Papers
1
Total Citations
7
H-Index
1
About
Anshika is a researcher whose work bridges the foundational principles of Vedic mathematics with the cutting-edge architecture of artificial neural networks (ANNs). Her key research areas include neural network design, logic gate implementation, and the application of ancient mathematical techniques to modern computational problems. Her most cited paper, "Neuronal Logic gates realization using Vedic mathematics" (2015, 7 citations), presents a novel approach to constructing fundamental logic circuits—the building blocks of all digital systems—using Vedic mathematical principles within an ANN framework. This work demonstrates how parallel processing capabilities of neural networks can be harnessed for efficient logic gate realization, offering potential advancements in pattern recognition, system identification, robotics, and control problems. By integrating Vedic mathematics into neural network design, Anshika has opened new pathways for optimizing computational efficiency and reducing hardware complexity. Her contributions highlight the value of cross-disciplinary thinking, showing how ancient mathematical wisdom can inform and enhance modern artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1Neuronal Logic gates realization using Vedic mathematics7 citations · 2015