AIMC Topic: Neural Networks, Computer

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Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network.

Computational intelligence and neuroscience
Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of thi...

An Optimization Analysis Model of Tourism Specialized Villages Based on Neural Network and System Dynamics.

Computational intelligence and neuroscience
With the rapid development of tourism, professional tourism villages emerge one after another, which has become the focus of the tourism industry. At present, there are some problems in tourism professional villages, such as imperfect management and ...

Ensemble model for rail surface defects detection.

PloS one
The detection of rail surface defects is vital for high-speed rail maintenance and management. The CNN-based computer vision approach has been proved to be a strong detection tool widely used in various industrial scenarios. However, the CNN-based de...

Tackling the class imbalance problem of deep learning-based head and neck organ segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: The segmentation of organs at risk (OAR) is a required precondition for the cancer treatment with image- guided radiation therapy. The automation of the segmentation task is therefore of high clinical relevance. Deep learning (DL)-based medi...

Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Salmon and Cod are economically significant world-class fish that have high economic value. It is difficult to accurately sort and process them by appearance during harvest and transportation. Conventional chemical detection means are time-consuming ...

Detection of adulteration in mutton using digital images in time domain combined with deep learning algorithm.

Meat science
A novel method based on digital images in time domain combined with convolutional neural network (CNN) is proposed for discrimination and analysis of the adulterated mutton. For this, 195 sample images during the constant temperature heating process ...

Sampled-data synchronization of complex network based on periodic self-triggered intermittent control and its application to image encryption.

Neural networks : the official journal of the International Neural Network Society
The aim of this paper is to investigate exponential synchronization issue of time-varying multi-weights network with time delays (TMNTD) via periodic self-triggered intermittent sampled-data control. In particular, it is the first time to combine per...

Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition.

Journal of neuroscience methods
BACKGROUND: Multimedia stimulation of brain activity is important for emotion induction. Based on brain activity, emotion recognition using EEG signals has become a hot issue in the field of affective computing.

Probabilistic Seismic Response Prediction of Three-Dimensional Structures Based on Bayesian Convolutional Neural Network.

Sensors (Basel, Switzerland)
Seismic response prediction is a challenging problem and is significant in every stage during a structure's life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural ne...

Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.

BMC psychiatry
BACKGROUND: The debate of whether machine learning models offer advantages over standard statistical methods when making predictions is ongoing. We discuss the use of a meta-learner model combining both approaches as an alternative.