In this work, silver nanoparticles (AgNPs) were synthesized biochemically at room temperature using aqueous extract of rhizome of Rheum australe plant. The as-synthesized AgNPs were further studied for their morphological, biological and electrical characterization. The morphological studies, such as scanning electron microscopy, X-ray diffraction and UV-vis spectrum confirmed their successful synthesis. Biological analysis revealed their antioxidant activity by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Electrical characterization showed that the conductivity of the mixture of AgNPs with DPPH assay is more than the AgNPs dispersed in distilled water. The obtained results may have potential applications as sensors.

The role of medical scans is vital in diagnosis and treatment. There is every possibility of distortion during the image acquisition process, which may badly affect the diagnosis based on these images. Thus, image processing has become an essential exercise to extract the exact information from the medical images or scans. In recent times, researchers made various attempts to enhance the biomedical images using various signal processing methods. Several techniques have been explored and reported for improving the quality of the medical images. Still, there is a scope of improvement in the area of quality enhancement of the medical scans. In this paper, we investigated an aura based technique for enhancing the quality of medical ultrasound images. An algorithm has been developed using aura transformation whose performance has been evaluated on a series of diseased and normal ultrasound images.

Skin diseases are common and are mainly caused by virus, bacteria, fungus, or chemical disturbances. Timely analysis and identification are of utmost importance in order to control the further spread of these diseases. Control of these diseases is even more difficult in rural and resource-poor environments due to a lack of expertise in primary health centers. Hence, there is a need for providing self-assisting and innovative measures for the appropriate diagnosis of skin diseases. Use of mobile applications may provide inexpensive, simple, and efficient solutions for early diagnosis and treatment. This paper investigates the application of the Gaussian mixture model (GMM) based on the analysis and classification of skin diseases from their visual images using a Mahalanobis distance measure. The GMM has been preferred over the convolution neural network (CNN) because of limited resources available within the mobile device. Gray-level co-occurrence matrix (GLCM) parameters contrast, correlation, energy, and homogeneity derived from skin images have been used as the input data for the GMM. The analysis of the results showed that the proposed method is able to predict the classification of skin diseases with satisfactory efficiency. It was also observed that different diseases occupy distinct spatial positions in multidimensional space clustered using the Mahalanobis distance measure.

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