6 citations · 11 across the 22 of their papers we have counts for
25 papers
A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining
Mostafa Abbasi, Homayoun Najjaran
Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on conformance metrics such as fitne…
Quantifying Volumetric Risk: Class-Aware Asymmetric Weighted Conformal Prediction for 3D Medical Image Segmentation
Shadi Alijani, Fereshteh Aghaee Meibodi, Homayoun Najjaran
Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated uncertainty under distribution…
Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking
Fereshteh Aghaee Meibodi, Amir Mehdi Soufi Enayati, Shadi Alijani +1
Visual object tracking typically assumes that the initial template and subsequent search frames share the same sensing modality. In practice, sensor availability or operation may c…
A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog
Amir Pouladi, Vesal Ahsani, Haijun Li +2
Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery, reducing the reliability of downstream detection and tracking. Thi…
SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models
Joel Sol, Homayoun Najjaran
As LLMs become more widely deployed, they are increasingly expected to work alongside other AI agents rather than operating in isolation. Effective coordination in these settings r…
A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems
Maryam Ahang, Todd Charter, Masoud Jalayer +1
Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial condition monitoring. This work develo…
Sub-Region-Aware Modality Fusion and Adaptive Prompting for Multi-Modal Brain Tumor Segmentation
Shadi Alijani, Fereshteh Aghaee Meibodi, Homayoun Najjaran
The successful adaptation of foundation models to multi-modal medical imaging is a critical yet unresolved challenge. Existing models often struggle to effectively fuse information…
Context Representation via Action-Free Transformer encoder-decoder for Meta Reinforcement Learning
Amir M. Soufi Enayati, Homayoun Honari, Homayoun Najjaran
Reinforcement learning (RL) enables robots to operate in uncertain environments, but standard approaches often struggle with poor generalization to unseen tasks. Context-adaptive m…
A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model
Mehran Ghafarian Tamizi, Homayoun Honari, Amir Mehdi Soufi Enayati +2
Path planning for a robotic system in high-dimensional cluttered environments needs to be efficient, safe, and adaptable for different environments and hardware. Conventional metho…
A Deep Dive into Generic Object Tracking: A Survey
Fereshteh Aghaee Meibodi, Shadi Alijani, Homayoun Najjaran
Generic object tracking remains an important yet challenging task in computer vision due to complex spatio-temporal dynamics, especially in the presence of occlusions, similar dist…
LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Jamil Fayyad, Nourhan Bayasi, Ziyang Yu +1
Deep learning models for skin disease classification require large, diverse, and well-annotated datasets. However, such resources are often limited due to privacy concerns, high an…
WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts
Shadi Alijani, Homayoun Najjaran
Conformal prediction (CP) provides a framework for constructing prediction sets with guaranteed coverage, assuming exchangeable data. However, real-world scenarios often involve di…
Domain Adaptive Skin Lesion Classification via Conformal Ensemble of Vision Transformers
Mehran Zoravar, Shadi Alijani, Homayoun Najjaran
Exploring the trustworthiness of deep learning models is crucial, especially in critical domains such as medical imaging decision support systems. Conformal prediction has emerged…
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction
Ali Mohajerzarrinkelk, Maryam Ahang, Mehran Zoravar +2
Precise estimation of the Remaining Useful Life (RUL) of rolling bearings is an important consideration to avoid unexpected failures, reduce downtime, and promote safety and effici…
Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis
Hootan Mahmoodiyan, Maryam Ahang, Mostafa Abbasi +1
Ensuring the reliable operation of power transformers is critical to grid stability. Dissolved Gas Analysis (DGA) is widely used for fault diagnosis, but traditional methods rely o…
Sliced Wasserstein Discrepancy in Disentangling Representation and Adaptation Networks for Unsupervised Domain Adaptation
Joel Sol, Shadi Alijani, Homayoun Najjaran
This paper introduces DRANet-SWD as a novel complete pipeline for disentangling content and style representations of images for unsupervised domain adaptation (UDA). The approach b…
An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring
Hadi Zare, Mostafa Abbasi, Maryam Ahang +1
Next activity prediction in predictive business process monitoring is crucial for operational efficiency and informed decision-making. While machine learning and Artificial Intelli…
An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring
Mostafa Abbasi, Maziyar Khadivi, Maryam Ahang +3
The application of artificial intelligence and machine learning in business process management has advanced significantly, however, the full potential of these technologies remains…
Conformal-in-the-Loop for Learning with Imbalanced Noisy Data
John Brandon Graham-Knight, Jamil Fayyad, Nourhan Bayasi +2
Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world c…
Debiasify: Self-Distillation for Unsupervised Bias Mitigation
Nourhan Bayasi, Jamil Fayyad, Ghassan Hamarneh +2
Simplicity bias poses a significant challenge in neural networks, often leading models to favor simpler solutions and inadvertently learn decision rules influenced by spurious corr…
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning
Homayoun Honari, Amir Mehdi Soufi Enayati, Mehran Ghafarian Tamizi +1
Safe Reinforcement Learning (Safe RL) is one of the prevalently studied subcategories of trial-and-error-based methods with the intention to be deployed on real-world systems. In s…
Sim-to-Real Domain Adaptation for Deformation Classification
Joel Sol, Jamil Fayyad, Shadi Alijani +1
Deformation detection is vital for enabling accurate assessment and prediction of structural changes in materials, ensuring timely and effective interventions to maintain safety an…
Visual Deformation Detection Using Soft Material Simulation for Pre-training of Condition Assessment Models
Joel Sol, Amir M. Soufi Enayati, Homayoun Najjaran
This paper addresses the challenge of geometric quality assurance in manufacturing, particularly when human assessment is required. It proposes using Blender, an open-source simula…
Vision transformers in domain adaptation and domain generalization: a study of robustness
Shadi Alijani, Jamil Fayyad, Homayoun Najjaran
Deep learning models are often evaluated in scenarios where the data distribution is different from those used in the training and validation phases. The discrepancy presents a cha…