No abstract

Symbiotic seed germination for conservation and cultivation of orchids holds colossal merit asmycorrhizal fungus in its system improves their growth and adaptability. Symbiotic activity ishighly specific in some species, but in other cases the same fungus from one species is effective in aseries of related species. The present work describes inter-specific activity of three fungal isolatesfrom seedling root of Vanda thwaitesii to support seed germination and seedling growth of V.wightii, a closely related species from India. Among the three isolates, two designated as Wyd2 andIdk were identified as Ceratobasidium sp through sequencing of ITS1 and ITS4 regions. Oneisolate designated as Wyd1 did not clad with any described genera, but remained as an out-groupunder the family Ceratobasidiaceae. All the three isolates possessed binucleate hyphae producingellipsoidal, oval or barrel shaped monilioid cells and supported 80–95% seed germination,transforming 70–85% of them into protocorms in 30 days duration. Symbiotic seedlingdevelopment starting with the promeristem formation, first leaf development and second leafinitiation occurred in 95% of the protocorms in a time interval of 60 days compared to 90–120 daysthrough asymbiotic method. The fungal isolates from Vanda thwaitesii evaluated are provedeffective in V. wightii for its symbiotic seed germination and thus useful to mycorrhiza assistedconservation.

In masquerade attack, attacker impersonates legitimate user. Most of the masquerade detection techniques done so far are based on supervised learning techniques. But here in this paper masquerade detection based on unsupervised learning techniques are used. Various clustering algorithms used are K-Means, K-Medoid, Agglomerative clustering algorithm and DBSCAN. A comparative study is done based on the detection capability of these four clustering algorithms. The experiment is conducted on Schonlau data set [1]. From the experiment it was found that K-Medoid algorithm, agglomerative clustering algorithm and DBSCAN algorithm outperforms K-means clustering.