Intelligent system

The production of counterfeit paper currencies has become cheaper because of the advancement in the printing technologies. The circulation of counterfeit currencies down the economy of a country. By leveraging this, there is a mandate to develop an intelligent technique for the detection and classification of counterfeit currencies. The intelligent techniques play a major role in the field of Human Computer Interaction (HCI) too. This paper deals with the detection of counterfeit Indian currencies. The proposed method feature extraction is based on the characteristics of Indian paper currencies. The first order and second order statistical features are extracted initially from the input. The effective feature vectors are given to the SVM classifier unit for classification. The proposed method produced classification accuracy of 95.8%. The experimental results are compared with state-of-the methods and produced reliable results.

Problems caused by the growth of traffic in cities require modern management approaches to improve the situation with the routing of traffic flow. This article aims to develop a conceptual system that uses computer vision technology to collect and process data from vehicles. It describes the technology of computer vision as an opportunity to improve routing algorithms by processing large data streams that reflect real situations and causes that affect route optimization. The result of using the color vision system is to provide more accurate and timely information to drivers, allowing them to make informed decisions about their routes. This will reduce traffic congestion, improve transport efficiency, and minimize the negative impact on the environment.