Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Mehrkanoon, Siamak"'
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-su
Externí odkaz:
http://arxiv.org/abs/2403.08592
Autor:
Reulen, Eloy, Mehrkanoon, Siamak
In recent years, data-driven modeling approaches have gained significant attention across various meteorological applications, particularly in weather forecasting. However, these methods often face challenges in handling extreme weather conditions. I
Externí odkaz:
http://arxiv.org/abs/2401.09881
Autor:
Vatamany, Lorand, Mehrkanoon, Siamak
Accurate precipitation nowcasting is essential for various applications, including flood prediction, disaster management, optimizing agricultural activities, managing transportation routes and renewable energy. While several studies have addressed th
Externí odkaz:
http://arxiv.org/abs/2401.07958
Autor:
Kazatzidis, Sergio, Mehrkanoon, Siamak
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 pronounced in fields such as the electroencephalography (EEG) research do
Externí odkaz:
http://arxiv.org/abs/2312.09623
Autor:
Renault, Mathieu, Mehrkanoon, Siamak
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 proposes a novel architecture called Small Attention Residual UNet (SAR-U
Externí odkaz:
http://arxiv.org/abs/2303.06663
Designing early warning systems for harsh weather and its effects, such as urban flooding or landslides, requires accurate short-term forecasts (nowcasts) of precipitation. Nowcasting is a significant task with several environmental applications, suc
Externí odkaz:
http://arxiv.org/abs/2302.04102
Autor:
Kuang, Sheng, Woodruff, Henry C., Granzier, Renee, van Nijnatten, Thiemo J. A., Lobbes, Marc B. I., Smidt, Marjolein L., Lambin, Philippe, Mehrkanoon, Siamak
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 arises from different vendors, acquisition protocols, and biological het
Externí odkaz:
http://arxiv.org/abs/2301.02554
Accurate sound localization in a reverberation environment is essential for human auditory perception. Recently, Convolutional Neural Networks (CNNs) have been utilized to model the binaural human auditory pathway. However, CNN shows barriers in capt
Externí odkaz:
http://arxiv.org/abs/2207.03927
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 nonlinear partial differential equations (PDEs). The model aims at incorp
Externí odkaz:
http://arxiv.org/abs/2204.13744
Autor:
Yang, Yimin, Mehrkanoon, Siamak
Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper introduces a novel data-driven predictive model based on TransUNet for pre
Externí odkaz:
http://arxiv.org/abs/2202.04996