104 citations · 148 across the 34 of their papers we have counts for
36 papers
Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall
Gijs van Nieuwkoop, Siamak Mehrkanoon
Precipitation nowcasting is increasingly being approached with deep learning models that learn directly from recent radar observations. Although such models can efficiently capture…
Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
Gijs van Nieuwkoop, Siamak Mehrkanoon
Deep-learning precipitation nowcasting models are often optimized using pointwise losses such as mean squared error or mean absolute error, which can lead to overly smooth forecast…
A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions
Jie Shi, Siamak Mehrkanoon
Accurate weather nowcasting remains one of the central challenges in atmospheric science, with critical implications for climate resilience, energy security, and disaster preparedn…
EEG-MFTNet: An Enhanced EEGNet Architecture with Multi-Scale Temporal Convolutions and Transformer Fusion for Cross-Session Motor Imagery Decoding
Panagiotis Andrikopoulos, Siamak Mehrkanoon
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices, providing critical support for individuals with motor impairments. However, acc…
SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting
Nikolas Stavrou, Siamak Mehrkanoon
Weather forecasting supports critical socioeconomic activities and complements environmental protection, yet operational Numerical Weather Prediction (NWP) systems remain computati…
MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting
Samuel van Wonderen, Siamak Mehrkanoon
Precipitation nowcasting (short-term forecasting) is still often performed using numerical solvers for physical equations, which are computationally expensive and make limited use…
AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts
Niklas Grieger, Jannik Raskob, Siamak Mehrkanoon +1
Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care. Across centers, polysomnography (PSG)…
Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon
This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address…
From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysis
Niklas Grieger, Siamak Mehrkanoon, Philipp Ritter +1
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to…
SSA-UNet: Advanced Precipitation Nowcasting via Channel Shuffling
Marco Turzi, Siamak Mehrkanoon
Weather forecasting is essential for facilitating diverse socio-economic activity and environmental conservation initiatives. Deep learning techniques are increasingly being explor…
Integrating Weather Station Data and Radar for Precipitation Nowcasting: SmaAt-fUsion and SmaAt-Krige-GNet
Jie Shi, Aleksej Cornelissen, Siamak Mehrkanoon
Short-term precipitation nowcasting is essential for flood management, transportation, energy system operations, and emergency response. However, many existing models fail to fully…
Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting
Haotian Li, Arno Siebes, Siamak Mehrkanoon
Nowcasting, the short-term prediction of weather, is essential for making timely and weather-dependent decisions. Specifically, precipitation nowcasting aims to predict precipitati…
Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding
Lucas Goene, Siamak Mehrkanoon
This paper presents the novel Dual Stream Graph-Transformer Fusion (DS-GTF) architecture designed specifically for classifying task-based Magnetoencephalography (MEG) data. In the…
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
Niklas Grieger, Siamak Mehrkanoon, Stephan Bialonski
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small data…
GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcasting
Eloy Reulen, Siamak Mehrkanoon
In recent years, data-driven modeling approaches have gained significant attention across various meteorological applications, particularly in weather forecasting. However, these m…
Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting
Lorand Vatamany, Siamak Mehrkanoon
Accurate precipitation nowcasting is crucial for applications such as flood prediction, disaster management, agriculture optimization, and transportation management. While many stu…
A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification
Sergio Kazatzidis, Siamak Mehrkanoon
Self-supervised learning addresses the challenge encountered by many supervised methods, i.e. the requirement of large amounts of annotated data. This challenge is particularly pro…
TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start
Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon
This paper proposes an interpretable two-stream transformer CORAL networks (TransCORALNet) for supply chain credit assessment under the segment industry and cold start problem. The…
SAR-UNet: Small Attention Residual UNet for Explainable Nowcasting Tasks
Mathieu Renault, Siamak Mehrkanoon
The accuracy and explainability of data-driven nowcasting models are of great importance in many socio-economic sectors reliant on weather-dependent decision making. This paper pro…
WF-UNet: Weather Fusion UNet for Precipitation Nowcasting
Christos Kaparakis, Siamak Mehrkanoon
Designing early warning systems for harsh weather and its effects, such as urban flooding or landslides, requires accurate short-term forecasts (nowcasts) of precipitation. Nowcast…
MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets
Sheng Kuang, Henry C. Woodruff, Renee Granzier +5
Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which ar…
BAST: Binaural Audio Spectrogram Transformer for Binaural Sound Localization
Sheng Kuang, Jie Shi, Kiki van der Heijden +1
Accurate sound localization in a reverberation environment is essential for human auditory perception. Recently, Convolutional Neural Networks (CNNs) have been utilized to model th…
GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations
Onur Bilgin, Thomas Vergutz, Siamak Mehrkanoon
This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of non…
AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks
Yimin Yang, Siamak Mehrkanoon
Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper intr…