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In response to globalization, International Financial Reporting Standards (IFRS) has become the norm of the global capital markets. Companies preparing financial statements using IFRS may make the financial situation fully disclosed. Nevertheless, an overestimated accrual expense of a balance sheet may not only underestimate the earnings data, but also increase the cash outflows of the statement of...
Closing prices of the financial stock market change daily at the end of each session. These changes happen because of many factors that affect the prices of the stocks. This study attempts to accurately predict closing prices by applying a data mining approach and investigate and identify the most influential factors of Dubai Financial Stock Market prices. The main objective of this study is to help...
Regression analysis is one of the components of data mining techniques. Various regression algorithms have been proposed to mine the data efficiently and to propose a suitable business model. Every algorithm caters to a particular need and not necessarily produces the best fit futuristic model for all types of data. On the other hand, todays Web is expanding rapidly and affecting all aspects of our...
This paper investigates the different attributes used in evaluating faculty performance to come up with a regression model that predicts faculty performance. The main objective of this paper is to develop a model for predicting faculty performance and design a framework of data mining implementing ETL. The outcome of this research could be used as basis in improving the instruction in an academic...
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.
Based on the real data of a Chinese commercial bank's credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit...
In this study, a hybrid model using multivariate adaptive regression splines (MARS) and SVR is proposed for sales forecasting of information technology (IT) products. Support vector regression (SVR) has become a promising alternative for forecasting due to its generalization capability in obtaining a unique solution. However, one of the key problems is that SVR can not identify which forecasting variables...
Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
This paper uses GMDH method to establish a prediction model to forecast the output value of transport & storage of Guangdong in China, since the original samples of the output value of transport & storage are less enough to be used with the traditional methods. Compared with traditional linear regression and artificial neural network, the predicted results show that GMDH method is an effective...
In recent years the kind of nerve network's analysis technique was on the rise, this technology's discovery was when the information project department made Data Mining was born, this program had from the huge data discovers the regularity or correlation formidable ability, therefore this article will study the case to focus in the traditional PCB industry Micro-drill system regulation process capability...
Predictive modelling of multivariate data where both the covariates and responses are high-dimensional is becoming an increasingly popular task in many data mining applications. Partial Least Squares (PLS) regression often turns out to be a useful model in these situations since it performs dimensionality reduction by assuming the existence of a small number of latent factors that may explain the...
Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous...
The current study was to investigate game attendance in the Chinese Professional Baseball League using data mining. Simultaneous multiple linear regression was utilized to perform data analysis. Results show that the proposed model explained 54.2% of the total variance of game attendance. Additionally,weekend game attracted more attendance than non-weekend games; games played in home field attracted...
This paper use Microsoft SQL Server 2005 data mining tools and three methods of neural networks, decision trees and logistic regression to establish the financial crisis early-warning model of listed companies. The conclusion is that the three kinds of methods have good results and the prediction accuracy rate are 80% or more. The accuracy of the decision tree algorithm model is higher than others.
Several flaviviruses are important human pathogens, including dengue virus, a disease against which neither a vaccine nor specific antiviral therapies currently exist. QSAR study was carried out with the purpose of searching new competitive dengue inhibitors with similar properties to the existence inhibitors (i.e. data set). The approach began with the development of rigorously validated QSAR model...
Social networks have generated great expectations connected with their potential business value. The purpose of our research is to present that even a rudimentary application of data mining techniques can bring statistically significant improvement in marketing response accuracy throughout the virtual community. In our test the C&RT (classification and regression tree) approach was used to generate...
In order to predict the performance of a manufacturing process or system, proper mathematical models are needed. This research investigates the use of two competitive unsupervised data mining methods - regression and neural networks - in developing an empirical model for two electronics fabrication processes/systems. A case study from experimental data of electronics fabrication is used to demonstrate...
This paper applies DEA model to a sample of 58 power plate listed companies in the securities market in China in 2008, with a view to identifying the financial risk companies and non-financial risk companies, instead of using ST in the past. Then, after comparing logit regression model and neural network LVQ in predicting the company financial risks, the conclusion was drawn that neural network LVQ...
A new calculation method for the input of the neural network ensemble prediction (NNEP) model has been developed based on the data mining technology using the feature extraction method of Empirical Orthogonal Function(EOF) and the stepwise regression method, for investigating the effect of different model input with the same dimension on the prediction capacity of the NNEP model. Taking typhoon intensity...
With 112 listed companies from A-share market in Chinese securities markets as research sample, authors selected 56 newly ST companies among 2007 and 2008 as distressed enterprises group, and other 56 non-ST companies as comparison group. 13 financial indicators of these companies at three year before being ST were screened out, and the factor analysis and logistic regression were conducted. It found...
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