Excluding fitness helps improve robustness of evolutionary algorithms p. 8 Testing voice mimicry with the YOHO speaker verification corpus p. 15 Image multi-noise removal via Levy process analysis p. 22 Evaluating the size of the SOAP for integration in B2B p. 29 Personalised search on electronic information p. 35 Data mining coupled conceptual spaces for intelligent agents in data-rich environ...

Data Mining is being actively applied to stock market since 1980s. It has been used to predict stock prices, stock indexes, for portfolio management, trend detection and for developing recommender systems. The various algorithms which have been used for the same include ANN, SVM, ARIMA, GARCH etc. Different hybrid models have been developed by combining these algorithms with other algorithms li...

this paper, a pattern-based stock data mining approach which transforms the numeric stock data to symbolic sequences, carries out sequential and non-sequential association analysis and uses the mined rules in classifying/predicting the further price movements is proposed. Two formulations of the problem are considered. They are intra-stock mining which focuses on finding frequently appearing pa...

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-...

The application of AI techniques for stock price prediction leads to voluminous growth of wealth of investors with the advent of technology. Several prediction and estimations are coming up for almost all sectors of the market. Particularly any kind of stock price prediction is not at all possible without excessive data manipulation which can be done effectively only thru data mining. The syste...

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems ...

Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

Fuzzy time series forecasting model is one of the tools that can be used to identify factors in order to solve the complex process and uncertainty, nowadays widely used in forecasting problems, but having appropriate universe of discourse and interval length are two subjects that exist in the Fuzzy time series. Recently Adaptive Time-Variant Model for fuzzy time series (ATVF) has been proposed ...

Global ocean salinity/temperature variations are attracting increasing attention, due to their influence on ocean-atmospheric changes and their potential for improved climate forecasting. The goal is to analyze historic salinity/temperature data to make predictions about future variations. Traditional statistical models that assume data independence are not applicable as ocean data are often in...