Shengli Pang

Xi’an University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Shengli Pang is a researcher focused on the intersection of Internet of Things (IoT) technology, data analytics, and robotic applications. His key research areas include RFID system optimization, machine learning, and rough set theory. Pang’s major contribution lies in developing predictive models for RFID identification rates, which are critical for improving hardware deployment strategies in complex environments. In his most-cited work, "Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios" (2020), he introduced a novel hybrid approach combining neighborhood rough sets with random forest algorithms to enhance prediction accuracy. This work, which has garnered 4 citations, addresses a pressing challenge in IoT: optimizing system efficiency for real-world robot scenarios. By enabling more effective RFID deployment, Pang’s research supports advancements in automation, logistics, and smart environments. His work is particularly notable for bridging theoretical data-mining methods with practical engineering problems, offering a scalable solution for industries relying on RFID technology. With a focus on actionable insights, Pang continues to contribute to the evolution of intelligent systems.