AIMC Topic: Neural Networks, Computer

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Unsupervised convolutional variational autoencoder deep embedding clustering for Raman spectra.

Analytical methods : advancing methods and applications
Unsupervised deep learning methods place increased emphasis on the process of cluster analysis of unknown samples without requiring sample labels. Clustering algorithms based on deep embedding networks have been recently developed and are widely used...

Pavement Disease Detection through Improved YOLOv5s Neural Network.

Computational intelligence and neuroscience
An improved Ghost-YOLOv5s detection algorithm is proposed in this paper to solve the problems of high computational load and undesirable recognition rate in the traditional detection methods of pavement diseases. Ghost modules and C3Ghost are introdu...

Construction and Application of the Financial Early-Warning Model Based on the BP Neural Network.

Computational intelligence and neuroscience
In order to further improve the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crisis, by taking listed companies as examples and combining the operating conditions of listed companies...

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition.

Computational intelligence and neuroscience
Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural n...

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.

Computational intelligence and neuroscience
Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately refle...

Evaluation of a Self-Supervised Machine Learning Method for Screening of Particulate Samples: A Case Study in Liquid Formulations.

Journal of pharmaceutical sciences
Imaging is commonly used as a characterization method in the pharmaceuticals industry, including for quantifying subvisible particles in solid and liquid formulations. Extracting information beyond particle size, such as classifying morphological sub...

Image based beef and lamb slice authentication using convolutional neural networks.

Meat science
Meat adulteration affects customers and the market. Existing meat authentication methods usually rely on special devices, and thus are limited to professional use only. Fake lamb or beef slices made from duck and fat appear in some Chinese hotpot res...

E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation.

Computers in biology and medicine
BACKGROUND: U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by tradition...

The -MDA: An Invariant to Shifting, Scaling, and Rotating Variance for 3D Object Recognition Using Diffractive Deep Neural Network.

Sensors (Basel, Switzerland)
The diffractive deep neural network (DNN) can efficiently accomplish 2D object recognition based on rapid optical manipulation. Moreover, the multiple-view DNN array (MDA) possesses the obvious advantage of being able to effectively achieve 3D object...

HRpI System Based on Wavenet Controller with Human Cooperative-in-the-Loop for Neurorehabilitation Purposes.

Sensors (Basel, Switzerland)
There exist several methods aimed at human-robot physical interaction (HRpI) to provide physical therapy in patients. The use of haptics has become an option to display forces along a given path so as to it guides the physiotherapist protocol. Critic...