AIMC Topic: Neural Networks, Computer

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Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network.

Sensors (Basel, Switzerland)
The prevalent convolutional neural network (CNN)-based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. However, these methods may ignore the underlying distribution of clean ima...

EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human-Computer Interactions.

Sensors (Basel, Switzerland)
The parking problem, which is caused by a low parking space utilization ratio, has always plagued drivers. In this work, we proposed an intelligent detection method based on deep learning technology. First, we constructed a TensorFlow deep learning p...

Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariabl...

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning.

International journal of environmental research and public health
The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in...

Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks.

Scientific reports
The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense ...

Merging enzymatic and synthetic chemistry with computational synthesis planning.

Nature communications
Synthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability to leverage rare chemical transformations. This challenge is acute for enzymatic reactions, which...

Sparse RNNs can support high-capacity classification.

PLoS computational biology
Feedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers. Although convergent output-unit like neurons may exist in some biological neural circuits, n...

Handover Optimization Algorithm Based on T2RFS-FNN.

Computational intelligence and neuroscience
As a key technology for highly reliable communication in the fifth generation mobile communication for railway (5G-R) high-speed railway wireless communication system, once the handover fails, it will pose a serious risk to the safe operation of high...

Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach.

PloS one
We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the dr...

Artificial Intelligence in Pediatric Nephrology-A Call for Action.

Advances in kidney disease and health
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical ...