activity
20222024
most citedIoT Anomaly Detection Methods and Applications: A Survey

313 citations · 321 across the 12 of their papers we have counts for

collaborators

12 papers

cs.DB20241 cited

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…

cs.AI2024

An Adaptive Metaheuristic Framework for Changing Environments

Bestoun S. Ahmed

The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF)…

cs.DC2024

Optimizing Service Placement in Edge-to-Cloud AR/VR Systems using a Multi-Objective Genetic Algorithm

Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed +1

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to…

cs.AI2023

Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework

Manal Rahal, Bestoun S. Ahmed, Jorgen Samuelsson

Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data a…

cs.LG20234 cited

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…

cs.LG2023

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…