• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.213-226
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• 2024

This study uses box office data collected by the Korean Film Council (KOFIC) to develop and compare predictive models for cinema attendance and revenue. Data preprocessing removed irrelevant variables and handled missing values separately for categorical and numerical data to ensure consistency. Exploratory data analysis identified key variables, including Seoul audience size, revenue, total number of screens, film genre, rating, and month of release, which revealed a strong correlation between Seoul audience size and revenue with box office performance. Based on this analysis, predictive models were developed using CatBoost and PyCaret AutoML. CatBoost was chosen for its effectiveness in handling categorical variables such as director name, production company, and genre, while PyCaret AutoML was chosen for its ability to automate the modeling process, making it easy for non-experts to compare different models. The performance of the models was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R²), with CatBoost demonstrating superior accuracy. In addition, the SHAP technique was used to interpret the models, identifying Seoul's audience size and revenue as the most significant predictors. This research presents reliable box office prediction models that will improve decision-making in the film industry and support the development of data-driven strategies.

• Steel and Composite Structures
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• v.46 no.2
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.