Minahil Naseer, Hamna Kashif, Kashif Sultan, Hina Ghafoor, Awais Majeed, Prince Semba Yawada
Oluwaseun Racheal Ojekemi, Dervis Kirikkaleli
Abstract The GWO-XGBoost model achieved an R² of 0.991 in Gross Domestic Product (GDP) prediction, exceeding all other machine learning models compared in this study. Thus, GWO-XGBoost provides a transparent and reliable decision-making support system for all those who develop ec…
RatnaKumari Neerukonda, B. Hariharan
Cloud computing has evolved into a mature technology, seamlessly integrating with modern internet services and functioning as utility computing that delivers infrastructure, platforms, and software on a pay-per-use basis. A key challenge in cloud computing is task scheduling, whi…
Zengtao Geng, Xiangqian Ding, Bangyong Liang, Yaping Fu
Nowadays, maritime trade becomes the mainstream mode of commerce and transportation, and port operation plays important roles in improving the efficiency of global trades. In recent years, many studies focus on scheduling handling equipment in container terminals. Nevertheless, r…
Raul Rodriguez, K. Hemachandran, Ghanshyam Ghatole, Manjeet Rege, Balamurugan Balusamy
Abstract We propose a machine learning framework to predict human decision failures in AI-assisted systems using automated analysis of behavioral patterns. Ensemble classifiers trained on 127 engineered features achieve 89% accuracy (AUC = 0.89) in identifying accountability defi…