Saroj Kumar Lenka
Papers
1
Total Citations
7
H-Index
1
About
Saroj Kumar Lenka is a researcher whose work bridges computational intelligence and industrial automation, with a primary focus on neural network optimization and robotic system selection. His most cited paper, "Gradient Descent with Momentum Based Backpropagation Neural Network for Selection of Industrial Robot" (2016), has garnered 7 citations, demonstrating his contribution to improving the efficiency of backpropagation algorithms by integrating momentum-based gradient descent. This work addresses a critical challenge in manufacturing: selecting optimal industrial robots through enhanced neural network training, which reduces convergence time and improves decision accuracy. Lenka’s research has practical implications for automating complex selection processes in robotics, making him a notable figure in the intersection of machine learning and industrial engineering. His approach to refining backpropagation methods reflects a broader commitment to advancing computational tools for real-world applications, particularly in resource-constrained environments where precise robot selection impacts productivity and cost. While his citation count remains modest, his work serves as a foundational reference for researchers exploring momentum-based optimization in neural networks for industrial tasks, highlighting his role in developing accessible, efficient AI solutions for manufacturing challenges.
Research Focus
Key Achievements
Top Papers
- 1