5 papers
Boosting Predictive Performance on Tabular Data through Data Augmentation with Latent-Space Flow-Based Diffusion
Md. Tawfique Ihsan, Md. Rakibul Hasan Rafi, Ahmed Shoyeb Raihan +2
Severe class imbalance is common in real-world tabular learning, where rare but important minority classes are essential for reliable prediction. Existing generative oversampling m…
LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention
Hamed Khosravi, Mohammad Reza Shafie, Ahmed Shoyeb Raihan +2
Existing contextual multi-armed bandit (MAB) algorithms fail to effectively capture both long-term trends and local patterns across all arms, leading to suboptimal performance in e…
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints
Ahmed Shoyeb Raihan, Zhichao Liu, Tanveer Hossain Bhuiyan +1
Accelerating the discovery and manufacturing of advanced materials with specific properties is a critical yet formidable challenge due to vast search space, high costs of experimen…
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers
Israt Zarin Era, Fan Zhou, Ahmed Shoyeb Raihan +5
Directed Energy Deposition (DED) offers significant potential for manufacturing complex and multi-material parts. However, internal defects such as porosity and cracks can compromi…
A Data-Efficient Sequential Learning Framework for Melt Pool Defect Classification in Laser Powder Bed Fusion
Ahmed Shoyeb Raihan, Austin Harper, Israt Zarin Era +4
Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects…