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Abstract: | The composition and variability of heterotrophic bacteria along the shelf sediments of south west coast of India and its relationship with the sediment biogeochemistry was investigated. The bacterial abundance ranged from 1.12 x 103 – 1.88 x 106 CFU g-1 dry wt. of sediment. The population showed significant positive correlation with silt (r = 0.529, p< 0.05), organic carbon (OC) (r = 0.679, p< 0.05), total nitrogen (TN) (r = 0.638, p< 0.05), total protein (TPRT) (r = 0.615, p< 0.05) and total carbohydrate (TCHO) (r = 0.675, p< 0.05) and significant negative correlation with sand (r = -0.488, p< 0.05). Community was mainly composed of Bacillus, Alteromonas, Vibrio, Coryneforms, Micrococcus, Planococcus, Staphylococcus, Moraxella, Alcaligenes, Enterobacteriaceae, Pseudomonas, Acinetobacter, Flavobacterium and Aeromonas. BIOENV analysis explained the best possible environmental parameters i.e., carbohydrate, total nitrogen, temperature, pH and sand at 50m depth and organic matter, BPC, protein, lipid and temperature at 200m depth controlling the distribution pattern of heterotrophic bacterial population in shelf sediments. The Principal Component Analysis (PCA) of the environmental variables showed that the first and second principal component accounted for 65% and 30.6% of the data variance respectively. Canonical Correspondence Analysis (CCA) revealed a strong correspondence between bacterial distribution and environmental variables in the study area. Moreover, non-metric MDS (Multidimensional Scaling) analysis demarcated the northern and southern latitudes of the study area based on the bioavailable organic matter |
Description: | Advances in Applied Science Research, 2013, 4(3):315-328 |
URI: | http://dyuthi.cusat.ac.in/purl/4079 |
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Biogeochemistry ... bacterial heterotrophs.pdf | (634.3Kb) |
Abstract: | Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively |
Description: | Soft Comput (2014) 18:1093–1112 DOI 10.1007/s00500-013-1129-0 |
URI: | http://dyuthi.cusat.ac.in/purl/4219 |
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Learning disabi ... ol using ANN and ANFIS.pdf | (2.950Mb) |
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