In this present fast growing environment the automatic coin reorganization and identification machines has a vital role in all financial allied fields. At present most of coin recognition techniques are depends on physical properties of the coin like length, width, weight etc. Whereas image processing techniques are based on extraction of colour of the coin, edge features of the coin and shape of the coin. For recognition and detection of Brazil currency we have designed Machine Learning, Deep Learning (DL) models in this paper. We have designed various a Machine Learning (ML) models with Convolutional Neural Network for identification and reorganization of Brazil currency. Brazil currency consisting of 5 Centavos, 10 Centavos, 25 Centavos etc. Each of which has different shapes and designs. Different deep learning models are designed and applied ensemble to find the better accuracy. The trained ensemble model is tested on various the datasets which consists of shifting of images, rotation and translated images.

Protein-Protein Interactions (PPI) have important role in drug binding with the Proteins called drug targets. For identifying the potential drug targets there are different techniques. In this paper we are presenting application of Centrality Measures for identifying the drug targets. Centrality measure indicates importance of node in the graph or network. Protein-Protein Interactions for proteins which are involved in a particular disease are identified and centrality measures will be calculated based on the graph built suing the PPI interactions. Further the nodes which are playing crucial role will be identified using the various centrality measures and these drug targets can be used for drug discovery of a particular disease through insilico docking studies.

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Online reviews and ratings are considered extremely important in any business. Any customer who wants to purchase a particular product online will have a look on the ratings before placing an order, any person who wants to book a hotel or restaurant in a particular location will have a glance on online ratings, any person who wants to choose a best hospital will check ratings. The question which arises here is how far the ratings can be reliable? The ratings cannot be trusted completely as they are not feature specific. The overall rating leaves the people in chaos and they cannot conclude that on which basis the ratings have been awarded. To overcome this problem feature based online rating is introduced. Where ratings for different features have been awarded separately. It helps people to take decisions easily and can purchase a product of their requirement. It helps in the growth of the business as customer satisfaction is equally important and it increases the quality of service.