AIMC Topic: Neural Networks, Computer

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Classification-based prediction of network connectivity robustness.

Neural networks : the official journal of the International Neural Network Society
Today, there is an increasing concern about malicious attacks on various networks in society and industry, against which the network robustness is critical. Network connectivity robustness, in particular, is of fundamental importance, which is genera...

The effect of Gaussian noise on pneumonia detection on chest radiographs, using convolutional neural networks.

Radiography (London, England : 1995)
INTRODUCTION: Chest X-rays (CXR) with under-exposure increase image noise and this may affect convolutional neural network (CNN) performance. This study aimed to train and validate CNNs for classifying pneumonia on CXR as normal or pneumonia acquired...

Towards Interpretable Camera and LiDAR Data Fusion for Autonomous Ground Vehicles Localisation.

Sensors (Basel, Switzerland)
Recent deep learning frameworks draw strong research interest in application of ego-motion estimation as they demonstrate a superior result compared to geometric approaches. However, due to the lack of multimodal datasets, most of these studies prima...

Multi-objective data enhancement for deep learning-based ultrasound analysis.

BMC bioinformatics
Recently, Deep Learning based automatic generation of treatment recommendation has been attracting much attention. However, medical datasets are usually small, which may lead to over-fitting and inferior performances of deep learning models. In this ...

Embedding cognitive framework with self-attention for interpretable knowledge tracing.

Scientific reports
Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to...

Flexible learning of quantum states with generative query neural networks.

Nature communications
Deep neural networks are a powerful tool for characterizing quantum states. Existing networks are typically trained with experimental data gathered from the quantum state that needs to be characterized. But is it possible to train a neural network of...

Deep learning-based classification for lung opacities in chest x-ray radiographs through batch control and sensitivity regulation.

Scientific reports
In this study, we implemented a system to classify lung opacities from frontal chest x-ray radiographs. We also proposed a training method to address the class imbalance problem presented in the dataset. We participated in the Radiological Society of...

PM2.5 forecasting for an urban area based on deep learning and decomposition method.

Scientific reports
Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the hea...

A real-time driver fatigue identification method based on GA-GRNN.

Frontiers in public health
It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification ba...

Spike-Based Approximate Backpropagation Algorithm of Brain-Inspired Deep SNN for Sonar Target Classification.

Computational intelligence and neuroscience
With the development of neuromorphic computing, more and more attention has been paid to a brain-inspired spiking neural network (SNN) because of its ultralow energy consumption and high-performance spatiotemporal information processing. Due to the d...