Mahesh Motwani

Image Clustering Using Color and Texture

With the advancement in image capturing device, the image data been generated at high volume. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Due to semantic gap between low-level image features and the richness of human semantics, a challenge with image contents is to extract meaning from the data they contain. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Proposed framework focuses on color and texture as feature. Color Moment and Gabor filter is used to extract features for image dataset. K-Means and Hierarchical clustering algorithm is applied to group the image dataset into various clusters

Integration of Artificial Intelligence and Database Management System: An Inventive Approach for Intelligent Databases

The integration of AI and DBMS technologies promises to play a significant role in shaping the future of computing. AI/DB integration is crucial not only for next generation computing but also for the continued development of DBMS technology. Both DBMS and AI systems represent well established technologies, research and development in the area of AI/DB integration is comparatively new. The motivations driving the integration of these two technologies include the need for (a) access to large amounts of shared data for knowledge processing, (b) efficient management of data as well as knowledge, and (c) intelligent processing of data. In addition to these motivations, the design of Intelligent Database Interface (IDI) was also motivated by the desire to preserve the substantial investment represented by most existing databases. Several general approaches to AI/DB integration and various developments in the field of intelligent databases have been investigated and reported in the paper.

Sum of Distance based Algorithm for Clustering Web Data

Clustering is a data mining technique used to make groups of objects that are somehow similar in characteristics. The criterion for checking the similarity is implementation dependent.Clustering analyzes data objects without consulting a known class label or category i.e. it is an unsupervised data mining technique. K-means is a widely used clustering algorithm that chooses random cluster centers (centroid), one for each centroid. The performance of K-means strongly depends on the initial guess of centers (centroid) and the final cluster centroids may not be the optimal ones as the algorithm can converge to local optimal solutions. Therefore it is important for K-means to have good choice of initial centroids. An algorithm for clustering that selects initial centroids using criteria of finding sum of distances of data objects to all other data objects have been formed. The proposed algorithm results in better clustering on synthetic as well as real datasets when compared to the K-means technique.

A Framework for Load Sharing in Clustered Ad Hoc Networks

ad hoc networks, Clustering provides a hierarchical structure in which certain nodes are assigned the extra task (such as routing) of the network. Ordinary nodes do not participate in the routing instead they rely on coordinators of the clusters (clusterheads) for packet delivery. If a suitable tap is not applied on the number of nodes that join a clusterhead as its members, formation of bottleneck can takes place at the overloaded clusterheads. The performance of the network may get affected due to the bottleneck. This paper proposes a cluster formation algorithm in which, if the number of members of a clusterhead exceeds the predefined threshold value, a procedure of cluster division is executed. This relieves the clusterheads from the burden of excessive members. Simulation study of the proposed algorithm justifies the facts by observing an improvement in the performance in terms of E2E delay, PDF and throughput. Keywordsad hoc network, clustering, clusterhead, load, energy

Optimized Ad-hoc on Demand Distance Vector Routing Protocol

A Mobile Ad-hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without using any centralized access point, infrastructure, or centralized administration. Data transmission between two nodes in MANET’s may be requires multiple hops as the nodes transmission range is limited. Mobility of the different nodes makes the situation even more complicated. Multiple routing protocols especially for these conditions have been developed during the last few years, to find optimized routes from a source to some destination. Ad-hoc network suffer from the lot of issues and congestion and security are the major issues which lead to severe degradation of network throughput and increases the routing overheads. This paper presents Optimized AODV which the modified version Enhance local repair AODV by improving its Route Error message format. The result shows that OAODV performs better in terms of routing overhead and end to end delay than AODV. The simulation is done through network simulator ns2 gives a detailed comparison of research on AODV. Open research direction is also discussed to serve as a starting point to protocol design and evaluation

Protocol for Coordinated Checkpointing using Smart Interval with Dual Coordinator

Checkpointing is a very popular technique for fault tolerance in distributed systems. The proposed protocol tolerates the transient faults. In the protocol, all processes take checkpoints to form a global consistent checkpoint. The protocol handles the failures of initiator and non-initiator.