The Trivial association rule m.ning which should be fixed in order to avoid both that early trivial rules is retained and also that interesting rules are not *** fact the situations which use the relative com.arison t...
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The Trivial association rule m.ning which should be fixed in order to avoid both that early trivial rules is retained and also that interesting rules are not *** fact the situations which use the relative com.arison to express association rules are m.re com.lete than the situations that generate association rules that use the absolute *** traditional concept of association rule m.ning will lose som. inform.tion in generating association *** user does have to determ.ne the degree of support and confidence thresholds before generating association *** our paper,we proposed new approach in finding association *** new approach uses the concept of rough set theory and Bayesian network classification to generate association *** provides a way for decision m.ker to get m.re inform.tion to generate association rules than traditional *** new approach for revving association rules has the ability to handle the certainty in the classifying process so that we can reduceinform.tion loss and enhance the result of data *** new algorithm.can sim.late the value of probability which is based on continuous data set.
Rank Aggregation problem.is to find a com.ined ordering for objects, given a set of rankings obtained from.different rankers. Rank aggregation is a technique that com.ines results of various rankings on the sets of en...
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Rank Aggregation problem.is to find a com.ined ordering for objects, given a set of rankings obtained from.different rankers. Rank aggregation is a technique that com.ines results of various rankings on the sets of entities (e.g. Docum.nts or web pages of search result) to generate an overall ranking of the entities. In the context of the World Wide Web, Rank aggregation is frequently used in m.tasearching. In this paper, we discuss the developm.nt of a supervised rank aggregation system.that is based on “neural networks”. A supervised rank aggregation system.provides an aggregation of rankings of entities by learning rules for com.ining the different individual rankings of the entities on the basis of training data. In case of a m.tasearch system. the training data m.y be the user feedback based ranking of the search results. The m.in contribution of the paper is the form.lation of the rank aggregation problem.as a function approxim.tion problem. As the m.ltilayer perceptrons are considered as the universal approxim.tor, we use a m.ltilayer perceptron for the supervised rank aggregation. For experim.ntal purpose, we apply this supervised rank aggregation technique to m.tasearching. We train our m.tasearch system.with the user feedback based ranking of the search results from.7 public search engines for a set of 15 queries. We also evaluate the perform.nce of our trained m.tasearch system.using the feedback from.three independent evaluators and find that our system.gives a very good perform.nce.
A m.tasearch engine is a search engine, which uses the results of several search engines to produce a collated list of search results. The actual success of a m.ta-search engine directly depends on the aggregation tec...
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A m.tasearch engine is a search engine, which uses the results of several search engines to produce a collated list of search results. The actual success of a m.ta-search engine directly depends on the aggregation technique underlying it. User satisfaction is obviously the m.st im.ortant factor in m.asuring the quality of search results. Therefore, we propose a m.tasearch system.that m.dels user feedback based m.tasearching. Our system.uses the m.dified rough set based rank aggregation for m.tasearching. In the m.dified rough set based rank aggregation technique, we incorporate the confidence of the rules in predicting a class for a given set of data. We associate a score variable to the predicted class of the record, where the value of the variable is equal to the confidence m.asure of the rule. For each query in the training set, we m.ne the ranking rules using rough set theory and select the best rules-set by perform.ng cross-validation test. Once the system.is trained, we m.y use the best rule set to get the overall ranking for the results returned from.different search system. in response to other queries. Previously m.dified rough set based rank aggregation technique has been already used for the perform.nce evaluation of Web Search system. but here, we apply this m.thod for m.ta searching. We also perform.an evaluation of our proposed system.by three independent evaluators. We com.are our m.thod with m.tasearching m.thod based on rough set based rank aggregation and find that our m.thod perform. better than the m.tasearching m.thod based on rough set based rank aggregation.
We report the fabrication and characterization of a m.crostrip patch antenna com.osed entirely out of m.lti-walled carbon nanotubes on a silicon substrate. The antenna showed excellent response in the X-band, for whic...
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Texture features alone cannot help us to recognize faces, because there m.y be several people with sim.lar texture features. Likewise geom.try based features can also be sim.lar in different people. When the two m.tho...
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Texture features alone cannot help us to recognize faces, because there m.y be several people with sim.lar texture features. Likewise geom.try based features can also be sim.lar in different people. When the two m.thods are experim.nted separately, there m.y be inaccurate results. There m.ght be better results when the two m.thods are com.ined and used. So this paper tries to evaluate the perform.nce of both the features independently and jointly in face recognition. Texture feature extraction m.thods such as Grey Level Co-occurrence m.trix (GLCm., Local Binary Pattern (LBP) and Elliptical Local Binary Tem.late (ELBT) are proposed to com.ine with geom.try based m.thod. Japanese Fem.le Facial Expression (JAFFE) database is used to conduct the test. Experim.nts are carried out in two ways. First, all the m.thods are experim.nted with im.ges having less expression. Then they are tested with all im.ges in the database. Experim.ntal results show that ELBT outperform.the other m.thods and also the recognition rate is higher when texture features are com.ined with geom.try based features. For classification K-nearest neighbourhood algorithm.is used. And for recognition Chi square statistic (χ 2) is used as dissim.larity m.asure.
We report on focus group feedback regarding the services provided by existing education-related Digital Libraries (DL). Participants provided insight into how they seek educational resources online, and what they perc...
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Im.ge denoising is a com.on procedure in digital im.ge processing aim.ng at the rem.val of noise which m.y corrupt an im.ge during its acquisition or transm.ssion while sustaining its quality. A statistical m.del is p...
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Im.ge denoising is a com.on procedure in digital im.ge processing aim.ng at the rem.val of noise which m.y corrupt an im.ge during its acquisition or transm.ssion while sustaining its quality. A statistical m.del is proposed depending on the m.gnitude of wavelet coefficients and noise variance for each coefficient is estim.ted based on the subband it belongs to using m.xim.m.Likelihood (m.) estim.tor or a m.xim.m.a Posterior (m.P) estim.tor. An adaptive thresholding is proposed which is applied to each subband coefficient except the low pass or approxim.tion subband. This is done by fixing the optim.m.thresholding value depending on the decom.osition level. The proposed m.thod describes a new m.thod for suppression of noise in im.ge by fusing the wavelet denoising technique with optim.zed thresholding function to which a m.ltiplying factor (α) is included to m.ke the threshold value dependent on decom.osition level. Due to this, the proposed technique yields significantly superior im.ge quality by preserving the edges, producing a better PSNR value. The efficiency is proved on com.aring with Bayes shrink (BS), m.dified Bayes Shrink (m.S) and Norm.l Shrink (NS) for different noise level.
In this study, the authors focused on children from.2-8 years of age and asked the sim.le question: what do engineers do? The num.er one response was: “I don’t know”, the num.er two response was “they drive a tra...
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In this study, the authors focused on children from.2-8 years of age and asked the sim.le question: what do engineers do? The num.er one response was: “I don’t know”, the num.er two response was “they drive a train.” While children are very fam.liar with professionals such as doctors, teachers, nurses, firefighters and policem.n, they are rarely introduced to engineers. With this m.tivation, the authors developed a novel children’s book on engineering: engineering Elephants. This book is an outreach tool that introduces children to the dynam.c world of engineering design through roller coasters, fireworks, and a plethora of other exciting adventures. The book teaches children about relevant topics such as nanotechnology, renewable energy, and prosthetics by engaging them.through an interactive journey of an elephant and his questioning of the world around him. The text was strategically developed using the language of science (asking questions) and introducing vocabulary relevant to science and m.th using a lyrical pattern. This presentation will highlight the developm.nt of this book as an instructional aid but also detail the response of various age groups to engineering activities presented as a com.anion to this book. In particular, an elem.ntary school district in West Texas designed a 4-5th grade 3-week sum.er school curriculum.around this book. Results from.this study will have an im.act on future generations by inspiring them.to consider the exciting profession of engineering at an early age.
We investigate num.rically and experim.ntally the on-chip nanoscale focusing of surface plasm.n polaritons (SPPs) in m.tallic nanotip coupled to the silicon waveguide. Strong field enhancem.nt is observed at the apex ...
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We investigate num.rically and experim.ntally the on-chip nanoscale focusing of surface plasm.n polaritons (SPPs) in m.tallic nanotip coupled to the silicon waveguide. Strong field enhancem.nt is observed at the apex of the tip.
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