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共 3354 条记录，以下是251-260 订阅

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Steady-State Topology Discovery of Target Networks Based on Statistics Method 5th

Steady-State Topology Discovery of Target Networks Based on ...

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5th international conference on Artificial Intelligence and Security, ICAIS 2019

作者： Yang, Di Liu, Yan Chen, Jing China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030242671

The progress of target network topology discovery research is slow due to the uncertainty of the target network range, and large-scale network measurement activities lead to a large accumulation of "false links" in the results, it is difficult to obtain a relatively accurate network topology. In this paper, we proposed a statistical-based target network steady-state topology discovery method. By statistical analyzing the characteristics of the measured data, we have proposed corresponding solutions to network boundary recognition, "false links" deletion and network steady-state topology construction, so that a relatively complete and accurate target network steady topology can be obtained. We also use this method to probe the HK (Hong Kong) and TW (Tai Wan) network and compare it with the data of CAIDA in the same period. Not only do the number of nodes and edges found are increased by two or three orders of magnitude, but also the number of "false links" in the results is greatly reduced. © 2019, Springer Nature Switzerland AG.

关键词： Network topology

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A Comparative Analysis of the Different data mining Tools by Using Supervised Learning Algorithms 8th

A Comparative Analysis of the Different Data Mining Tools by...

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8th international conference on Soft computing and Pattern Recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Goyal, Akarsh Khandelwal, Ishan Anand, Rahul Srivastava, Anan Swarnalatha, P. VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

These days a lot of raw data is generated from various common sources. This large amount of data, which would appear useless at first glance, is very important for companies and researchers as could provide a lot of helpful information. The data could be mined to get useful knowledge that could be used to make fruitful decisions. A lot of online tools and proprietary toolkits are available to the users and it becomes all the more cumbersome for them to know which is the best tool among these for the supervised learning algorithm and datasets they are applying. In order to aid this process, the paper progresses in this direction by doing a comparison of various data mining tools on the basis of their classification finesse. The various tools used in the paper are weka, knime and tanagra. Rigorous work on this has given the result that the performance of the tools is affected by the kind of datasets used and the way in which the supervised learning is done.

关键词： Weka Tanagra Knime mining tools Supervised learning

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Fusion of IoT and Machine Learning Approach to Prevent Confidential data from Digital Crimes and Cyber Mugging for Covert Transmission

Fusion of IoT and Machine Learning Approach to Prevent Confi...

引用

international conference on Manufacturing, advanced computing, Renewable Energy and Communication, MARC 2018

作者： Shakya, Amit Kumar Ramola, Ayushman Pokhariya, Hemant Singh Kandwal, Akhilesh 566/6 Bell Road Clement Town Dehradun Uttarakhand India

ISBN: (纸本)9789811367717

We are aware of the scenario of the twenty-first century where each and every nation of the world wants to progress at a rapid rate, weaker nations are destabilized by adopting policies like state-sponsored terrorism and online hacking which are part of the government official agenda. In this situation, there is strong need for policies to prevent data from online hacking and cyber mugging. Every nation wants to secure its data in terms of future plans, satellite images, new military setups, new river linkage plans, etc. Here, we have developed a new model advanced private content-based image retrieval (APCBIR) of data privacy based on content-based image query and retrieval (CBIQ/R). In this approach, data is smartly encrypted with only 10% of the original data, features of the original and newly encrypted images are matched by statistical parameters, and finally, the transmission of the data is done in a manner so that no one is able to reveal the identity of the original data. The efficiency of this system becomes more effective as IoT is attached to the model;data of usefulness is automatically selected and encrypted accordingly. Finally, the data gets secured from various types of malware attacks. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Robot Simulation and Reinforcement Learning Training Platform Based on Distributed Architecture 4th

Robot Simulation and Reinforcement Learning Training Platfor...

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4th international conference on Cognitive Systems and Information Processing, ICCSIP 2018

作者： Jiang, Yun-Liang Huang, Zeng-Qiang Cao, Jun-Jie Liu, Yong Ma, Xinqiang Huang, Yi School of Computer Science Hangzhou Dianzi University Hangzhou310018 China School of Information Engineering Huzhou University Huzhou313000 China Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China Institute of Intelligent Computing and Visualization Based on Big Data Chongqing University of Arts and Sciences Chongqing402160 China

ISBN: (纸本)9789811379826

In recent years, reinforcement learning, which enables robots to learn previously missing abilities, plays an increasingly important role in robotics, such as learning hard-to-code behaviors or optimizing problems without an accepted closed solution. The main problem of RL in robotics is that it is expensive and takes a long time to learn and operate. Another problem: advanced robot simulators like Gazebo are inefficient and time-consuming. In order to cope with these problems, a hybrid computing platform based on traditional robot simulation architecture and distributed architecture (hereinafter referred to as RDTP) is proposed in this paper, which helps to save cost, shorten time and speed up simulation and training. Additionally, the platform is optimized to a certain extent in terms of ease of use and compatibility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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A Taxonomy on Machine Learning Based Techniques to Identify the Heart Disease 4th

A Taxonomy on Machine Learning Based Techniques to Identify ...

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4th international conference on Next-Generation computing Technologies, NGCT 2018

作者： Srivastava, Anand Kumar Singh, Pradeep Kumar Kumar, Yugal Department of Computer Science and Engineering ABES Engineering College GhaziabadUP India Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat SolanHP India

ISBN: (纸本)9789811517174

Every year average death of human being is 17.7 million caused by Heart Disease or Cardiovascular diseases (CVDs), which is 31% of all global deaths reflected in Survey of World Heart Day 2017 [33]. In September 2016, many countries have taken the various Global Hearts Initiative in prediction and diagnosis of heart Diseases at earlier stages so that it can be cure perfectly [5]. Many authors have studied in this filed to optimize the performance of various ML techniques using various approaches. In latest studies, many groups have uncovered that many optimization algorithm like Differential Evolution, Genetic Variants, and Particle Swarm optimization are associated with prediction algorithm like K-Nearest neighbor, Decision Tree, Neural Network, Support Vector machine, Logistic Regression etc. to make efficient medical system for CVDs. The Objective of our current study is to analyze the comparatively study of the ML techniques in terms of performance measure of different ML techniques that have been used by various authors in their research work in earlier studies of heart disease prediction and diagnosis process. © 2019, Springer Nature Singapore Pte Ltd.

关键词： data mining

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A survey of state-of-the-art short text matching algorithms 4th

A survey of state-of-the-art short text matching algorithms

引用

4th international conference on data mining and Big data, DMBD 2019

作者： Hu, Weiwei Dang, Anhong Tan, Ying State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China and Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (纸本)9789813295629

The short text matching task uses an NLP model to predict the semantic relevance of two texts. It has been used in many fields such as information retrieval, question answering and dialogue systems. This paper will review several state-of-the-art neural network based text matching algorithms in recent years. We aim to provide a quick start guide to beginners on short text matching. The representation based model DSSM is first introduced, which uses a neural network model to represent texts as feature vectors, and the cosine similarity between vectors is regarded as the matching score of texts. Word interaction based models such as DRMM, MatchPyramid and BERT are then introduced, which extract semantic matching features from the similarities of word pairs in two texts to capture more detailed interaction information between texts. We analyze the applicable scenes of each algorithm based on the effectiveness and time complexity, which will help beginners to choose appropriate models for their short text matching applications. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Combining Vector Space Features and Convolution Neural Network for Text Sentiment Analysis 12th

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12th international conference on Complex, Intelligent, and Software Intensive Systems (CISIS)

作者： Wang Yun Wang Xu An Zhang Jindan Yu, Chenghai Engn Univ CAPF Xian Shaanxi Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian Shaanxi Peoples R China Xianyang Vocat Tech Coll Xianyang Shaanxi Peoples R China Zhejiang Sci Tech Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783319936598;9783319936581

With the advanced tendency of big data, there are billions of text messages posted on the Internet to express peoples' views and opinions all the time. And with the explosive growth of text information flow in social network, the topic detection and the sentiment analysis have become important researches in the field of NLP (natural language processing). There are two ways to analyze the sentiment tendency: traditional statistics method and machine learning method. At present, with the extensive application of machine learning method in NLP, various neural network models have achieved commendable results in the research of sentiment classification. However, such methods require a long period in training and learning process, and the training effect may be over fitting depended on the training set. In addition, such methods usually do not consider the vector space characteristics and lacks of the use of the word sentiment label. Standing around problem, this paper creates a sentiment classification method which combined with the feature of text vector space and the convolutional neural network method. Firstly, the words are ranked and selected according to the spatial distribution features in text information. Then the processed words are mapped into abstract vectors based on the existing dictionary resources. And the convolutional neural network is utilized to extract features of abstract vectors for the sentiment classification. This paper puts forward the relevant methods obtained superb performance in the Chinese tendency analysis evaluation (COAE 2014) data sets.

关键词： Big data Convolution Neural Network Sentiment analysis Vector space features Word embedding

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Applying Business Intelligence Technology for Equipment Maintenance and Repair Plan of Telecommunications Services Provider 20

Applying Business Intelligence Technology for Equipment Main...

引用

20th international conference on advanced Communication Technology (ICACT)

作者： Tanphet, Sasiwimon Wanchai, Paweena Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen 40002 Thailand

ISBN: (纸本)9791188428014

The purpose of this study was to apply business intelligence (BI) technology for planning of equipment maintenance and repair. Multidimensional data model was designed as a star schema for multidimensional analysis, ad hoc analytics and online analytical processing. Moreover, data mining technique was utilized to figure out association rules of cause of difficulty and time of difficulty. This research applied Oracle Business Intelligence and Weka 3.6 as research tools to analyze the data of Maintenance Department of CAT Telecom Public Company Limited in Thailand from the past four years during 2013-2016 including 131,456 records. The findings revealed that applying BI to the organization was beneficial to executives since it provided various aspects of data which could be used for more appropriate and faster decision making and strategic planning.

关键词： Business Intelligence (BI) data Warehouse data mining Association Rules Apriori

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Analysis of MOOC Learning Rhythms 20

Analysis of MOOC Learning Rhythms

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20th IEEE international conference on High Performance computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on data Science and Systems (DSS)

作者： He, Jingjing Men, Chang Fang, Senbiao Du, Zhihui Liu, Jason Li, Manli Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA Tsinghua Univ Inst Educ Beijing Peoples R China

ISBN: (纸本)9781538666142

With the increasing popularity of Massive Open Online Course (MOOC), a large amount of data has been collected by the MOOC platforms about the users and their interactions with the platforms. Many studies analyze the data to understand the online learning behavior of the students in order to improve the courses and services. In this paper, we propose the concept of learning rhythms. We divide the students into three groups corresponding to the level of engagement with the course. We capture the learning behavior on different learning units by observing the delay time and the study time of the students, and use them to infer the eagerness and intensity applied to studying the materials. We use the frequent tree mining technique to extract frequent patterns. The most frequently occurred subtrees are identified as typical learning rhythms. To evaluate our method, we analyze the data provided by XuetangX, an online learning platform in China, and study the learning rhythms using one of its most popular courses.

关键词： Vegetation Videos Education Delays data mining Web pages conferences

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Matching Biomedical Ontologies Through Compact Evolutionary Simulated Annealing Algorithm 12th

Matching Biomedical Ontologies Through Compact Evolutionary ...

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12th international conference on Genetic and Evolutionary computing, ICGEC 2018

作者： Xue, Xingsi Chen, Jie Liu, Jianhua Chen, Dongxu College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fujian350118 China Fujian Medical University Union Hospital Fuzhou Fujian350001 China

ISBN: (纸本)9789811358401

To overcome biomedical ontology’s heterogeneous problem, biomedical ontology matchers are developed to search for bridges of knowledge (or alignment) between heterogeneous biomedical ontologies. Since it is a complex problem, Evolutionary Algorithm (EA) can present a good methodology for matching biomedical ontologies. To improve the efficiency, in this paper, a Compact Evolutionary Simulated Annealing algorithm (CESA) is proposed to match the biomedical ontologies. In particular, CESA utilizes a Probability Vector (PV) to simulate the behavior of population-based EA, and introduces the Simulated Annealing algorithm (SA) as a local search in each generation. The experiment is conducted on the Large Biomed track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the experimental results show the effectiveness of our proposal. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Simulated annealing

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