Titlebook: IT Convergence and Security 2017; Volume 1 Kuinam J. Kim,Hyuncheol Kim,Nakhoon Baek Conference proceedings 2018 Springer Nature Singapore P

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書(shū)目名稱(chēng)IT Convergence and Security 2017影響因子(影響力)




書(shū)目名稱(chēng)IT Convergence and Security 2017影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)IT Convergence and Security 2017網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)IT Convergence and Security 2017網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)IT Convergence and Security 2017被引頻次




書(shū)目名稱(chēng)IT Convergence and Security 2017被引頻次學(xué)科排名




書(shū)目名稱(chēng)IT Convergence and Security 2017年度引用




書(shū)目名稱(chēng)IT Convergence and Security 2017年度引用學(xué)科排名




書(shū)目名稱(chēng)IT Convergence and Security 2017讀者反饋




書(shū)目名稱(chēng)IT Convergence and Security 2017讀者反饋學(xué)科排名

碎石頭

Kuinam J. Kim,Hyuncheol Kim,Nakhoon BaekIncludes supplementary material:

種植,培養(yǎng)

cartilage

監(jiān)禁

Oafishness

Image-Based Content Retrieval via Class-Based Histogram Comparisonss have been proposed in the past, most of them suffer from poor image representation and comparison methods, returning images that match the query image rather poorly when judged by a human. The recent rebirth of deep learning neural networks has been a boon to CBIR, producing much higher quality re

GLADE

Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks anywhere such as workplace, home and even schools. Nevertheless, not all the web contents are appropriate for all users, especially children. An example of these contents is pornography images which should be restricted to certain age group. Besides, these images are not safe for work (NSFW) in whi

GROUP

柔美流暢

AGGER

Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Networkognosis. In this paper, a reliable prediction system for the disease of diabetes is presented using a dropout method to address the overfitting issue. In the proposed method, deep learning neural network is employed where fully connected layers are followed by dropout layers. The proposed neural net