1,648 research outputs found
Classification of Macronutrient Deficiencies in Maize Plant Using Machine Learning
Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in non-destructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets
Age and growth of Jhinga prawn Metapenaeus affinis Milne Edwards (Decapoda, Penaeidae) in Mumbai waters
The results of the studies on age and growth of Metapenaeus affinis, one of the dominant species of penaeid shrimps in the
coastal waters off Maharashtra are presented. From monthly size-frequency data, the growth parameters for males and
females were estimated employing modal progression and computer based FiSAT software package using ELEFAN program,
Bhattacharya method, Gulland-Holt plot, Faben’s method, Appeldoorn’s method and von Bertalanffy plot. The estimates
obtained by Bhattacharya analysis and Gulland-Holt plot were: L� = 162 mm, K = 2.25 for males and L� = 204 mm, K = 1.91
for females. Males and females were found to attain 145 mm and 174 mm at the end of one year and their life spans were 1.16
and 1.4 years respectively
Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity
Time series analysis is the process of building a model using statistical
techniques to represent characteristics of time series data. Processing and
forecasting huge time series data is a challenging task. This paper presents
Approximation and Prediction of Stock Time-series data (APST), which is a two
step approach to predict the direction of change of stock price indices. First,
performs data approximation by using the technique called Multilevel Segment
Mean (MSM). In second phase, prediction is performed for the approximated data
using Euclidian distance and Nearest-Neighbour technique. The computational
cost of data approximation is O(n ni) and computational cost of prediction task
is O(m |NN|). Thus, the accuracy and the time required for prediction in the
proposed method is comparatively efficient than the existing Label Based
Forecasting (LBF) method [1].Comment: 11 page
EFFECTS OF SUNFLOWER SEEDS ON CHOLESTEROL AND LOW-DENSITY LIPOPROTEIN LEVELS IN PATIENTS WITH DYSLIPIDEMIA
Objective: The present study was conducted with a goal to analyze and assess the effect of sunflower seeds on the serum cholesterol and low-density lipoprotein (LDL) levels.
Methods: A total of 60 patients comprising 34 males and 26 females were selected for the given study. The patients were divided into case and control groups. Various anthropometric measurements such as weight, height, and blood pressure along with certain biochemical parameters including cholesterol and LDL were recorded for these patients pre- and post-supplementation of 2 g of sunflower seeds for 6 months.
Results: The patients in the experimental group showed a significant and rapid difference (p<0.05) in comparison to the control intervention. The cholesterol levels in the case group reduced from 254.6±21.40 to 183.40±3.01 mg/dl, whereas, in control group, it reduced from 234.53±13.54 to 194.50±6.16 mg/dl. Similarly, the LDL levels in the case group decreased from 155.28±8.48 to 122.70±2.94 mg/dl; in contrast, in control group, it decreased from 159.53±6.04 to 140.53±1.11 mg/dl, respectively.
Conclusion: The study conducted concluded that sunflower seeds can be used as an adjuvant in treating the raised cholesterol and LDL levels in the blood serum which could otherwise lead various cardiac disorders (both major and minor)
Hyperleptinemia - an independent predictor of metabolic syndromein the adult population in Kerala, India
Background: Hyperleptinemia, associated with obesity which is a major risk factor for metabolic syndrome. Kerala has the highest prevalence of most of the cardio metabolic disorders and risk factors. So we analysed the ability of serum leptin level to predict the risk of developing metabolic syndrome among the adult population in Kerala, India.Methods: The study included 149 men and 155 women in the age group of 20-60years. Anthropometric measurements and Blood pressure were recorded. BMI and WHR were calculated. Fasting blood sample was used to measure serum leptin, insulin, lipid profile and glucose. HOMA-IR and HOMA-β were calculated. Baseline characterestics (means ± SEM) of men and women were examined by quartiles of serum leptin levels using ANOVA. The strength of association between leptin and components of metabolic syndrome was expressed as Odds Ratio (OR) using logistic regression analysis. p values <0.05 were considered significant.Results: In men and women, participants in the upper leptin quartiles were more likely to have factors associated with metabolic syndrome including waist circumference, systolic BP, decreased HDL cholesterol etc. Furthermore, those with metabolic syndrome were more likely to be in the upper leptin quartiles. On multivariate binary logistic regression analysis of leptin, the OR was: BMI (OR=3.51), waist circumference (OR=3.14), insulin (OR=4.43), and HOMA-IR (OR=2.4) in men, while in women the association of leptin was strong with abdominal obesity (OR=7.6), insulin (OR=2.8) and Insulin resistance (OR=4.1).Conclusions: Serum leptin levels had a strong association with components of metabolic syndrome, especially abdominal obesity and insulin resistance. Elevated leptin level should be taken as a warning sign of metabolic syndrome
Endogenous erythropoietin at birth is associated with neurodevelopmental morbidity in early childhood
Background New biomarkers that predict later neurodevelopmental morbidity are needed. This study evaluated the associations between umbilical cord serum erythropoietin (us-EPO) and neurodevelopmental morbidity by the age of 2-6.5 years in a Finnish cohort. Methods This study included 878 non-anomalous children born alive in 2012 to 2016 in Helsinki University Hospitals and whose us-EPO concentration was determined at birth. Data of these children were linked to data from the Finnish Medical Birth Register and the Finnish Hospital Discharge Register. Neurodevelopmental morbidity included cerebral palsy, epilepsy, intellectual disability, autism spectrum disorder, sensorineural defects, and minor neurodevelopmental disorders. Results In the cohort including both term and preterm children, us-EPO levels correlated with gestational age (r = 0.526) and were lower in premature children. High us-EPO levels (>100 IU/l) were associated with an increased risk of severe neurodevelopmental morbidity (OR: 4.87; 95% CI: 1.05-22.58) when adjusted for the gestational age. The distribution of us-EPO levels did not differ in children with or without the later neurodevelopmental diagnosis. Conclusions Although high us-EPO concentration at birth was associated with an increased risk of neurodevelopmental morbidity in early childhood, the role of us-EPO determination in clinical use appears to be minor. Impact We determined whether endogenous umbilical cord serum erythropoietin would be a new useful biomarker to predict the risk of neurodevelopmental morbidity. This study evaluated the role of endogenous erythropoietin at birth in neurodevelopmental morbidity with a study population of good size and specific diagnoses based on data from high-quality registers. Although high umbilical cord serum erythropoietin concentration at birth was associated with an increased risk of neurodevelopmental morbidity in early childhood, the clinical value of erythropoietin determination appears to be minor.Peer reviewe
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