Performance Evaluation of Location Management in GSM Networks
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Sun Mar 01 2020
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Computer Networks
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Telecommunication Systems
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Mon Jan 28 2019
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Soft Computing
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Thu Dec 05 2019
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Advances In Intelligent Systems And Computing
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Sat Apr 01 2023
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Digital Communications And Networks
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Sat Aug 25 2012
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Wireless Personal Communications
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Tue Mar 25 2014
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Sensors
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Thu Dec 01 2011
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Swarm And Evolutionary Computation
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Mon Oct 05 2020
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International Journal Of Advanced Science And Technology
Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
Publication Date
Fri May 01 2020
Journal Name
International Journal Of Advanced Science And Technology
Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
Zernike Moments
Deep learning
Convolutional Neural Networks
Wavelet
transforms
Slanlet transform
Informational retrieval
Background/Objectives: The purpose of current research aims to a modified image representation framework for Content-Based Image Retrieval (CBIR) through gray scale input image, Zernike Moments (ZMs) properties, Local Binary Pattern (LBP), Y Color Space, Slantlet Transform (SLT), and Discrete Wavelet Transform (DWT). Methods/Statistical analysis: This study surveyed and analysed three standard datasets WANG V1.0, WANG V2.0, and Caltech 101. The features an image of objects in this sets that belong to 101 classes-with approximately 40-800 images for every category. The suggested infrastructure within the study seeks to present a description and operationalization of the CBIR system through automated attribute extraction system premised on CN
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