Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing is a part of digital signal processing. Digital image processing has many significant advantages over analog image processing. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. Wavelet transforms have become a very powerful tool for de-noising an image.

The importance of fennel is well realized on account of its high remunerative prices, domestic consumption, medicinal value and means to get earn foreign exchange. Despite the economic importance of fennel, disease resistant, locally acceptable and high yielding crop. The present investigation was therefore, carried out to estimate the magnitude and nature of genetic variability in terms of variation, heritability, genetic advance and genetic diversity for different traits like seed yield per plant and yield contributing traits in a set of 50 germplasm with four checks and extent of environmental influence on these traits, form the basis on which a breeder can predict the extent of dependence on phenotypic selection for improvement of traits. The analysis of variance revealed that significant amount of variability was present in germplasm lines for almost all morphological traits studied as days to germination, 50 per cent flowering, king umbel anthesis, number of number of primary branches, number of secondary branches, plant height (cm), diameter of king umbel (cm), number of umbels per plant, number of umbellates per umbel, number of seeds per umbel, at a test-weight (g) and seed yield (g). A wide range of mean for yield and some of its contributing traits indicates good chance for improvement of yield through direct selection or by transferring desired traits. , were found to be superior. The variability of characters was compared on the basis of co-efficient of variation. The genotypic co-efficient of variation (GCV) and phenotypic co-efficient of variation (PCV) were worked out. Higher GCV (genotypic co-efficient of variation) was recorded for number of umbels per plant (15.7), seed yield (12.4) and number of secondary branches per plant (12.3), it expresses the true genetic potential which indicated the presence of high amount of genetic variability for these characters thus, selection may be more effective for these characters because the response to selection is directly proportional to the component of variability, while, number of seeds per umbellate (11.9), king umbel diameter (10.8) and umbellate per umbel showed moderate to high genotypic co-efficient of variation. Whereas primary branches (9.6), test weight (8.1) showed low magnitude of genotypic co-efficient of variation. Higher PCV was recorded for number of umbels per plant (16.7), king umbel diameter (14.3) and number of secondary branches per plant (14.0), while, seed yield (g) (12.5), number of seeds per umbellate (12.1) and number of umbellates per umbel (11.1) showed moderate to high phenotypic co-efficient of variation. Whereas number of primary branches (10.6), test weight (g) (8.9) showed low magnitude of phenotypic co-efficient of variation.

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Background Liquid medication dosing errors (LMDE) made by caregivers affect treatment in children, but this is not a well-studied topic in many low-and middle-income countries including in India. Methods An intervention study was conducted among mothers attending a pediatric outpatient clinic of a tertiary care setting in Ujjain, India. The mothers randomly measured 12 volumes of a paracetamol liquid preparation by using a dropper (0.5 and 1 mL), measuring cup (2.5 and 5 mL), and calibrated spoon (2.5 and 5 mL) each with two instructions—oral-only measurement session (OMS) and oral plus pictogram measurement session (OPMS, the intervention). The main outcome was dosing error prevalence. The effectiveness of the intervention was assessed by measuring effect size. Risk factors for maximum LMDE were explored using backward multivariate logistic regression models. A P value of < 0.05 was considered statistically significant. Results In total, 310 mothers [mean (± SD) age, 30.2 (± 4.18) years] were included. LMDE prevalence in the OMS versus OPMS for dropper 0.5 mL was 60% versus 48%; for l mL dropper was 63% versus 54%; for 2.5 mL cup 62% versus 54%; for 2.5 calibrated spoon 66% versus 59%; 5 mL cup 69% versus 57%; and 5 mL calibrated spoon 68% versus 55%. Comparing OMS with OPMS, underdosing was minimum with the calibrated spoon for 2.5 mL (OR 4.39) and maximum with the dropper for 1 mL (OR 9.40), and overdosing was minimum with the dropper for 0.5 mL (OR 7.12) and maximum with the calibrated spoon for 2.5 mL (OR 13.24). The effect size (dCohen) of the intervention OPMS was 1.86–6.4. Risk factors for the most prevalent dosing error, that is, with the calibrated spoon for 2.5 mL, were increasing age of the mother (aOR 1.08; P = 0.026) and nuclear family (aOR 2.83; P = 0.002). The risk of dosing errors decreased with higher education of the mothers. Conclusions Pictograms can effectively minimize LMDE even in less educated mothers.

In a world of fast paces and a general adoption of poor health care habits, with the same speed that the 21st century demands of us, it is common to feel that the fields of illnesses and their spread have grown dramatically. It is a group of business intelligence (BI) appliances that employ advanced machine learning approaches to find connections and patterns in huge quantities of facts. These facts-driven relationships and patterns help us anticipate behavior and occurrences. By utilizing previous occurrences, predictive analytics gives you a glimpse into the future. Although the predictive model is not based on the creation of a mathematical model or approaches for the development of the prognosis, it is essential to point out that it was constructed using past predictive methods, but on the use of approaches available in the identified appliance. Through the use of the model, it is proposed that the entities that provide public and private health services implement it in a commercial context, using the predictive abilities of the model for the benefit of the client's diagnosis and the optimization of consultation processes. This work proposed a review of 10 papers on diabetes prognosis using various machine learning approaches namely Logistic Regression, Random Forest, KNN, Naïve Bayes, ANN, Gradient Boosting, CNN, Support Vector Machine, and Ada Boost Approach.