This paper presents a new multi-objective coupled energy and reactive power market clearing model namely the MO-CERPMC model in day-ahead competitive market environment. In proposed model, both the active and reactive power markets are considered as coupled markets and cleared in a same time frame. The multi-objective optimization problem involves the minimization of total payment functions for...

A fuzzy-genetic data-mining algorithm for extracting both association rules and membership functions from quantitative transactions is shown in this paper. It used a combination of large 1-itemsets and membershipfunction suitability to evaluate the fitness values of chromosomes. The calculation for large 1itemsets could take a lot of time, especially when the database to be scanned could not to...

We conduct evolutionary programming experiments to evolve artificial neural networks for forecast combination. Using stock price volatility forecast data we find evolved networks compare favorably with a naı̈ve average combination, a least squares method, and a Kernel method on out-of-sample forecasting ability—the best evolved network showed strong superiority in statistical tests of encompassi...

The growth of internet technology spread a large amount of data communication. The communication of data compromised network threats and security issues. The network threats and security issues raised a problem of data integrity and loss of data. For the purpose of data integrity and loss of data before 20 year Anderson developed a model of intrusion detection system. Initially intrusion detect...

Bayesian Networks are increasingly being used to model complex socio-economic systems by expert knowledge elicitation even when data is scarce or does not exist. In this paper, a Multi-Objective Evolutionary Algorithm (MOEA) is presented for assessing the parameters (input relevance/weights) of fuzzy dependence relationships in a Bayesian Network (BN). The MOEA was designed to include a hybrid ...

There are various techniques for data mining and data analysis. Data mining is very important in the information retrieval areas especially when the data amounts are very large. Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. In this paper, we present an application of evolutionary-fuzzy classification...

Most models for the time series of stock prices have centered on autoregressive (AR) processes. Traditionaly, fundamantal Box-Jenkins analysis [2] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. A fuzzy regression model is then introduced. Following this description, an artificial fu...

Most models for the time series of stock prices have centered on autoregressive (AR) processes. Traditionally, fundamental Box-Jenkins analysis have been the mainstream methodology used to develop time series models. We briefly describe developing a classical AR model for stock price forecasting. Then a fuzzy regression model is introduced. Following this description, an artificial fuzzy neural...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

Disease forecasting using soft computing techniques is major area of research in data mining in recent years. To classify heart and diabetes diseases, this paper proposes a diagnosis system using cuckoo search optimized rough sets based attribute reduction and fuzzy logic system. The disease prediction is done as per the following steps 1) feature reduction using cuckoo search with rough set th...