Danial Jahed Armaghani^1,*, Zida Liu², Hadi Khabbaz¹, Hadi Fattahi³, Diyuan Li², Mohammad Afrazi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2421-2451, 2024, DOI:10.32604/cmes.2024.052210 - 31 October 2024

Abstract Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s)… More >

Saadia Malik¹, Nadia Tabassum², Muhammad Saleem³, Tahir Alyas⁴, Muhammad Hamid^5,*, Umer Farooq⁴

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 471-480, 2022, DOI:10.32604/iasc.2022.019837 - 03 September 2021

Abstract With the ongoing revolution in the Internet of Things (IoT) and cloud computing has made the potential of every stack holder that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction with devices as very helpful and serviceable in their daily life. However, an improperly configured network system is a soft target to security threats, therefore there is a dire need for a security embedded framework for IoT and cloud communication models is the latest research area. In this paper, different IoT and cloud computing frameworks are… More >