AIMC Topic: Neural Networks, Computer

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iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.

BMC bioinformatics
Enhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement struc...

Recognizing intertwined patterns using a network of spiking pattern recognition platforms.

Scientific reports
Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making...

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

Scientific reports
The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data,...

Defending Person Detection Against Adversarial Patch Attack by Using Universal Defensive Frame.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of person detection networks have been improved dramatically, they are vuln...

A class of doubly stochastic shift operators for random graph signals and their boundedness.

Neural networks : the official journal of the International Neural Network Society
A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper L-boundedness for locally stationary random graph signals, (ii) L-isometry for i.i.d. random graph signals with the asymptotic increa...

One-Class Convolutional Neural Networks for Water-Level Anomaly Detection.

Sensors (Basel, Switzerland)
Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging f...

Efficient Integrity-Tree Structure for Convolutional Neural Networks through Frequent Counter Overflow Prevention in Secure Memories.

Sensors (Basel, Switzerland)
Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNN...

Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks.

Seminars in nuclear medicine
Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment ...

Revisiting graph neural networks from hybrid regularized graph signal reconstruction.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have shown strong graph-structured data processing capabilities. However, most of them are generated based on the message-passing mechanism and lack of the systematic approach to guide their developments. Meanwhile, a uni...

SD-CNN: A static-dynamic convolutional neural network for functional brain networks.

Medical image analysis
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology o...