This study presents two machine learning models, namely, the light gradient boosting machine
(LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood …

This study aims to examine three machine learning (ML) techniques, namely random forest (RF),
LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu …

The integration of solar energy in smart grids and other utilities is continuously increasing
due to its economic and environmental benefits. However, the uncertainty of available solar …

Many renewable energy resources, including wind energy, are uncertain and often
unavailable when needed, with high variability and dependency on atmospheric and climatic …

This paper introduces an automatic strategy for the segmentation of medical images from
Magnetic Resonance Imaging (MRI) and Computed Topography (CT). A new segmentation …

Wind energy is one of the sustainable and clean energy resources that are uncertain
because of its high fluctuation and stochastic volatility. Its uncertainty follows wind speed and …

In wireless sensor/ad hoc networks, all wireless nodes frequently flood the network channel
by transmitting control messages causing “broadcast storm problem”. Thus, inspired by the …

Meta-heuristics are general frameworks of heuristics methods for solving combinatorial
optimization problems, where exploring the exact solutions for these problems becomes very …

For centuries, the study of prime numbers has been regarded as a subject of pure
mathematics in number theory. Recently, this vision has changed and the importance of prime …

Sediment Bypass Tunnels (SBTs) effectively mitigate reservoir sedimentation by diverting
flood-laden flows, but they face significant challenges due to hydroabrasive erosion, which …