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With the rapid development of science and technology and the growing popularity of computer networks, the scale of network users is gradually expanding, and the behavior of network users is becoming more and more complicated. A large number of studies show that the user's actual interest is closely related to the browsing behavior on the web page. Through the user browsing behavior analysis can obtain...
Product bundling is widely adopted for information goods and online services because it can increase profit for companies. For example, cable companies often bundle Internet access and video streaming services together. However, it is challenging to obtain an optimal bundling strategy, not only because it is computationally expensive, but also that customers’ private information (e.g., valuations...
Granger causality is proposed to fuse stock prices and social media sentiment information for stock market prediction. Sentiment extraction is performed on the Twitter data from major stock companies. Analysis shows that authoritative user's sentiment affects the other users after an event with the lag of 3 days. The prediction is performed for Twitter and stock data from four companies. The sentiment...
In this paper, we study relations ranking and object classification for multi-relational data where objects are interconnected by multiple relations. The relations among objects should be exploited for achieving a good classification. While most existing approaches exploit either by directly counting the number of connections among objects or by learning the weight of each relation from labeled data...
This paper deals with modeling human behavior routines during driving. We propose a new vision of the maximum causal entropy framework for inverse reinforcement learning to predict actions to be triggered in particular situation (lane change). We designed a plugin to enhance functionalities of the vCar platform which is presents an open source solution for the analysis and visualization of data from...
The use of information technology in the study of human behavior is a subject of great scientific interest. Cultural and personality aspects are factors that influence how people interact with one another in a crowd. This paper presents a methodology to detect cultural characteristics of crowds in video sequences. Based on filmed sequences, pedestrians are detected, tracked and characterized. Such...
This paper presents a rapidly and lower neural networks to treat those waste water index that is difficult to be measured. Model called soft sensor is composited two parts: one is used to estimate the principal linear output, the other one is used to adjust estimated error to obtain better accuracy. Selection of features that effects greatly computation scale and predict accuracy is discussed also...
In data classification mining, the decision tree method is a key algorithm. ID3 (Iterative Dichotomiser 3) algorithm which was presented by Quinlan is a famous decision tree algorithms, but ID3 has some shortcomings such as high complex computation in computing the information entropy expression, multivalue bios problem in the process of selecting an optimal attribute, large scales, etc. In order...
The method principal component (PCA) allows to allocate from a matrix of these several objects with a large amount of signs only 1–3 vectors containing 90–95% of information. Usually measuring problem of assessment of these main components is solved by the iterative NIPALS procedure or the algebraic SVD procedure, however both of these methods often give ambiguous estimates. For the purpose of elimination...
The presence of gaps in time series makes the data analysis process difficult. Although there are several methods for filling such gaps, they do not present satisfactory results as the gap widens. The proposal of this paper is to present a new methodology that uses techniques of extraction of characteristics and identification of systems to fill the missing data. The proposed methodology was applied...
DNA Microarray data is a high-dimensional data that enables the researchers to analyze the expression of many genes in a single reaction quickly and in an efficient manner. Its characteristics such as small sample size, class imbalance, and data complexity causes it difficult to classified. Feature selection is a process that automatically selects features that are most relevant to the predictive...
This paper describes a position estimation approach based on high-frequency voltage injections for dual three-phase permanent magnet synchronous machines (DTP-PMSM) whose sets of windings are spatially shifted by 360 electrical degrees. Fail-operational drives gain more and more importance in automotive applications. Typically, multiphase machines are used to realize fail-operational properties. Besides...
The article describes typical problems solved by means of inductive modeling, provides information on the development of this scientific direction in Ukraine and abroad, characterizes the basic fundamental, applied and technological achievements, and formulates the most promising ways of further research.
This paper investigates an efficient modal method for reducing weak transmission stability boundaries and identifying voltage control areas. This method divides the power system network into regions to eventually reduce the control candidates for each controller and minimize the interaction between each voltage control area. To determine the optimized number of areas, proper selection of threshold...
The article studies the issue of identification of literary texts with artificial neural networks. We showed that artificial neural networks for solving the problem of classifying literary texts makes it possible to obtain a true result for determining the category of a text with a probability of 95%. However, determining the genre of a literary text is more difficult and in the worst case it is possible...
In this paper, we formally prove that the classification rules formed on the basis of contrast patterns are guaranteed to be of a high quality. We propose to use the new ‘Sets of Contrasting Rules’ pattern for the identification of local differences between the classes of the dataset. Being essentially a contrast pattern formed of several classification rules, ‘Sets of Contrasting Rules’ pattern is...
When studies in literature are examined, it is seen that different approaches have been used to solve facility layout problems. The relationship between departments in layout is always important. In this study, data mining technique is used for analyzing relations among departments and then association rules are obtained. Determining closeness relationships between the departments in facility are...
In the last few years, with the emergence of ambient assisted living, the study of human behavioral pattern took a wide interest from research communities around the world. In many literatures, pattern recognition was widely adopted approach to implements in human behavior study from computing perspective. Pattern recognition brings a promising results in terms of accuracy for modeling human behavior...
Aiming at the problem that the semantic explanation of the existing topic model is poor and the accuracy is not high, a semi-supervised topic learning and representation method based on association rules and metadata is proposed. First, we used the metadata as a priori knowledge to guide the topic learning, and got the probability distribution of the term in the document. Then, we got the frequent...
It is more and more common to use function words as an important text feature of Chinese, such as the research on “A Dream of Red Mansions” of Li Xianping. But the effect of using all function words as a feature in distinguishing writers' writing style is not prominent. Our study finds that using the classical Chinese function words and sentence tail function words as a feature is better than differentiated...
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