Graph representation learning based on deep generative gaussian mixture models

Niknam, Ghazaleh; Molaei, Soheila; Zare, Hadi; Clifton, David A.; Pan, Shirui

doi:10.1016/j.neucom.2022.11.087

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…Microstructure is main ice core's primary characteristics. Ice-core microstructure holds priceless information on optical characteristics, melting events, global warming [12,13].The transition from snow to ice usually happens in the upper 120 metres [14]. Therefore, research into this depth range is required to comprehend the driving processes.…”

Section: Introductionmentioning

confidence: 99%

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Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model

Haiqin Zhu

2024

jes

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Polar ice sheets, or ice cores, are among the most well-known natural archives that might provide crucial historical details about our planet's previous environment. An important factor in establishing the fundamental characteristics of ice, likes pore close-off, albedo, melt events, is the ice-core microstructure. To engulf these complications Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model (ICMCTS-WSOA-DSSDNN-GMM)is proposed. Initially, micro scale CT images are collect from Alfred Wegener Institute ice-core storage as input. Then,data’s are given to pre-processing. In pre-processing, it enhance image brightness, remove salt pepper noise, crop outer ring (carbon fiber casing) to have only ice particles in image utilizing Federated Neural Collaborative Filtering (FNCF).The pre-processing output is given to segmentation. Here, micro scale CT image is utilized for segmenting high-resolution scans using Gaussian Mixture Model (GMM). After that, the segmented images are given to Dual Stream Spectrum Deconvolution Neural Network optimized with Water Strider Optimization Algorithm for classifying micro scale CT images as sintered snow, compacted firn and bubbly ice. The proposed ICMCTS-WSOA-DSSDNN-GMM method is executed in python. The performance of ICMCTS-WSOA-DSSDNN-GMM approach attains 16.24%, 17.90% and 27.7% high accuracy, 14.04%, 25.51% and 19.31% higher precision and 14.36%, 12.65%, 14.51% higher recall analyzed with existing techniques likes Ice-Core Micro-CT Image Segmentation With Deep Learning and Gaussian Mixture Method (ICMCTS-U-net-GMM), Computer Aided Detection of COVID 19 from CT Images Depend on Gaussian Mixture Method with Kernel Support Vector Machines Classifer (ICMCTS-KNN-GMM) and the Gmmseg: Gaussian mixture based generative semantic segmentation methods(ICMCTS-FCN-GMM) respectively.

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Section: Introductionmentioning

confidence: 99%

“…Loss function is used mean-Square error (MSE). In equation(13), the parameter serves an initial value determining the learning pace as follows,…”

mentioning

confidence: 99%

Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model

Haiqin Zhu

2024

jes

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show abstract

“…Mixture models such as Gaussian Mixture Models (GMM) are an absolute solution for these kinds of datasets. These models describe the probability distribution of observations in the whole population [12,13,14].…”

Section: Introductionmentioning

confidence: 99%