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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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书目名称IT Convergence and Security 2017
副标题Volume 1
编辑Kuinam J. Kim,Hyuncheol Kim,Nakhoon Baek
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Electrical Engineering
图书封面Titlebook: IT Convergence and Security 2017; Volume 1 Kuinam J. Kim,Hyuncheol Kim,Nakhoon Baek Conference proceedings 2018 Springer Nature Singapore P
描述.This is the first volume of proceedings including selected papers from the International Conference on IT Convergence and Security (ICITCS) 2017, presenting a snapshot of the latest issues encountered in this field. It explores how IT convergence and security issues are core to most current research, and industrial and commercial activities. It consists of contributions covering topics such as machine learning & deep learning, communication and signal processing, computer vision and applications, future network technology, artificial intelligence and robotics.. .ICITCS 2017 is the latest in a series of highly successful International Conferences on IT Convergence and Security, previously held in Prague, Czech Republic(2016), Kuala Lumpur, Malaysia (2015) Beijing, China (2014), Macau, China (2013), Pyeong Chang, Korea (2012), and Suwon, Korea (2011)..
出版日期Conference proceedings 2018
关键词ICITCS 2017; Machine Learning & Deep Learning; Communication and Signal Processing; Computer Vision and
版次1
doihttps://doi.org/10.1007/978-981-10-6451-7
isbn_softcover978-981-13-4881-5
isbn_ebook978-981-10-6451-7Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightSpringer Nature Singapore Pte Ltd. 2018
The information of publication is updating

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