Objectives: To enhance the microarray data classification accuracy, to accelerate the convergence speed of classifier, and Modified Whale Optimization Algorithm (MWOA), refine the best balance among local exploitation and global exploration, a Search space enhanced Modified Whale Optimization Algorithm (SMWOA) is the proposed task. Methods: The SMWOA selects the optimal features stands on the Levy flight method and quadratic interpolation method. Levy flight which employs for acceleration convergence speed of SMWOA andalso holds the result from local optima builds up by the population assortment.A quadratic interpolation takes up the exploitation stage for deeper searching within the search area. Finding: In addition to this, a self-adaptive control parameter is introduced to make a clear variation to the solution quality. Itrefines the best equity among the local exploitation method by global exploration method. After selection of features, those are processed in Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers for cancer detection. Novelty: The classification accuracy is improved by processing the most discriminative features in the classifiers. The overall accuracy, specificity, sensitivity, F1-score and average error of SMWOA-ANN are 6.7%, 5.6%, 7.3% and 5.6% greater than MWOA-ANN respectively for cancer detection. Keywords: Gene expression data; dimensionality reduction; feature selection; modified whale optimization algorithm (MWOA); search space enhanced modified whale optimization algorithm (WOA)
Recurrent fires in forested areas are a leading disturbance that can strongly affect the vegetation dynamics, structure and regeneration. In this study, we examined tree species regeneration in response to fire in Sathyamangalam Tiger Reserve. Fire frequency map was prepared using Landsat 5, 7 and 8 satellite images for the period between 1999 and 2015 (17 years). The study resulted in the occurrence of ten fire frequency classes along with an unburned class. For each class, three plots were randomly laid in the field, and data were collected on the three growth forms such as seedlings, saplings and trees. A total of 62 species belonging to 50 genera and 23 families were recorded. There was a negative linear correlation between the numbers of species in all three growth forms and fire frequencies. Lower fire frequency classes (F1-F4) sustained the individual density of seedlings and saplings. Friedman's two-way analysis of variance by rank showed that the number of individuals of three dominant species (Anogeissus latifolia, Phyllanthus emblica and Tectona grandis), in seedling and sapling, exhibited a significant difference among each other in fire frequency classes, whereas in tree form they were not significant. The species with more seedlings were P. emblica, Terminalia chebula and Ziziphus rugosa. This study found that even in the short fire intervals P. emblica species could sustain in all three life forms, thus this species can be recommended for afforestation in high fire frequency areas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.