21 research outputs found

    Comparative Study on Physicochemical and Biological Parameters of Water among Fish Culture and Reconstructed Pond at Jahangirnagar University Campus, Bangladesh

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    The study was conducted to investigate the physicochemical and biological parameters of fish culture and reconstructed pond at Jahangirnagar university campus. The physicochemical parameters of water in culture and reconstructed pond were analyzed during February to September, 2014 and the mean value of temperature were 30.21±0.89 ºC and 29.96±0.91 ºC, pH value were 7.20±0.29 and 6.97±0.39, Dissolve Oxygen (DO) value were 6.44±0.40mg-1 and 6.22±0.30mg-1, Biochemical Oxygen Demand (BOD5) value were 1.02±0.32mg-1 and 0.78±0.18mg-1, Total Dissolve Solid (TDS) were 0.69±0.04mg-1 and 0.64±0.04mg-1, Electric Conductivity (EC) value were 215.38±21.27?Scm-1 and 128.58±1.10?Scm-1. From the study of biological parameter, it was found that Chlorophyceae and Euglenophyceae were dominant in studied ponds and the abundance of phytoplankton are in the order of Chlorophyceae &gt;Euglenophyceae &gt; Bacillariophyceae &gt; Cyanophyceae. The highest productivity was found in culture pond which indicates the suitability of using for aquaculture.J. Environ. Sci. &amp; Natural Resources, 9(1): 1-7 2016</jats:p

    Adaptive differential evolution based feature selection and parameter optimization for advised SVM classifier

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    © Springer International Publishing Switzerland 2015. This paper proposes a pattern recognition model for classification. Adaptive differential evolution based feature selection is used for dimensionality reduction and a new advised version of support vector machine is used for evaluation of selected features and for the classification. The tuning of the control parameters for differential evolution algorithm, parameter value optimization for support vector machine and selection of most relevant features form the datasets all are done together. This helps in dealing with their interdependent effect on the overall performance of the learning model. The proposed model is tested on some latest machine learning medical datasets and compared with some well-developed methods in literature. The proposed model provided quite convincing results on all the test datasets
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