6 citations · 15 across the 13 of their papers we have counts for
13 papers
GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback
Mohamed Abdelaal, Samuel Lokadjaja, Gilbert Engert
This paper introduces GLLM, an innovative tool that leverages Large Language Models (LLMs) to automatically generate G-code from natural language instructions for Computer Numerica…
DataLens: ML-Oriented Interactive Tabular Data Quality Dashboard
Mohamed Abdelaal, Samuel Lokadjaja, Arne Kreuz +1
Maintaining high data quality is crucial for reliable data analysis and machine learning (ML). However, existing data quality management tools often lack automation, interactivity,…
EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection
Kanishk Chaturvedi, Johannes Gasthuber, Mohamed Abdelaal
This paper introduces EdgeMLOps, a framework leveraging Cumulocity IoT and thin-edge.io for deploying and managing machine learning models on resource-constrained edge devices. We…
AI in Manufacturing: Market Analysis and Opportunities
Mohamed Abdelaal
In this paper, we explore the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, highlighting its potential to revolutionize industry practices and…
Open-Source Drift Detection Tools in Action: Insights from Two Use Cases
Rieke Müller, Mohamed Abdelaal, Davor Stjelja
Data drifts pose a critical challenge in the lifecycle of machine learning (ML) models, affecting their performance and reliability. In response to this challenge, we present a mic…
LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs
Fabian Biester, Mohamed Abdelaal, Daniel Del Gaudio
Machine learning's influence is expanding rapidly, now integral to decision-making processes from corporate strategy to the advancements in Industry 4.0. The efficacy of Artificial…
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
Truong Thanh Hung Nguyen, Tobias Clement, Phuc Truong Loc Nguyen +5
LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. Despite…
Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction
Tobias Clement, Hung Truong Thanh Nguyen, Nils Kemmerzell +2
This paper presents an approach integrating explainable artificial intelligence (XAI) techniques with adaptive learning to enhance energy consumption prediction models, with a focu…
XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
Tobias Clement, Truong Thanh Hung Nguyen, Mohamed Abdelaal +1
Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ computer vision and machine learning for precise, rapid defect detection. However, th…
AutoCure: Automated Tabular Data Curation Technique for ML Pipelines
Mohamed Abdelaal, Rashmi Koparde, Harald Schoening
Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a…
RTClean: Context-aware Tabular Data Cleaning using Real-time OFDs
Daniel Del Gaudio, Tim Schubert, Mohamed Abdelaal
Nowadays, machine learning plays a key role in developing plenty of applications, e.g., smart homes, smart medical assistance, and autonomous driving. A major challenge of these ap…
REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
Mohamed Abdelaal, Christian Hammacher, Harald Schoening
Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In essence, building well-performing ML applications requires the provision of high-quality da…
DiffML: End-to-end Differentiable ML Pipelines
Benjamin Hilprecht, Christian Hammacher, Eduardo Reis +2
In this paper, we present our vision of differentiable ML pipelines called DiffML to automate the construction of ML pipelines in an end-to-end fashion. The idea is that DiffML all…