Informatics Publishing Limited

Anthoniraj Amalanathan ¹, S. Margret Anouncia ¹, S. Vairamuthu ¹, Magesh Vasudevan ¹

Affiliations
1 School of Computing Science and Engineering, VIT University, Vellore – 632014, Tamil Nadu, IN

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 Web

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Anisha M. Lal ¹, S. Margret Anouncia ¹, Omar Haji Kombo ¹

Affiliations
1 School of Computing Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

Background/Objectives: Image fusion in remote sensing is a challenging task for fusing minute differences in multispectral images for further analysis. The objective of this paper is to propose a hybrid approach for image fusion in remotely sensed images. Methods/Statistical Analysis: The objective of this paper is accomplished by a hybrid approach combining Stationary Wavelet Transform (SWT) and sparse representation. SWT is used for pre-processing and sparse is used for fusing the multitemporal LANDSAT image. Results: Various fusion metrics like RMSE, PSNR and FMI are evaluated and the obtained results show that the proposed hybrid approach outperforms well than the existing methods. The proposed approach gives better results in terms of all the features has been fused correctly, less mean square error and high signal to noise ratio compared to the existing methods such as DWT, SWT and Ehlers. Conclusion/Application: The application of this work is mainly on multispectral multitemporal images. It can be used for change detection also can be used to fuse low resolution image with high resolution remote sensing image. Finally this approach provides an efficient fusion result compared to the traditional methods.

Keywords

FMI, Image Fusion, Multispectral, Multitemporal, Sparse Representation, SWT

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Anthoniraj Amalanathan ¹, S. Margret Anouncia ¹

Affiliations
1 School of Computing Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN

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 Behavior

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V. Balasubramanian ¹, S. Margret Anouncia ¹

Affiliations
1 School of Computing Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

Background/Objectives: Adaptive learning has become a key accelerator to provide personalized content based on the learners’ preferences, as existing Learning management system support to manage the content and learner data analysis. Methods: To provide suitable content for the learner, Learning Style (LS) of the learner plays an important role. The limitation in existing learning environment is identifying the individual’s learning skill based on the mental processing capability of the learners. In this paper, a model is proposed to incorporate an effective learner model for adaptive learning environment through cognitive skill based learner classification. Findings: A Multiple Choice Questionaries’ was conducted to test the cognitive capability. Around 107 learners participated. The questionnaire was prepared based on the four cognitive skills such as Memory, Concentration, Perception and Logical Thinking. Each of the students is evaluated and the scores earned by them are normalized to assess them uniformly. Based on the mean score earned by each of the student under each cognitive skill category, the skill level is set as low, medium and high. The skill set categorization helps in classifying the learners into three broad categories which would facilitate the suitable learning object to be followed and hence the knowledge competency level can be improved. Most of the existing methodology permits no classification of learners and it permitted the direct approach on improving the memory based skills alone. Thus the attempted approach is observed to be helpful as the learners are classified based on their cognitive skill. Application/Improvements: The results obtained by the proposed architecture are promising in assessing the learner’s ability. The approach can be applied in any education system starting from high school to higher education.

Keywords

Adaptive Learning, Cognitive Skill, Learner Classification, Learning Management System.

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