In the era of grid computing, resource allocation plays a vital role for assigning the available resources. This paper describes how to reduce the search time for the best available resources and assure instant provisioning of the lately added resources to the grid thereby using clustering and artificial neural networks. The efficacy is achieved through K-Means clustering algorithm which is used to cluster the similar type of resources on the basis of their configuration as high, medium or low thereby decreasing the search time by searching only into the cluster of high availability instead of searching for the best from all of the available resources. Thereafter artificial neural network trained with feed forward propagation is deployed to automatically assign the newly added resources to appropriate cluster. This approach significantly reduces the computational time of resource allocation.
The deployment mechanism in wireless sensor networks (WSN) affects the coverage, connectivity, bandwidth, packet loss, lifetime of network, etc. features [1]. Depending upon the application, the sensor nodes are deployed in either random or deterministic fashion, accordingly WSN has different requirements and features. In this work, a new clustering technique, Energy Efficient Deterministic ClusterHead Selection Algorithm (E 2 DCH), is proposed for deterministically deployed WSN. Better coverage with less number of nodes, minimum traffic from nodes to base station, balanced energy consumption are the main features of E 2 DCH to improve life time of WSN. The proposed algorithm uses dynamic routing from nodes to respective cluster head by considering the number of nodes and residual node energy of all the involved nodes. It includes an efficient technique for reorganizing the clusters. Analysis and simulation results demonstrate the correctness and effectiveness of the proposed algorithm.
Molecular markers are useful tool for assessing genetic variations and resolving genotype identity. In the current study, genetic diversity among 20 rice genotypes was assessed using the random amplified polymorphic DNA (RAPD) and simple sequence repeat (SSR). In RAPD analysis, 20 primers generated a total of 116 bands of which 114 were polymorphic. The number of amplification products produced by each primer varied from 4 to 7 with an average of 5.8 bands per primer. Twenty (20) SSR primers generated a total of 65 alleles with an average 3.2 alleles per primer. Genetic diversity of 20 genotypes estimated by polymorphic information content (PIC) value ranged from 0.62 to 0.97 in SSR and 0.33 to 0.88 with RAPD analysis. The cluster dendrogram by SSR revealed two major clusters. Rajeshwari was the only genotype in cluster I. The cluster II further divided into two sub clusters IIA and IIB. II A consisted of 17 genotypes while II B consisted of two genotypes (Apo and Kalakeni). The information generated from this study can be used to maximize selection of diverse parents and broaden the germplasm base for the future rice breeding programs.
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