AIMC Topic: Neural Networks, Computer

Clear Filters Showing 17601 to 17610 of 31376 articles

Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning.

Journal of chemical information and modeling
To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that mach...

Edge deep learning for neural implants: a case study of seizure detection and prediction.

Journal of neural engineering
Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease. Classification performance is critical to identifying brain states appro...

Computational Logistics for Container Terminal Handling Systems with Deep Learning.

Computational intelligence and neuroscience
Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonli...

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

PloS one
Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for d...

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

Computers in biology and medicine
The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, includin...

Enhancing Graph Neural Networks by a High-quality Aggregation of Beneficial Information.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs), such as GCN, GraphSAGE, GAT, and SGC, have achieved state-of-the-art performance on a wide range of graph-based tasks. These models all use a technique called neighborhood aggregation, in which the embedding of each node...

Generative Adversarial Network with Multi-branch Discriminator for imbalanced cross-species image-to-image translation.

Neural networks : the official journal of the International Neural Network Society
There has been an increased interest in high-level image-to-image translation to achieve semantic matching. Through a powerful translation model, we can efficiently synthesize high-quality images with diverse appearances while retaining semantic matc...

A deep neural network-based approach for prediction of mutagenicity of compounds.

Environmental science and pollution research international
We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of m...

Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus.

Trends in cognitive sciences
Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training A...

Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution.

Computers in biology and medicine
Liver tumor segmentation networks are generally based on U-shaped encoder-decoder network with 2D or 3D structure. However, 2D networks lose the inter-layer information of continuous slices and 3D networks might introduce unacceptable parameters for ...