A Hybrid Machine Learning Technique For Feature Optimization In Object-Based Classification of Debris-Covered Glaciers

Object-based features like spectral, topographic, and textural are supportive to determine debris-covered glacier classes. The original feature space includes relevant and irrelevant features. The inclusion of all these features increases the complexity and renders the classifier’s performance. Therefore, feature space optimization is requisite for the classification process. Previous studies have shown a rigorous exercise in manually selecting the best combination of features to define the target class and proven to be a time consuming task. The present study proposed a hybrid feature selection technique to automate the selection of the best suitable features. This study aimed to reduce the classifier’s complexity and enhance the performance of the classification model. Relief-F and Pearson Correlation filter-based feature selection methods ranked features according to the relevance and filtered out irrelevant or less important features based on the defined condition. Later, the hybrid model selected the common features to get an optimal feature set. The proposed hybrid model was tested on Landsat 8 images of debris-covered glaciers in Central Karakoram Range and validated with present glacier inventories. The results showed that the classification accuracy of the proposed hybrid feature selection model with a Decision Tree classifier is 99.82%, which is better than the classification results obtained using other mapping techniques. In addition, the hybrid feature selection technique has sped up the process of classification by reducing the number of features by 77% without compromising the classification accuracy

Performance Assessment of a Sensor-Based Variable-Rate Real-Time Fertilizer Applicator for Rice Crop

Variable-rate technology (VRT) may reduce input costs, increase crop productivity and quality, and help to protect the environment. The present study was conducted to evaluate the performance of a variable-rate fertilizer applicator for rice (Oryza sativa L.). Three replications were conducted, each of which was divided into four plots. Field performance of the system was assessed at different nitrogen levels (N1 to N4, i.e., 75, 125, 175, 225 kg ha−1), growth stages (tillering, panicle initiation, heading), and heights (40, 60, 80, 100 cm) of the sensor from the crop canopy. Fertilizer rate was at minimum 12.59 kg ha−1 at 10 rpm of drive-shaft rotational speed and at maximum 50.41 kg ha−1 at 40 rpm. The system response time was within the range of 3.53 to 4.93 s, with overall error ranging between 0.83% to 4.92%. Across different growth stages, when fertilizer rate was increased from N1 to N4, NDVI increased from 0.49 to 0.69. Hence, drive-shaft rotational speed is decreased from 25 to 7 rpm to shift the application rate from 30.83 to 9.15 kg ha−1. There was a 45% reduction in total fertilizer rate applied by the system, with respect to the recommended rate

Prevalence, risk factors and virological profile of chronic hepatitis b virus infection in pregnant women in India

A large program was conducted by the Government of India to study the prevalence and profile of chronic hepatitis B virus (HBV) infection and its risk factors in pregnant women attending a tertiary care hospital in India. From September 2004 to December 2008 consecutive pregnant women attending the antenatal clinic were screened and those found positive for HBsAg were enrolled. Healthy non-pregnant women of child-bearing age, who presented for blood donation during the same period, served as controls. Women with symptoms of liver disease or those aware of their HBsAg status were excluded. Of the 20,104 pregnant women screened, 224 (1.1%) and of the 658 controls, 8 (1.2%) were HBsAg positive (P=ns). Previous blood transfusions and surgery were significant risk factors for infection with HBV. Of the women who were HBsAg positive, the ALT levels were normal in 54% of the women and HBV DNA levels were above 2,000 IU/ml in 71% of women. The median HBV DNA levels were higher in women who were HBeAg positive compared to the HBeAg negative group. The most common HBV genotype was D (84%) followed by A + D and A (8% each). In conclusion, the prevalence of HBsAg positivity among asymptomatic pregnant women in North India is 1.1% with 71% having high HBV DNA levels. These women may have a high risk of transmitting infection to their newborns