Publications

Abstract

Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using deep Q learning (DQL) and a novel multi-objective binary grey wolf optimizer (MOBGWO). We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.

Abstract

The COVID-19 pandemic served as a stark reminder of a critical vulnerability in our global infrastructure: the health supply chain. The complex network responsible for delivering everything from pharmaceuticals to personal protective equipment (PPE) was strained to its breaking point, leading to widespread shortages that impacted healthcare providers and patients alike. This research examines the challenges and proposes a clear, actionable framework for strengthening this vital system through four fundamental pillars for building a resilient health supply chain.