AIMC Topic: Neural Networks, Computer

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Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.

PLoS computational biology
DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of chromosomes. DNA methylation modification sites should be identified, which lays a solid basis for gai...

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

Computational biology and chemistry
Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exh...

Automatic detection and segmentation of chorda tympani under microscopic vision in otosclerosis patients via convolutional neural networks.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Artificial intelligence (AI) techniques, especially deep learning (DL) techniques, have shown promising results for various computer vision tasks in the field of surgery. However, AI-guided navigation during microscopic surgery for real-t...

PEPNet: A barotropic primitive equations-based network for wind speed prediction.

Neural networks : the official journal of the International Neural Network Society
In wind speed prediction technologies, deep learning-based methods have achieved promising advantages. However, most existing methods focus on learning implicit knowledge in a data-driven manner but neglect some explicit knowledge from the physical t...

Hybrid learning mechanisms under a neural control network for various walking speed generation of a quadruped robot.

Neural networks : the official journal of the International Neural Network Society
Legged robots that can instantly change motor patterns at different walking speeds are useful and can accomplish various tasks efficiently. However, state-of-the-art control methods either are difficult to develop or require long training times. In t...

Evaluation and Improvement of Employee Performance with respect to Health, Safety, and Environment (HSE) Factors: A Case of Complex Transport Construction Project.

Computational intelligence and neuroscience
Risk control in complex transport construction is complicated due to the dangerous nature of high variation and unpredictability. Most of the current research analysis focuses on the health, safety, and environment (HSE) risk assessment and employee ...

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T.

NMR in biomedicine
At ultrahigh field strengths images of the body are hampered by B -field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a "bias field" to the ideal image. Current bias field correction m...

Enhancing neurodynamic approach with physics-informed neural networks for solving non-smooth convex optimization problems.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a deep learning approach for solving non-smooth convex optimization problems (NCOPs), which have broad applications in computer science, engineering, and physics. Our approach combines neurodynamic optimization with physics-inform...

PIMedSeg: Progressive interactive medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate object segmentation in medical images is a crucial step in medical diagnosis and other applications. Despite years of research on automatic segmentation approaches, achieving clinically acceptable image quality rema...

DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters.

Neural networks : the official journal of the International Neural Network Society
Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relati...