4 citations · 9 across the 9 of their papers we have counts for
10 papers
Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs
Firas Bayram, Maxime Cordy
Adversarial detection on evolving attributed graphs faces two challenges: fraud labels arrive late or not at all, and natural distributional drift erodes any trained decision bound…
End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment
Firas Bayram, Bestoun S. Ahmed, Erik Hallin
This paper introduces a novel end-to-end framework that efficiently integrates data quality assessment with machine learning (ML) model operations in real-time production environme…
Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing
Bestoun S. Ahmed, Ludwig Otto Baader, Firas Bayram +2
This paper presents a comprehensive framework for testing and evaluating quality characteristics of Large Language Model (LLM) systems enhanced with Retrieval-Augmented Generation…
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
Firas Bayram, Bestoun S. Ahmed
Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years,…
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…
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors
Firas Bayram, Bestoun S. Ahmed, Andreas Kassler
The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained…