Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)

Abstract

Price hike is common and one of the major issues all over the world. The prices of daily necessities are increasing day by day. Many shops, restaurants and transport systems are charging extra price from the customers over the products’ expected or maximum retail price. Moreover, in special occasions, such as festivals, the sellers take high price from customers. Furthermore, unauthorized VAT or other taxes are charged on products on which such taxes are exempted by the government. This study proposes a Cloud of Things (CoT)-based model for monitoring the products’ price during transactions, where a cloud server maintains historical pricing data and maximum retail prices. Two algorithms run on the server based on live invoice data from sellers and customer feedback to detect unfair price hike.