Hashibah Hamid
Papers
1
Total Citations
17
H-Index
1
About
Hashibah Hamid is a researcher whose work lies at the intersection of data fusion, statistical modeling, and classification techniques. Her most cited paper, "Principal Component Analysis – A Realization of Classification Success in Multi Sensor Data Fusion" (2012, 17 citations), addresses a persistent challenge in multi-sensor data fusion (MSDF)—a field that, despite its origins in defense and robotics, has expanded into diverse nonmilitary applications. Hamid’s contribution demonstrates how principal component analysis can be effectively harnessed to improve classification accuracy within MSDF frameworks, offering a practical solution to the field’s longstanding lack of a one-size-fits-all method. This work underscores her focus on making complex data integration more reliable and interpretable. While her citation count reflects a niche but growing impact, her research is particularly valuable for students and practitioners working on sensor networks, pattern recognition, and real-world data fusion problems. Hamid’s approach bridges theoretical statistical methods with applied engineering challenges, making her a notable figure for those exploring how dimensionality reduction can enhance multi-source data interpretation.
Research Focus
Key Achievements
Top Papers
- 1