AIMC Topic: Neural Networks, Computer

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Low-Light Image Enhancement Based on Multi-Path Interaction.

Sensors (Basel, Switzerland)
Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhan...

Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks.

Sensors (Basel, Switzerland)
The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using...

Supervised Learning of Neural Networks for Active Queue Management in the Internet.

Sensors (Basel, Switzerland)
The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router's queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies th...

Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.

Radiation oncology (London, England)
BACKGROUND: This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvi...

Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray.

Computational intelligence and neuroscience
Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimental...

Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network.

Computational intelligence and neuroscience
Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale l...

Signal Recognition Based on APSO-RBF Neural Network to Assist Athlete's Competitive Ability Evaluation.

Computational intelligence and neuroscience
The advanced analysis and research methods of big data will provide theoretical support for the integration of athletes' talent training. The advanced technological methods of big data will also give full play to the advantages of tapping the potenti...

Machine learning models as an alternative to determine productivity losses caused by weeds.

Pest management science
BACKGROUND: Weed control can be economically viable if implemented at the necessary time to minimize interference. Empirical mathematical models have been used to determine when to start the weed control in many crops. Furthermore, empirical models h...

Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.

International journal of computer assisted radiology and surgery
PURPOSE: In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At ...

A Long Short-Term Memory Network Stock Price Prediction with Leading Indicators.

Big data
The accuracy of the prediction of stock price fluctuations is crucial for investors, and it helps investors manage funds better when formulating trading strategies. Using forecasting tools to get a predicted value that is closer to the actual value f...