Association Rule play very important role in recent scenario of data mining. But we have only generated positive rule, negative rule also useful in today data mining task. In this paper we are proposing “A new method for generating all positive and negative Association Rules” (NRGA).NRGA generates all association rules which are hidden when we have applied Apriori Algorithm. For representation ...

This article proposes a novel Pareto-based multiobjective meta-heuristic algorithm named non-dominated ranking genetic algorithm (NRGA) to solve multi-facility location-allocation problem. In NRGA, a fitness value representing rank is assigned to each individual of the population. Moreover, two features ranked based roulette wheel selection including select the fronts and choose solutions from ...

Streptococcus mutans, a Gram-positive bacterium, is considered to be a major etiologic agent of human dental caries and reported to form biofilms known as dental plaque on tooth surfaces. This organism is also known to possess a large number of transport proteins in the cell membrane for export and import of molecules. Nitrogen is an essential nutrient for Gram-positive bacteria, though alterna...

Bacillus subtilis uses glutamine as the best source of nitrogen. In the absence of glutamine, alternative nitrogen sources such as ammonium can be used. Ammonium utilization involves the uptake of the gas or the ammonium ion, the synthesis of glutamine by the glutamine synthetase and the recycling of the glutamate by the glutamate synthase. In this work, ammonium transport in B. subtilis was st...

Multi-objective evolutionary algorithms (EAs) that use non-dominated sorting and sharing have been criticized. Mainly for their: 1) ( 3 MN O computational complexity (where M is the number of objectives and N is the population size). 2Non-elitism approach; 3-the need for specifying a sharing parameter. In this paper, a method combining the new Ranked based Roulette Wheel selection algorithm wit...

Support Vector Machine (SVM) is a popular and landmark classification method based on the idea of structural risk minimization, which has obtained extensive adoption across numerous domains such as pattern recognition, regression, ranking, etc. In order to achieve satisfying generalization, penalty and kernel function parameters of SVM must be carefully determined. This paper presents an origin...

Evolutionary algorithms are becoming increasingly valuable in solving large-scale, realistic engineering multiobjective optimization problems, which typically require consideration of conflicting and competing design issues. A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search ...

Abstract This paper presents a bi-objective model for the design and optimization of sustainable hierarchical multi-modal hub network. The proposed focuses on sustainability by considering economic, environmental, social aspects decisions in A case Turkish network freight transportation is used to validate model. To solve small-sized problems, augmented epsilon constraint method version 2 (AUGM...

In this research, a bi-objective scheduling problem with controllable processing times on identical parallel machines is investigated. The direction of this paper is mainly motivated by the adoption of the just-in-time (JIT) philosophy on identical parallel machines in terms of bi-objective approach, where the job processing times are controllable. The aim of this study is to simultaneously min...

Gene expressions by microarray data technique have been effectively utilized for classification and diagnostic of cancer nodules. Numerous data mining techniques like clustering are presently applied for identifying cancer using gene expression data. An unsupervised learning technique is a clustering technique used to find out grouping structure in a set of data. The problem of feature selectio...