• 경영정보학연구
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• 제3권2호
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• pp.277-288
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• 2001

Of critical importance to the success of any e-commerce site are the two factors: rapid application development and quick response time. A three-tier architecture composed of presentation layer, business layer, and data access layer emerges to allow rapid changes in user interface, business logic, and database structures. Too often, such a logical three-tier architecture is considered as requiring a three-tier physical architecture: Web server, application server, and database server running on separate computers. Contrary to the common belief, a Web stress test reveals that the three-tier logical architecture implemented on a two-tier physical platform guarantees a quicker response time due to the reduction in cross-machine communications. This would lead business firms to economize their spending on e-commerce: increasing the number of physical servers to expedite transaction is not necessarily the best solution. Before selecting a particular hardware configuration, a Web stress test needs to be conducted to compare the relative merits of alternative physical architectures. Together with capacity planning, Web stress test emerges as a powerful tool to build robust, yet economical e-commerce sites.

• ETRI Journal
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• 제46권2호
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.