4 citations · 7 across the 5 of their papers we have counts for
5 papers
Adaptive Data Quality Scoring Operations Framework using Drift-Aware Mechanism for Industrial Applications
Firas Bayram, Bestoun S. Ahmed, Erik Hallin
Within data-driven artificial intelligence (AI) systems for industrial applications, ensuring the reliability of the incoming data streams is an integral part of trustworthy decisi…
DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks
Firas Bayram, Phil Aupke, Bestoun S. Ahmed +3
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid s…
A Domain-Region Based Evaluation of ML Performance Robustness to Covariate Shift
Firas Bayram, Bestoun S. Ahmed
Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and…
DQSOps: Data Quality Scoring Operations Framework for Data-Driven Applications
Firas Bayram, Bestoun S. Ahmed, Erik Hallin +1
Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world a…
A Drift Handling Approach for Self-Adaptive ML Software in Scalable Industrial Processes
Firas Bayram, Bestoun S. Ahmed, Erik Hallin +1
Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learnin…