Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and...
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Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and explores the topic distribution characteristics and evolution rules of them. First, image features are extracted through scale-invariant feature transform (SIFT) and Canny and scale-invariant feature transform (CSIFT), a visual dictionary is generated through the k-means clustering algorithm, and the term frequency-inverse document frequency (TF-IDF) vector is calculated and combined with the colour feature vector extracted via hue, saturation and value (HSV). Second, Dunhuang mural images are collected and the support vector machine (SVM) classifier is built. Finally, the knowledge graph-based topic maps are constructed, and graph theory is introduced to analyse the topic distribution and evolution of Dunhuang murals in different dynasties. The results show that the Dunhuang murals of different dynasties can be effectively classified through the bag of words, HSV and support vector machine (BOW_HSV_SVM) based on their visual features. Through topic maps, the topic distribution characteristics and evolution rules of Dunhuang murals with the dynasties are revealed.
This paper introduce the application of Big Data Analytic Technique to predict the severity of various Single Transmission line outages. The severity of the outage is assessed by computing the Line Voltage Stability I...
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ISBN:
(纸本)9781728191805
This paper introduce the application of Big Data Analytic Technique to predict the severity of various Single Transmission line outages. The severity of the outage is assessed by computing the Line Voltage Stability Index (LVSI) and is used for ranking purpose under different loading condition. This results in generation of large volume of data. The data obtained from the simulations for various scenarios is processed and applied to machinelearning to predict the rank and severity condition of the line. The severity is predicted for various test systems to ascertain the suitability of the technique applied and the results of the study conducted on the IEEE 30 Bus system are presented with the analysis needed. The MATLAB and the WEKA software are used for simulation purpose.
Phishing is an online scandalous act that occurs when a malevolent web page impersonates as legitimate web page in the intension of exploiting the confidential information from the user. Phishing attack continues to p...
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Phishing is an online scandalous act that occurs when a malevolent web page impersonates as legitimate web page in the intension of exploiting the confidential information from the user. Phishing attack continues to pose serious risk for web users and annoying threat in the field of electronic commerce. Feature selection is the process of removing unrelated features and thus reduces the dimensionality of the feature. This paper focuses on identifying the foremost features that categorise legitimate websites from phishing websites based on feature selection. In real world identifying phishing URL with low computational time and accuracy is very important and thus feature selection is considered in this work. A comparative study is carried out on different data mining classifiers before and after feature selection and the performance are evaluated in terms of accuracy and computational rate. The results indicate that the proposed approach detects phishing websites with considerable accuracy.
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