8 papers
Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting
John Sengendo, Zineddine Bettouche, Khalid Ali +2
As mobile networks transition from Beyond 5G (B5G) towards 6G, accurate traffic forecasting is a prerequisite for improving network management. However, with increasing heterogenei…
Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid Forecasting
Zineddine Bettouche, Khalid Ali, Andreas Fischer +1
Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need fo…
HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Zineddine Bettouche, Khalid Ali, Andreas Fischer +1
Cellular traffic forecasting is essential for network planning, resource allocation, or load-balancing traffic across cells. However, accurate forecasting is difficult due to intri…
Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting
Khalid Ali, Zineddine Bettouche, Andreas Kassler +1
Accurate spatiotemporal traffic forecasting is vital for intelligent resource management in 5G and beyond. However, conventional AI approaches often fail to capture the intricate s…
Synthetic Data Augmentation for Table Detection: Re-evaluating TableNet's Performance with Automatically Generated Document Images
Krishna Sahukara, Zineddine Bettouche, Andreas Fischer
Document pages captured by smartphones or scanners often contain tables, yet manual extraction is slow and error-prone. We introduce an automated LaTeX-based pipeline that synthesi…
Contextual Categorization Enhancement through LLMs Latent-Space
Zineddine Bettouche, Anas Safi, Andreas Fischer
Managing the semantic quality of the categorization in large textual datasets, such as Wikipedia, presents significant challenges in terms of complexity and cost. In this paper, we…
Mapping Researcher Activity based on Publication Data by means of Transformers
Zineddine Bettouche, Andreas Fischer
Modern performance on several natural language processing (NLP) tasks has been enhanced thanks to the Transformer-based pre-trained language model BERT. We employ this concept to i…
Improving Image Tracing with Convolutional Autoencoders by High-Pass Filter Preprocessing
Zineddine Bettouche, Andreas Fischer
The process of transforming a raster image into a vector representation is known as image tracing. This study looks into several processing methods that include high-pass filtering…