Policy exchange and management for Policy Compliance and Change Detection System in managed service in data networks

Search engines use crawlers to traverse the Web in order to download web pages and build their indexes. Maintaining these indexes up-to-date is an essential task to ensure the quality of search results. However, changes in web pages are unpredictable. Identifying the moment when a web page changes as soon as possible and with minimal computational cost is a major challenge. In this article we present the Web Change Detection system that, in a best case scenario, is capable to detect, almost in real time, when a web page changes. In a worst case scenario, it will require, on average, 12 minutes to detect a change on a low PageRank web site and about one minute on a web site with high PageRank. Meanwhile, current search engines require more than a day, on average, to detect a modification in a web page (in both cases).

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A novel synthetic aperture radar image change detection system using radial basis function-based deep convolutional neural network

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02091-y ◽

2020 ◽

Author(s):

B. Pandeeswari ◽

J. Sutha ◽

M. Parvathy

Keyword(s):

Neural Network ◽

Synthetic Aperture Radar ◽

Change Detection ◽

Convolutional Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Detection System ◽

Synthetic Aperture Radar Image ◽

Synthetic Aperture ◽

Image Change Detection

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An Efficient Web Page Change Detection System Based on an Optimized Hungarian Algorithm

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2007.1014 ◽

2007 ◽

Vol 19 (5) ◽

pp. 599-613 ◽

Cited By ~ 9

Author(s):

Imad Khoury ◽

Rami M. El-Mawas ◽

Oussama El-Rawas ◽

Elias F. Mounayar ◽

Hassan Artail

Keyword(s):

Change Detection ◽

Detection System ◽

Web Page ◽

Hungarian Algorithm

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A Development of Change Detection System for Urban Monitoring

2006 IEEE International Symposium on Geoscience and Remote Sensing ◽

10.1109/igarss.2006.389 ◽

2006 ◽

Author(s):

Seung Joon Kwon ◽

Sung Woong Shin ◽

Kyung Ok Kim ◽

Yong Il Kim ◽

Ki Yun Yoo

Keyword(s):

Change Detection ◽

Detection System ◽

Urban Monitoring

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Development of a remote sensing change detection system based on neighborhood correlation image analysis and intelligent knowledge-based systems

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. ◽

10.1109/igarss.2005.1526438 ◽

2005 ◽

Cited By ~ 1

Author(s):

Jungho Im ◽

J.R. Jensen ◽

J.A. Tullis

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Change Detection ◽

Detection System ◽

Knowledge Based Systems ◽

Knowledge Based ◽

Correlation Image ◽

Neighborhood Correlation

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Scalable Change Detection Using Place-Specific Compressive Change Classifiers

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p0705 ◽

2019 ◽

Vol 23 (4) ◽

pp. 705-714

Author(s):

Kanji Tanaka ◽

Keyword(s):

Change Detection ◽

Large Scale ◽

Detection System ◽

Visual Image ◽

Level Change ◽

Specific Object ◽

Training Examples ◽

Classifier Learning ◽

Object Level

With recent progress in large-scale map maintenance and long-term map learning, the task of change detection on a large-scale map from a visual image captured by a mobile robot has become a problem of increasing criticality. In this paper, we present an efficient approach of change-classifier-learning, more specifically, in the proposed approach, a collection of place-specific change classifiers is employed. Our approach requires the memorization of only training examples (rather than the classifier itself), which can be further compressed in the form of bag-of-words (BoW). Furthermore, through the proposed approach the most recent map can be incorporated into the classifiers by straightforwardly adding or deleting a few training examples that correspond to these classifiers. The proposed algorithm is applied and evaluated on a practical long-term cross-season change detection system that consists of a large number of place-specific object-level change classifiers.

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