AIMC Topic: Neural Networks, Computer

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Variable Stiffness Object Recognition with a CNN-Bayes Classifier on a Soft Gripper.

Soft robotics
Soft grippers significantly widen the palpation capabilities of robots, ranging from soft to hard materials without the assistance of cameras. From a medical perspective, the detection of size and shape of hard inclusions concealed within soft three-...

A zeroing neural dynamics based acceleration optimization approach for optimizers in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
The first-order optimizers in deep neural networks (DNN) are of pivotal essence for a concrete loss function to reach the local minimum or global one on the loss surface within convergence time. However, each optimizer possesses its own superiority a...

BFENet: A two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Early fundus screening and timely treatment of ophthalmology diseases can effectively prevent blindness. Previous studies just focus on fundus images of single eye without utilizing the useful relevant information of the lef...

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events.

Journal of chemical theory and computation
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to ...

Emergence of Direction-Selective Retinal Cell Types in Task-Optimized Deep Learning Models.

Journal of computational biology : a journal of computational molecular cell biology
Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. P...

Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Ireland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore ther...

AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval.

Sensors (Basel, Switzerland)
Image retrieval techniques are becoming famous due to the vast availability of multimedia data. The present image retrieval system performs excellently on labeled data. However, often, data labeling becomes costly and sometimes impossible. Therefore,...

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance.

Sensors (Basel, Switzerland)
This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and t...

Comparative Study of Classification Algorithms for Various DNA Microarray Data.

Genes
Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of ...

Reconstructing high fidelity digital rock images using deep convolutional neural networks.

Scientific reports
Imaging methods have broad applications in geosciences. Scanning electron microscopy (SEM) and micro-CT scanning have been applied for studying various geological problems. Despite significant advances in imaging capabilities, and image processing al...