AIMC Topic: Neural Networks, Computer

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Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models.

Sensors (Basel, Switzerland)
Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) t...

Novel Methodology to Recover Road Surface Height Maps from Illuminated Scene through Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Road surface properties have a major impact on pavement's life service conditions. Nowadays, contactless techniques are widely used to monitor road surfaces due to their portability and high precision. Among the different possibilities, laser profilo...

SiamOT: An Improved Siamese Network with Online Training for Visual Tracking.

Sensors (Basel, Switzerland)
As a prevailing solution for visual tracking, Siamese networks manifest high performance via convolution neural networks and weight-sharing schemes. Most existing Siamese networks have adopted various offline training strategies to realize precise tr...

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data.

International journal of environmental research and public health
Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect public safety. It is helpful for traffic operation and management to predict the duration of traffic incidents. Most of the previous studies have been in a c...

Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.

BMC bioinformatics
BACKGROUND: Presently, there is a wide variety of classification methods and deep neural network approaches in bioinformatics. Deep neural networks have proven their effectiveness for classification tasks, and have outperformed classical methods, but...

3D convolutional neural network for machining feature recognition with gradient-based visual explanations from 3D CAD models.

Scientific reports
In the manufacturing industry, all things related to a product manufactured are generated and managed with a three-dimensional (3D) computer-aided design (CAD) system. CAD models created in a 3D CAD system are represented as geometric and topological...

One shot ancient character recognition with siamese similarity network.

Scientific reports
Ancient character recognition is not only important for the study and understanding of ancient history but also has a profound impact on the inheritance and development of national culture. In order to reduce the study of difficult professional knowl...

Deep learning for twelve hour precipitation forecasts.

Nature communications
Existing weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasin...

A Deeply Supervised Convolutional Neural Network for Pavement Crack Detection With Multiscale Feature Fusion.

IEEE transactions on neural networks and learning systems
Automatic crack detection is vital for efficient and economical road maintenance. With the explosive development of convolutional neural networks (CNNs), recent crack detection methods are mostly based on CNNs. In this article, we propose a deeply su...

Application Based on Artificial Intelligence in Substation Operation and Maintenance Management.

Computational intelligence and neuroscience
To fulfill state grid Industry's demands for smart and digitized business growth, traditional technological approaches have fallen short. Artificial intelligence (AI) technology enables coming up with solutions because electricity business types and ...