Manjula Selvam
Papers
2
Total Citations
6
H-Index
2
About
Manjula Selvam’s research bridges the critical domains of multi-criteria decision-making (MCDM) and embedded systems engineering, with a focus on optimizing project selection and sensor-based automation. In her widely cited work, “Selection of Candidate for a Project Using WASPAS Method” (2022), Selvam advances decision science by critically comparing the WASPAS and EDAS methodologies, demonstrating that WASPAS offers superior ranking precision—identifying BS4 as the optimal candidate—by addressing EDAS’s limitation of neglecting distance importance. This contribution provides a robust framework for evaluators facing complex, multi-attribute choices. Simultaneously, her investigation into microcontroller-based sensor interfaces (2022) explores the integration of MCUs in automated control systems for power tools and automotive engines, emphasizing real-time responsiveness and reliability. With both papers accumulating 3 citations each, Selvam’s work is gaining traction among scholars seeking practical MCDM tools and efficient embedded solutions. Her dual expertise highlights a unique ability to apply computational intelligence to tangible engineering challenges, making her a rising voice in operational research and IoT systems.
Research Focus
Key Achievements
Top Papers
- 1Selection of Candidate for a Project Using WASPAS Method3 citations · 2022
- 2Microcontroller Based Sensor Interface and Its Investigation3 citations · 2022