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共 4173 条记录，以下是3861-3870 订阅

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Artificial Intelligence and learning Techniques in Intelligent Fault Diagnosis 4

Artificial Intelligence and Learning Techniques in Intellige...

引用

4th international conference on computer Science and Network Technology (ICCSNT)

作者： Sun Yuanyuan Guo Lili Wang Yongming Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 100094 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9781467381734

At present, based on computer and information technology, intelligent diagnosis technology is in rapid development. In this paper, the application of artificial intelligence and learning techniques in intelligent fault diagnosis are demonstrated, such as Rule-Based Reasoning, Case-based Reasoning, Network neural, Fuzzy Logic, Genetic algorithm, Rough set theory, Bayesian network theory, Multi-agents, Reinforcement learning, Support Vector machine. Some kinds of applications are introduced. these intelligent fault diagnosis methods are widely used in complex fault diagnosis system. We will try to use them in our future intelligent fault diagnosis system for space station.

关键词： RBR CBR NN FL GA RST Bayesian network Multi-agents RL SVM

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Research on Image Recognition Based on Deep learning Technology

Research on Image Recognition Based on Deep Learning Technol...

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2016 4th international conference on Advanced Materials and Information Technology Processing(AMITP 2016)

作者： Hao Zhai School of Computer and Communication Engineering Tianjin University of Technology

Nowadays image recognition technology is widely used, and plays a very important in various fields. Deep learning technology uses multilayer structure to analyze and deal with image features, which can improve the performance of image recognition. the popular models of deep learning contain Auto Encoder, Restricted Boltzmann machine(RBM), Deep Belief Network(DBN), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN) and other improved methods. the applications of image recognition based on deep learning technology including image classification, facial recognition, image search, object detection, pedestrian detection, video analysis. We believe that in the future deep learning will develop rapidly in theory, algorithm, and application and they will make our lives more intelligent.

关键词： Image Recognition Deep learning

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X-Ray Medical Image Classification based on Multi Classifiers 4

X-Ray Medical Image Classification based on Multi Classifier...

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4th international conference on Advanced computer Science applications and Technologies (ACSAT)

作者： Abdulrazzaq, M. M. Noah, Shahrul Azman Fadhil, Moayad A. Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi Selangor Malaysia Philadelphia Univ Fac Informat Technol Amman Jordan

ISBN: (纸本)9781509004249

Advances in the medical imaging technology has lead to a growth in the number of digital images that needs to be classified, stored and retrieved properly. Content Based Image Retrieval (CBIR) systems represent the application of specific computer vision techniques to retrieve images from large databases based on their visual features, such as color, texture and shape. Practically, the use of these visual features only does not offer appropriate measurement performance and accuracy since those features cannot express the high-level semantics of users. therefore, image classification systems based on machine learning techniques are used as solutions for this problem of CBIR systems. In our previous works, performance of different feature types were investigated by using two techniques of machine learning which are k-Nearest Neighbor (k-NN) and Support Vector machine (SVM). In this paper, we extend that work by exploring the effect of combining these two classifiers. Our experiments show accuracy improvements based on using ImageCLEF2005 dataset.

关键词： Content Based Image Retrieval (CBIR) k-Nearest Neighbor (k-NN) Support Vector machine (SVM) ImageCLEF2005 machine learning

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Discovery Science: 18th international conference, DS 2015, Banff, AB, Canada, October 4–6, 2015, proceedings 1

引用

丛书名： Lecture Notes in computer Science 9356

2015年

作者： Nathalie Japkowicz, Stan Matwin (eds.)

ISBN: (数字)9783319242828

ISBN: (纸本)9783319242811;9783319242828

this book constitutes the proceedings of the 17th international conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. the 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 *** combination of recentadvances in the development and analysis of methods for discovering scienti cknowledge, coming from machine learning, data mining, and intelligent dataanalysis, as well as their application in various scienti c domains, on the onehand, with the algorithmic advances in machine learning theory, on the otherhand, makes every instance of this joint event unique and attractive.

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Enhance QoS by learning Data flow rates in Wireless Networks using Hierarchical Docition 4th

Enhance QoS by Learning Data flow rates in Wireless Networks...

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4th international conference on Eco-Friendly Computing and Communication Systems (ICECCS)

作者： Simi, S. Varghese, Sruthi Ann AMRITA Univ Kollam 690525 India

Wireless Wireless network finds application in military environments, emergency, rescue operations and medical monitoring due to its self-configuring nature. As the availability of resources such as processing power, buffer capacity and energy are limited in wireless networks;it is required to devise efficient algorithms for packet forwarding. Due to the dynamic nature of the wireless environment, the traditional packet forwarding strategies cannot guarantee good network performance every time. this paper proposes a method for learning data flow rates in wireless network to improve quality of service in the network. Each node in the network learns the environment using reinforcement learning approach and selects appropriate neighbours for packet forwarding. In order to improve the learning capacity of nodes, the hierarchical docition technique is employed. Docition applied to each layer of network, which selects a set of special nodes which has more information about the environment and share this information with less informative nodes. the algorithm is tested in a geographical routing protocol and the results indicate improved network performance. (C) 2015 the Authors. Published by Elsevier B.V.

关键词： Wireless network Reinforcement learning

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Design and Development of a Prototype application for Intrusion Detection using Data mining 4

Design and Development of a Prototype Application for Intrus...

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4th international conference on Reliability, Infocom Technologies and Optimization (ICRITO)

作者： Jaswal, Kalpana Kumar, Praveen Rawat, Seema Amity Univ Noida UP India

ISBN: (纸本)9781467372312

In the world of communication, security is a big concern. Most of our crucial data is stored in a computer system and in most cases we exchange it over a network. But it's not just our data transmitting over the network but different types of attacks too. these attacks can harm our stored data. Monitoring computer system and its logs (administration logs, security logs, system logs, network logs) and protecting our crucial data is necessary. For these necessities we use intrusion detection system. An intrusion detection system is an application that provides protection from malicious activities or policy violations and generates various rules to defend computer security. Intrusion detection system can be designed and developed on any platform but for its better functionality we are using data mining technique. In past years, many techniques have been introduced to improvise the detection rate. Earlier, in the initial stages of its designing, hardware had to be installed to detect and monitor the system. But, with the help of data mining it has become easier to work with software and algorithm development. In the recent trends, many new algorithms have been introduced to increase its efficiency. they are categorized under machine learning algorithms: supervised, unsupervised and hybrid. though hybrid has not yet been categorized finely but various authors have used it by merging different machine learning algorithms. machine learning algorithms provide a process of detecting intrusion and generating rules for its detection and prevention. Rule generation is defined by association rule mining and apriori algorithm. An intrusion detection system is not a new application but developing a prototype which will work for the saved logs (administration logs, security logs and system logs) and monitor network logs on host system as well as on client system, so that the Intrusion detection system can alert the user on regular basis, is. In this paper we are using a hybrid mach

关键词： IDs apriori algorithm association rule supervised approach unsupervised approach hybrid approach

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learning dynamic systems from time-series data an application to gene regulatory networks 4

Learning dynamic systems from time-series data an applicatio...

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4th international conference on Pattern Recognition applications and Methods, ICPRAM 2015

作者： Timoteo, Ivo J.P.M. Holden, Sean B. University of Cambridge Computer Laboratory William Gates Building 15 JJ Thomson Avenue CambridgeCB3 OFD United Kingdom

ISBN: (纸本)9789897580772

We propose a local search approach for learning dynamic systems from time-series data, using networks of differential equations as the underlying model. We evaluate the performance of our approach for two scenarios: first, by comparing with an l1-regularization approach under the assumption of a uniformly weighted network for identifying systems of masses and springs;and then on the task of learning gene regulatory networks, where we compare it with the best performers in the DREAM4 challenge using the original dataset for that challenge. Our method consistently improves on the performance of the other methods considered in both scenarios.

关键词： Differential equations

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PCA-IBP model application in medium and long-term runoff forecasting 4

PCA-IBP model application in medium and long-term runoff for...

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4th international conference on computer Science and Network Technology, ICCSNT 2015

作者： Daidi, Hu Yuan, Sang Mingtao, Ge School of Electronics and Information Engineering SIAS International University Xin Zheng Henan451150 China

ISBN: (纸本)9781467381727

this paper proposes a med-long term runoff forecasting model based on the principal component analysis (PCA) and the improved BP Neural Network. PCA was utilized to eliminate the relevance between input data, reducing input dimension and effectively reducing the model's structural complexity, improving the model's learning efficiency and forecast performance. the proposed model was predicted and verified with actual data, compared and analyzed with the traditional BP neural network model. the results show that the proposed model is superior to the traditional BP neural network model in terms of forecasting efficiency and accuracy. © 2015 IEEE.

关键词： Principal component analysis

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PCA-IBP Model application in Medium and Long-term Runoff Forecasting

PCA-IBP Model Application in Medium and Long-term Runoff For...

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4th international conference on computer Science and Network Technology (ICCSNT)

作者： Hu Daidi Sang Yuan Ge Mingtao SIAS Int Univ Sch Elect & Informat Engn Xin Zheng 451150 Henan Peoples R China

ISBN: (纸本)9781467381734

关键词： runoff forecasting PCA neural network BP neural network

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4th international conference on Design, User Experience, and Usability, DUXU 2015 Held as Part of 17th international conference on Human-computer Interaction, HCI international 2015

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4th international conference on Design, User Experience and Usability, DUXU 2015 Held as Part of 17th international conference on Human-computer Interaction, HCI international 2015

ISBN: (纸本)9783319208886

the proceedings contain 71 papers. the special focus in this conference is on Designing the Social Media Experience, Designing the learning Experience, Designing the Playing Experience and Designing the Urban Experience. the topics include: Social media interactions and the use of third-party management applications on effectiveness and perception of information;design process of a social network system for storage and share files in the workplace;visualizing group user behaviors for social network interaction design iteration;understanding the semantics of web interface signs;clicking through endless seas;origins and perspectives on designing virtual communities of practice for permanent education;the challenges and opportunities of designing national digital services for cross-border use;lessons learned from a co-creation of a technology-enhanced playful learning environment;application of dashboards and scorecards for learning models it risk management;mapping metaphors for the design of academic library websites;antique school furniture, new technological features needs;ergonomic and usability analysis of interactive whiteboards in the academic environment;a usability study with children on an online educational platform;evaluating an education department portal;ads-on games and fake brands;a biofeedback board game to improve coordination and emotional control and evaluating and customizing user interaction in an adaptive game controller.

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