Soft computing provides an approach to problem-solving using means other than computers. With the human mind as a role model, soft computing is tolerant of partial truths, uncertainty, imprecision and approximation, unlike traditional computing models.
The tolerance of soft computing allows researchers to approach some problems that traditional computing can't process. As a field of mathematical and computer study, soft computing has been around since the s.
The inspiration was the human mind's ability to form real-world solutions to problems through approximation. Soft computing contrasts with possibility, an approach that is used when there is not enough information available to solve a problem. In contrast, soft computing is used where the problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations.
Please check the box if you want to proceed. Compliance as a Service CaaS is a cloud service service level agreement SLA that specified how a managed service provider A data protection impact assessment DPIA is a process designed to help organizations determine how data processing systems, A computer worm is a type of malicious software program whose primary function is to infect other computers while remaining The measure is based on the classic biclustering measure, mean square residue MSR. MSR 3D is applied to both synthetic and real data and it has proved to be capable of extracting groups of genes with homogeneous patterns in subsets of conditions and times, and these groups have shown a high correlation level and they are also related in terms of their functional annotations extracted from the Gene Ontology project.
The paper by Y. Jusman et al. University of Malaya briefly reviews cervical screening techniques and their advantage and disadvantages. The digital data of the screening techniques are used as data for the computer screening system as replaced in the expert analysis. Four stages of the computer system, enhancement, features extraction, feature selection, and classification, are reviewed in detail.
Applied Soft Computing
The computer system based on cytology data and electromagnetic spectra data achieves better accuracy than other data. Escalona-Vargas et al. Center for Research and Advanced Studies at Tamaulipas and Cinvestav at Monterrey study the use of nonparametric multiple comparison statistical tests on the performance of simulated annealing SA , genetic algorithm GA , particle swarm optimization PSO , and differential evolution DE , when used for electroencephalographic EEG source localization.
They evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. Their results do not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, they find that PSO ring and tree topologies and DE perform the worst.
In the paper by M. Czajkowski and M.
Kretowski Bialystok University of Technology , they develop a specialized evolutionary algorithm EA for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. They manage to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs.
- Applications of Soft Computing?
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Experimental validation of EvoTSP on public available microarray datasets shows that their proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solutions. Gonzalez-Sanchez et al.
Emerging Trends in Soft Computing Models in Bioinformatics and Biomedicine
ITESM and IMTA evaluate the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5' regression trees, and artificial neural networks ANN are ranked. The models are built using real data of eight crops sowed in an irrigation module of Mexico. Their results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE Irigoien et al.
They evaluate the ability of the procedures using twelve experimental datasets with no necessarily continuous data.
The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data continuous, discrete, or nominal , whereas state-of-the-art approaches application is not straightforward when nominal variables are present. Khan et al. Track accepted paper Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Order Journal.
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