Shubha Mishra
Papers
2
Total Citations
126
H-Index
2
About
Shubha Mishra’s research bridges the critical intersection of artificial intelligence and neuroscience, with a primary focus on combating digital misinformation and modeling neural computation. Her most impactful work, “Analyzing Machine Learning Enabled Fake News Detection Techniques for Diversified Datasets” (2022), has garnered 115 citations, establishing her as a key voice in developing robust AI systems to identify deceptive content across varied data sources—a pressing challenge for preserving societal trust and democratic discourse. In parallel, Mishra explores the computational principles of memory and pattern separation in the brain. Her 2023 study, “Design Partition in an Active to Imbalanced Excitation/Inhibition Hippocampus Neural Arrange,” proposes a three-layer spiking neural network model inspired by the dentate gyrus and hippocampus, achieving 11 citations for its innovative approach to understanding how neural circuits encode distinct memories. By integrating machine learning with biological neural modeling, Mishra’s work not only advances practical tools for fake news detection but also deepens our grasp of hippocampal function. Her dual focus on societal impact and fundamental neuroscience makes her research both timely and foundational, offering valuable insights for students and researchers working at the nexus of AI, cognitive science, and information integrity.
Research Focus
Key Achievements
Top Papers
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