Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …

Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …

Artificial intelligence has the potential to alter the agricultural and food processing industries,
with significant ramifications for sustainability and global food security. The integration of …

Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …

This paper offers a comprehensive overview of machine learning (ML) methodologies and
algorithms, highlighting their practical applications in the critical domain of water resource …

The advancement of 5G and the Internet of Things (IoT) has ushered in an era of super-
interconnected intelligence, which promises high-quality social development. Triboelectric …

Recent years have experienced sustained focus in research on software defect prediction
that aims to predict the likelihood of software defects. Moreover, with the increased interest …

The escalating use of novel chemicals and nanomaterials (NMs) across diverse sectors
underscores the need for advanced risk assessment methods to safeguard human health …

Due to large specific surface area, abundant functional groups and low cost, biochar is
widely used for pollutant removal. The adsorption performance of biochar is related to …

Many individual studies in the literature observed the superiority of tree-based machine
learning (ML) algorithms. However, the current body of literature lacks statistical validation of …