crossrefResults in EngineeringCited by 1

Machine learning prediction and interpretability analysis of the association between digital transformation and enterprise performance: Based on the XGBoost-SHAP integrated framework

Related papers

semantic_scholarResults in Engineering

Accelerating compact UWB fractal antenna design using interpretable machine learning with forward and inverse prediction

Nayan Sarker, Sanjay Das, Kanad Basu, Ifana Mahbub

View free PDFSource page

semantic_scholarResults in Engineering

Strength-class-specific correction ratios for concrete core testing: Dual-branch machine learning and SHAP interpretability

Mahyar Arabani, R. Madandoust, Morteza Safari, K. Momeni

View free PDFSource page

semantic_scholarResults in Engineering

Machine learning framework for predicting mechanical properties of fiber-reinforced recycled aggregate concrete with sustainability optimization

Zixu Zhou, M. A. Ismail

View free PDFSource page

crossrefResults in Engineering2026-06-01Cited by 1

Using deep generative-based machine and ensemble learning framework for predicting ultimate bending capacity of circular steel tubes

Mahmood Sami Salah, Mohamed El Amine Ben Seghier, Sushant Gautam, Osama Ahmed Mohamed

crossrefResults in Engineering

Machine learning in membrane-based desalination and wastewater treatment: An AI-assisted bibliometric analysis

Azzam Abuhabib, Cao Yuan, Mohd Fadhil Bin Md Din, Ahmed Albahnasavi, Siti Nur Fatihah Binti Moideen, Chen Hong, et al.

crossrefResults in Engineering

Fully automated pavement crack quantification and PCI estimation via a digital twin-ready vision-based deep learning pipeline

Mohammad Amin Talaghat, Amir Golroo, Vahid Shahhosseini, Mehdi Rasti