We explore the challenges of re-using machine learning models originally developed for patient-level predictions to forecast system-wide quantities in hospitals. As a running example, we examine the use of patient-level predictions of length of stay to predict short-term occupancy in an...

We propose a new machine learning-based framework for long-term mortality forecasting. Based on ideas of neighbouring prediction, model ensembling, and tree boosting, this framework can significantly improve the prediction accuracy of long-term mortality. In addition, the proposed framework...

The proliferation of cameras and their video data in retail marketing presents new opportunities for academics to study customer behavior with the newer video analytics tools. In collaboration with a large retail chain store in Asia, we obtained a unique video dataset collected from in-store...

In this paper, an opportunistic predictive maintenance is developed for a system composed one continuous operation component (COC) and one intermittent operation component (IOC). The COC continuously operates in consistent with the system. In contrast, the IOC alternatively works in operation...

Recent advancements in machine learning (ML) algorithms have shown that ML predictive models can be used as a rapid and efficient monitoring tool. This study proposes the capability of Support Vector Regression (SVR) and Extreme Gradient Boost (XGB) algorithms to predict effluent Total Suspended...

The bottlenecks of supply chains are shifting as individual companies are adopting smart production and reducing their inventories. Since the after-market orders mainly depend on repair and maintenance, addressing demand fluctuation is critical to reduce the risks of oversupply and shortage for...

A circular economy requires managing and reducing the environmental and social impacts of products and materials across their lifecycle from production to end-of-life, including clean energy technologies. The unprecedented growth in wind turbine (WT) deployment in Australia as a clean energy...

Highlights• Simulate dissolved oxygen in 5 lakes via multiple machine learning models.• A 2-step machine learning model workflow combines Gradient Boost Regressor and Longshort-term-memory.• A one-dimensional process-based hydrodynamic model provides ML models with predictors, including...

In supply chain inventory management, the backorder of products is a common occurrence. A backorder can only be successful if the supplier can be relied upon to provide the out-of-stock product when required or if the consumer is prepared to wait for the product without becoming impatient. So,...

Biochar adsorption is one of the most effective methods for mitigating heavy metal pollution; however, it remains quite challenging for its limited adsorption capacity and selectivity. To address these issues and enhance the adsorption performance of biochar for heavy metals in soil, we proposed...