Uswah Khairuddin

Papers

Total Citations

H-Index

About

Uswah Khairuddin is a researcher at the forefront of applying deep learning to environmental monitoring and resource management. Her primary research areas center on computer vision, object detection, and automated systems for ecological sustainability, with a particular focus on riverine and tropical forest ecosystems. Khairuddin’s most significant contributions lie in developing optimized YOLO (You Only Look Once) models for waste management. Her landmark 2022 paper, "An automated solid waste detection using the optimized YOLO model for riverine management," has garnered 42 citations, demonstrating its impact on urban riverine pollution control. This work, along with her 2023 follow-up on automatic garbage detection (15 citations), provides scalable, real-time solutions for identifying and quantifying debris in waterways—a critical tool for mitigating threats to human health and ecosystem services. She has also advanced tropical wood species identification with her 2019 online system, addressing the shortage of certified experts in wood anatomy. Her 2021 study on YOLO-based network fusion for floating debris monitoring further solidifies her role in pioneering automated, visual-inspection systems for environmental stewardship. Khairuddin’s research bridges artificial intelligence and conservation, offering practical tools for sustainable urban and forest management.