Informatics Publishing Limited

Anupama Angadi ¹, P. Suresh Varma ²

Affiliations
1 IT Department, GMR Institute of Technology, Rajam - 532127, Andhra Pradesh, IN
2 Department of CSE, University College of Engineering, Adikavi Nannaya University, Rajahmundry - 533296, inAndhra Pradesh, IN

Abstract

Background/Objectives: Social Network Analysis (SNA) is the analysis of a social structure that is made up of a set of social players and a pile of the interactions between these social players. An individual such as a person, or an institution such as a college, agency and a federation, can be taken to be a social player. In late years, with the extensive function of social networking such as Facebook and Twitter, a vast sum of social interaction data has established social network analysis go beyond sociology and invite analysts from many fields. Methods/Statistical Analysis: Analysts have offered many different metrics to assess different topological features of a social player such as degree, betweenness centrality, eigen vector centrality etc. A distinctmetric is not adequate to examine multiple features of a social player, since each indicator designate a network in a dissimilar way so it is a reasonable solution to employ collective metrics with strong correlation (Spearman’s or Pearson’s). Findings: To find out the influential nodes the framework considers three egocentric metrics replacing social centric measures in temporal networks. Previous studies applied multiple social centrality measures with a strong correlation in static (or constant) networks. But many online social networks are naturally dynamic, propagate quickly in terms of social communications. Not all social players are born identical in a network, some might be superior in the sense they interconnected with almost all others and some might not contribute at all. The framework identifies these Hubs and Outliers at every snapshot. We have done experiments on undirected and unweighted EMAIL-ENRON real-world network. Application/Improvements: Influenial nodes can reveal new insights such as viral marketing, epidemic control, super-spreaders of disease and more generally in information dissemination.

Keywords

Combination of Genetic and Decision Tree, Consensus of Classifiers

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Anupama Angadi ¹, P. Suresh Varma ²

Affiliations
1 IT Department, GMR Institute of Technology, Rajam - 532127, Andhra Pradesh, IN
2 Department of CSE, University College of Engineering, Adikavi Nannaya University Rajahmundry - 53329, Andhra Pradesh, IN

Abstract

Background/Objectives: One of the most commonly observed features of Online Social Networks is Community Structure. This feature provides great benefit focusing on insights of network structure, hidden patterns and the flow of information between actors. Methods/Statistical Analysis: Most real-world social networks are inherently dynamic, grow rapidly in terms of social interactions. These interactions in network are reflected by edges in a graph. Instead of updating a network structure for every edge change, the proposed method tracks the edges at every unique time stamp in a subgraph and modify the network only with the changed edges. Findings: There are many static community detection algorithms for discovering communities in networks, but very few deal with incremental structural changes in the network. The proposed algorithm DOMLPA (Dynamic Overlapping Multi-Label Propogation) deals with dynamic networks where data arrives as a stream to find the overlapping nodes in communities. To find the new edges the proposed algorithm lists out the differences in edges between the subgraph and the network for every snapshot. Based on the differences the label edges would be added or removed from the network and adjacency entries, neighbors’ list and label distribution entries are modified eventually. Speaker node function is activated to start the propogation process inorder to get the labels for every node. If a node contains a only one label it belongs to single community. If a node carries more than one label with more than one maximum probability entry, then it belongs to multiple communities. The opportunity to capture the evolutionary patterns in dynamic networks is lost by not considering the time of interaction in static algorithms. Application/Improvements: The study of communities is helpful in examining patterns leading to understand the structure of networks, finding the information flow and events taking place between a group of social actors over a period of time and to identify trending sentiments about brands based on tweets.

Keywords

Actors, Community Detection, Overlapping Nodes, Social Networks, Temporal Networks

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Mohammed Ali Hussain ¹, P. Suresh Varma ², K. Satya Rajesh ³, Hussain Basha Pathan ³, Leela Madhav Sarraju ⁴

Affiliations
1 Dept.of Computer Science and Engineering, Sri Sai Madhavi Institute of Science and Technology, Rajahmundry, A.P., IN
2 Dept. of Computer Science and Engineering, Adikavi Nannaya University, Rajahmundry, A.P., IN
3 Dept. of Computer Science, Rayalaseema University, Kurnool, A.P., IN
4 Dept. of Computer Science, Dravidian University, Kuppam, A.P., IN

Abstract

The capacity of ad hoc networks can be severely limited due to interference constraints. One way of using improving the overall capacity of ad hoc networks is by the use of smart antennas. Smart antennas allow the energy to be transmitted or received in a particular direction as opposed to disseminating energy in all directions. This helps in achieving significant spatial re-use and thereby increasing the capacity of the network. However, the use of smart antennas presents significant challenges at the higher layers of the protocol stack. In particular, the medium access control and the routing layers will have to be modified and made aware of the presence of such antennas in order to exploit their use. In this paper we examine the various challenges that arise when deploying such antennas in ad hoc networks and the solutions proposed thus far in order to overcome them. The current state of the art seems to suggest that the deployment of such antennas can have a tremendous impact in terms of increasing the capacity of ad hoc networks.

Keywords

Directional Antennas, Medium Access Control, Routing.

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Uma Mahesh Kumar Gandham ¹, P. Suresh Varma ²

Affiliations
1 Department of CSE, AKNU University, GIET Engineering College, Rajamahendravaram – 533296, Andhra Pradesh, IN
2 Department of CSE, University College of Engineering, Adikavi Nannaya University, Rajamahendravaram – 533296, Andhra Pradesh, IN

Abstract

Objective: MapReduce is an encoding representation and a connected execution for handing out and generate huge data set. The objective of the present paper is that retrieve the data from enormous dataset in efficient manner a MapReduce. Methodology: The present paper uses structured parallel efficient execution Database Management System i.e. Parallel Database Management Systems (PDBMS). The present paper uses the Matlab for implementing PDBMS. This paper uses the broad concept of the paradigms quite than the exact implementations of MapReduce and Parallel DBMS. Such enormous information investigation on large clusters present new opportunity and challenge for mounting an extremely scalable and competent dispersed calculation system which is informal to strategy and multi- composite scheme optimization to exploit presentation and dependability to conquer this problem realize a new algorithm called Structured Parallel Efficient Execution Database 'Management (SPEED'MS) System' over Enormous Dataset with MapReduce. Findings: An optimizer is answerable for converting script into well-organized implementation plans for the dispersed calculation engine. Speed is living thing utilized day by day for assorted qualities of data study and data mining applications driving Bing, and other online services. The algorithm has been tested with the Matlab. Applications: MapReduce concept has potential applications like Clinical big data analysis, Bioinformatics Distributed programming.

Keywords

DBMS, Enormous Dataset Speed, MapReduce, Parallel DBMS.

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P. Chandra Sekhar ¹, P. S. Avadhani ², P. Suresh Varma ³, N. R. M. R. Bhargava ⁴

Affiliations
1 IT Department, Visakhapatnam Steel Plant, RINL, A.P, IN
2 Department of CS, Andhra Univrsity, Vizag, A.P, IN
3 Department of CSE, Adikavi Nannaya University, A.P, IN
4 Department of Metallurgy, Andhra University, A.P, IN

Abstract

In this paper the authors have ventured into scheduling of Steel Making process from LD converters to Continuous casters including Argon Rinsing stations using Earliest Dead line First (EDF) scheduling algorithms. The authors have evaluated performance metrics of the model like Turnaround time, Average waiting time and Total dead line deviation. The results of EDF scheduling is compared with First Cum First Scheduling (FCFS), Shortest Job First(SF) scheduling and the results of EDF scheduling has shown better performance over the other two.

Keywords

EDF, Steel Making, Three Stage Scheduling.

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