Preeti Raj Verma
Papers
1
Total Citations
18
H-Index
1
About
Preeti Raj Verma is a rising scholar in artificial intelligence and computational modeling, with a primary focus on neural network architectures and their evolution. Her most-cited work, "Neural network developments: A detailed survey from static to dynamic models" (2024), has already garnered 18 citations, reflecting its timely impact on the field. In this comprehensive survey, Verma systematically maps the transition from traditional static neural networks to dynamic, adaptive models, offering a critical taxonomy that bridges foundational concepts with cutting-edge advancements. Her contribution lies in synthesizing fragmented research into a coherent framework, enabling researchers and practitioners to better understand the trajectory of neural network design—from feedforward structures to recurrent, spiking, and reservoir computing paradigms. This work not only highlights her expertise in deep learning and model dynamics but also positions her as a key voice in guiding future innovations. Beyond this survey, Verma’s research continues to explore adaptive learning systems, with an emphasis on efficiency and scalability. Her ability to distill complex technical landscapes into accessible insights makes her a valuable resource for students and researchers navigating the rapidly evolving AI domain.
Research Focus
Key Achievements
Top Papers
- 1Neural network developments: A detailed survey from static to dynamic models18 citations · 2024