AIMC Topic: Neural Networks, Computer

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DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.

BMC bioinformatics
BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep lear...

Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.

Scientific reports
Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection ...

Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.

Journal of neural engineering
Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require re...

Multitask learning of a biophysically-detailed neuron model.

PLoS computational biology
The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective meth...

Soft sensing of NOx emission from waste incineration process based on data de-noising and bidirectional long short-term memory neural networks.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Continuous emission monitoring system is commonly employed to monitor NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significa...

Self-supervised learning for improved calibrationless radial MRI with NLINV-Net.

Magnetic resonance in medicine
PURPOSE: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training.

Enhanced 3D dose prediction for hypofractionated SRS (gamma knife radiosurgery) in brain tumor using cascaded-deep-supervised convolutional neural network.

Physical and engineering sciences in medicine
Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant ...

Unsupervised domain adaptive building semantic segmentation network by edge-enhanced contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that classifies images in the target domain when the source domain has labeled samples, and the target domain has unlabeled samples. Due to the complexity of imaging condi...

Deep self-supervised spatial-variant image deblurring.

Neural networks : the official journal of the International Neural Network Society
Most existing model-based and learning-based image deblurring methods usually use synthetic blur-sharp training pairs to remove blur. However, these approaches do not perform well in real-world applications as the blur-sharp training pairs are diffic...

Improved fixed-time stability analysis and applications to synchronization of discontinuous complex-valued fuzzy cellular neural networks.

Neural networks : the official journal of the International Neural Network Society
This article mainly centers on proposing new fixed-time (FXT) stability lemmas of discontinuous systems, in which novel optimization approaches are utilized and more relaxed conditions are required. The conventional discussions about Vt>1 and 0