Building extraction has been a challenging task due to complex structures and features of various land use with matching spectral and spatial attributes in a satellite data. We attempted to extract building as features using machine-learning algorithms such as Support Vector Machine (SVM), Random Forests (RF), Artificial Neural Network (ANN) and Improved Ensemble Technique as Gradient Boosting. The techniques used increases their classification accuracies using spectral properties as well as indices such as Normalized Difference Vegetation Index (NDVI) as attributes. Extracted results through various methods, performance of three different machine learning such as Ensemble method, RF and SVM are applied and results are analyzed for their behavior in different building distribution. Different algorithms showed variations in accuracies and performance in different built-up conditions. Ensemble algorithm performed very well in all conditions followed by RF and SVM performed better in coarse resolution, while ANN performed better in high resolution and overall accuracies of all algorithms increased with better spatial resolution. Ensemble algorithm showed relatively efficient performance in regions with extensive heterogeneous features. These analyses can helpful to provide quantitative data for various stocktaking analysis and city managers for better administration capabilities.
In this present investigation, two Multi Antibiotics Resistant (MAR) bacterial strains namely SR2 and SR4 were isolated from clinical waste. The bacterial isolate SR2 was resistant to most of the antibiotics tested, but sensitive to levofloxacin. While, the other strain SR4 was sensitive to Cefixime and levofloxacin. Morphological, biochemical and 16S rRNA sequence analysis identified these 2 bacterial strains SR2 and SR4 as Acinetobacter sp. (GenBank Acc. No. KJ879241) and Aeromonas hydrophila (GenBank Acc. No. KJ879242), respectively. The conjugal transfer efficiency of antibiotics resistant gene to another bacterial strain was also tested, using Corynebacterium alkanolyticum ATH3 as a recipient. In order to control these pathogenic bacterial strains, leaf extracts obtained from three Cassia plants named C. siamea, C. allata and C. occidentalis were used at different concentration. Cassia siamea was recorded to be most effective against both of these bacterial strains. Growth kinetics of these two bacterial strains revealed that growth was decreased with gradual increase of concentration of leaf extract, obtained from C. siamea and ultimately negligible growth at 30 mg mLG 1 (3%).
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