmatism to rethink editorial principles and data modelling, arguing for a broader conception of the edition rooted in data collections and multimedia experience. The Conclusion (Section 5) draws attention to the challenges of publishing digital editions, and why digital editions have failed to be supported by the publishing industry. If publications are conceived as pragmatic inventions based on reliable, open-access data collections, then editing can embrace the critical, aesthetic, and experimental affordances of editions of experience.

An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction

In recent years, big data has become an important branch of computer science. However, without AI, it is difficult to dive into the context of data as a prediction term, relying on a large feature of improving the process of prediction is connected with big data modelling, which appears to be a significant aspect of improving the process of prediction. Accordingly, one of the basic constructions of the big data model is the rule-based method. Rule-based method is used to discover and utilize a set of association rules that collectively represent the relationships identified by the system. This work focused on the use of the Apriori algorithm for the investigations of constraints from panel data using the discretization preprocess technique. The statistical outcomes are associated with the improved preprocess that can be applied over the transaction and it can illustrate interesting rules with confidence approximately equal to one. The minimum support provided to the present rule considers constraint as a milestone for the prediction model. The model makes an effective and accurate decision. In nowadays business, several guidelines have been produced. Moreover, the generation method was upgraded because of an association data algorithm that works for dissimilar principles of the structures compared with fewer breaks that are delivered by the discretization technique.

Statistical Inference on Nonparametric Spline Models and its Applications

One of the components of the Human Development Index which is still a problem and concern in the world today is the Life Expectancy Rate (LER). United Nations Development Program (UNDP). United Nations Development Program (UNDP) uses the LER to measure community health status as well as a benchmark for development success. LER in Indonesia continues to increase almost throughout the year. That is, the hope of a newborn baby to be able to live longer is getting higher. LER data modelling with parametric regression is not necessarily suitable to be applied because the LER relationship pattern has a pattern that varies at certain age intervals. Spline regression is a regression method that can handle data whose pattern changes at certain intervals. Spline is one of the models in nonparametric regression that has a very special and very good visual statistical interpretation. In addition, splines are also able to handle data characters or functions that are smooth (smooth). This study aims to derive the form of the estimator and the shortest confidence interval for the quadratic spline model and model the LER data in Indonesia.

The Modelling of Heteroscedastics IDR-USD Exchange Rate with Intervention and Outlier Factors

The nonstationary in time series data may be caused by the existence of intervention, outliers, and heteroscedastic effects. The outliers can represent an intervention so that it creates a heteroscedastic process. This research investigates the involvements of these three factors in time series data modelling. It is also reviewed how long the effects of the intervention and outliersfactors will last. The weekly IDR-USD exchange rate in period of May 2015 to April 2020 be evaluated. It is obtained that ARIMA model with the intervention factor gives the best re-estimation result, with smallest average of errors squared. Meanwhile for prediction, the heteroscedastic effect combined with outlier factors gives better results with the lowest percentage of errors. One of the phenomenal interventions in this data is the Covid-19 pandemic, which was started in Indonesia on March 2020. It is found that the effect of the intervention lasts less than five months and the prediction shows that the volatility of IDR-USD exchange rate starts to decline. This shows the stability of the process is starting to be maintained.