the ever increasing diversification of Web services and software applications poses a real challenge to developers and designers when creating software that has to cope with a myriad of interaction situations, as well...
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the ever increasing diversification of Web services and software applications poses a real challenge to developers and designers when creating software that has to cope with a myriad of interaction situations, as well as specific directives for ensuring an accessible interaction. Utilizing an advanced web services accessibility assessment tool, they can obtain a better understanding of the accessibility constraints for people with disabilities within Web services and software application's user interfaces. the proposed Web services assessment tool will assist them, with a minimal effort, to explore user-centered design and important accessibility issues for their software implementations. In an effort to solve such issues, this paper takes a step forward and introduces the notion of accessibility in the web service domain, in order to enhance web services with accessibility features capable to ensure that HCI through applications utilizing them is accessible.
Identification of an Orthopaedic Implant before a revision surgery is very important. Failure to identify an implant causes surgical planning delays, inability to plan for the correct equipment requirements, and can r...
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In this research work an ensemble of bagging, boosting, rotation forest, decorate and random subspace methods with 5 symbolic sub-classifiers in each one is presented. then a voting methodology is used for the final p...
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ISBN:
(纸本)9781467393126
In this research work an ensemble of bagging, boosting, rotation forest, decorate and random subspace methods with 5 symbolic sub-classifiers in each one is presented. then a voting methodology is used for the final prediction. In order to decrease training time, before building the ensemble redundant features were removed using a slight filter feature selection method. A comparison with simple bagging, boosting, rotation forest, decorate and random subspace methods ensembles with 25 symbolic sub-classifiers is performed, as well as other well-known combining methods, on standard benchmark datasets. the proposed technique is shown to be more accurate than other related methods in most cases.
Motivation: With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper n...
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Motivation: With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance;results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data. Results: We propose a fully probabilistic Bayesian model to cluster gene-expression profiles. the number of classes does not need to be specified in advance;instead it is adjusted dynamically using a Reversible Jump Markov Chain Monte Carlo sampler. Imputation of missing values is integrated into the model. With simulations, we determined the speed of convergence of the sampler as well as the accuracy of the inferred variables. Results were compared withthe widely used k-means algorithm. With our method, biologically related co-expressed genes could be identified in a yeast transcriptome dataset, even when some values were missing. Availability: the code is available at http://***/BayesianClustering/.
Motivation: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is pro...
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Motivation: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. this means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. this is not a trivial decision. We propose an alternative approach to overcome this problem. Results: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes;find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results.
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