Abstract This paper describe the extension mechanisms of the UML in real time system and it introduces a new approach of using Stereotypes and shows its utility by implementing in road traffic management system. This paper also discusses the process of specifying, translating and verifying UML specifications for road traffic management system. Different kinds of existing UML Meta-models used in road traffic management system... This paper describe the extension mechanisms of the UML in real time system and it introduces a new approach of using Stereotypes and shows its utility by implementing in road traffic management system. This paper also discusses the process of specifying, translating and verifying UML specifications for road traffic management system. Different kinds of existing UML Meta-models used in road traffic management system are analyzed UML traffic system by implementing the Stereotypes will be created based on the real time system using the analysis report. Read More Read Less

Abstract The purpose of this paper is to statically analyze the android application package files to detect zero-day attacks. The methodology deals with attribute extraction using dissemblers, feature reduction by sparse feature elimination, feature selection and ranking by implementing various feature selection techniques, aggregation of attribute categories followed by classification and prediction. Feature selection techniques such as Bi-Normal separation (BNS), Mutual Information (MI), Feature to... The purpose of this paper is to statically analyze the android application package files to detect zero-day attacks. The methodology deals with attribute extraction using dissemblers, feature reduction by sparse feature elimination, feature selection and ranking by implementing various feature selection techniques, aggregation of attribute categories followed by classification and prediction. Feature selection techniques such as Bi-Normal separation (BNS), Mutual Information (MI), Feature to class correlation (F-CC), Feature to feature correlation (F-FC), combination of feature to class and feature to feature correlation (FCFF), Comprehensive measurement feature selection (CMFS) and Optimal orthogonal centroid feature selection (OCFS) are implemented to choose the significant attributes. Prominent features of five different attribute categories like permissions, count of permissions, hardware features, software features as well as API calls from 1175 application packages are extracted to generate the classification model. Attribute aggregation is performed to build the ensemble model. The intention of this framework is to evaluate the effectiveness of ensemble features with respect to individual features; find out the best feature selection method with fewer feature length and classification algorithm. The framework developed here by implementing dimensionality reduction and machine learning algorithms depicts an overall classification accuracy of 93.02% using ensemble features. Evaluating the performance of ensemble model with independent model, the former provides better results with Bi-Normal separation. Read More Read Less

Abstract With the advent of mobile technology, providing accurate information at a critical time for those in need has become easy. People moving around in the crowd may get lost or face medical emergencies due to previous health problems. In such a scenario, the mobile technology can be deployed to provide information to those who are lost or need emergency medical help. This study... With the advent of mobile technology, providing accurate information at a critical time for those in need has become easy. People moving around in the crowd may get lost or face medical emergencies due to previous health problems. In such a scenario, the mobile technology can be deployed to provide information to those who are lost or need emergency medical help. This study presents a real-time system based on Quick Response Code specifically designed for any crowded environments where there is a requirement for people to be assisted for navigating to their respective destinations. Compressed and encrypted information about individual participants is encoded in the Quick Response Code and is provided to them. If somebody in the crowd is lost or injured, volunteers and organizers can use smart phones to scan this Quick Response Code to retrieve information and provide the necessary help. The same system can also be implemented for any other scenarios like huge exhibitions, airports, shopping malls or even the battlefields where there is a chance of an individual getting lost or requires direction. Read More Read Less