AIMC Topic: Neural Networks, Computer

Clear Filters Showing 13651 to 13660 of 31376 articles

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.

Sensors (Basel, Switzerland)
Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-qualit...

A novel liver cancer diagnosis method based on patient similarity network and DenseGCN.

Scientific reports
Liver cancer is the main malignancy in terms of mortality rate, accurate diagnosis can help the treatment outcome of liver cancer. Patient similarity network is an important information which helps in cancer diagnosis. However, recent works rarely ta...

Chalcogenide optomemristors for multi-factor neuromorphic computation.

Nature communications
Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase...

Learning aerodynamics with neural network.

Scientific reports
We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two...

Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches.

Genome medicine
The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled ...

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.

Frontiers in public health
PURPOSE: To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.

The Impact of Corporate Capital Structure on Financial Performance Based on Convolutional Neural Network.

Computational intelligence and neuroscience
Capital structure is an important indicator to measure the source, composition, and proportion of a company's equity and debit capital. It is not only related to the internal operating environment of listed companies but also related to the rights an...

Research on Rice Yield Prediction Model Based on Deep Learning.

Computational intelligence and neuroscience
Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people'...

DeepBBBP: High Accuracy Blood-brain-barrier Permeability Prediction with a Mixed Deep Learning Model.

Molecular informatics
Blood-brain-barrier permeability (BBBP) is an important property that is used to establish the drug-likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not suppos...

Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations.

Journal of the American College of Radiology : JACR
Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging because of the...