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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.
Oriented graphs belong to a part of Mathematics - Combinatorics called Graph Theory. One of the fundamental terms here is a tree. The tree structures have widespread use not only in Mathematics. They can be used in Decision Theory as data mining tools as well. In the present paper we point out to the use of decision trees as models for financial services, namely, by credit scoring, fraud and churn...
Discovery of temporal dependence is the basic idea for evaluating gene networks using Granger causality. However, with the advancement of technology, now we can analyze multiple genes simultaneously that result in high dimensional data. Recent studies suggest that more causal information can be retrieved if we reverse the time stamp of time series data along with standard time series data. Based on...
State-of-the-art Computed Tomography Angiography (CTA) scanners are capable of acquiring rigorous 3D vasculature information. Blood filled vessels are extracted from the data cloud for pathological analysis on the basis of intensity value, measured in Hounsfield units. Setting a hard threshold in CTA images for differentiating coronaries from fatty muscles of heart could be misleading as it lacks...
The paper presents a data mining technique for qualitative analysis of functional differential equations of compartmental type. As a result we get the decision tree able to classify the system behaviour depending on relations between initial conditions and between rate constants. Antitumour immunity example is presented.
This paper describes a methodology for the design of a supervisory system applied to a biotechnological process. A discrete-event system (DES) is build by the joint participation of the process expert along with clustering and classification techniques applied to measured signals. The automaton becomes a heuristic model of the process under supervision. In the application example, a yeast batch culture,...
Credit risk assessment is the groundwork of the individual housing loans. However, what banks really care about is how to make the most money with least risk. The most difficult problem of the credit risk management for the banks is that they both have to encourage loans, but also they need to avoid bad debt. This paper proposes a new risk assessment system of the individual housing loans by integrating...
Feature (gene) selection is an important preprocessing step for performing data mining on large-scale bioinformatics datasets. However, one known concern is that feature selection can sometimes give very different results when applied to very similar data sets. Ensemble gene selection is a promising new approach which may help resolve this concern, producing more stable gene lists and better classification...
In the context of financial storm, financial crisis early warning has become an important object of research in the field of corporate financial management. This paper collected the data from China Stock Market Trading Database (CSMAR) and sorted out the financial indices of listed companies. We first used factor analysis to select variables and extract common factors, and then established the financial...
The paper firstly briefly introduces the remote sensing investigation in vegetation and wetlands, preprocessing procedure of remote sensing image in the natural resources monitoring, and the commonly-used extraction methods of wetland vegetation information. In addition, the paper discusses the research status of the remote sensing inversion model, and elaborates on the three commonly-used models:...
Attracting more students into science and engineering disciplines concerned many researchers for decades. Literature used traditional statistical methods and qualitative techniques to identify factors that affect student retention up most and predict their persistence. In this paper we developed two neural network models using a feed-forward backpropagation network to predict retention for students...
Machine Science, or Data-driven Research, is a new and interesting scientific methodology that uses advanced computational techniques to identify, retrieve, classify and analyse data in order to generate hypotheses and develop models. In this paper we describe three recent biomedical Machine Science studies, and use these to assess the current state of the art with specific emphasis on data mining,...
This paper describes an appropriate category discovery method that simultaneously involves a customer's lifestyle category and item category for the sustainable management of retail services, designated as ``category mining''. Category mining is realized using a large-scale ID-POS data and customer's questionnaire responses with respect to their lifestyle. For the heterogeneous data fusion, we propose...
Multi-factor of regional social relates to the landscape pattern, Model can simulate the drive mechanism can result by computer simulation . This paper inferred the forecast model of fuzzy weight Markov chain. The new model which is called FWMC to predict the future, combining the character of landscape and the theory of Markov chain, value of cultivated land demand in land use planning. Factors to...
Application of the rough set theory and BP neural network model in disease diagnosis is discussed in this paper. BP neural network model was established, and trained by the real diagnosis data of nephritis, utilizing the neural network toolbox in Matlab software. In this way we were able to provide a good solution to the problem of diagnose for new patients based on their chemical test data. By data...
When a telemarketing staff or a seat calls, whether the customer will answer the phone is under uncertainty. This is a common risk faced by telemarketing companies: holding the telephone too long will bring a big lose to the company, while waiting too short won't be beneficial also. In this paper, we want to solve the problem and propose the definition optimal holding time, and then we conduct data...
Spatial data analysis and mining is more difficult to put into practice than classical data analysis due to complexity of geographical phenomena. This paper preliminary analyzed main problem faced by SDM, provided a basic framework for SDM with spatial statistical methods. Logistic regression is popular in LUCC for building relationships between land use types and influential factors by spatial sampling...
For the production of coal enterprises in new mine after the formation of a strategic alliance, the article takes into account the Union,s overall development strategy of coal resources for a certain period and the interests of the coal enterprises, establishes the alliance production planning model in pursuit of the minimum cost. The application of multi-objective genetic algorithm, each enterprise...
A detailed discussion on contributions from feature attributes to the classifying attribute in the nonlinear classification model based on the Choquet integral is given in this paper. The work provides a new understanding to the geometric structure of the model with contribution rates from the feature attributes towards the classification, as well as the interaction among them.
Water quality is an important global issue, requiring effective management, which needs good predictive tools. While good methods for lake water quality prediction have previously been developed, accurate prediction of river water quality has hitherto been difficult. This project combines process-model and data mining approaches through evolutionary methods, resulting in tools for more effective water...
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