Abstract

Online advertising provides an opportunity for product sellers and service providers to reach customers and has become a key factor in the growth of economy. It is a major source of revenue for the major search engine and social networking sites. Search engine, context-specific and banner advertising are the major modes of online advertising. The banner advertisement mode has certain advantages over other modes of advertising. Currently, the number of websites registered comes to a billion. Each day, a typical website receives the number of visitors ranging from hundreds to millions. In a few years, the entire population of the globe is going to be connected to Internet and browse websites. It is possible for a product seller or service provider to reach every potential customer through banner advertising. In this paper, a framework is proposed to harvest the pages views of web by forming the clusters of similar websites. Rather than managing a single website, the publisher manages the aggregated advertising space of a collection of websites. As a result, the advertisement space could be expanded significantly and it will provide the opportunity for increased number of publishers to market the aggregated advertisement space of millions of websites to advertisers for reaching potential customers. It will also help in balancing the management of banner advertising market.

Abstract

In banner advertisement scenario, advertiser expects his advertisement should be displayed to certain percentage of visitors. He also expects the advertisement to be relevant to the content of web page. On the other hand, to generate more revenue for a given website, publisher has to meet the coverage demands of several advertisers by providing appropriate sets of pages. Coverage patterns (CPs) are a set of web pages visited by certain percentage of visitors. The model of CPs does not reflect the real world scenario as they do not capture the aspect of relevance of web pages to the advertisement. In this paper, we propose a model of content-specific CPs and a methodology to extract content-specific CPs from click-through data, given keywords describing every web page. Content-specific CP is a set of web pages visited by certain percentage of users interested in particular content of the web pages. Experimental results show that the proposed model extracts CPs relevant to the topics of interest of advertiser over the previous model.

Abstract

The process of frequent pattern extraction finds interesting information about the association among the items in a transactional database. The notion of support is employed to extract the frequent patterns. Normally, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns

Abstract

Protein-protein interaction (PPI) networks are valuable biological source of data which contain rich information useful for protein function prediction. The PPI networks face data quality challenges like noise in the form of false positive edges and incompleteness in the form of missing biologically valued edges. These issues can be handled by enhancing data quality through graph transformations for improved protein function prediction. We proposed an improved method to extract similar proteins based on the notion of relaxed neighborhood. The proposed method can be applied to carry out graph transformation of PPI network data sets to improve the performance of protein function prediction task by adding biologically important protein interactions, removing dissimilar interactions and increasing reliability score of the interactions. By preprocessing PPI network data sets with the proposed methodology, experiment results on both un-weighted and weighted PPI network data sets show that, the proposed methodology enhances the data quality and improves prediction accuracy over other approaches. The results indicate that the proposed approach could utilize underutilized knowledge, such as distant relationships embedded in the PPI graph.

Abstract

The systems for weather observation and forecast are being operated to deal with adverse weather in general to mankind. Weatherbased decision support systems (DSSs) are being build to improve the efficiency of the production systems in the domains of health, agriculture, livestock, transport, business, planing, governance and so on. The weather-based DSS provides appropriate suggestions based on the weather condition of the given period for the selected domain. In the literature, the notion of reuse is being employed in improving the efficiency of DSSs. In this paper, we have proposed a framework to identify similar weather conditions, which could help in improving the performance of weather-based DSSs with better reuse. In the proposed framework, the range of weather variable is divided into categories based on its influence on that domain. We form a weather condition for a period which is the combination of category values of weather variables. By comparing the daily/weekly weather conditions of a given year to weather conditions of subsequent years, the proposed framework identifies the extent of reuse. We have conducted the experiment by applying the proposed framework on 30 years of weather data of Rajendranagar, Hyderabad and using the categories employed by India Meteorological Department in Meteorology0 domain. The results show that there is a significant degree of similarity among daily and weekly weather conditions over the years. The results provide an opportunity to improve the efficiency of weather-based DSSs by improving the degree of reuse of the developed suggestions/knowledge for the corresponding weather conditions.

Abstract

Legal judgements are complex in nature and contain cita-tions to other judgements. Research efforts are going on to develop meth-ods for efficient search of relevant legal information by extending the pop-ular approaches used in information retrieval and web searching research areas. In the literature, it was shown that it is possible to find similarjudgements by exploiting citations or links. In this paper, an approachh as been has been proposed using the notion of “paragraph-link” to im-prove the efficiency of link-based similarity method. The experiments on real-world data set and user evaluation study show the encouraging results.

Abstract

We propose a model of coverage patterns and a methodology to ex-tract coverage patterns from transactional databases. We have discussed how thecoverage patterns are useful by considering the problem of banner advertisementsplacement in e-commerce web sites. Normally, advertiser expects that the banneradvertisement should be displayed to a certain percentage of web site visitors. Onthe other hand, to generate more revenue for a given web site, the publisher has tomeet the coverage demands of several advertisers by providing appropriate setsof web pages. Given web pages of a web site, a coverage pattern is a set of pagesvisited by a certain percentage of visitors. The coverage patterns discovered fromclick-stream data could help the publisher in meeting the demands of several ad-vertisers. The efficiency and advantages of the proposed approach is shown byconducting experiments on real world click-stream data sets.

Abstract

A model of crop specific virtual labs is proposed to improve practical agricultural education by considering the agricultural educa-tion system in India. In agricultural education, the theoretical concepts are being imparted through class room lectures and laboratory skills are imparted in the dedicated laboratories. Further, practical agricultural education is being imparted by exposing the students to the field prob-lems through Rural Agricultural Work Experience Program (RAWEP),experiential learning and internships. In spite of these efforts, there isa feeling that the level of practical skills exposed to the students is not up to the desired level. So we have to devise the new ways and means to enhance the practical knowledge and skills of agricultural students to understand the real-time crop problems and provide the corrective stepsat the field level. Recent developments in ICTs, thus, provide an oppor-tunity to improve practical education by developing virtual crop labs.The virtual crop labs contain a well organized, indexed and summarized digital data (text, photograph, and video). The digital data corresponds to farm situations reflecting life cycles of several farms of different crops cultivated under diverse farming conditions. The practical knowledge of the students could be improved, if we systematically expose them to virtual crop labs along with course teaching. We can employ cloud com-puting platform to store huge amounts of data and render to students and other stakeholders in an online manner

Abstract

Frequent patterns are an important class of regularities thatexist in a transaction database. Certain frequent patternswith low minimum support (minsup) value can provide use-ful information in many real-world applications. However,extraction of these frequent patterns with singleminsup-based frequent pattern mining algorithms such as Aprioriand FP-growth leads to “rare item problem.” That is, athighminsupvalue, the frequent patterns with lowminsupare missed, and at lowminsupvalue, the number of frequentpatterns explodes. In the literature,“multipleminsupsframe-work” was proposed to discover frequent patterns. Further-more, frequent pattern mining techniques such as MultipleSupport Apriori and Conditional Frequent Pattern-growth(CFP-growth) algorithms have been proposed. As the fre-quent patterns mined with this framework do not satisfydownward closure property, the algorithms follow differenttypes of pruning techniques to reduce the search space. Inthis paper, we propose an efficient CFP-growth algorithmby proposing new pruning techniques. Experimental resultsshow that the proposed pruning techniques are effective.

Abstract

Recommender systems employ the popular K-nearest neigh-bor collaborative filtering (K-CF) methodology and its vari-ations for recommending the products. In K-CF approach,recommendation for a given user is computed based on theratings of K-nearest neighbors. In K-CF approach, it can benoted that, the system identifiesKneighbors for each userirrespective of the number of products he/she has rated.As a result, the user who have rated few products may getthe less-similar neighbors and the user who have rated moreproducts may miss the genuine neighbors. In the literature,the notion of lower-bound similarity has been proposed toimprove the clustering performance in which the clusters areextracted by fixing the similarity threshold. In this paper,we have extended the notion of lower-bound similarity torecommender systems to improve the performance of K-CFapproach. In the proposed approach, instead of fixingKfor finding the neighborhood, the similarity threshold valueis fixed to extract the neighbors for each user. As a re-sult, each user gets appropriate number of neighbors basedon the number of products rated by him/her in a dynamicmanner. The experimental results, on MovieLens dataset,show that the proposed lower bound similarity CF approachimproves the performance of recommender systems over K-CF approach.