Research · 03
Intelligent Information Processing
The laboratory develops learning-based methods for inference over structured and spatio-temporal data, with attention to reliability rather than benchmark scores alone.
Topics include graph and temporal neural models, classifier fusion, feature construction and decision support.
- Machine learning
- Spatio-temporal inference
- Classifier fusion
Related Publications
All publications →2025
An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus
Mohammad Fatahi, Danial Sadrian Zadeh, Benyamin Ghojogh, Behzad Moshiri, Otman Basir
arXiv (Cornell University)
2025
Detection of Autism Spectrum Disorder Using Quantum Support Vector Machines Algorithm
Arshia Eftekhari Zadeh, Ali Mikaeili Barzili, Mohammad-Hossein Nemati, Mohammad Khoshnevisan, Hamid Azadegan, Behzad Moshiri
2025
Entropy-based genetic feature engineering and multi-classifier fusion for anomaly detection in vehicle controller area networks
Mohammad Fatahi, Danial Sadrian Zadeh, Behzad Moshiri, Otman Basir
Future Generation Computer Systems
2025
Fault Detection in Induction Motors Using Thermal Images and Interpretable Machine Learning: An Approach Based on Efficientnet and Advanced Simulation Algorithms
Seyed Reza Hashemi Dogahe, Behzad Moshiri
2025
Improving the Identification of Fraudulent Activity with Cascade Attention-Based Ensemble Learning: An Interpretable AI Approach
Mehdi Hosseini Chagahi, Abolfazl Yekaneh, Saeed Mohammadi Dashtaki, Hyun-Han Kwon, Behzad Moshiri, Md. Jalil Piran