arXiv subjects

Elham Bagheri

Publications and source records attributed to Elham Bagheri.

3 recordsLinked to original sources

arXiv2025

A Graph Neural Network Approach for Localized and High-Resolution Temperature Forecasting

Heatwaves are intensifying worldwide and are among the deadliest weather disasters. The burden falls disproportionately on marginalized populations and the Global South, where under-resourced health systems, exposure to urban heat islands, and the lack of adaptive infrastructure amplify risks. Yet current numerical weather prediction models often fail to capture micro-scale extremes, leaving the most vulnerable excluded from timely early warnings. We present a Graph Neural Network framework for localized, high-resolution temperature forecasting. By leveraging spatial learning and efficient computation, our approach generates forecasts at multiple horizons, up to 48 hours. For Southwestern Ontario, Canada, the model captures temperature patterns with a mean MAE of 1.93$^{\circ}$C across 1-48h forecasts and MAE@48h of 2.93$^{\circ}$C, evaluated using 24h input windows on the largest region. While demonstrated here in a data-rich context, this work lays the foundation for transfer learning approaches that could enable localized, equitable forecasts in data-limited regions of the Global South.

cs.LG↗

arXiv2024

Modeling Visual Memorability Assessment with Autoencoders Reveals Characteristics of Memorable Images

Image memorability refers to the phenomenon where certain images are more likely to be remembered than others. It is a quantifiable and intrinsic image attribute, defined as the likelihood of an image being remembered upon a single exposure. Despite advances in understanding human visual perception and memory, it is unclear what features contribute to an image's memorability. To address this question, we propose a deep learning-based computational modeling approach. We employ an autoencoder-based approach built on VGG16 convolutional neural networks (CNNs) to learn latent representations of images. The model is trained in a single-epoch setting, mirroring human memory experiments that assess recall after a single exposure. We examine the relationship between autoencoder reconstruction error and memorability, analyze the distinctiveness of latent space representations, and develop a multi-layer perceptron (MLP) model for memorability prediction. Additionally, we perform interpretability analysis using Integrated Gradients (IG) to identify the key visual characteristics that contribute to memorability. Our results demonstrate a significant correlation between the images' memorability score and the autoencoder's reconstruction error, as well as the robust predictive performance of its latent representations. Distinctiveness in these representations correlated significantly with memorability. Additionally, certain visual characteristics were identified as features contributing to image memorability in our model. These findings suggest that autoencoder-based representations capture fundamental aspects of image memorability, providing new insights into the computational modeling of human visual memory.

cs.CV↗

arXiv2020

Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detecting System (TDS), Shallow Learning-based Detecting System (SLDS), and Deep Learning-based Detecting System (DLDS). These systems are evaluated on channel-, segment- and EEG-level. The TDS, SLDS, and DLDS performs prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30-minutes EEG in 4 seconds and can be deployed to assist clinicians in interpreting EEGs.

eess.SP↗