Abstract

Insect pests are a major cause of crop loss globally. Pest management will be effective and efficient if we can predict the occurrence of peak activities of a given pest. Research efforts are going on to understand the pest dynamics by applying analytical and other techniques on pest surveillance data sets. In this study we make an effort to understand pest population dynamics using Neural Networks by analyzing pest surveillance data set of Helicoverpa armigera or Pod borer on chickpea (Cicer arietinum L.) crop. The results show that neural network method successfully predicts the pest attack incidences for one week in advance.

Abstract

Distributed locking is commonly adopted for performing concurrency control in distributed systems. It incorporatesadditional steps for handling deadlocks. This activity is carried out by methods based on wait-for-graphs or probes. The present studyexamines detection of conflicts based on enhanced local processing for distributed concurrency control. In the proposed “edgedetection” approach, a graph-based resolution of access conflicts has been adopted. The technique generates a uniform wait-forprecedence order at distributed sites for transactions to execute. The earlier methods based on serialization graph testing are difficultto implement in a distributed environment. The edge detection approach is a fully distributed approach. It presents a unified techniquefor locking and deadlock detection exercises. The technique eliminates many deadlocks without incurring message overheads

Abstract

The blocking phenomena in two-phase commit (2PC) reduces the availability of the system as the blocked transactionskeep all the resources until the recovery of the coordinator. The three-phase commit (3PC) protocol involves an extra roundof message transmission to resolve the blocking problem. In this paper, we propose a backup commit (BC) protocol to reducethe blocking problem by attaching multiple backup sites to the coordinator site. In BC, after receiving responses from theparticipants, the coordinator quickly communicates the final decision to the backup sites, before it sends the final decision tothe participants. When blocking occurs, the participant sites can terminate the transaction by consulting a backup site of thecoordinator. The BC protocol resolves the blocking in most of the coordinator site failures without involving an expensivecommunication cycle as in 3PC. The simulation experiments indicate that the throughput performance of BC is close to 2PC.2002 Elsevier Science B.V. All rights reserved.

Abstract

This paper describes a new approach for script separation. A characterlevel script separation scheme is combined with a Viterbi algorithm to get an optimal sequence of scripts which could generate such a text. This method complements the popular approaches for script separation at paragraph level using texture features or at line level using structural features.

Abstract

In E-commerce sites, recommendation systems are used torecommend products to their customers. Collaborative filtering (CF) iswidely employed approach to recommend products. In the literature,researchers are making efforts to improve the scalability and onlineperformance of CF. In this paper we propose a graph based approach toimprove the performance of CF. We abstract a neighborhood communityof a given customer through dense bipartite graph (DBG). Given adata set of customer preferences, a group of neighborhood customersfor a given customer is extracted by extracting corresponding DBG.The experimental results on the MovieLens data set show that therecommendation made with the proposed approach matches closelywith the recommendation of CF. The proposed approach possesses apotential to adopt to frequent changes in the product preference data set.

Abstract

In this paper we propose an approach to extract community structures in the Web by considering a community structure as a group of content creators that manifests itself as a set of interlinked pages. We abstract the community structure as a dense bipartite graph (DBG) over a group of Web pages and proposed an algorithm to extract the DBGs from the given data set. Also, a high-level community is abstracted as a DBG over a set of a low-level communities. Using the proposed approach, a community hierarchy can be constructed for the given data set that generalizes a large number of low-level communities into a few highlevel communities. Using the proposed approach, we have extracted a a three-level community hierarchy from 10 GB TPEC (Text PEtrieval Conference) data set. We believe that the extracted community hierarchy facilitates easy analysis of the low-level communities, helps in reorganizing the Web sites, and provides a way to understand the sociology of the Web

Abstract

Fuzzy thresholding schemes preserve the structural details embedded in the original gray distribution. In this paper, various fuzzy thresholding schemes are analysed in detail. Thresholding scheme based on fuzzy clustering has been extended to a possibilistic framework. The characteristic di!erence for assignment of membership of fuzzy algorithms and their correspondence with conventional hard thresholding schemes have been investigated. A possible direction towards unifying a number of hard and fuzzy thresholding schemes has been presented. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved

Abstract

Image databases index in them using features extracted from the image. The indexing scheme is decided apriori and is optimized for a specific querying criterion. This is not suitable for a generic database of images which may be queried by multiple users based on different criteria. IN this paper, we present a flexible scheme which adapts itself to the user 's preferences. Though the method uses a conservative set of features during indexing that includes a large number and type of fundamental features the query processing time does not increase due to this redundancy. A boot-strapping mechanism allows the user to build up a "desired class" from a few samples. The feature selection computation scales linearly with the size of the desired class, rather than that of the entire database. THis feature makes our algorithm viable for very large databases. We present an implementation of our scheme and some results from it.