AIMC Topic: Neural Networks, Computer

Clear Filters Showing 10801 to 10810 of 31376 articles

Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain.

Computational intelligence and neuroscience
Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully...

An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images.

Journal of healthcare engineering
Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Present...

A deep learning generative model approach for image synthesis of plant leaves.

PloS one
OBJECTIVES: A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this s...

Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution.

Journal of digital imaging
Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RAN...

Artificial neural networks in contemporary toxicology research.

Chemico-biological interactions
Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed,...

A fractional gradient descent algorithm robust to the initial weights of multilayer perceptron.

Neural networks : the official journal of the International Neural Network Society
For multilayer perceptron (MLP), the initial weights will significantly influence its performance. Based on the enhanced fractional derivative extend from convex optimization, this paper proposes a fractional gradient descent (RFGD) algorithm robust ...

The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage.

Journal of the mechanical behavior of biomedical materials
The growing health and economic burden of bone fractures, their intricate multiscale features and the existing knowledge gaps in the comprehension of micro-scale bone damage occurrence make fracture diagnosis a challenging issue. In this scenario, de...

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Acta biomaterialia
Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods appr...

Deffini: A family-specific deep neural network model for structure-based virtual screening.

Computers in biology and medicine
Deep learning-based virtual screening methods have been shown to significantly improve the accuracy of traditional docking-based virtual screening methods. In this paper, we developed Deffini, a structure-based virtual screening neural network model....

Machine Learning with Neural Networks to Enhance Selectivity of Nonenzymatic Electrochemical Biosensors in Multianalyte Mixtures.

ACS applied materials & interfaces
Nonenzymatic biosensors hold great potential in the field of analysis and detection due to long-term stability, high sensitivity, and low cost. However, the relative low selectivity, especially the overlapped oxidation peaks of biomarkers, in the bio...