Ketan Rathor
Papers
1
Total Citations
33
H-Index
1
About
Ketan Rathor is a researcher at the forefront of applying computational intelligence to retail analytics and ergonomic systems. His work primarily focuses on developing machine learning frameworks that bridge the gap between predictive modeling and real-world retail operations. His most cited contribution, "A Novel Method for Exploring the Store Sales Forecasting using Fuzzy Pruning LS-SVM Approach" (2023), has garnered 33 citations, demonstrating its impact on the field. In this work, Rathor introduces a hybrid approach that combines fuzzy logic with least-squares support vector machines to enhance sales prediction accuracy in convenience stores. This method addresses the growing complexity of product assortments and competitive dynamics in modern retail environments. Beyond sales forecasting, his research explores the integration of computer-aided ergonomics into intelligent systems—spanning robots, mobile devices, and smart stores—to optimize human-machine interactions. Rathor’s work is particularly notable for its practical relevance, offering scalable solutions that help retailers adapt to data-driven decision-making. His contributions are shaping the next generation of intelligent retail ecosystems, making him a key figure in the convergence of AI, ergonomics, and commerce.
Research Focus
Key Achievements
Top Papers
- 1