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As the number of cooking recipes posted on the Web increases, it becomes difficult to find a cooking recipe that a user needs. Moreover, even if it can be done, it is still difficult for users to arrange the cooking recipe, for example, by replacing ingredients with different ones. To deal with such problems, we propose a framework for typicality analysis of the combination of ingredients. The framework...
Peer-to-peer system is a promising solution to manage a large amount of data, but similarity search on peer-to-peer network with a restricted small number of messages is a challenging problem. Existing methods that can perform similarity search work only with low-dimensional data. We propose a method to transform the very high-dimensional data into low-dimensional vectors in order to perform similarity...
Security is the biggest challenge for the digital data of information systems and computer networks. Some systems are used for providing security to this data. Like these systems intrusion detection system (IDS) is used for providing security to computer networks and information systems. In IDS many systems uses number of techniques for providing accuracy by selecting complete features of dataset...
In this paper, we propose a general smartphone user activity prediction framework utilizing the general concept of partial repetitive behavior (instead of the stronger periodicity condition) for similarity scoring and the landmark behaviors (representative behaviors to identify groups of similar behavior vectors). Prediction of the next-day(s) behavior is based on a weighted sum of the most similar...
A new method combined PCA (Principal Component Analysis) with SOM (Self-Organizing Maps) neural network is presented for clustering analysis of gene expression data. Firstly, the principal components are extracted from the genetic data set by PCA, in order to get a low dimensional data set. These principal components with lower dimension can basically express comprehensive information of original...
As a widely used medium platform, Micro-blog influence research is a hotspot. The community micro-blog, which is used as an effective tool by social managers in virtual community, has developed rapidly in recent years. As the basis of government micro-blog system in China, the community blog influence has great importance to guide the public popular feelings and guarantee the safety of the virtual...
In this work, based on the ACF model and the SVM classifier, succeeded on trials mining information that it's more effective to analyze the subcellular localization prediction of apoptosis proteins when adopting hydrophobicity property. This information is obtained in three benchmark datasets by using the ACF model and SVM to scan the AAindex database, which contains 544 kinds of amino acids. The...
Class labels and pairwise constraints are adopted as the prior information to present the semi-supervised dimensionality reduction for hyperspectral image. In this paper, we extend semi-supervised probabilistic principal component analysis (S2PPCA), semi-supervised local fisher discriminant analysis (S2LFDA) and semi-supervised dimensionality reduction with pairwise constraints (S2DRpc) to extract...
In this paper, we propose an adaptive implementation of a fast-convergent algorithm for principal component extraction. Our approach consists of first estimating a basis of the principal subspace through the use of OPAST algorithm. The obtained basis is then fed to a second process where at each iteration one or several Givens transformations are applied to estimate the principal components. Later...
Based on the massive floating car data and traffic flow detector data of intelligent movement platform, the average travel speed, the average delay per unit distance and the saturation of road sections are selected to build traffic congestion evaluation system. The accuracy of floating car data is validated with field observations of the typical roads in Guangzhou, the validation result shows that...
Over the past century, time based and frequency based is used for analyzing Electroencephalography (EEG) signals. EEG is a scientific tool for measure signal from human brain. This paper proposes a time-frequency approach or spectrogram image processing technique for analyzing EEG signals. Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted from spectrogram image and then Principal...
Stock market prediction is of great interest to stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are: feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this paper, an automated...
Customer satisfaction degree(CSD) is the increasingly important decision factor to all third -party logistics firms seeking maximum service profits. It is necessary for the third party logistics company's customer satisfaction degree evaluation-makers to comprehensively measure many qualitative and quantitative factors. This paper established a two-level index system of logistics company's CSD, which...
This paper mainly discusses the study of models for financial distress pre-warning, trying to select general financial indexes by principal component analysis, and meanwhile adding nonfinancial indexes which reflect corporate governance state to complement. Logit Model which is more accurate in prediction is selected, with the 56 company samples including both delisting pre-warned companies and counterparts...
The paper applies support vector machine (SVM) based on principal component to credit management in E-commerce, and in this paper not only principal component analysis is used to do operation of dimension reduction in multi-dimensional variable system so as to get low-dimensional variable system characters with a high precision, but also the favorable generalization capability of SVM to small sample...
In this paper a system for multi-scale change detection with automatic scale selection is proposed. The generation of the multi-scale data set is based on fractal net evolution approach. The set of scales used are selected optimally from the scale domain to ensure good enough representation of the scale domain. The change detection is performed on each scale image independently then the information...
Credit risk assessment has been an important research topic in customer relationship management. It is also an important field for commercial banks because discriminating good creditors from bad ones is becoming more and more crucial for banks. A Fuzzy Support Vector Machine (FSVM) classification model based on principal component analysis (PCA-FSVM) was advanced, which adapted PCA to extract principal...
This study compares six change detection techniques to study land cover change associated with tropical forest (El Rawashda forest reserve, Gedaref State, Sudan). For this site, Landsat 7 Enhanced Thematic Mapper (ETM+) data acquired on March 22, 2003 and Aster data acquired on February 26, 2006 were used. The change detection techniques employed in this study were Post-Classification Comparison (PCC),...
According to the monitoring and analysis of chloroform in the water distribution network in a northern city of China, the variations of chloroform in the water distribution network and the major influence factors were studied. Using principle component analysis method, the prediction model which including 9 water quality indexes was established to predict the concentration of chloroform and the average...
According to mathematical analysis, using primary components and factor analysis (PCFA) and its advantages, the main function indexes of intrusion detection system in network security are analyzed and assessed. Based on PCFA model and the scores by the experts, the authors computed some results that can reflect some aspects about main function indexes of intrusion detection system in network security...
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