26 citations · 29 across the 4 of their papers we have counts for
6 papers
When More Modalities Hurt: Modality Dropout for Heavy-Duty Vehicle Engine Diagnostics
Adeel Zafar, Slawomir Nowaczyk, Hamid Sarmadi +1
Heavy-duty vehicle diagnostics generate three disconnected data modalities: unstructured multi- lingual service complaints, high-dimensional sensor telemetry with over 80% missing…
Bridging Forecast Accuracy and Inventory KPIs: A Simulation-Based Software Framework
So Fukuhara, Abdallah Alabdallah, Nuwan Gunasekara +1
Efficient management of spare parts inventory is crucial in the automotive aftermarket, where demand is highly intermittent and uncertainty drives substantial cost and service risk…
Neuroplasticity-inspired dynamic ANNs for multi-task demand forecasting
Mateusz Żarski, Sławomir Nowaczyk
This paper introduces a novel approach to Dynamic Artificial Neural Networks (D-ANNs) for multi-task demand forecasting called Neuroplastic Multi-Task Network (NMT-Net). Unlike con…
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks
Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami +2
This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs). Randomization, like weight noise and dropout, aids in reducing overfitting and enha…
Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles
Yuantao Fan, Zhenkan Wang, Sepideh Pashami +2
Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understandin…
Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study
Alexander Galozy, Sadi Alawadi, Victor Kebande +1
This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on…