AIMC Topic: Neural Networks, Computer

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Analysis Model of Image Colour Data Elements Based on Deep Neural Network.

Computational intelligence and neuroscience
At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency bu...

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model.

Computational intelligence and neuroscience
Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependabili...

All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks.

Computational intelligence and neuroscience
Coronary heart disease (CHD) has become one of the most serious public health issues due to its high morbidity and mortality rates. Most of the existing coronary heart disease risk prediction models manually extract features based on shallow machine ...

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.

BioMed research international
Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the...

Auxiliary Pneumonia Classification Algorithm Based on Pruning Compression.

Computational and mathematical methods in medicine
Pneumonia infection is the leading cause of death in young children. The commonly used pneumonia detection method is that doctors diagnose through chest X-ray, and external factors easily interfere with the results. Assisting doctors in diagnosing pn...

Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.

NMR in biomedicine
Self-supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self-supervised learning methods for physics-guided recons...

Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition.

Biomolecules
Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented...

PDE-READ: Human-readable partial differential equation discovery using deep learning.

Neural networks : the official journal of the International Neural Network Society
PDE discovery shows promise for uncovering predictive models of complex physical systems but has difficulty when measurements are noisy and limited. We introduce a new approach for PDE discovery that uses two Rational Neural Networks and a principled...

Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy.

Neural networks : the official journal of the International Neural Network Society
Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as adversarial training) greatly hurt generalization performance on the un...

An analysis of the influence of transfer learning when measuring the tortuosity of blood vessels.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Convolutional Neural Networks (CNNs) can provide excellent results regarding the segmentation of blood vessels. One important aspect of CNNs is that they can be trained on large amounts of data and then be made available, fo...