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A covering algorithm Based on Competition

Procedia Engineering 2012年 29卷 3175-3178页

作者： Qingsheng Wang Yueqin Zhang Xianyang Sun Bin Liang College of Computer Science and Technology Taiyuan University of Technology No.79 West Yingze Street Taiyuan 030024 China

In covering algorithm, the order of learning samples is random, but experiments show that the learning sequence has significant impact on the network performance. This paper proposes a new algorithm which is covering algorithm based on competition (CAC). In this algorithm, sphere neighborhoods can be adjusted gradually, the ill-suited sphere neighborhoods will be removed and the whole neural network will performs more stable. Finally the algorithm is applied to a widely used database. The experiment results show that it can effectively decrease the number of rejected samples, reduce the number of sphere domains and improve the recognition accuracy.

关键词： neural network covering algorithm learning sequence

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Where To Locate Tethered Aerostats for an Effective Surveillance System: A Case Study on Southern Turkey

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2018年第1期31卷 189-200页

作者： Yilmaz, Nahit Gencer, Cevriye Temel Turkish Gen Staff Logist Branch TR-06654 Ankara Turkey Gazi Univ Dept Ind Engn Fac Engn & Architecture TR-06570 Ankara Turkey

Due to its geostrategic position, security problems such as terrorism and illegal immigration have been experienced in southern Turkey so far. While Turkey uses UAVs to prevent the threats that may come from this region, National Defense Ministry is planning to use alternative technologies such as Tethered Aerostats. Because of the high investment cost, a considerable planning period is required before implementation of these systems. In this study;considering project budget, camera sensor capabilities, geographical analysis data and appropriateness parameters of candidate locations, three scenarios are developed for the site selection problem of Aerostats on southern Turkey. Goal Programming approach including Set covering algorithm and fuzzy-TOPSIS is applied and the results are tested with Viewshed Analysis of GIS. The study results present important recommendations for the probable success of TAs in southern Turkey.

关键词： Site Selection covering algorithm Goal Programming Fuzzy-TOPSIS

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A New Mobile Malware Classification for SMS Exploitation 2nd

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2nd International Conference on Soft Computing and Data Mining (SCDM)

作者： Zaizi, Nurzi Juana Mohd Saudi, Madihah Mohd Khailani, Adiebah USIM Fac Sci & Technol Nilai 71800 Negeri Sembilan Malaysia

ISBN: (纸本)9783319512815;9783319512792

Mobile malware is ubiquitous in many malicious activities such as money stealing. Consumers are charged without their consent. This paper explores how mobile malware exploit the system calls via SMS. As a solution, we proposed a system calls classification based on surveillance exploitation system calls for SMS. The proposed system calls classification is evaluated and tested using applications from Google Play Store. This research focuses on Android operating system. The experiment was conducted using Drebin dataset which contains 5560 malware applications. Dynamic analysis was used to extract the system calls from each application in a controlled lab environment. This research has developed a new mobile malware classification for Android smartphone using a covering algorithm. The classification has been evaluated in 500 applications and 126 applications have been identified to contain malware.

关键词： Mobile malware Data classification Android Big data covering algorithm

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Multi-granular mining for boundary regions in three-way decision theory

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KNOWLEDGE-BASED SYSTEMS 2016年 91卷 287-292页

作者： Chen, Jie Zhang, Yan-ping Zhao, Shu Minist Educ Key Lab Intelligent Comp & Signal Proc Beijing Peoples R China Anhui Univ Ctr Informat Support & Assurance Technol Hefei 230601 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol 111 Jiulong Rd Hefei 230601 Anhui Peoples R China

In three-way decision theory, all samples are divided into three regions: a positive region, a negative region, and boundary regions. A lack of detailed information may make a definite decision impossible for samples in boundary regions, and hence the third non-commitment option is used. Reducing boundary regions is a new problem. In this paper, the multi-granular three-way decision (MGTD) algorithm is presented to reduce boundary regions. At the beginning of the multi-granular process, samples are divided using the covering algorithm, which does not need a threshold. Then pairs of heterogeneous points (HPs) are defined in boundary regions to obtain diversity information. This detailed information is used to define attribute subsets. Eventually, boundary regions are further investigated using multiple-views of granularity. Each view corresponds to an attribute subset. Experiments have shown that the MGTD algorithm is beneficial for reducing boundary regions and improving classification precision in most cases. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Boundary regions Multi-granular three-way decision algorithm covering algorithm Multiple-views of granularity Pairs of heterogeneous points

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A novel trajectory similarity-based approach for location prediction

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2016年第11期12卷 1-1页

作者： Liu, Zelei Hu, Liang Wu, Chunyi Ding, Yan Zhao, Jia Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Changchun Univ Technol Sch Comp Sci & Engn Changchun 130012 Peoples R China INESC TEC High Assurance Software Lab Braga Portugal Univ Minho Braga Portugal

Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people's future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user's social relations in real world and variances in trajectory and then utilize this association to model the user's mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers' trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity-based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity-based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.

关键词： Mobility modeling location prediction social contagion Markov Chain covering algorithm

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Classifying Continuous Classes with Reinforcement Learning RULES 7

Classifying Continuous Classes with Reinforcement Learning R...

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7th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： ElGibreen, Hebah Aksoy, Mehmet Sabih King Saud Univ Dept Informat Technol Coll Comp & Informat Sci Riyadh 11415 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11543 Saudi Arabia

ISBN: (纸本)9783319157054;9783319157047

Autonomous machines are interesting for both researchers and regular people. Everyone wants to have a self control machine that do the work by itself and deal with all types of problems. Thus, supervised learning and classification became important for high-dimensional and complex problems. However, classification algorithms only deals with discrete classes while practical and real-life applications contain continuous labels. Although several statistical techniques in machine learning were applied to solve this problem but they act as a black box and their actions are difficult to justify. covering algorithms (CA), however, is one type of inductive learning that can be used to build a simple and powerful repository. Nevertheless, current CA approaches that deal with continuous classes are bias, non-updatable, overspecialized and sensitive to noise, or time consuming. Consequently, this paper proposes a novel non-discretization algorithm that deal with numeric classes while predicting discrete actions. It is a new version of RULES family called RULES-3C that learns interactively and transfer experience through exploiting the properties of reinforcement learning. This paper will investigate and assess the performance of RULES-3C with different practical cases and algorithms. Friedman test is also applied to rank RULES-3C performance and measure its significance.

关键词： Continuous classes Classification covering algorithm RULES family Reinforcement learning

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Android Mobile Malware Surveillance Exploitation Via Call Logs: Proof of Concept 17

Android Mobile Malware Surveillance Exploitation Via Call Lo...

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17th IEEE UKSim-AMSS International Conference on Computer Modelling and Simulation (UKSim)

作者： Saudi, Madihah Mohd Ridzuan, Farida Basir, Nurlida Nabila, N. F. Pitchay, Sakinah Ali Ahmad, Intan Nurfarahin Univ Sains Islam Malaysia USIM FST Bandar Baru Nilai Nilai Malaysia

ISBN: (纸本)9781479987122

The invention of smartphone have made life easier as it is capable of providing important functions used in user's daily life. While different operating system (OS) platform was built for smartphone, Android has become one of the most popular choice. Nonetheless, it is also the most targeted platform for mobile malware attack causing financial loss to the victims. Therefore, in this research, the exploitation on system calls in Android OS platform caused by mobile malware that could lead to financial loss were examined. The experiment was conducted in a controlled lab environment using open source tools by implementing dynamic analysis on 1260 datasets from the Android Malware Genome Project. Based on the experiment conducted, a new system call classification to exploit call logs for mobile attacks has been developed using covering algorithm. This new system call classification can be used as a reference for other researcher in the same field to secure against mobile malware attacks by exploiting call logs. In the future, this new system call classification could be used as a basis to develop a new model to detect mobile attacks exploitation via call logs.

关键词： system calls similarity analysis exploitation of call logs using system calls covering algorithm data transformation and system call classification

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Incremental Learning for Compressed Pornographic Image Recognition 1

Incremental Learning for Compressed Pornographic Image Recog...

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IEEE First International Conference on Multimedia Big Data

作者： Wang, Chao Zhang, Jing Zhuo, Li Liu, Xin Beijing Univ Technol Signal & Informat Proc Lab Beijing Peoples R China

ISBN: (纸本)9781479986880

A compressed pornographic image recognition method is proposed by using incremental learning. For describing pornographic image, visual words are created from low-resolution (LR) image reconstructed from the compressed stream of the pornographic image. covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic image. At last, incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples. The experimental results show that the proposed incremental learning method for compressed pornographic image has higher recognition rate as well as costs less recognition time.

关键词： recognition incremental learning compressed pornographic image covering algorithm visual words

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Fuzzy Classification algorithm Based on Kernel covering and its Application

Fuzzy Classification Algorithm Based on Kernel Covering and ...

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22nd Chinese Control and Decision Conference

作者： Duan, Zhen Wu, Tao Cheng, Jiaxing Zhang, Ling Anhui Univ Minist Educ Key Lab IC & SP Hefei 230039 Peoples R China

ISBN: (纸本)9781424451821

This paper firstly introduces the basic concept of covering algorithm and kernel covering algorithm (KCA) adopting kernel function, then analyzes the influence of proximity principle used to judge rejection points on classifier's effect. FKCA, i.e. Fuzzy Kernel covering algorithm, is proposed to improve the performance of classifier. The main improvement of FKCA is the change of radius selection and introduction of membership function. Also we discuss the influence of isolated covering and provide a method to reduce the number of coverings by the combination of second scanning with contribution calculation. Experiments show the performance gap between KCA and FKCA, and comparisons with other fuzzy classifiers are also performed. We apply FKCA to character recognition of car plates and the result is satisfactory.

关键词： covering algorithm Kernel Function Fuzzy Kernel covering Character Recognition

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Study of the model of probability-based covering algorithm

Study of the model of probability-based covering algorithm

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Conference on Pattern Recognition and Computer Vision

作者： Zhou, Ying Xie, Yangqun Zhang, Ling Anhui Univ Sch Management Hefei 230039 Anhui Peoples R China Anhui Univ Dept Comp Hefei 230039 Anhui Peoples R China

ISBN: (纸本)9780819469526

Probability-Based covering algorithm (PBCA) is a new algorithm based on probability distribution. It uses the probability of samples and decides the class of the sample on the border of coverage by voting. In the original covering algorithm, there are many tested samples that can't be classified by the spherical neighborhood gained. The network structure of PBCA is mixed structure composed of feed-forward network and feedback network. The method of adding some samples of different class and enlarging the coverage radius is used to decrease the number of refused samples and improve the rates of recognition. The algorithm is effected in improving the study precision.

关键词： covering algorithm constructive study algorithm probability

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