Research Publications (Accounting and Informatics)
Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/212
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Item Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification(Indian Academy of Sciences, 2014-06-12) Marwala, T.; Abe, B. T.; Olugbara, Oludayo O.The performances of regular support vector machines and random forests are experimentally com-pared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimiza-tion algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gra-dient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.Item Hyperspectral image classification using random forests and neural networks(International Association of Engineers, 2012) Abe, B. T.; Olugbara, Oludayo O.; Marwala, T.Spectral unmixing of hyperspectral images are based on the knowledge of a set of unknown endmembers. Unique characteristics of hyperspectral dataset enable different processing problems to be resolved using robust mathematical logic such as image classification. Consequently, pixel purity index is used to find endmembers from Washington DC mall hyperspectral image dataset. The generalized reduced gradient algorithm is used to estimate fractional abundances in the hyperspectral image dataset. The WEKA data mining tool is selected to construct random forests and neural networks classifiers from the set of fractional abundances. The performances of these classifiers are experimentally compared for hyperspectral data land cover classification. Results show that random forests give better classification accuracy when compared to neural networks. The study proffers solution to the problem associated with land cover classification by exploring generalized reduced gradient approach with learning classifiers to improve overall classification accuracy. The classification accuracy comparison of classifiers is important for decision maker to consider tradeoffs in accuracy and complexity of methods.Item Meta analysis of heuristic approaches for optimizing node localization and energy efficiency in wireless sensor networks(Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2020-10) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, Timothy T.; Ogunsakin, Ropo E.Background: In the literature node localization and energy efficiency are intrinsic problems often experienced in wireless sensor networks (WSNs). Consequently, various heuristic approaches have been proposed to allay the challenges faced by WSNs. However, there is little to nothing in the literature to support which of the heuristic approaches is best in optimizing node localization and energy efficiency problems in WSN. The aim of this paper is to assess the best heuristic approach to date on resolving the node localization and energy efficiency in WSNs. Method: The extraction of the relevant articles was designed following the technique of preferred reporting items for systematic reviews and meta-analyses (PRISMA). All the included research articles were searched from the widely used databases of Google Scholar and Web of Science. All statistical analysis was performed with the fixed-effects model and the random-effects model implementation in RStudio. The overall pooled global estimate and categorization of performance for the heuristic approaches were presented in forest plots. Results: A total of 18 studies were included in this meta-analysis and the overall pooled estimated categorization of the heuristic approaches was 35% (95% CI (13%, 67%)). According to subgroup analysis the pooled estimation of heuristic approach with hyper-heuristic was 71% (95% CI: 6% to 99%), I2 = 100%) while the hybrid heuristic, was 31% (95% CI: 3% to 87%, I2 = 100%) and metaheuristic was 21%(95% CI: 9% to 41%, I2 = 100%). Conclusion: It can be concluded based on the experimental results that hyper-heuristic approach outclassed the hybrid heuristic and metaheuristic approaches in optimizing node localization and energy efficiency in WSNs.