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2022 IEEE international workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings

2022 IEEE International Workshop on Metrology for Extended R...

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1st IEEE international workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022

ISBN: (纸本)9781665485746

The proceedings contain 130 papers. The topics discussed include: deep learning model for blood pressure estimation from ppg signal;a comprehensive UX index to evaluate industrial tasks from a human-centered perspective;analyzing the needs of homeless people using feature selection and mining association rules;machine learning prediction of the expected performance of football player during training;recognition of recurrent movement patterns of football players via machine learning;determining the difficulties of students with dyslexia via virtual reality and artificial intelligence: an exploratory analysis;estimating finite-time delay in dynamical soft sensors: an industrial case of study;application of data distribution metrics for soft sensors in industrial scenarios;a combined approach using Lorentzian fitting and ANNs for microwave resonator modeling;and artificial neural networks for the forecasting of wave climate in proximity of harbor area.

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Based on the Semantics Analysis of the URL Identification and Malicious Code Detection 24

Based on the Semantics Analysis of the URL Identification an...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Wang Mingxin Wuhan Res Inst Posts & Telecommun Wuhan Peoples R China

ISBN: (纸本)9798400707032

The traditional malicious URL identification methods usually adopt blacklist technology, heuristic algorithm and machine learning algorithm. This paper considers that natural language processing technology can be introduced when dealing with text with context. This technique is used to help computers understand, interpret, and manipulate human language. It is often used to deal with contextual problems, and when combined with malicious URL directions, it can produce more accurate models than existing methods. In addition to the traditional TF-IDF detection method, this paper introduces N-Gram and Word2vec methods for the first time, a total of three different natural language processing technologies to process and extract URL data. Through a series of experiments, this paper proves that semantic analysis can improve the accuracy of malicious code detection successfully by adjusting parameters of the optimization model. The final experimental results show that the detection rate of malicious URLs by TF-IDF method and N-Gram method combined with various machine learning models is about 85%, while the detection rate of Word2vec method combined with deep learning model reaches 99%, and the detection accuracy is significantly improved.

关键词： Malicious URL detection Natural Language Processing (NLP) machine learning Deep learning

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One More Reason to Reject Manuscript about machine learning for structural Health Monitoring 1

One More Reason to Reject Manuscript about Machine Learning ...

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1st international Conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Gunawan, Fergyanto E. Soewito, Benfano Surantha, Nico Mauritsius, Tuga Bina Nusantara Univ BINUS Grad Program Dept Ind Engn Ind Engn Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Dept Comp Sci Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Dept Management Informat Syst Informat Syst Management Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

Two major journals in mechanical engineering field put forward a special note for author(s) of articles discussing structural health monitoring. Only contributing new classifiers or new features without applying engineering knowledge or principle is considered insufficient. This article intends to provide empirical evidence to support the decision. This work shows the classification accuracy of a machine learning approach strongly depends on the number of training data. A high accuracy can simply be obtained by training the classifier with a large dataset, which is hardly realizable in practice. Meanwhile, the features and classifier that based on a sound engineering principle can provide a level of classification accuracy independent of the number of training data.

关键词： structural health monitoring machine learning power spectral density F statistic

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Research on Power Grid Infrastructure Business Management Platform Based on data Layer Analysis Method 24

Research on Power Grid Infrastructure Business Management Pl...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Song, Baosong Zhang, Chenyu Ding, Lei Beijing Guodiantong Network Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798400707032

In order to carry out the concept of green development and respond to the construction of digital China, it is imperative to transform the energy industry into digital intelligent development. The power grid infrastructure project is regional and complex, and the traditional information architecture is difficult to support its data management and value realization, the application of multi-source and visual data analysis combining with power grid infrastructure engineering data, geological data, meteorological data and so on is developing gradually. The research of power grid infrastructure business management platform based on data layer analysis method, firstly analyzes the power grid infrastructure business demand, then carries out the application design and the construction function, finally carries on the example study. It realizes the unified structured management and visual display of various types of power grid infrastructure resources, breaks the data barriers, makes the heterogeneous data get fusion analysis and application, releases the value of power grid data elements and helps new power systems construct and develop.

关键词： data layer analysis method Grid infrastructure business management Geographic information system

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Web usage mining for automatic link generation

Web usage mining for automatic link generation

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10th international workshop on Modelling, Simulation, Verification and Validation of Enterprise Information Systems, MSVVEIS 2012 and 1st international workshop on Web Intelligence, WEBI 2012, in Conjunction with ICEIS 2012

作者： Arbelaitz, Olatz Gurrutxaga, Ibai Lojo, Aizea Muguerza, Javier Pérez, Jesús M. Perona, Iñigo Dept. of Computer Architecture and Technology University of the Basque Country UPV-EHU M. Lardizabal 1 20018 Donostia Spain

ISBN: (纸本)9789898565143

During the last decades, the information in the web has increased drastically but larger quantities of data do not provide perse added value for web visitors;there is a need for more efficient access to the required information and adaptation to user preferences or needs. The use of machine learning techniques to build user profiles allows to take into account users' real preferences. We present in this work a preliminary system, based on the collaborative filtering approach, to identify and generate interesting links for the users while they are navigating. The system uses only web navigation logs stored in any web server (according to the Common Log Format) and extracts information from them combining unsupervised and supervised classification techniques and frequent pattern mining techniques. It also includes a generalization procedure in the data preprocessing phase and in this work we analyze its effect on the final performance of the whole system. We also analyze the effect of the cold start (0 day problem) in the proposed system. The experiments show that the proposed generalization option improves the results of the designed system, which performs efficiently w.r.t. a web-accessible database and is even able to deal with the cold start problem.

关键词： Collaborative filtering

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A deep analysis on age estimation

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pattern recognition LETTERS 2015年第Part2期68卷 239-249页

作者： Huerta, Ivan Fernandez, Carles Segura, Carlos Hernando, Javier Prati, Andrea Univ IUAV DPDCE I-30135 Venice Italy Herta Secur Barcelona 08037 Spain Univ Politecn Cataluna ES-08034 Barcelona Spain

The automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search optimization in large databases. Nevertheless, it becomes challenging to estimate age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity and high within-range variance. These difficulties have been recently addressed with complex and strongly hand-crafted descriptors, difficult to replicate and compare. This paper presents two novel approaches: first, a simple yet effective fusion of descriptors based on texture and local appearance;and second, a deep learning scheme for accurate age estimation. These methods have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Age estimation Deep learning DNN CCA HOG LBP

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GK-means: An Efficient K-means Clustering Algorithm Based On Grid

GK-means: An Efficient K-means Clustering Algorithm Based On...

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1st international Symposium on Computer Network and Multimedia Technology

作者： Chen, Xiaoyun Su, Youli Chen, Yi Liu, Guohua Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9781424452729

As an important tool, clustering analysis is used in many applications such as pattern recognition, data mining, machine learning and statistics etc. K-means clustering, based on minimizing a formal objective function, is perhaps the most widely used and studied. But k the number of clusters needs users specify and the effective initial centers are difficult to select. Meanwhile, it is sensitive to noise data points. In this paper, we focus on choice the better initial centers to improve the quality of k-means and to reduce the computational complexity of k-means method. The proposed algorithm called GK-means, which combines grid structure and spatial index with k-means algorithm. Theoretical analysis and experimental results show the algorithm has high quality and efficiency.

关键词： data mining clustering analysis k-means algorithm grid technology

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Video shot segmentation using graph-based dominant-set clustering 09

Video shot segmentation using graph-based dominant-set clust...

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1st international Conference on Internet Multimedia Computing and Service, ICIMCS 2009

作者： Li, Li Zeng, Xianglin Li, Xi Hu, Weiming Zhu, Pengfei National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781605588407

Video shot segmentation is a solid foundation for automatic video content analysis, for most content based video retrieval tasks require accurate segmentation of video boundaries. In recent years, using the tools of data mining and machine learning to detect shot boundaries has become more and more popular. In this paper, we propose an effective video segmentation approach based on a dominant-set clustering algorithm. The algorithm can not only automatically determine the number of video shots, but also obtain accurate shot boundaries with low computation complexity. Experimental results have demonstrated the effectiveness of the proposed shot segmentation approach. Copyright 2009 ACM.

关键词： Clustering algorithms

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PDM 2013 - Proceedings of the 1st ACM international workshop on Personal data Meets Distributed Multimedia, Co-located with ACM Multimedia 2013

PDM 2013 - Proceedings of the 1st ACM International Workshop...

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1st ACM international workshop on Personal data Meets Distributed Multimedia, PDM 2013 - Co-located with ACM Multimedia 2013

ISBN: (纸本)9781450323970

The proceedings contain 9 papers. The topics discussed include: the power of the data: opportunities and challenges in big and personal data mining;situation fencing: making geo-fencing personal and dynamic;crowds, Bluetooth, and rock'n'roll: understanding music festival participant behavior;building health persona from personal data streams;a mobile personal informatics system with interactive visualizations of mobility and social interactions;an evaluation of wearable activity monitoring devices;combining crowd-generated media and personal data: semi-supervised learning for context recognition;the influence of social norms on synchronous versus asynchronous communication technologies;and 'whaT's in it for me?' how can big multimedia aid quantified-self applications.

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2014 IEEE 1st international workshop on Artificial Intelligence for Requirements Engineering, AIRE 2014 - Proceedings

2014 IEEE 1st International Workshop on Artificial Intellige...

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2014 IEEE 1st international workshop on Artificial Intelligence for Requirements Engineering, AIRE 2014 - Proceedings

ISBN: (纸本)9781479963553

The proceedings contain 9 papers. The topics discussed include: pragmatic ambiguity detection in natural language requirements;weka meets TraceLab: toward convenient classification: machine learning for requirements engineering problems: a position paper;transferring research into the real world: how to improve re with AI in the automotive industry;customizable rule-based verification of requirements ontology;content-based recommendation techniques for requirements engineering;on requirements representation and reasoning using answer set programming;a case study of applying data mining to sensor data for contextual requirements analysis;using AI to model quality attribute tradeoffs;and applying knowledge representation and reasoning to (simple) goal models.

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