AIMC Topic: Neural Networks, Computer

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Automatic Implementation Algorithm of Pressure Relief Drilling Depth Based on an Innovative Monitoring-While-Drilling Method.

Sensors (Basel, Switzerland)
An innovative monitoring-while-drilling method of pressure relief drilling was proposed in a previous study, and the periodic appearance of amplitude concentrated enlargement zone in vibration signals can represent the drilling depth. However, there ...

Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.

Sensors (Basel, Switzerland)
In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context...

Robust Spatial-Spectral Squeeze-Excitation AdaBound Dense Network (SE-AB-Densenet) for Hyperspectral Image Classification.

Sensors (Basel, Switzerland)
Increasing importance in the field of artificial intelligence has led to huge progress in remote sensing. Deep learning approaches have made tremendous progress in hyperspectral image (HSI) classification. However, the complexity in classifying the H...

Multihydrophone Fusion Network for Modulation Recognition.

Sensors (Basel, Switzerland)
Deep learning (DL)-based modulation recognition methods of underwater acoustic communication signals are mostly applied to a single hydrophone reception scenario. In this paper, we propose a novel end-to-end multihydrophone fusion network (MHFNet) fo...

Deep Neural Network for Point Sets Based on Local Feature Integration.

Sensors (Basel, Switzerland)
The research of object classification and part segmentation is a hot topic in computer vision, robotics, and virtual reality. With the emergence of depth cameras, point clouds have become easier to collect and increasingly important because of their ...

Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks.

International journal of environmental research and public health
Time series data from environmental monitoring stations are often analysed with machine learning methods on an individual basis, however recent advances in the machine learning field point to the advantages of incorporating multiple related time seri...

Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining.

Scientific reports
To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unlabeled cell culture images captured by a light microscope as input. In this study, A549, HEK293, and ...

Fast environmental sound classification based on resource adaptive convolutional neural network.

Scientific reports
Recently, with the construction of smart city, the research on environmental sound classification (ESC) has attracted the attention of academia and industry. The development of convolutional neural network (CNN) makes the accuracy of ESC reach a high...

Introduction of Lazy Luna an automatic software-driven multilevel comparison of ventricular function quantification in cardiovascular magnetic resonance imaging.

Scientific reports
Cardiovascular magnetic resonance imaging is the gold standard for cardiac function assessment. Quantification of clinical results (CR) requires precise segmentation. Clinicians statistically compare CRs to ensure reproducibility. Convolutional Neura...

Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study.

JMIR mHealth and uHealth
BACKGROUND: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. How...