Earticle

Web clustering engine greatly simplifies the effort of the user from browsing the large set of search results by reorganizing them into smaller clusters. Current web clustering engines result in additional clusters and misses out few relevant, leading to lack of predictability of clustering outputs. Web clustering engines produces inconsistent results as the content of the cluster do not always correspond to its label. In this paper, a new web clustering engine named SRCluster has been proposed to overcome these deficiencies, in specific for the polysemy unigram search keywords. SRCluster identifies the possible categories and its label for the given polysemy keyword based on Wikipedia. The system determines the improved Lesk score (termed, SRLesk score) for each of the category. The search result is clustered to the category with the maximum SRLesk score. The hypertext of the disambiguation Wikipedia page is utilized for labeling the cluster. The experimental result on AMBIENT dataset shows that the inconsistency and the lack of predictability of clustering outputs is being improved using SRCluster.

This paper will explain a research of some effort in Guidance Counseling (GC) class. This research carried out by the fact that some class VIII of Junior High School (JHS) students in Indonesia does not have the insight and good preparation for his/her career. The research was carried out two cycles. First cycle, applying career guidance services indirectly and not programmed. Second cycle, implementing career guidance services indirectly and programmed using ICT (Information and communications technology) media with mobile phone and PC. Research subject was 120 JHS students of class VIII ABCD, Academic Year 2008/2009. The subject consist of 55 male and 65 female. All students are followed its development until the rise in class IX ABCD. But specifically to be followed by the development and given special treatment is 44 students consisting of 20 men and 24 women who have value to the 9th stage of development aspects of insight and career preparation (TP9) ≤ 3. Special treatment in question is to increase individual counseling services either using or not using. On the average (120 students) there is an increase of 23.1% from the initial conditions to the second cycle, and for 44 students (which is specifically observed for TP9 value ≤ 3) the increase is higher at 40.6%.This suggests that GC service needs to be done directly and indirectly by using various media, especially ICT.

Extracting useful knowledge from data sets measuring in gigabytes and even terabytes is a challenging research area for the data mining community. Sequential approaches suffer from a performance problem due to the fact that they have to mine voluminous databases. Parallelism is introduced as an important solution that could improve the response time and the scalability of these approaches. However, parallelization process is not trivial and still facing many challenges including the workload balancing problem. In this paper, we propose a hierarchical dynamic load balancing strategy for parallel association rule mining algorithms in the context of a Grid computing environment. The French research grid “Grid’5000” is used as our experimental test-bed. Through a detailed experimental study, we show that our strategy improves the performance and helps the parallel algorithm to scale very well with the number of computational nodes available.

For almost a full decade, the bio-based vehicular fuels industry has been anticipating the establishment of a continually expanding industry within a reasonably stable market base. However, a variety of events in the bio-based diesel fuel industry has resulted in this promising market still remaining dynamic. Of the five renewable, bio-based fuels that appear to have potential to displacement petroleum diesel within the United States and elsewhere, biodiesel and green diesel are believed to be the fuels that in the near-term represent the most promising options. Even though they are both viable displacement options and blend well with petroleum-based diesel these fuels are chemically different, created using completely different processing systems. While both are considered promising, biodiesel is fully commercialized with green diesel generally considered to be at the demonstration stage. Upon review of how the two fuels are produced and potentially marketed, there does exist the potential for both feedstock and market competition to develop between biodiesel and renewable diesel.

In this paper, we present a novel approach to process the image using different filtering methods by Image Restoration. The aim is to enhance the digital image, reconstruct it into the original form from the noisy image. This paper is an overview of effective algorithms that can be used for image restoration. For which, techniques are used on the basis of non linear filters to restore the image. The performance of Histogram Adaptive Fuzzy (HAF) filter is carefully examined and compared with other filters like, Weighted Fuzzy Mean (WFM) filter, Minimum-maximum Detector Based (MDB) filter, Adaptive Fuzzy Mean (AFMF) filter, Centre Weighted Mean (CWM) filter, and Min-max Exclusive Mean(MMEM) filter on the basis of (Peak Signal to Noise Ration) PSNR. Experimental results on images will illustrate the capabilities of all the studied approaches.

This paper presents a Petri net based analysis of ontology for indoor location-based services. The proposed method builds up a Petri net model for the given ontology at the first step. Then, it analyzes the Petri net model by running the simulation on it. The proposed method of building a Petri net model for the given ontology is given in the paper. Then algorithms of performing inferences on the Petri net are proposed. Our proposed method is applied on a simple ontology for a sample Indoor Location-based service.