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 perspecti...
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;machinelearning prediction of the expected performance of football player during training;recognition of recurrent movement patterns of football players via machinelearning;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.
The traditional malicious URL identification methods usually adopt blacklist technology, heuristic algorithm and machinelearning algorithm. This paper considers that natural language processing technology can be intr...
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ISBN:
(纸本)9798400707032
The traditional malicious URL identification methods usually adopt blacklist technology, heuristic algorithm and machinelearning 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 machinelearning 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.
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 engin...
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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 machinelearning 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.
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 infrastru...
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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.
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 i...
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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 machinelearning 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 patternmining 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.
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 searc...
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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.
As an important tool, clustering analysis is used in many applications such as patternrecognition, datamining, machinelearning and statistics etc. K-means clustering, based on minimizing a formal objective function...
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ISBN:
(纸本)9781424452729
As an important tool, clustering analysis is used in many applications such as patternrecognition, datamining, machinelearning 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.
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 d...
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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 datamining and machinelearning 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.
The proceedings contain 9 papers. The topics discussed include: the power of the data: opportunities and challenges in big and personal datamining;situation fencing: making geo-fencing personal and dynamic;crowds, Bl...
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 datamining;situation fencing: making geo-fencing personal and dynamic;crowds, Bluetooth, and rock'n'roll: understanding music festival participant behavior;building health persona from personal datastreams;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.
The proceedings contain 9 papers. The topics discussed include: pragmatic ambiguity detection in natural language requirements;weka meets TraceLab: toward convenient classification: machinelearning for requirements e...
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: machinelearning 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 datamining 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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