AIMC Topic: Neural Networks, Computer

Clear Filters Showing 20171 to 20180 of 31376 articles

Machine learning goes wild: Using data from captive individuals to infer wildlife behaviours.

PloS one
1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour cl...

Fast Haar Transforms for Graph Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data. However, as GNNs typically define the graph convolution ...

Using artificial neural networks to predict impingement and dislocation in total hip arthroplasty.

Computer methods in biomechanics and biomedical engineering
Dislocation after total hip arthroplasty (THA) remains a major issue and an important post-surgical complication. Impingement and subsequent dislocation are influenced by the design (head size) and position (anteversion and abduction angles) of the a...

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks.

Nano letters
Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experimen...

Multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images.

Medical & biological engineering & computing
Many studies in the rigid gas permeable (RGP) lens fitting field have focused on providing the best fit for patients with irregular astigmatism, a challenging issue. Despite the ease and accuracy of fitting in the current fitting methods, no studies ...

A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).

Expert review of molecular diagnostics
BACKGROUND: A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. T...

Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer.

Sensors (Basel, Switzerland)
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson's Disease (PD) electromyography (EMG) signals. The experimental results indicate...

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions.

Journal of computer-aided molecular design
Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models re...

An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs' Compliance with CFPR Guidelines.

Biosensors
Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an a...

Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye.

Sensors (Basel, Switzerland)
In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the metho...