iosr-JCE   Volume-14 ~ Issue-2

Abstract: There are patterns buried within the mass of data in the various editions of population census figures in this country. These are patterns that will be impossible for humans working with bare eyes and hands, to uncover without computer system to give geo-spatial distribution of population in that area. This paper is an effort towards harnessing the power of data-mining technique to develop mining model applicable to the analysis of census data that could uncover some hidden patterns to get their geo-spatial distribution. This could help better-informed business decisions and provide government with the intelligence for strategic planning, tactical decision-making and better policy formulation. Decision tree learning is a method for approximating discrete-valued target function, in which the leaned function is represented by a decision tree. Decision tree algorithm was used to predict some basic attributes of population in the census database. Structured System Analysis and Design Methodology were used.

Key words: Census, Data-mining and GIS.

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Abstract: Analysis of routing in Mesh Network reveals that Proactive routes are fast but suffers vulnerability of route failure under high mobility. Reactive routes on the other hand add extra overhead in the network for obtaining a route before every communication session. As Link state routing provides a Network Map at each node, It is well suited for a mesh network. However due to uncertain demand in the mesh network, link states needs frequent updation and refreshing. A refreshing phase halts current session thus adding latency to the session. In order to avoid this pitfall we propose a unique Cross layer Based Link State Routing for Mesh network. Nodes keep monitoring the link quality as an when a packet is received in a link. Signal to Noise Ratio and Received power is measured at the MAC layer. Any change in the stored value raises an event which is read directly by the network layer. Once Network layer gets the notification, it automatically updates the route table entry with new metric values. Thus there is no specific refresh phase and nodes automatically update the links and route cache. Once a node realizes that the link quality with its next hop has degraded and a better one is available, it opts a handover of the connection called vertical handover. Thus proposed system provides seamless connectivity under varying load and mobility.

Keywords: Cross layer routing, Link state routing protocol, Wireless mesh networks (WMNs).

[1] J. Ren, "Wireless mesh network resource allocation and congestion control algorithm research," Ph.D. dissertation, Beijing Jiaotong University, Beijing, 2010 (in Chinese).

[2] S. Waharte, R. Boutaba, Y. Iraqi, and B. Ishibashi, "Routing protocols in wireless mesh networks: challenges and design considerations," Multimedia Tools and Applications, vol. 29, no. 3, pp. 285−303, 2006.

[3] T.-H. Liu and W.-J. Liao, "Capacity-aware routing in multi-channel multi-rate wireless mesh networks," in Proc. of 2006 IEEE International Conf. on Communications, Istanbul, 2006, pp. 1971−1976.

[4] W. Song and X.-M. Fang, "Routing with congestion control and load balancing in wireless mesh networks," in Proc. of the 6th International Conf. on ITS Telecommunications, Chengdu, 2006, pp. 719−724.

[5] Y.-F. Wai, Y. Zhang, M. Song, and J. Song, "An improved AODV routing protocol for WiFi mesh networks," Journal of Beijing University of Posts and Telecommunication, vol. 30, no. 4, pp. 120−124, 2007 (in Chinese).

[6] Q. Shen and X.-M. Fang, "An integrated metrics based extended dynamic source routing protocol in wireless mesh networks," in Proc. of International Conf. on Communications, Circuits and Systems, Guilin, 2007, pp. 1457−1461.

[7] Y.-L. Yang, J. Wang, and R. Kravets, "Designing routing metrics for mesh networks," in Proc. of the IEEE Workshop on Wireless Mesh Networks (WiMesh), Santa Clara, 2005, pp. 1−9.

[8] P. Jacquet, P. Muhlethaler, T. Clausen, A. Laouiti, A. Qayyum, and L. Viennot, "Optimized link state routing protocol for ad hoc networks," IEEE International Multi Topoic Conf., Lahore, 2001, pp. 62−68

Abstract: In the information technology ground people are using various tools and software for their official use and for their personal reasons. Nowadays people are worrying to choose data accessing tools and software's at the time of buying and selling the products and they are also worrying about various constraints such as cost, life time of the product, color and size of the product etc. In this paper we generated the solutions to the existing unsolved problems. Here we proposed the algorithm Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective decision at all the levels of data extraction, using the above technique and we analyzed the results at various datasets, finally the results were observed and compared with the existing methods such as PCC and VSS. The result accuracy was higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson's Correlation Coefficient (PCC), VSS (Vector Space Similarity)

[1]. Das,J. Heritage Inst. of Technol., Heritage Acad., Kolkata, India, Voronoi based location aware collaborative filtering.
[2]. Mittal, N. MNIT, Jaipur, India Nayak, R.; Govil, M.C.; Jain, K.C. Recommender System Framework Using Clustering and
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[3]. Akshi Kumar, Abhilasha Sharma Alleviating Sparsity and Scalability Issues in Collaborative Filtering Based Recommender
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[4]. Nidhi Gupta, Trends in Collaborative filtering Recommendation Technique.
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Based Collaborative Filtering Technique.
[7]. Sotirios P. Chatzis Department of Electrical Engineering, Computer Engineering and Informatics.
[8]. Cyprus University of Technology, A Coupled Indian Buet Process Model for Collaborative Filtering.
[9]. Xuejun Zhang, John Edwards *, Jenny Harding, Personalised online sales using web usage data mining.
[10]. Seok Kee Lee a,1, Yoon Ho Cho b,*, Soung Hie Kim, Collaborative filtering with ordinal scale-based implicit ratingsfor mobile
music recommendations