AIMC Topic: Neural Networks, Computer

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E-Commerce Information System Management Based on Data Mining and Neural Network Algorithms.

Computational intelligence and neuroscience
The rapid development of artificial intelligence technology has led to rapid development in various fields. It has many hidden related customer behavior information and future development trends in the e-commerce information system. The data mining t...

MC-GCN: A Multi-Scale Contrastive Graph Convolutional Network for Unconstrained Face Recognition With Image Sets.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, a Multi-scale Contrastive Graph Convolutional Network (MC-GCN) method is proposed for unconstrained face recognition with image sets, which takes a set of media (orderless images and videos) as a face subject instead of single media (a...

Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Deep feature embedding aims to learn discriminative features or feature embeddings for image samples which can minimize their intra-class distance while maximizing their inter-class distance. Recent state-of-the-art methods have been focusing on lear...

Deep learning based object tracking for 3D microstructure reconstruction.

Methods (San Diego, Calif.)
In medical and material science, 3D reconstruction is of great importance for quantitative analysis of microstructures. After the image segmentation process of serial slices, in order to reconstruct each local structure in volume data, it needs to us...

Calculation of volume fractions regardless scale deposition in the oil industry pipelines using feed-forward multilayer perceptron artificial neural network and MCNP6 code.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
During the production of oil and gas, barium sulfate (BaSO) scale occurs on the inner walls of the tubes leading the reduction of the internal diameter, making the fluid passage difficult and complicating the calculation of volume fractions of fluids...

Can a computer "learn" nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes.

Journal of chromatography. A
The design and optimization of chromatographic processes is essential for enabling efficient separations. To this end, hyperbolic partial differential equations (PDEs) along with nonlinear adsorption isotherms must be solved using computationally exp...

Deep learning approach for bubble segmentation from hysteroscopic images.

Medical & biological engineering & computing
Gas embolism is a potentially serious complication of hysteroscopic surgery. It is particularly necessary to monitor bubble parameters in hysteroscopic images by computer vision method for helping develop automatic bubble removal devices. In this wor...

Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning.

Sensors (Basel, Switzerland)
To identify the unknown values of the parameters of Burger's constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where eac...

Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems.

Sensors (Basel, Switzerland)
Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the ma...

Towards a robust out-of-the-box neural network model for genomic data.

BMC bioinformatics
BACKGROUND: The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and...