AIMC Topic: Neural Networks, Computer

Clear Filters Showing 9261 to 9270 of 31376 articles

An end-to-end convolutional neural network for automated failure localisation and characterisation of 3D interconnects.

Scientific reports
The advancement in the field of 3D integration circuit technology leads to new challenges for quality assessment of interconnects such as through silicon vias (TSVs) in terms of automated and time-efficient analysis. In this paper, we develop a fully...

A memristor fingerprinting and characterisation methodology for hardware security.

Scientific reports
The modern IC supply chain encompasses a large number of steps and manufacturers. In many applications it is critically important that chips are of the right quality and are assured to have been obtained from the legitimate supply chain. To this end,...

Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics.

Frontiers in immunology
BACKGROUND: Recently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagnostic and therapeutic tools available for the disease are insufficient, necessitating the screening ...

LSTM-AE for Domain Shift Quantification in Cross-Day Upper-Limb Motion Estimation Using Surface Electromyography.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and time-varying properties of surface ...

Automated permanent tooth detection and numbering on panoramic radiograph using a deep learning approach.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to assess the performance of the deep learning (DL) model for automated tooth numbering in panoramic radiographs.

Deep Ensembles Are Robust to Occasional Catastrophic Failures of Individual DNNs for Organs Segmentations in CT Images.

Journal of digital imaging
Deep neural networks (DNNs) have recently showed remarkable performance in various computer vision tasks, including classification and segmentation of medical images. Deep ensembles (an aggregated prediction of multiple DNNs) were shown to improve a ...

Continuous online prediction of lower limb joints angles based on sEMG signals by deep learning approach.

Computers in biology and medicine
Continuous online prediction of human joints angles is a key point to improve the performance of man-machine cooperative control. In this study, a framework of online prediction method of joints angles by long short-term memory (LSTM) neural network ...

Development of in silico classification models for binding affinity to the glucocorticoid receptor.

Chemosphere
The endocrine disrupting properties of chemicals acting through the glucocorticoid receptor (GR) have attracted considerable interest. Since there are few data for most chemicals on their endocrine properties in silico approaches seem to be the most ...

Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform.

International journal of biological macromolecules
Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Un...

Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network.

Metabolic engineering
Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent...