AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Dynamic graph-based bilateral recurrent imputation network for multivariate time series.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series imputation using graph neural networks (GNNs) has gained significant attention, where the variables and their correlations are depicted as the graph nodes and edges, offering a structured way to understand the intricacies of ...

Advertising or adversarial? AdvSign: Artistic advertising sign camouflage for target physical attacking to object detector.

Neural networks : the official journal of the International Neural Network Society
Deep learning models are often vulnerable to adversarial attacks in both digital and physical environments. Particularly challenging are physical attacks that involve subtle, unobtrusive modifications to objects, such as patch-sticking or light-shoot...

HFFTrack: Transformer tracking via hybrid frequency features.

Neural networks : the official journal of the International Neural Network Society
Numerous Transformer-based trackers have emerged due to the powerful global modeling capabilities of the Transformer. Nevertheless, the Transformer is a low-pass filter with insufficient capacity to extract high-frequency features of the target and t...

Attention-augmented multi-domain cooperative graph representation learning for molecular interaction prediction.

Neural networks : the official journal of the International Neural Network Society
Accurate identification of molecular interactions is crucial for biological network analysis, which can provide valuable insights into fundamental regulatory mechanisms. Despite considerable progress driven by computational advancements, existing met...

Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources.

Neural networks : the official journal of the International Neural Network Society
Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation can lead to the waste of renewable energy. As renewable energy is becoming a larger part of the grid, ...

A novel deep learning model combining 3DCNN-CapsNet and hierarchical attention mechanism for EEG emotion recognition.

Neural networks : the official journal of the International Neural Network Society
Emotion recognition plays a key role in the field of human-computer interaction. Classifying and predicting human emotions using electroencephalogram (EEG) signals has consistently been a challenging research area. Recently, with the increasing appli...

ZS-MNET: A zero-shot learning based approach to multimodal named entity typing.

Neural networks : the official journal of the International Neural Network Society
The task of named entity typing (NET) on social platforms is significant as it involves identifying the various types of named entities within unstructured text. The existing methods for NET only utilize the text modality to classify the types of nam...

Neural transition system abstraction for neural network dynamical system models and its application to Computational Tree Logic verification.

Neural networks : the official journal of the International Neural Network Society
This paper proposes an explainable abstraction-based verification method that prioritizes user interaction and enhances interpretability. By partitioning the system's state space using a data-driven process, we can abstract the dynamics into words co...

Learning temporal regularized spatial-aware deep correlation filter tracking via adaptive channel selection.

Neural networks : the official journal of the International Neural Network Society
In recent years, deep correlation filters have demonstrated outstanding performance in robust object tracking. Nevertheless, the correlation filters encounter challenges in managing huge occlusion, target deviation, and background clutter due to the ...

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

Neural networks : the official journal of the International Neural Network Society
While numerous studies strive to exploit the complementary potential of MRI and PET using learning-based methods, the effective fusion of the two modalities remains a tricky problem due to their inherently distinctive properties. In addition, current...