Forthcoming articles


International Journal of Social Computing and Cyber-Physical Systems


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International Journal of Social Computing and Cyber-Physical Systems (2 papers in press)


Regular Issues


  • Implementation of an Efficient and Intelligent Indian Maritime Borderline Alert System using IoT   Order a copy of this article
    by Asha Jerlin, Anindya Kumar Das 
    Abstract: The sea border issues are crucial to nations where its hard to have a track of boarder for fishermens. Quite a few occurrences of Indian fishermen from Tamilnadu, have been facing problems near the historic maritime boundary. About 180000 fishing vessels of assorted forms are involved in fishing along the Indian-Sri Lankan maritime border. But fishing activities in that region has always been an agitating factor, ever since violence broke out in Sri Lanka about 3 decades ago. This problem however this can be solved only by an efficient and intelligent boundary alert system using IoT. Internet of Things has the technology to make it possible for monitoring the sea border levels to know the border location. The Intelligent Maritime Borderline Alter System or IMBAS incorporates the new methodology of IoT for saving the fishermens life as well as their sole assets in which their mode of income depends. The methodology includes tracking the position of the boat in real time using a differential GPS system and keeping the boat in safe waters, well inside the Indian Territory. The system will also notify the ground control as and when the boat approaches the maritime borderline. The main objective of the system is to help the fishermen navigate safely inside our maritime country border and also prevent them from crossing it at all costs.
    Keywords: maritime borderline; India-Sri Lanka; GPS; border security; vessel monitoring; alert system; Tamil Nadu; fishing community; IoT.

  • A Novel Approach to Text Clustering using Shift K-Medoid   Order a copy of this article
    by Mohit , A. Charan Kumari, Meghna Sharma 
    Abstract: As the amount of data is growing day by day, we need to convert it into some effective manner so as to extract some useful information from huge data. Text Mining is used to perform this task. We use text clustering to convert the large data into different cluster forms to extract the meaningful information for the purpose of analysis so as to get the summarized data. Three partitioning-based clustering techniques i.e. k-means, k-means fast and k-medoids are compared, and a new algorithm named shift k-medoid is proposed, which is hybrid of k-medoid and mean shift clustering algorithms. Cosine Similarity, Correlation Coefficient and Jaccard Similarity measures are used to check the performance of the algorithms and two measures i.e. Randomized feature and Normalized Mutual Information (NMI) feature are used to test the accuracy of the algorithms. The outcomes demonstrate that the best performance is accomplished by using our proposed algorithm.
    Keywords: Text Clustering; Cosine measure; Jaccard measure; Correlation coefficient; Shift k-medoid.