AIMC Topic: Neural Networks, Computer

Clear Filters Showing 15301 to 15310 of 31376 articles

Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks.

International journal of neural systems
Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed ...

Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network.

Sensors (Basel, Switzerland)
UNLABELLED: Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) came...

Artificial neural network-based adaptive control for a DFIG-based WECS.

ISA transactions
This paper presents an artificial neural network-based adaptive control approach for a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The control objectives are: (1) extraction of maximum available power from the wi...

Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization.

Big data
One of the world's most widely grown crops is corn. Crop loss due to diseases has a major economic effect, putting the food supply in jeopardy. In many parts of the world, lack of infrastructure still slows disease diagnosis. In this context, effecti...

Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: This study aims to develop and compare the use of deep neural networks (DNN) and support vector machines (SVM) to clinical prognostic scores for prognosticating 30-day mortality and 90-day poor functional outcome (PFO) in spontaneous intra...

Deep graph convolutional network for US birth data harmonization.

Journal of biomedical informatics
In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph base...

A deep learning method for eliminating head motion artifacts in computed tomography.

Medical physics
PURPOSE: Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a ...

Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Sensors (Basel, Switzerland)
Phishing has become one of the biggest and most effective cyber threats, causing hundreds of millions of dollars in losses and millions of data breaches every year. Currently, anti-phishing techniques require experts to extract phishing sites feature...

Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.

Sensors (Basel, Switzerland)
The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related ...

Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network.

Sensors (Basel, Switzerland)
A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of...