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Predicting of the Number of Diners in School Cafeteria; Including COVID-19 Pandemic Period Data
Chae-eun Baek, Yesl Kwon, Jangmin Oh
http://doi.org/10.5626/JOK.2024.51.7.634
Accurately predicting the number of diners in institutional food service is essential for efficient operations, reducing leftovers, and ensuring customer satisfaction. University cafeterias, in particular, face additional challenges in making these predictions due to various environmental factors and changes in class formats caused by the COVID-19 pandemic. To tackle this issue, this study utilized specialized data collected during the pandemic period in university cafeteria environments. The data was used to train and compare the performance of five different models. The three best-performing ensemble tree-based models -- RandomForest, LightGBM, and XGBoost -- were averaged to obtain a final prediction with a Mean Absolute Error (MAE) of 30.96. By regularly providing prediction results to on-campus cafeterias using this final model, practical support can be offered to optimize operations. This study presents an effective methodology for accurately predicting of the number of diners, even in abnormal situations such as the COVID-19 pandemic.
Analysis of QoQ GDP Prediction Performance Using Deep Learning Time Series Model
Yeonhee Lee, Youngmin Kim, Taewan You
http://doi.org/10.5626/JOK.2022.49.10.873
In this paper, we proposed an algorithm for predicting GDP growth rate using a deep learning time series model spotlighted recently. The proposed algorithm adopts an ensemble deep learning method to ensure stable prediction performance using a large number of economic time series data with low frequency. It also uses a gradual learning method to ensure adaptive performance even in business fluctuations. By demonstrating that the performance could be improved by using economic sector information in learning, the necessity of convergence with domain knowledge was confirmed and the importance of AI operation technology to provide adaptive predictive power was emphasized. Through performance comparison with traditional machine learning models for the COVID-19 period, we proved that deep learning could be a relatively reasonable predictive tool under rapid economic fluctuations. The deep learning-based adaptive AI algorithm presented in this paper is expected to be developed into a deep learning-based autonomous adaptive economic prediction system through combination with AI operation technology.
Information Collection of COVID-19 Pandemic Using Wikipedia Template Network
Danu Kim, Damin Lee, Jaehyeon Myung, Changwook Jung, Inho Hong, Diego Sáez-Trumper, Jinhyuk Yun, Woo-Sung Jung, Meeyoung Cha
http://doi.org/10.5626/JOK.2022.49.5.347
Access to accurate information is essential to reduce the social damage caused by the Coronavirus Disease 2019 (COVID-19) pandemic. Information about ongoing events, such as COVID-19, is quickly updated on Wikipedia, an accessible internet encyclopedia that allows users to edit it themselves. However, the existing Wikipedia information retrieval method has a limitation in collecting information, including relationships between documents. The template format of Wikipedia reflects the structure of information as a link that is selectively applied to documents with high relevance. This study collected information on COVID-19 in 10 languages on Wikipedia using a template and reorganized it into networks. Among the 10 networks with 130,662 nodes and 202,258 edges, languages with a large number of active users had a template network with a large size and depth, and documents highly related to COVID-19 existed within a 3-hop connection structure. This research proposed a new information retrieval method applicable to multiple languages and contributes to the construction of document lists related to specific topics.
ConvLSTM-Based COVID-19 Outbreak Prediction using Feature Combination of Multivariate Dataset
Yejin Kim, Seokyeon Kim, Yun Jang
http://doi.org/10.5626/JOK.2021.48.4.405
COVID-19 is transmitted through the droplets expelled by infected people. The propagation of splash is affected by space-time. The transmission of infectious diseases depends on the interaction of various factors such as the health status of the infected and the non-infected people and different environmental factors. However, it is difficult to include all information related to the epidemic in the predictive model and understand the relationship between the information. In this research, we propose a method to include the infectious features of COVID-19 in a learning dataset of the deep learning model and understand the effect of the combination of COVID-19 spreading data on the predictive performance of deep learning. Before predicting, the infectious features of COVID-19 are identified and considerations for including the COVID-19 spreading features are defined in the data preprocessing step. In deep learning modeling, a prediction model using ConvLSTM is designed for spatiotemporal prediction. In the process of testing the model, various features related to COVID-19 spread are combined and the effect of the combination on the performance of the model is analyzed. We tested 120 feature combinations with 47 features composed of personal information of confirmed patients and spatial characteristics of the places that they had visited. We used MAPE as an indicator to evaluate performance of the models. In the case of COVID-19 dataset, the MAPE value of the model with combined features was 1.234, and that of the model with not combined features was 2.217.
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