Sasmita Nayak
Papers
2
Total Citations
24
H-Index
2
About
Dr. Sasmita Nayak is a researcher whose work lies at the intersection of artificial intelligence, neural networks, and industrial automation. Her primary research focuses on optimizing backpropagation algorithms to solve complex engineering selection problems, particularly in the domain of industrial robotics. Dr. Nayak’s most notable contribution is the development of a Scaled Conjugate Gradient Backpropagation Algorithm for the selection of industrial robots, a seminal paper that has garnered 17 citations. This work addresses the critical challenge faced by manufacturing industries in choosing the most suitable robot for diverse applications, offering a systematic, computationally efficient approach to a traditionally complicated decision-making process. She further advanced this field with her study on Gradient Descent with Momentum Based Backpropagation Neural Networks, which received 7 citations and refined the accuracy of robot selection models. By applying advanced neural network training techniques to real-world industrial problems, Dr. Nayak has provided engineers and decision-makers with powerful tools to enhance productivity and precision in automated manufacturing. Her research bridges the gap between theoretical machine learning and practical engineering, making a tangible impact on how industries leverage AI for robotic system optimization.
Research Focus
Key Achievements
Top Papers
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