AIMC Topic: Neural Networks, Computer

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PhyTransformer: A unified framework for learning spatial-temporal representation from physiological signals.

Neural networks : the official journal of the International Neural Network Society
As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG), and eye tracking (ET) signals are widely used to decode human intention, promoting the development of human-computer interaction systems. Extensive ...

Dual-structure community preserving network embedding.

Neural networks : the official journal of the International Neural Network Society
Network embedding, an effective method for learning low-dimensional representations of nodes, plays a crucial role in various network learning scenarios. However, existing network embedding learning methods fail to learn node embeddings from the pers...

S2LIC: Learned image compression with the SwinV2 block, Adaptive Channel-wise and Global-inter attention Context.

Neural networks : the official journal of the International Neural Network Society
Recently, deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. It is crucial to design an effective and efficient entropy model to estimate the probability distribu...

Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback.

Scientific reports
This work presents an embedded solution for detecting and classifying head-level objects using stereo vision to assist blind individuals. A custom dataset was created, featuring five classes of head-level objects, selected based on a survey of visual...

3D Micro-Expression Recognition Based on Adaptive Dynamic Vision.

Sensors (Basel, Switzerland)
In the research on intelligent perception, dynamic emotion recognition has been the focus in recent years. Small samples and unbalanced data are the main reasons for the low recognition accuracy of current technologies. Inspired by circular convoluti...

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.

Sensors (Basel, Switzerland)
Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to promin...

TCoCPIn reveals topological characteristics of chemical protein interaction networks for novel feature discovery.

Scientific reports
Understanding chemical-protein interactions (CPIs) is crucial for drug discovery and biological research, yet their complexity often challenges traditional methods. We propose TCoCPIn, a novel framework integrating graph neural networks (GNN) with th...

Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends.

TheScientificWorldJournal
The investigation and diagnosis of hematologic malignancy using blood cell image analysis are major and emerging subjects that lie at the intersection of artificial intelligence and medical research. This survey systematically examines the state-of-t...

Dual-stream interactive networks with pearson-mask awareness for multivariate time series forecasting.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series forecasting (MTSF) aims to predict time series data containing multiple variates, which requires the consideration of both intra-series temporal trends and inter-series interactions. Benefiting from the success of Transformer...

Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI.

Medical image analysis
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational an...