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We propose a data mining approach to predict the wine's quality level in order to improve the quality of products for wine enterprises in this paper. A large dataset is considered and three regression techniques were applied. Through the comparison, we get the conclusion that the model established by neural network is more accurate and it can improve the quality of wine's production.
As the development of Web service, reliable service quality evaluation has become the key in the service optimization. This paper addresses this problem by proposing a reliable service quality evaluation model in the Web services community. The model initially imports Web service community aiming at evaluation unreliable when involved criterions are beyond service domain. The assistant domain Ontology...
This study proposed a novel HPSO-SVR model that hybridized the particle swarm optimization (PSO) and support vector regression (SVR) to improve the regression accuracy based on the type of kernel function and kernel parameter value optimization with a small and appropriate feature subset, which is then applied to forecast the monthly rainfall. This optimization mechanism combined the discrete PSO...
The traditional prediction models of business failure are usually constructed upon the research sample without missing values, that is, the training and testing procedure of the prediction model are not able to be completed if some observations of the relevant variables are missing. This study solves this problem by applying for the data imputation technique of which the autoassociative neural networks...
It is known that Logistic Regression coupled with Partial Least Squares dimension reduction (PLSDR-LD) is capable of extracting a great deal of useful information for classification from gene expression profile and getting a rather high classification accuracy rate. In this study, we replace the logistic function of Logistic Regression with several functions which are similar to logistic function...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
Node localization is one of the key technologies in wireless sensor networks. Taking into account the reasons for the hardware and cost, DV-Hop algorithm is wildly used. The main DV-Hop localization error is the distance between unknown nodes and anchor nodes. In this paper, the estimation of average hop-length and RSSI value has been applied to reduce the error of measurement, which is the distance...
Minimum redundancy maximum relevancy (mRMR) is one of the successful criteria used by many feature selection techniques to evaluate the discriminating abilities of the features. We combined dynamic sample space with mRMR and proposed a new feature selection method. In each iteration, the weighted mRMR values are calculated on dynamic sample space consisting of the current unlabelled samples. The feature...
Liver biopsy is considered as mandatory for the management of patients infected with the hepatitis C virus (HCV), particularly for staging of fibrosis degree. However, due to its invasive nature and limitations of sampling error, the tendency is to substitute the liver biopsy with non-invasive method. The objective of this study is to combine the serum biomarkers and histopathological findings to...
Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due...
Multi-core and many-core Systems-on-Chip (SoC) are growing more complex than ever. Consequently, developing system models for such SoCs to guide and validate architectural and implementation decisions is becoming a daunting task. It consumes a huge amount of time and effort just to get the model up and running. Although these system models can be fairly abstracted, they still require the setup of...
Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute B, find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute B. This problem is motivated by the fact that often available historic data is biased due to discrimination, e.g., when B denotes ethnicity. Using...
Throughout the 1990s, four global waves of financial turmoil occurred. The beginning of the 21st century has also suffered from several crisis episodes, including the severe sub prime crisis. However, to date, the forecasting results are still disappointing. This paper examines whether new insights can be gained from the application of the Self-Organizing Map (SOM) - a non-parametric neural network-based...
A novel Neural Network Based Fuzzy Inference System for financial default forecast will be introduced. A wide range of financial forecasts is known. This method is focusing on the economical default forecast, but the method can be used generally for other financial forecasts as well, for example for calculating the Value at Risk. This hybrid method is combined by two classical methods: the artificial...
Suppliers plays a vital role in terms of its ability to provide reliable quality services, which directly determines the credibility of corporate success. In order to solve the problems related to selection of suppliers, we, in this paper, introduce the concept of module design while combining with mixed update mechanism, and then propose a choice model for best trustworthy supplier called BTSM. First...
Under the analysis of the present situation of urban roads at home and abroad, the paper reviews the development of tripper guiding information system, traffic-flow distribution model and route choice model. Then it has three multi-attribute decision-making model based on the influence factors of tripper route choice and uncertainty theories, in order to analyze the decision-making process of tripper...
In this paper, we present an integrated framework for transcribing Mandarin-English code-mixed lectures with improved acoustic and language modeling. The target corpus considered here has almost all utterances in the host language of Mandarin, while many of them are embedded with terms (mostly special terminologies for the course) produced in the guest language of English. For acoustic modeling, we...
From a new view of financial distress concept drift, this paper attempts to put forward a new method for dynamic financial distress prediction modeling based on slip time window and multiple support vector machines (SVMs). A new algorithm is designed to dynamically select the proper time window to handle concept drift, and then a dynamic classifier selection method is used to build a combined model...
Quality of organization decision making can be increased greatly by group decision, and more and more managers begin pay attention to this method. But there are still some problems existed in group decision, such as in some case, experts' ability, attitude and confidence will greatly affect decision result, and how to find abnormal expert and reduce his decision weight or dismiss him from the decision...
In this paper, we consider fuzzy variable weight combining forecasting which based on optimal combining forecasting and integrate those two modeling methods. Studies show that: optimal combining forecasting combines forecasting methods in a period of time and makes forecast through fixing the weight of forecasting methods that in the combination. But it does not consider that the weight will vary...
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