This paper proposes a fuzzy logic based DSS for stock market. This system will help investors of the stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this...

Title of dissertation: Mining of Business Data Shu Zhang, Doctor of Philosophy, 2009 Dissertation directed by: Associate Professor Wolfgang Jank AMSC and R.H.Smith School of Business Applying statistical tools to help understand business processes and make informed business decisions has attracted enormous amount of research interests in recent years. In this dissertation, we develop and apply ...

In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have ...

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 ...

In this paper, we propose a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD). The proposed method which is consisted of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization use genetic algorithms (GA) to perform efficient and effecti...

The use of intelligent systems for stock market predictions has been widely established. This chapter introduces two Genetic Programming (GP) techniques: Multi-Expression Programming (MEP) and Linear Genetic Programming (LGP) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm and Takagi-Sugeno n...

this study proposes a modified version of cultural algorithms (cas) which benefits from rule-based system for influence function. this rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. this is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. this rule ...

The Agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also the Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the classification technique like neural networks such as self build up the model of Back-propagation neural network (BPNN) to predict vegetab...

The increased popularity of financial time series forecasting in recent times lies to its great importance in predicting the best stock market timing. In this paper, we develop the concept of a pattern modeling and recognition system for predicting future behavior of time series using local approximation. In order to improve the performance of this system, we propose a systematic and automatic ...

Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, making an exhaustive search infeasible. Therefore, efficient search strategies are of vital importance. Search strategies based on ge...