AIMC Topic: Neural Networks, Computer

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Crowding in humans is unlike that in convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Object recognition is a primary function of the human visual system. It has recently been claimed that the highly successful ability to recognise objects in a set of emergent computer vision systems-Deep Convolutional Neural Networks (DCNNs)-can form...

Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.

Medical & biological engineering & computing
High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning ...

Margin-Based Deep Learning Networks for Human Activity Recognition.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have m...

Environmental Noise Classification Using Convolutional Neural Networks with Input Transform for Hearing Aids.

International journal of environmental research and public health
Hearing aids are essential for people with hearing loss, and noise estimation and classification are some of the most important technologies used in devices. This paper presents an environmental noise classification algorithm for hearing aids that us...

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module.

Sensors (Basel, Switzerland)
Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessar...

Multi-way backpropagation for training compact deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet (He et al., 2016), we are able to train very deep CNNs as the gradient vanishing issue has been largely addressed by the introduction of skip con...

New optimization algorithms for neural network training using operator splitting techniques.

Neural networks : the official journal of the International Neural Network Society
In the following paper we present a new type of optimization algorithms adapted for neural network training. These algorithms are based upon sequential operator splitting technique for some associated dynamical systems. Furthermore, we investigate th...

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs.

Molecules (Basel, Switzerland)
In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorb...