S. Koteeswaran ¹, R. Srinivasan ¹, K. A. Varunkumar ¹

Affiliations
1 Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology (Vel Tech Dr.RR & Dr.SR Technical University), Chennai-62. TamilNadu, IN

Abstract

Wireless Sensor Networks in real time applications are vastly increasing due to their sensing feature. In this paper we propose an efficient routing protocol for heterogeneous wireless sensor network. Two scenarios are considered: 20 advanced nodes&80 normal nodes are used in simulation. In scenario one, Nodes transmitted data directly to the base station. In scenario two, Nodes transmitted their data to base station with assigned cluster node. After assigned rounds are completed, number of alive nodes is considered for analyzing energy efficiency, throughput, stability and network life time. Currently, two efficient routing protocols like Leech, Sep are induced to the simulations and compared with the proposed RSE routing protocol. Results proved that throughput, stability, network life time, energy efficiency are increased in our proposed model.

Keywords

Clustering, Energy Efficiency, Election, Regions, Routing, Wireless Sensor Network.

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S. Koteeswaran ¹, P. Visu ¹, E. Kannan ¹

Affiliations
1 Department of CSE, Vel Tech University, Chennai-62. TamilNadu, India, IN

Abstract

Hadoop is an advanced framework with separated File storage system to organize these data’s in distributed environment.Hadoop is a form of cluster with which is subjected to wide range of visualized data. Job sequence is one of the most peculiar sequences often handled by the scheduler in order to split and merge the job and its probable environment in organizing and utilizing the data. Once the scheduler assigns the job to its sequence and then it is visualized in terms of tracking, reordering and distributing those data in any distributed environment. Here the major focus of the research is concentrated on enormous amount of data to distinguish its pattern and way of organizing those data’s. The major scope is switched in the context of analyzing the data distribution using next generation yarn structure of HADOOP. The experimental results show that the problem addressed here has a vast advantage over the existing visualization techniques.

Keywords

Data Visualisation, Hadoop, Job Sequence, Scheduler, Yarn, Big Data.

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A. N. Arularasan ¹, S. Koteeswaran ¹

Affiliations
1 Department of Computer Science and Engineering, Vel Tech University, Chennai - 62, Tamil Nadu, IN

Abstract

Objectives: The objective of the work is to identify the most influential nodes and to classify the information hubs in the social network in promoting the viral marketing.  Methods: N-gram model classification approach is used for categorizing the information hubs of different domains.  Findings: The proposed method aims at estimating influence of each user in the network and categorizes the information hubs by using domain based classification to advertise a product in a social network.  Applications/Improvements: The work can further be extended to slice the longer string into N characters to measure the similarity value depending on the set of characters between words and relationship among them.

Keywords

Information Hub, Influence Maximization, Influential Node, Social Network, Viral Marketing

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K. Silambarasan ¹, S. Ambareesh ², S. Koteeswaran ³

Affiliations
1 Department of CSE, Vel Tech University, Chennai - 600062, Tamil Nadu, IN
2 Department of CSE, Vemana Institute of Technology, Bangalore - 560034, Karnataka, IN
3 Department of CSE, Vel Tech University, Chennai - 600062, Tamil Nadu

Abstract

Background/Objectives: Overseeing resources at mega scale while giving performance isolation and efficient utilization of basic hardware is a key test for any cloud management software. Aside from scalability issue, a cloud-level resource management layer requirements to settle the heterogeneity of frameworks, compatibility imperatives between virtual machines and basic hardware, islands of resources made because of storage and network connectivity and restricted scale of storage resources. Methods/Statistical Analysis: Upshots prospects promising optimizable brooks initiated probe in various filed, in this work we projected an effectual topology for solving the resource model for resource problem solution. Deployment of optimization algorithm opted for multi objective problem is Artificial Bee Colony Algorithm (ABC) which delivered best optimized result and less computation time is utilized to unfurl the determined objective, upshots of the proposition topology has depicted promising and effectual results and minimized computational exertion. Findings: In our proposed method, the execution time is reduced largely when compared to the existing method. Applications/Improvements: Optimization of cloud computing resource utilization.

Keywords

ABC, Cloud Computing, Multi Objective Problem, Map Reduce, Mass Storage Capacity etc, Resource Problem

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Jeevanandam Jotheeswaran ¹, S. Koteeswaran ¹

Affiliations
1 Department of CSE, Vel Tech University, Chennai - 600062, Tamil Nadu, IN

Abstract

Background/Objectives: Online review has become important decision support system for the customers to decide on the subscription or purchse. This paper is aiming to suggest a method that improves the accuracy of the classifier. Methods/Statistical analysis: Feature selection for sentiment analysis using decision forest method and Principal Component Analysis (PCA) is used for the feature reduction. The proposed method is evaluated using twitter data set. Findings: It is proved, that the proposed decision forest based feature extraction improves the precision of the classifiers in the range of 12.49% to 62.5% when compared to PCA and by 49.5% to 62.5% when compared to decision tree based feature selection. Application/Improvements: This method is applicable to product reviews, emotion detection, Knowledge transformation, and predictive analytics.

Keywords

Inverse Document Frequency (IDF), Learning Vector Quantization (LVQ), Opinion Mining, Principal Component Analysis (PCA), Sentiment Analysis, Twitter

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