Author

Pradyut Anand

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#reinforcement learning Open access Oct 2026

New hybrid models integrating the aquila optimizer with machine learning algorithms to predict the strength of geosynthetic-reinforced subgrade soil

Accurately predicting the California Bearing Ratio (CBR) of geosynthetic-reinforced subgrades is critical for pavement design but remains challenging due to the complex, nonlinear soil-reinforcement interaction. This study develops a novel hybrid modeling framework that integrates the Aquila Optimization Algorithm (AOA...

Jun-Jiat Tiang, Bayram Ateş, Mohammad Azım Eırgash et al. · Frontiers in Built Environme... · 0 citations

#federated learning Open access Sep 2026

Fairness-constrained explainable machine learning with differentially private federated training for employee attrition prediction in civil engineering and law

Employee attrition in knowledge-intensive sectors such as civil engineering and law presents compounding risks to project continuity, regulatory compliance, and organisational profitability. Despite growing adoption of machine learning (ML) in human resource (HR) analytics, no prior study has simultaneously targeted th...

Rashmi Kumari, Suresh Pratap, Pradyut Anand et al. · Scientific Reports · 0 citations