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1st International Conference on Big Data Computing and Communications (BigCom)

作者： Wang, Lin Hawbani, Ammar Wang, Xingfu Univ Sci & Technol China Comp Sci & Technol Hefei 230022 Anhui Peoples R China

ISBN: (纸本)9783319220475;9783319220468

Currently, Most back-end web databases cannot be indexed by traditional hyperlink-based search engines due to their requirement of users' interactive queries via page form submission. In order to make hidden-Web information more easily accessible, this paper proposes a hierarchical classifier to locate domain-specific hidden Web entries at a large scale. The classifier is trained by appropriately selected page form features to get rid of non-relevant domains and non-searchable forms. Experiments conducted on eight different topics demonstrate that the technique can discover deep web interfaces accurately and efficiently.

关键词： Deep web Focused crawler Searchable forms HTML analysis SVM classifier decision tree algorithm

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Storage-Efficient tree Structure with Level-Ordered Unary Degree Sequence for Packet Classification 3

Storage-Efficient Tree Structure with Level-Ordered Unary De...

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3rd International Symposium on Computing and Networking (CANDAR)

作者： Kitamura, Yuta Iwata, Akira Mohri, Masami Shiraishi, Yoshiaki Nagoya Inst Technol Dept Sci & Engn Simulat Nagoya Aichi Japan Gifu Univ Informat & Multimedia Ctr Gifu Japan Kobe Univ Dept Elect & Elect Engn Kobe Hyogo Japan

ISBN: (纸本)9781467397971

Packet classification is an essential function in Internet routers that provides advanced network services such as virtual private networks (VPNs) and quality of service (QoS). decision tree-based packet classification algorithms such as HiCuts and HyperCuts allow the tradeoff between storage and throughput in the experimental result with the real life rulesets. However, the memory consumption of these algorithms remains high when high throughput is required. In this paper, we propose the tree data structure whose storage is small maintaining high throughput. It is based on the two ideas: The first is to use Level Ordered Unary Degree Sequence (LOUDS) which is a succinct representation of a tree structure. A general tree of n nodes is usually represented in pointer form, requiring 64n bits in 64-bit architecture, whereas LOUDS requires 2n+o(n) bits. Because LOUDS can access any child node from its parent one in constant time, the tree traversal is fast. The second is reducing the information contained in the tree node. This also reduces storage requirements without affecting search speed. The proposed data structure requires up to approximately one ninth less memory than pointer-formed tree, and the time complexity of a packet classification is equivalent to the pointer-formed tree.

关键词： packet classification decision tree algorithm succinct representation level-ordered unary degree sequence

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Improved edge detection algorithm based on decision tree

Improved edge detection algorithm based on decision tree

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International Conference on Mechatronics and Industrial Informatics (ICMII 2013)

作者： Cai Aiping Jiangxi Univ Technol Dept Digital Technol Nanchang 330029 Peoples R China

ISBN: (纸本)9783037856949

The edge detection is the important role in the image disposal. Traditional methods had some limitations more or less in practical applications such as pseudo-edge or need setting parameters by manual. Now, proposed a method can solve these problems in this paper. The histogram of gradient effective features was selected to composite the feature space, and during the process of classifier training, combined with AdaBoost and decision tree algorithm to improve the classification accuracy. Finally, the application of the algorithm proposed to image of Lena edge detection and comparative experimental show that the algorithm has better self-adaptability and good edge can be detected through this new algorithm.

关键词： edge detection AdaBoost algorithm decision tree algorithm

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Rapid and automated determination of rusted surface areas of a steel bridge for robotic maintenance systems

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AUTOMATION IN CONSTRUCTION 2014年 42卷 13-24页

作者： Son, Hyojoo Hwang, Nahyae Kim, Changmin Kim, Changwan Chung Aug Univ Dept Architectural Engn Seoul 156756 South Korea

There has been an increased interest in the use of robotic systems to automate the blasting tasks in steel bridge maintenance. To utilize such robotic systems effectively, an automated process for determining the rusted areas on a steel bridge to be blasted is a prerequisite. This study proposes a method to rapidly and accurately determine rusted surface areas on a steel bridge that are to be blasted, within current standards. The proposed method consists of three steps: color space conversion, classification of rusted area via the J48 decision tree algorithm, and determination of blasting area. The method was validated using 119 test images showing both normal states and various degrees of rusting and rust distribution types. The experimental results showed that the success rate for determining the rusted areas that needed blasting was 97.48%. The average processing time was 0.57 s/image. The results demonstrate that the proposed method rapidly and accurately indicated whether blasting was necessary, and if so, where blasting should be performed, based on current standards of practice. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Rust detection Rusted surface area determination Steel bridge inspection and maintenance Color space conversion decision tree algorithm

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The Application of decision tree in Security Audit

The Application of Decision Tree in Security Audit

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2012 International Conference Mechatronic Systems and Materials Application(ICMSMA 2012)

作者： Dan Shao Gang Ma Department of Computer Changchun University Network Information Center Beijing University of Posts and Telecommunications

Network security audit is an important part in network security system, which, to some extent, makes up for the deficiencies in firewalls and intrusion detectionsAccording to the characteristics of the network data, the traditional decision tree model is modified, and the application pattern in which the decision tree model is used to network data security audit is also proposedSimulations show that the decision tree classification model not only is adaptive to the characteristics of network data, but also can prevent malicious attacks effectivelyThe effects of the attributes such as IP, port, and protocol type on classification are also uncovered.

关键词： Network data Security audit decision tree algorithm Classification of data

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Fast Numerical Threshold Search algorithm for C4.5 3

Fast Numerical Threshold Search Algorithm for C4.5

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3rd IIAI International Conference on Advanced Applied Informatics (IIAI-AAI)

作者： Chong, Wen-Mau Goh, Chien-Le Bau, Yoon-Teck Lee, Kian-Chin Multimedia Univ Fac Comp & Informat Cyberjaya Malaysia

ISBN: (纸本)9781479941735

This paper presents a new algorithm to improve the speed of threshold searching process in C4.5 by using the technique of genetic algorithms. In the threshold searching process in C4.5, the values in a numerical attribute are sorted first and then the mid-point between every two consecutive values is calculated and designated as a candidate threshold. This process can be time consuming and it is not practical for large data. Our algorithm generates a population of possible thresholds and converges to the best threshold value rapidly. Our experimental results have shown that significant time reduction has been achieved by using our algorithm in threshold searching process.

关键词： classification decision tree algorithm numerical attribute genetic algorithm

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Privacy Threats from Social Networking Service Aggregators 5

Privacy Threats from Social Networking Service Aggregators

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5th Cybercrime and Trustworthy Computing Conference CTC

作者： Jaafor, Omar Birregah, Babiga Lemercier, Marc Perez, Charles Univ Technol Troyes Charles Delaunay Inst UMR CNRS 6281 Troyes France ESG Management Sch Paris France

ISBN: (纸本)9781479988259

Social networking services (SNS) have increased in popularity over the last decade. They have become major platforms for e-commerce, personal branding, socialization and information. The success of social networking services like Facebook and Twitter as well as LinkedIn, LiveJournal and Foursquare and the variety of their usages leads their users to create a set of profiles on different SNS. Recently, social networking service aggregators have proposed centralizing the multiple social networking profiles of a given user in order to facilitate his interactions with social networking services. Such aggregators allow the messages received by a profile over multiple SNS to be retrieved, edited and posted with much less effort. Despite their obvious advantages, we highlight in this paper the risk of potential data leaks due to the inexperienced use of such tools. For this purpose, we provide a classification of online SNS and present their specificities with regard to the publicly exposed data of a user. Based on this classification, we investigate the possible insecure use of aggregators with an inappropriate set of SNS, which could lead to rendering sensitive data accessible to people it wasn't intended for. We present a decision tree approach for identifying a possible data leak based on the three following criteria: opinion, interest and location. We finally show the result of this approach on popular social networking aggregators.

关键词： Social networking service social aggregators social network classification social networking based data disclosure decision tree algorithm

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Exploiting sound signals for fault diagnosis of bearings using decision tree

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MEASUREMENT 2013年第3期46卷 1250-1256页

作者： Amarnath, M. Sugumaran, V. Kumar, Hemantha Indian Inst Informat Technol Design & Mfg Jabalpur Madhya Pradesh India VIT Univ Chennai 600048 Tamil Nadu India Birla Inst Technol & Sci Pilani Goa 403726 India

Bearing is one of vital transmission elements, finding numerous applications in small, medium and large machineries. Sound and vibration signals of a rotating machine contain the dynamic information about its operating condition. There are many articles in the literature reporting suitability of vibration signals for fault diagnosis applications;however, the transducer used for vibration signals (accelerometers) is costly. This prevents small scale industries and low cost equipments from using diagnostic tools on affordability ground. On the other hand, transducers used for acquiring sound signals are relatively low cost or/and affordable. Hence, there is a need for studying the use of sound signal for fault diagnosis applications. This paper uses acoustic signals (sound) acquired from near field area of bearings in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from sound signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using C4.5 decision tree algorithm. The paper also discusses the effect of features, effect of various classifier parameters on classification accuracy. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Bearing fault diagnosis decision tree algorithm Feature selection Machine learning approach

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Fault Diagnosis of Welded Joints through Vibration Signals Using Naïve Bayes algorithm

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Procedia Materials Science 2014年 5卷 1922-1928页

作者： M. Girish Kumar K. Hemanth N. Gangadhar Hemantha Kumar Prasad Krishna Nano Manufacturing Technology Centre Central Manufacturing Technology Institute Tumkur RoadBangalore-560022India Department of Mechnaical Engineering National Institute of Technology Karanataka Surathkal Mangalore-575025 India

Inspection of welded joints plays a crucial role in finding internal flaws and defects in specimens, finding quality of welded joint in early stage will reduce damage and provides safety in many manufacturing industries. For this reason many testing methods have been used to test a material for surface or internal flaws without interfering in any way with its suitability for service. Vibration analysis can be used to identify the defect in welded joints. This paper uses the vibration signals acquired using the accelerometer in welded specimen for defect free welded joint and induced defect in welded joint for fault diagnosis through machine learning approach for online welded joint quality monitoring. The statistical features were extracted from the vibration signals. Significantly important features were selected using decision tree (J48) algorithm and Naive Bayes algorithm is used as a classifier. The selected features were given as inputs for the classifier and their classification accuracies were compared. Results of Naive Bayes Classifier algorithm were used to learn and classify the condition of welded joints, also found its accuracy as of 85.56%. Hence, the Naive Bayes Classifier model can be practically used for diagnosing the condition of the welded joint quality successfully.

关键词： Condition monitoring decision tree algorithm Accelerometer feature selection Naïve Bayes algorithm.

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The Application of decision tree in security audit

The Application of decision tree in security audit

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3rd International Conference on Advanced Design and Manufacturing Engineering (ADME 2013)

作者： Dan, Shao Bai, Wang Yan Changchun Univ Dept Comp Changchun Peoples R China

ISBN: (纸本)9783037858431

Network security audit is an important part in network security system, which, to some extent, makes up for the deficiencies in firewalls and intrusion detections. According to the characteristics of the network data, the traditional decision tree model is modified, and the application pattern in which the decision tree model is used to network data security audit is also proposed. Simulations show that the decision tree classification model not only is adaptive to the characteristics of network data, but also can prevent malicious attacks effectively. The effects of the attributes such as IP, port, and protocol type on classification are also uncovered.

关键词： Network data Security audit decision tree algorithm Classification of data

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