Advanced Machine Learning Approach for Detection of Multilinguistic Terror Message to save human Lives

Abstract

Solutions to avoid terrorist attacks, suspicion crimes, and misbehaving of law & order globally. With the use of multilingual through instant messaging applications via short-text messages are traced using the proposed framework. Criminals, terrorists, underworld dons are sitting in one place and implanting their criminal plans globally using different languages. To date, there are no stringent solutions were proposed for mitigating online crimes in Social networking sites, where multilingual words are used while chatting with other users. To date, no proper solution to stop the crimes that are happening through multilingual. Criminals use more than one language to pass activity messages among teammates who may be in living in any corner of the World. Earlier works in messaging applications were based on the prediction of suspicious messages for a unique language at a time (i.e. either English language or china) ignored multiple languages at the same time.  The proposed framework developed using a multilingual framework comprising of various components namely Semantic web Ontology, Suspicious Database assisted with Pre-defined decision rules, machine learning technique, language translator guided with past learning experiences.  When a user communicates the suspicious terminology using multilingual language this framework expedites in predicting the type of crime from microblogs before it gets executed by criminals. Details of criminals will be alerted to cybercrime the department that reduces the tension for the various security departments.