Achieving improved performance in construction projects: advanced time and cost optimization framework

Pham, Vu Hong Son; Nguyen Dang, Nghiep Trinh; Nguyen, Van Nam

doi:10.1007/s12065-024-00918-7

Evol. Intel.

2024

DOI: 10.1007/s12065-024-00918-7

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Achieving improved performance in construction projects: advanced time and cost optimization framework

Vu Hong Son Pham,

Nghiep Trinh Nguyen Dang,

Van Nam Nguyen

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Cited by 4 publications

References 37 publications

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Research on applying machine learning models to predict and assess return on assets (ROA)

Son,

Duong

2024

Asian J Civ Eng

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Return on Assets (ROA), a pro tability measure, is crucial in corporate nance for assessing how e ciently a company uses assets to generate pro t. Currently, the prediction of the ROA index at present is a tedious, manual process. It usually involves making educated guesses or waiting for the accurate data, which becomes available only after nancial reports have been compiled. This paper introduces a machine learning model for predicting the ROA index. The model draws data from 78 companies listed on the Vietnam Stock Exchanges (HOSE and HNX) over the span of 2012 to 2022.The Random Forest (RF) model was put to the test using datasets from selected Vietnamese businesses in 2023. The results demonstrated a high level of precision, with an error rate of less than 1%, an R2 value of 0.9762, and a Root Mean Square Error (RMSE) of 0.5826. These ndings indicate potential real-world uses in predicting and boosting business performance. In conclusion, the integration of machine learning in nancial analysis and prediction represents substantial progress. It enhances both accuracy and e ciency and holds promise for future advancements in nancial management practices. This study aims to encourage more research and development in this area, leading to more advanced and e cient nancial management tools.

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Research on applying machine learning models to predict and assess return on assets (ROA)

Son,

Duong

2024

Asian J Civ Eng

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An arithmetic optimization algorithm based on opposition jumping rate for time cost trade-off optimization problems

Sulub,

Eirgash,

Toğan

2024

Asian J Civ Eng

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Optimizing time–cost in construction projects using modified quasi-opposition learning-based multi-objective Jaya optimizer and multi-criteria decision-making methods

Eirgash

2024

Asian J Civ Eng

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Achieving improved performance in construction projects: advanced time and cost optimization framework

Cited by 4 publications

References 37 publications

Research on applying machine learning models to predict and assess return on assets (ROA)

Research on applying machine learning models to predict and assess return on assets (ROA)

An arithmetic optimization algorithm based on opposition jumping rate for time cost trade-off optimization problems

Optimizing time–cost in construction projects using modified quasi-opposition learning-based multi-objective Jaya optimizer and multi-criteria decision-making methods

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