Background: Anemia among women causes many serious health problems and is pervasive in developing country. Many research studies have documented that malnutrition affects body growth and development, especially during the crucial period of adolescence. The association between anemia and body mass index (BMI) is a measure of nutrition and health status of adults. Aim was to study the association between anemia and BMI among female students.Methods: An observational study was conducted among first year medical and dental female students (n=109) aged 18-20years. Hemoglobin (Hb) levels (g/dL) by Sahli’s hemoglobinometer and BMI (kg/m2) were estimated. Anemia was defined as Hb content <12g/dL. Subjects were classified by BMI categories as underweight (BMI <18.5kg/m2), normal weight (BMI:18.5-24.99kg/m2), overweight (BMI ≥25kg/m2) and obesity (BMI ≥30kg/m2) according to WHO. Then the relation between anemia and BMI were statistically analyzed.Results: Overall, 48.62% female students were anemic. Of which 43.4% were underweight, 22.6% normal weight and 34.5% were above normal weight (over weight and obese). Mean value of hemoglobin was significantly decreased in underweight and overweight compared to normal weight (p<0.001). Anemia was significantly associated with BMI (χ2 =46.48, p=0.000).Conclusions: The study concludes the occurrence of anemia in both undernourished and over-nourished individuals which were significantly associated. Further studies are needed with larger sample size to document the factors that may be associated with anemia in females.
Abstract-Processing the images to obtain the resultant images with challenging clarity and appealing visualization are the major challenges. So, pre-processing of images is required to meet these challenging needs. In many applications like rendering high dynamic range images, certain pre-processing is necessary. This kind of pre-processing includes detecting the high contrast edges, smoothing the noise using filters etc. In this paper, the main focus is on the application of edge detection in rendering high dynamic range (HDR) images using Retinex-based adaptive filter method. Different edge detection techniques are used in rendering HDR images using Retinex algorithm and an observation has been made that Canny edge detection yields visually appealing resultant image.
Early prediction of student performance helps to take action for better achievements of students. To achieve the better education standard, several attempts have been made to predict the performance of the student, but the prediction accuracy is not acceptable. To accomplish the enhanced prediction, neural network (NN) based method is proposed. In this paper, an approach to predict student academic performance in college education based on Lion-Wolf artificial neural network is proposed. Lion algorithm and Grey Wolf optimizer is integrated to develop a Lion-wolf training algorithm to find the optimal weight for every neuron in NN. The proposed prediction model is validated based on Mean Squared Error (MSE) & Root Mean Square Error (RMSE) with the existing NN based prediction model. The experimental results show that performance of proposed prediction model is improved compared to existing prediction model with MSE of 5.25 and RMSE of 2.3.
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