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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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Bilinear grid search strategy based support vector machines learning method

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Informatica (Slovenia) 2014年第1期38卷 51-58页

作者： Li, Lin Zhang, Xiaolong Zhang, Kai Liu, Jun School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Prov. Key Lab. of Intelligent Information Processing and Real-time Industrial System China

Support Vector Machines (SVM) learning can be used to construct classification models of high accuracy. However, the performance of SVM learning should be improved. This paper proposes a bilinear grid search method to achieve higher computation efficiency in choosing kernel parameters (C, γ) of SVM with RBF kernel. Experiments show that the proposed method retains the advantages of a small number of training SVMs of bilinear search and the high prediction accuracy of grid search. It has been proved that bilinear grid search method (BGSM) is an effective way to train SVM with RBF kernel. With the application of BGSM, the protein secondary structure prediction can obtain a better learning accuracy compared with other related algorithms.

关键词： Support vector machines

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Advances in Data and Web Management 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Qing Li Ling Feng Jian Pei Sean X. Wang Xiaofang Zhou Qiao-Ming Zhu

ISBN: (数字)9783642006722

ISBN: (纸本)9783642006715

This book constitutes the proceedings of the joint International Conference APWeb/WAIM 2009 which was held in Suzhou, China, during April 1-4, 2009. The 42 full papers presented together with 26 short papers and the abstracts of 2 keynote speeches were carefully reviewed and selected for inclusion in the book. The topics covered are query processing, topic-based techniques, Web data processing, multidimensional data analysis, stream data processing, data mining and its applications, and data management support to advanced applications.

关键词： computer Communication Networks Data Mining and Knowledge Discovery information Systems Applications (incl. Internet) information Storage and Retrieval Pattern Recognition information Systems and Communication Service

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Review of intelligent computing application 1st

Review of intelligent computing application

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Wang, Yiou Liu, Tianyuan Zhang, Fuquan Xu, Lin Ding, Gangyi Xiong, Rui Liu, Fei Beijing Institute of Science and Technology Information Beijing100044 China Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Digital Performance and Simulation Technology Lab. School of Software Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang Univeristy Fuzhou350121 China Innovative Information Industry Research Institute Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783319707297

Intelligent computing systems can automatically sense environmental changes in the sensor network, make judgments and prediction on the environmental status in time, and provide response strategies in different environments, applying some technologies of pattern recognition, time series prediction and big data analytics. Nowadays, intelligent computing is successfully used in a lot of areas such as transportation, healthcare, home, performance and environmental monitoring. In this paper, the concept of intelligent computing is introduced. Then, the applications in different areas are discussed detailedly. Finally, the future of intelligent computing is analyzed and the conclusion is briefly given. © Springer International Publishing AG 2018.

关键词： Wireless sensor networks

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Regional coverage monitoring planning technology for multi-UAV based on pruning PSO 2nd

Regional coverage monitoring planning technology for multi-U...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Wang, KaiXuan Shen, YuTing Zhang, FuQuan Liang, Zhuo Song, ZhiGuo Pan, YanPeng China Academy of Launch Vehicle Technology Beijing100076 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050000 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Digital Performance and Simulation Technology Lab School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783030045845

In recent years, with the application and gradual popularization of UAV technology in many fields, the normalization of UAV aerial photography has become a common phenomenon. There are few studies on Multi-UAV regional coverage monitoring. In this study, PSO algorithm based on pruning can be used to solve optimization problem, the multi-agent technology solve the problem of UAV group for self-perception and decision-making, the UAV group, namely multi-agent particles, can automatically initialize the perception agent particle whether meet the requirements of the iteration, whether to need to be pruned, and decide to optimize the iteration or increase the agent number, Finally, simulation verification shows that the optimized algorithm can finish the work faster under the premise of guaranteeing the effect. © Springer Nature Switzerland AG 2019.

关键词： Unmanned aerial vehicles (UAV)

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A Novel User Behavior Prediction and Optimization Algorithm for Single-User Multi-terminal Scenario

A Novel User Behavior Prediction and Optimization Algorithm ...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Hui Zhang Juan Chen Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509035595

For the single-user multi-terminal scenario, a novel user service behavior prediction and optimization algorithm is proposed in this paper, which can realize the accurate prediction of service requirements and optimal selection of service terminals, so as to improve user QoE effectively. Firstly, by using an adaptive feedback based weight correction method, a modified entropy weighted Markov model is formulated to accurately predict user future service states. Secondly, according to the above prediction results, the corresponding heavy-tailed model is used to estimate the service duration. Moreover, by integrating service preferences and terminal attributes, a matching metric of services and terminals is established, and then a terminal selection model is proposed for recommending the optimal service terminal to each user. Simulation results show the effectiveness of proposed algorithm.

关键词： Predictive models Markov processes Optimization Entropy Adaptation models Prediction algorithms Measurement

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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AUV dead-reckoning navigation based on neural network using a single accelerometer 11

AUV dead-reckoning navigation based on neural network using ...

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Xie, Yan-Xin Liu, Jun Hu, Cheng-Quan Cui, Jun-Hong Xu, Hongli State Key Laboratory of Robotics CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9781450346375

The accuracy of the Autonomous Underwater Vehicles (AUVs) navigation system determines whether they can safely operate and return. Traditional Dead-reckoning (DR) relies on the inertial sensors such as gyroscope and accelerometer. A major challenge for DR navigation is from measurement error of the inertial sensors (gyroscope, accelerometer, etc.), especially when the AUV is near or at the ocean surface. The AUV's motion is affected by ocean waves, and its pitch angle changes rapidly with the waves. This rapid change and the measurement errors will cause great noise to the direction measured by gyroscopes, and then lead to a large error to the DR navigation. To address this problem, a novel DR method based on neural network (DR-N) is proposed to explore the time-varying relationship between acceleration measurement and orientation measurement, which leverages acoustic localization and neural network estimate timely pitch angle through the explored time-varying relationship. This method enables AUV's DR navigation with a single acceleration, without relying on both acceleration and gyroscope. Most importantly, we can improve the accuracy of AUV navigation through avoiding DR errors caused by gyroscope noise at the sea surface. Simulations show DR-N significantly improves navigation accuracy.

关键词： Accelerometers

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A new type of dehumidifier for enclosed high-voltage switchgear cabinet

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Telkomnika - Indonesian Journal of Electrical Engineering 2012年第7期10卷 1637-1642页

作者： Zhang, Jinbo Shao, Tongguang Wang, Yu Wang, Jing College of Computer and Information Engineering Hohai University Changzhou 213022 China Jiangsu Provincial Key Lab. of Power Transmission Equipment Technology Changzhou 213022 China

In order to eliminate the internal condensation of enclosed switchgear cabinet, this paper introduced a subminiature and high-performance dehumidifier based on semiconductor refrigeration technology. This equipment utilized a Fuzzy self-tuning PID intelligent controller to monitor the humidity change in the cabinet continuously and judged the dehumidifier start-stop automatically. Besides, real-time communication with a host computer through RS485 interface was also considered. Experiments showed that this design could effectively reduce the internal humidity, eliminate the condensation, and provide a practical and effective solution to prevent the power device accidents caused by wet and condensation. © 2012 Universitas Ahmad Dahlan.

关键词： Condensation

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A Review of Artificial Fish Swarm Algorithms: Recent Advances and Applications

arXiv

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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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