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Amalanathan, Anthoniraj
- A Framework for E-Governance System using Linked Data and Belief-desire-intention Agent
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Authors
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1 School of Computing Science and Engineering, VIT University, Vellore – 632014, Tamil Nadu, IN
1 School of Computing Science and Engineering, VIT University, Vellore – 632014, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 15 (2015), Pagination:Abstract
E-Governance systems are built for the government to interact with citizens and efficiently provide workflow between the organizations. These systems play major role in efficiently organizing the government knowledge and enabling citizen friendly services. Traditionally E-Governance systems are developed as a requirement basis trend, where the requirement of establishing a service is evaluated and a system for that particular requirement is deployed. A framework has been introduced to develop an E-Governance system in an evolutionary fashion where the core government model is deployed and further services are deployed over the core. The key element in achieving such evolutionary system is by adding semantics to the knowledge which is represented in the form of Linked Data.Keywords
BDI Agent, E-Governance, Linked Data, Ontology, Semantic WebFull Text
- Social Network User’s Content Personalization based on Emoticons
Abstract Views :205 |
PDF Views:0
Authors
Affiliations
1 School of Computing Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
1 School of Computing Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 23 (2015), Pagination:Abstract
The social networks help sharing user’s multidimensional content which includes text, image, audio and video at any time. Emoticons are helpful in precise content sharing as an alternative of text and need to be analyzed for sharing of the right content. The content being shared mostly reflects the behavioral characteristics of the users and imitates their emotions.Therefore, each emoticon needs to be mapped with standard emotions. The emoticons proposed by Unicode consortium are considered and mapped with nine basic emotions such as love, happiness, pity, furious, heroic, fearful, disgust, wonder and peace. A prediction model based on decision tree classifier is designed to classify user’s contentaccording to the emotions expressed through the emoticons, especially for tweets. The designed methodology is demonstrated using two thousand tweets. Tweets are adopted for its simplicity and limited processing with only hundred and forty characters. The outcome obtained by applying the designed methodology provided satisfactory results of 83% accuracy which is more than the average accuracy (75%) of standard machine learning classification process. Therefore, it is possible to guess the behavior of the users through sharing the different forms of emoticons at various instances. This classification of users’ content would reflect the dominant emotions possessed by them. This finding helps in understanding the basic nature of an individual in social networks. Having identified the basic nature of an individual through emoticons, it is very easy to personalize the user’s social network page to filter disinterested and disgusting content at any time.Keywords
Classification, Emoticons, Emotions, Personalization, Social Networks, User BehaviorFull Text