Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

Streptomyces sp. strain SK68, isolated from peanut rhizosphere, promotes growth and alleviates salt stress in tomato ( Solanum lycopersicum cv. Micro-Tom)

Abstract: A novel actinobacterium, strain SK68, was isolated from the rhizosphere of peanut plant and its salinity stress alleviation ability was studied using tomato (Solanum lycopersicum cv. Micro-Tom) plants. Based on 16S rDNA based phylogenetic analysis, strain SK68 has been identified as a Streptomyces sp. Strain SK68 had branched substrate mycelium bearing smooth surfaced spores and the spore colour is brownish grey on ISP4 medium. It exhibited enzyme activities such as xylanase, cellulase, amylase, and pectinase and degraded hypoxanthine, casein, and L-tyrosine. The strain SK68 differed in its banding pattern in BOX-PCR and RAPD fingerprinting compared to the closely matching type strains Streptomyces erythrochromogenes NBRC 3304T (AB184746), S. flavotricini NBRC 12770T (AB184132), S. racemochromogenes NBRC 12906T (AB184235), and S. polychromogenes NBRC 13072T (NR041109). Strain SK68 was evaluated for its salinity stress-alleviating activity in tomato plants with 180 mmol/L NaCl under gnotobiotic condition. A significant increase in plant biomass was observed in strain SK68-inoculated tomato plants under salt stress compared to control and salt-stressed non-inoculated plants.

Mouse gesture based authentication using machine learning algorithm

Abstract: To authenticate a computer user, classification based on mouse operating behavior is proposed. It is used to perform the authentication task in initial login stage by capturing the mouse movements. The mouse operating behavior of users can be captured as coordinate axes and elapsed time, based on movement of the mouse. The obtained mouse behavior data consist of outliers and behavioral variability; these can be addressed by Peirce's criterion and Weighted Least Square Regression (WLSR) respectively. The features are extracted from that mouse behavior data to categorize the user's unique mouse behavior. The extracted features are analyzed and performs authentication using Learning Vector Quantization (LVQ). This LVQ Algorithm is used as a classifier, to find whether the given sample is authorized or unauthorized identity. The test result proves that, the proposed method WLSR with learning vector quantization classifier provides low error rates with good accuracy than the existing method.