Artificial Intelligence based Temporal Material Identification for Improving Qulaity of Service in Communication

Abstract

Abstract The artificial intelligence based learning model helps in identifying temporal material by processing linguistic terms that express time duration in code mixed data. The data available on social media contents are written in mixed script format and from this content temporal material content identification is a challenging task. The retrieval of temporal information and its corresponding time duration expression terms can be identified using artificial intelligence technique based neural learning model. The temporal retrieval and its time duration representation are widely used to present the opinions over the social media. The work described in the paper gives the comparative view of different techniques used in the area of transliteration. The rule framed approach is presented which accepts the roman form text as input and as per the defined rules the system is developed to give the temporal details words available in the sentence. The evaluation measures used here to validate the hypothesis is based on statistical measures along with HLSTM learning model. Further the result is validated using the voting technique that can choose appropriate Temporal label which are not identifies by the learning model. The applied neural learning approach increases the precision value.