detection & classification of power quality (pq) disturbances are the most important problems in distribution systems. in this paper, a new approach for the detection and classification of single and combined pq disturbances is proposed which utilizes fuzzy logic and particle swarm optimization (pso) algorithms. in this approach, first suitable features of the waveform of pq disturbances ar...

we can reach by dna microarray gene expression to such wealth of information with thousands of variables (genes). analysis of this information can show genetic reasons of disease and tumor differences. in this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...

prediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the previous information of patients records. in this paper has been used adaptive nero fuzzy inference system and data mining techniqu...

Ant colony optimization (ACO) algorithms for classification in general employ a sequential covering strategy to create a list of classification rules. A key component in this strategy is the selection of the rule quality function, since the algorithm aims at creating one rule at a time using an ACObased procedure to search the best rule. Recently, an improved strategy has been proposed in the c...

Many classification algorithms produce a classifier that is a weighted average of kernel evaluations. When working with a high or infinite dimensional kernel, it is imperative for speed of evaluation and storage issues that as few training samples as possible are used in the kernel expansion. Popular existing approaches focus on altering standard learning algorithms, such as the Support Vector ...