AIMC Topic: Neural Networks, Computer

Clear Filters Showing 23751 to 23760 of 31376 articles

Nonlinear analysis and synthesis of video images using deep dynamic bottleneck neural networks for face recognition.

Neural networks : the official journal of the International Neural Network Society
Nonlinear components extracted from deep structures of bottleneck neural networks exhibit a great ability to express input space in a low-dimensional manifold. Sharing and combining the components boost the capability of the neural networks to synthe...

Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection could effectively interrupt HCV transmission. PHI targeting high-risk populations, e.g., people who ...

Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: During needle interventions, successful automated detection of the needle immediately after insertion is necessary to allow the physician identify and correct any misalignment of the needle and the target at early stages, which reduces needl...

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

BMC bioinformatics
BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. T...

diploS/HIC: An Updated Approach to Classifying Selective Sweeps.

G3 (Bethesda, Md.)
Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective s...

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks.

Computational intelligence and neuroscience
Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional metho...

Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

Medical & biological engineering & computing
Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reacti...

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

Molecular pharmaceutics
Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a co...

Leaderless synchronization of coupled neural networks with the event-triggered mechanism.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with leaderless synchronization of coupled delayed neural networks. A distributed event-triggered control strategy under the periodic sampling scheme is introduced to reduce control updates. By introducing a weighted average s...

Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features.

Journal of neuroscience methods
BACKGROUND: Identifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder research, because it is of therapeutic and economic benefits to optimal intervention and management of ...