Cover: International Journal of Intelligent Systems, Volume 37 Issue 6 June 2022. - Chonghyo Joo; Hyundo Park; Jongkoo Lim; Hyungtae Cho; Junghwan Kim (Int. J. Intell. Syst., 2022)

Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters. - Chonghyo Joo; Hyundo Park; Jongkoo Lim; Hyungtae Cho; Junghwan Kim (Int. J. Intell. Syst., 2022)

Chemical Property-Guided Neural Networks for Naphtha Composition Prediction. - Chonghyo Joo; Jeongdong Kim; Hyungtae Cho; Jaewon Lee; Sungho Suh; Junghwan Kim (INDIN, 2023)

Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition. - Jeongdong Kim; Chonghyo Joo; Minsu Kim; Nahyeon An; Hyungtae Cho; Il Moon; Junghwan Kim (Expert Syst. Appl., 2023)

A novel graph-based missing values imputation method for industrial lubricant data. - Soohwan Jeong; Chonghyo Joo; Jongkoo Lim; Hyungtae Cho; Sungsu Lim; Junghwan Kim (Comput. Ind., 2023)

pyAPEP: An all-in-one software package for the automated preparation of adsorption process simulations. - Seongbin Ga; Nahyeon An; Chonghyo Joo; Junghwan Kim (Comput. Phys. Commun., 2023)

Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations. - Chonghyo Joo; Hyundo Park; Jongkoo Lim; Hyungtae Cho; Junghwan Kim (Eng. Appl. Artif. Intell., 2023)

Novel natural gradient boosting-based probabilistic prediction of physical properties for polypropylene-based composite data. - Hyundo Park; Chonghyo Joo; Jongkoo Lim; Junghwan Kim (Eng. Appl. Artif. Intell., 2024)

Novel inverse predictive system integrated with industrial lubricant information. - Minseong Kim; Chonghyo Joo; Jongkoo Lim; Seungho Yeom; Il Moon; Meng Qi; Junghwan Kim (Eng. Appl. Artif. Intell., 2025)