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Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network.

Computational and mathematical methods in medicine
Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely us...

Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points.

Sensors (Basel, Switzerland)
In the present work, a neuronal dynamic response prediction system is shown to estimate the response of multiple systems remotely without sensors. For this, a set of Neural Networks and the response to the step of a stable system is used. Six basic c...

A Liver Segmentation Method Based on the Fusion of VNet and WGAN.

Computational and mathematical methods in medicine
Accurate segmentation of liver images is an essential step in liver disease diagnosis, treatment planning, and prognosis. In recent years, although liver segmentation methods based on 2D convolutional neural networks have achieved good results, there...

Music video emotion classification using slow-fast audio-video network and unsupervised feature representation.

Scientific reports
Affective computing has suffered by the precise annotation because the emotions are highly subjective and vague. The music video emotion is complex due to the diverse textual, acoustic, and visual information which can take the form of lyrics, singer...

Explaining Predictive Model Performance: An Experimental Study of Data Preparation and Model Choice.

Big data
Although confirmatory modeling has dominated much of applied research in medical, business, and behavioral sciences, modeling large data sets with the goal of accurate prediction has become more widely accepted. The current practice for fitting predi...

PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning.

IEEE transactions on neural networks and learning systems
This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time dela...

ML-Net: Multi-Channel Lightweight Network for Detecting Myocardial Infarction.

IEEE journal of biomedical and health informatics
Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable device...

Synaptic Weight Evolution and Charge Trapping Mechanisms in a Synaptic Pass-Transistor Operation With a Direct Potential Output.

IEEE transactions on neural networks and learning systems
We present an intensive study on the weight modulation and charge trapping mechanisms of the synaptic transistor based on a pass-transistor concept for the direct voltage output. In this article, the pass-transistor concept for a metal-oxide-semicond...

A Robust Deep Learning Segmentation Method for Hematoma Volumetric Detection in Intracerebral Hemorrhage.

Stroke
BACKGROUND AND PURPOSE: Hematoma volume (HV) is a significant diagnosis for determining the clinical stage and therapeutic approach for intracerebral hemorrhage (ICH). The aim of this study is to develop a robust deep learning segmentation method for...

Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network.

Computational and mathematical methods in medicine
The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in...