Abstract

Information systems receive data through theuser interface forms. Improperly designed user interface formincreases navigational burden which in turn increases data-entry cost and data-entry errors. Research efforts are goingon to investigate the improved methods for user interfacedesign. Normally, context-based approach is followed to designuser interfaces. In this approach, the input attributes aredivided into several contexts based on the functionality anduser interface form is designed for each context. It can beobserved that, in some information systems, some attributesreceive values only during certain time duration. We termthis property as temporality of the attribute. In this paper,we have proposed an approach to exploit the temporality ofattributes for identifying the active-contexts to design improveduser interface. The implementation results on a real systemshow that the proposed method reduces the navigationalburden significantly as compared to the existing context-basedapproach

Abstract

The eSaguTMsystem is an IT-based personalized agro advisory system. In this system, the agricultural experts generate the advice by using the latest information about the crop situation received in the form of both photographs and text. The agricultural expert advice is delivered to each farm on a regular basis (typically once in a week/two weeks depending on the type of crop) from the sowing stage to the harvesting stage to the farmer without farmer asking a question. Since 2004, the eSagu system is being developed by operating on several crops and farms in Andhra Pradesh state of India. It has been found that the agriculture expert can prepare the expert advice in an efficient manner based on the crop photographs and the related information. The impact results show that the expert advices helped the farmers to achieve significant savings in the capital investment and improvement in the yield. Encouraged by the results of eSagu experiment, a country-wide integrated agri-service program is planned by Media Lab Asia to provide expert advice and other agri-related services to Indian farming community. In this paper we briefly explain the development of eSagu system, advantages and future plan.

Abstract

In the E-commerce environment, a customer faces several difficul-ties for selecting the product. The technology like recommendationsystem is being developed to improve the performance of productselection. In this paper, we have investigated the problem of ‘se-lecting a product from group of similar products’ faced by the cus-tomer. For example, when a customer wants to buy a Sony camerathrough E-commerce Web site, the customer has to go through theinformation of several Sony camera models to select the appropri-ate one. In this paper, we have proposed an improved approachto help the customers to select the appropriate product. We haveexploited the fact that every product possesses some specialness,which is exhibited through few special features. The proposed ap-proach identifies the special features and organizes the features ofthe product in an effective manner. We have conducted the experi-ments on three real world data-sets related to Nokia-mobile phones,Sony-cameras and HP-laptops. The results indicate that the pro-posed approach has a potential to improve the performance of theproduct selection.

Abstract

Despite the growing importance of other sectors, agriculture continues to hold the key to the growth of GDP, food security, employment and income in India. Over a period of time, agriculture is undergoing a change towards high value crops, livestock, fisheries etc. Along with it substantial changes have occurred in seed varieties, use of chemical inputs, cultivation practices. Such a process has also culminated in distress conditions in the farm sector. Studies1 undertaken in the states of Andhra Pradesh, Karnataka, Maharastra and Punjab have revealed the serious crisis among the farming community. It is pertinent to note that net income is negative in some states( Narayan Moorthy: 2006) and nearly two thirds of the farmers are frustrated with their profession ( Desh

Abstract

For clustering, cosine similarity method is apopular approach to compute the similarity between twodocument vectors where each document vector consists ofweighted terms. It can be noted that, the value of theweight assigned to the terms influences the similarity value.Generally, the weight for each term is calculated by using thepopular TF-IDF method. In the TF-IDF method, the weightof a term is a function of its frequency in the document andin overall document collection. However, it can be observedthat, TF-IDF weighting method ignores the semantic asso-ciation between the terms. In this paper, we have proposedan improved term weighting method calledBoostWeightbyconsidering the semantic association between the terms. Forthis, we exploit the generalization ability of hierarchicalknowledge repositories such as “Open Web Directory” tosemantically associate the terms. The proposedBoostWeightmethod increases the weight of terms if they have commongeneralized term. The clustering experiments on the realdata-set show thatBoostWeightimproves both entropy andpurity values over traditional term weighting approaches.

Abstract

In this paper we have proposed an improved ap-proach to extract rare association rules. Rare association rulesare the association rules containing rare items. Rare items are lessfrequent items. For extracting rare itemsets, the single minimumsupport (minsup) based approaches like Apriori approach sufferfrom “rare item problem” dilemma. At high minsup value, rareitemsets are missed, and at low minsup value, the number offrequent itemsets explodes. To extract rare itemsets, an effort hasbeen made in the literature in which minsup of each item is fixedequal to the percentage of its support. Even though this approachimproves the performance over single minsup based approaches,it still suffers from “rare item problem” dilemma. If minsup forthe item is fixed by setting the percentage value high, the rareitemsets are missed as the minsup for the rare items becomes closeto their support, and if minsup for the item is fixed by setting thepercentage value low, the number of frequent itemsets explodes.In this paper, we propose an improved approach in which minsupis fixed for each item based on the notion of “support difference”.The proposed approach assigns appropriate minsup values forfrequent as well as rare items based on their item supportsand reduces both “rule missing” and “rule explosion” problems.Experimental results on both synthetic and real world datasetsshow that the proposed approach improves performance overexisting approaches by minimizing the explosion of number offrequent itemsets involving frequent items and without missingthe frequent itemsets involving rare items.

Abstract

Rare association rules (or rules) are the association rules involving rare items (low frequent items). To minerare association rules efficiently, an effort has been made in CFP-growth approach to mine frequent patternsusing multiple minimum support (minsup) values. This approach is an extension of FP-growth approach tomultiple minsup values. This approach involves construction of MIS-tree and generating frequent patternsfrom the MIS-tree. The issue in CFP-growth is constructing the compact MIS-tree because CFP-growth con-siders certain items which will generate neither frequent patterns nor rules. In this paper, we propose an effi-cient approach for constructing the compact MIS-tree. To do so, the proposed approach explores the notions“least minimum support” and “infrequent child node pruning”. Experimental results on both synthetic andreal world datasets show that the proposed approach improves the performance over CFP-growth approach.

Abstract

We report on surface-enhanced Raman scattering (SERS) from single star-shaped gold nanoparticles coated with self-assembled monolayers of 4-mercaptobenzoic acid. SERS is observed without the formation of gold nanoparticle aggregates or resonant excitation of the analyte. Total Raman scattering enhancement factors for single nanostars are comparable to those of nanoparticle assemblies exhibiting coupled plasmon resonances. This renders gold nanostars promising for Raman imaging applications in complex environments such as cells or membranes.

Abstract

Gold nanoparticle-loaded liposomes can serve as important biophysical and biochemical tools. A rapid, green, one-step synthetic method has been used for direct and controlled loading of gold nanoparticles onto liposomes. The effects of various synthetic parameters on nanoparticle loading as well as on optical properties of the liposome-anchored nanoparticles are discussed. Gold nanoparticle-loaded individual liposomes have been characterized by dark-field microscopy and optical spectroscopy.

Abstract

Retrieving answer containing passages is a challenging task in Question Answering. In this paper, we describe a novel passage retrieval methodology using answer type profiles. Our methodology includes two steps: estimation and ranking. In the estimation step, answer type profiles are constructed from question-answer sentence pairs parallel corpus using a statistical alignment model. Each answer type profile consists of triples: the query word, the answering sentence word and the probability of translation. In the ranking step, answer type profiles are incorporated into the Language Modeling framework called Statistical Machine Translation models for Information Retrieval. Using this framework a set of relevant passages are retrieved, given a question. We conducted experiments on FACTOID questions from TREC 2002 to 2006 QA tracks. The experimental results showed significant improvements over different retrieval models including TFIDF, Okapi BM25, Indri and KL-divergence.