OVA scheme vs. single machine approach in feature selection for microarray datasets p. 10 Similarity searching in DNA sequences by spectral distortion measures p. 24 Multispecies gene entropy estimation, a data mining approach p. 38 A unified approach for discovery of interesting association rules in medical databases p. 53 Named relationship mining from medical literature p. 64 Experimental st...

When the standard genetic algorithm is used to solve the fuzzy programming problem, poor convergence occurs. In order to overcome this defect, this paper presents a hybrid intelligent evolutionary algorithm based on nonlinear support vector machine (SVM) to solve the fuzzy programming problem. Firstly, based on the research of genetic algorithm, evolutionary strategy and genetic algorithm are c...

The Data mining technology is widely used in various fields. Temperature forecasting is also the domain area where data mining is used. Accuracy is always an issue in forecasting and it will remain a matter of the concern forever. This paper presents data mining model based on heuristic search, which leads to more accurate solution of forecasting problems. The suggested model is implemented on ...

Many techniques continue to be proposed so far to get rid of the noise through digital images with more optimistic method. Each technique has its very own drawbacks. Although Fuzzy Mean Median (FMM) has demonstrated promising results on the available techniques, given it utilizes the top features of data mining to get rid of mixed noise. This data mining method is employed to check which sort o...

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock pr...

Stock price crash risk has a significant impact on investors, creditors, managers, and shareholders, so the prediction of this phenomenon is a very important issue in investment and risk management decisions. This research investigates the effect of business strategy and stock price synchronicity on stock price crash risk. Following Bentley et al.[2], composite strategy score has been used to ...

Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the futur...

There is an old Wall Street adage goes, ‘‘It takes volume to make price move”. The contemporaneous relation between trading volume and stock returns has been studied since stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China’s stock market. Pacific-Basin Finace Journal...

While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computi...

this paper proposes the exchange market algorithm (ema) to solve the combined economic and emission dispatch (ceed) problems in thermal power plants. the ema is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. existence of two seeking operators in ema provides a high ability in exploiting global optimum point. in order to show the capabilities ...