AIMC Topic: Neural Networks, Computer

Clear Filters Showing 22471 to 22480 of 31376 articles

Asynchronous event-based sampling data for impulsive protocol on consensus of non-linear multi-agent systems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we discuss the consensus problem of non-linear multi-agent systems where an impulsive protocol with event-based asynchronously sampled data is adopted. Systems that communicate by data asynchronously sampled in limited time intervals a...

Equivalence between dropout and data augmentation: A mathematical check.

Neural networks : the official journal of the International Neural Network Society
The great achievements of deep learning can be attributed to its tremendous power of feature representation, where the representation ability comes from the nonlinear activation function and the large number of network nodes. However, deep neural net...

A deep learning-based decision support system for diagnosis of OSAS using PTT signals.

Medical hypotheses
Sleep disorders, which negatively affect an individual's daily quality of life, are a common problem for most of society. The most dangerous sleep disorder is obstructive sleep apnea syndrome (OSAS), which manifests itself during sleep and can cause ...

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype.

IEEE transactions on biomedical circuits and systems
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since b...

A data-driven approach to referable diabetic retinopathy detection.

Artificial intelligence in medicine
UNLABELLED: Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize.

Cardiac Rhythm Device Identification Using Neural Networks.

JACC. Clinical electrophysiology
OBJECTIVES: This paper reports the development, validation, and public availability of a new neural network-based system which attempts to identify the manufacturer and even the model group of a pacemaker or defibrillator from a chest radiograph.

A multi hidden recurrent neural network with a modified grey wolf optimizer.

PloS one
Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are...

Application of deep learning-based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy.

European radiology
OBJECTIVES: To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest radiographs obtained after percutaneous transthoracic needle biopsy (PTNB) for pulmonary lesions.

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.)
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...

Fully complex conjugate gradient-based neural networks using Wirtinger calculus framework: Deterministic convergence and its application.

Neural networks : the official journal of the International Neural Network Society
Conjugate gradient method has been verified to be one effective strategy for training neural networks due to its low memory requirements and fast convergence. In this paper, we propose an efficient conjugate gradient method to train fully complex-val...