AIMC Topic: Neural Networks, Computer

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Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism.

BMC medical imaging
BACKGROUND: A novel multi-level pyramidal pooling residual U-Net with adversarial mechanism was proposed for organ segmentation from medical imaging, and was conducted on the challenging NIH Pancreas-CT dataset.

Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening.

Computational intelligence and neuroscience
With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency ban...

Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model.

Computational intelligence and neuroscience
Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univa...

Spatially Adaptive Feature Refinement for Efficient Inference.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Spatial redundancy commonly exists in the learned representations of convolutional neural networks (CNNs), leading to unnecessary computation on high-resolution features. In this paper, we propose a novel Spatially Adaptive feature Refinement (SAR) a...

Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks.

Environmental research
Removing decolorizing acid blue 113 (AB113) dye from textile wastewater is challenging due to its high stability and resistance to removal. In this study, we used an artificial neural network (ANN) model to estimate the effect of five different varia...

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers.

Computers in biology and medicine
Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system...

Nostradamus: A novel event propagation prediction approach with spatio-temporal characteristics in non-Euclidean space.

Neural networks : the official journal of the International Neural Network Society
The prediction of event propagation has received extensive attention from the knowledge discovery community for applications such as virus spread analytics, social network analysis, earthquake location prediction, and typhoon tracking. The data descr...

GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention.

Sensors (Basel, Switzerland)
We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimat...

Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks.

Sensors (Basel, Switzerland)
Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose ...

A Fluorescent Biosensor for Sensitive Detection of Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network.

Biosensors
In this study, a fluorescent biosensor was developed for the sensitive detection of typhimurium using a low-gradient magnetic field and deep learning via faster region-based convolutional neural networks (R-CNN) to recognize the fluorescent spots on...