Fatema Tuz Johora
Papers
2
Total Citations
7
H-Index
2
About
Fatema Tuz Johora is an emerging researcher whose work spans two increasingly vital domains: intelligent transportation systems and AI-driven financial technology. Her most recognized contribution, "SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories" (2022), addresses one of the most pressing challenges in autonomous vehicle development — the ability of robots and self-driving vehicles to safely and interpretably navigate mixed-traffic environments alongside human pedestrians. By grounding neural network predictions in physical principles, her approach offers both improved accuracy and greater transparency in behavior modeling, garnering 5 citations since its publication. Her more recent work, "Enhancing Regulatory Compliance in the Modern Banking Sector" (2025), demonstrates her versatility as a researcher, exploring how AI can strengthen fraud detection and risk management within financial institutions — an area of growing urgency as banking systems face increasingly sophisticated threats. With 2 early citations, this paper signals timely relevance in fintech policy and automation. Across both domains, Johora's research reflects a consistent commitment to applying advanced computational methods to real-world safety and compliance challenges, positioning her as a promising interdisciplinary voice in applied AI research.
Research Focus
Key Achievements
Top Papers
- 1
- 2