Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Pérez-Campuzano, Darío; Andrada, Luís Rubio; Ortega, Patricio Morcillo; López-Lázaro, Antonio

doi:10.1016/j.jairtraman.2022.102194

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…As a result of the study, it was revealed how much the passenger airline of the USA was affected during the Covid-19 period [17]. In the study of Prasad et al, existing data visualization methods have been enhanced by spectral modeling to overcome the problem of cluster bias on non-CS datasets, which efficiently recognizes the spectral features of non-CS datasets and cluster patterns [18]. New visualization techniques are used not only for environmental data, but also for other types of data such as satellite information, X-ray spectra processing or big data [19][20][21][22].…”

Section: B Related Workmentioning

confidence: 99%

Actionable Data Visualization for Air Quality Data in the Istanbul Location

MENGÜŞ

Daş

2022

Balkan Journal of Electrical and Computer Engineering

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Air pollution is increasing day by day due to the increasing population, urbanization, and industrial development. In our country, the amounts of pollutants in the air are recorded every day at different points. These recorded data continue to be collected in an increasing amount day by day. Information overload, which renders the data meaningless, complicates the interpretation of these data. One of the ways to solve this problem is to visualize curves and trends in measured pollution concentrations over time. In this study, using the data provided by the continuous monitoring center of the Turkey Ministry of Environment, Urbanization and Climate Change, visualization of different pollutants in the air was provided. Scatter plots, line scatter plots, and bar plots were used as data visualization. Data visualization makes it easy for non-experts to estimate air quality information from the concentration profiles displayed.

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Section: B Related Workmentioning

confidence: 99%

Actionable Data Visualization for Air Quality Data in the Istanbul Location

MENGÜŞ

Daş

2022

Balkan Journal of Electrical and Computer Engineering

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“…DM can be defined as a method that allows the most valuable information to be obtained by analyzing data sets. This method includes data processing, model creation, feature extraction, and discovery studies (Pérez-Campuzano et al 2022). Using important data sources and data mining algorithms helps reduce costs effectively by making it easier to determine the optimal price.…”

Section: Introductionmentioning

confidence: 99%

Predicting prices of case furniture products using web mining techniques

Bardak

2023

BioRes

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This article presents a methodology based on web mining techniques for estimating furniture prices using e-commerce data. Data on different public e-commerce sites in the United States were collected and analyzed using web mining methods. Deep learning and random forest algorithms were used to predict the prices of different types of furniture. Bookcase and dresser type furniture, which are widely used in price estimation, were selected. The inquiry identified a collection of eight distinctive attributes linked to furniture items, spanning measurements such as width, depth, and height, alongside features encompassing frame material, partition count, drawer count, color, and price. In preparation for constructing predictive models, a dataset comprising 300 instances was compiled for comprehensive analysis. Models developed based on web mining to predict furniture prices gave satisfactory results. During the testing phase, the random forest algorithm outperformed deep learning, achieving high goodness of fit values of 0.89 and 0.94 for bookcase and dresser furniture, respectively. The results indicate that price estimation for dresser furniture was more accurate than for bookcases in all models. The findings demonstrate that web mining techniques can be used effectively in competitive furniture pricing, with potential to save time and cost in pricing for furniture purchasing.

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The role of artificial intelligence for management decision: a structured literature review

Oppioli,

Sousa,

Sousa

et al. 2023

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PurposeThe topic of artificial intelligence (AI) has been expanding rapidly in recent years, gaining the attention of academics and practitioners. This study provides a structured literature review (SLR) on AI and management decisions (MDs) by analysing the scientific output and defining new research topics.Design/methodology/approachThe study uses a rigorous methodological approach to summarise the state of the art of the past literature. The authors used Scopus as the database for data collection and utilised the Bibliometrix R package. In total, 204 peer-reviewed English articles were collected and analysed.FindingsThe results showed that literature in this field is emerging. Studies are focused on using AI as forecasting and classification for management decision-making, AI as a tool to improve knowledge management in organisations and extract information. The cluster analysis revealed the presence of five thematic clusters of studies on the topic.Originality/valueThe study’s originality lies in providing a new perspective on AI for MDs. In particular, the analysis reveals a new classification of research streams and provides fruitful research questions to continue research on the topic.

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Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Cited by 4 publications

References 39 publications

Actionable Data Visualization for Air Quality Data in the Istanbul Location

Actionable Data Visualization for Air Quality Data in the Istanbul Location

Predicting prices of case furniture products using web mining techniques

The role of artificial intelligence for management decision: a structured literature review

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