AIMC Topic: Neural Networks, Computer

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An Improved Vulnerability Exploitation Prediction Model with Novel Cost Function and Custom Trained Word Vector Embedding.

Sensors (Basel, Switzerland)
Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small frac...

Sensitivity - Local index to control chaoticity or gradient globally.

Neural networks : the official journal of the International Neural Network Society
Here, we introduce a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN). We also propose a learning method to adjust it named "sensitivity adjustment learning (SAL)". The index is...

Label-free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will con...

Using distance on the Riemannian manifold to compare representations in brain and in models.

NeuroImage
Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner ...

Autoencoder based self-supervised test-time adaptation for medical image analysis.

Medical image analysis
Deep neural networks have been successfully applied to medical image analysis tasks like segmentation and synthesis. However, even if a network is trained on a large dataset from the source domain, its performance on unseen test domains is not guaran...

QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Molecular diversity
Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph...

Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network.

Sensors (Basel, Switzerland)
As key equipment in modern industry, it is important to diagnose and predict the health status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have achieved excellent performance in recent years compared to traditional me...

Prediction and Early Warning of Regional Coordinated Development Based on Convolution Neural Network Algorithm.

Computational intelligence and neuroscience
In order to respond to the regional coordinated development of the country, it is necessary to put forward a method that can predict and analyze the development trend according to the current development situation. In view of this, the research will ...

Adversarial attack vulnerability of medical image analysis systems: Unexplored factors.

Medical image analysis
Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the...

A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards.

Sensors (Basel, Switzerland)
In modern production environments, advanced and intelligent process monitoring strategies are required to enable an unambiguous diagnosis of the process situation and thus of the final component quality. In addition, the ability to recognize the curr...