• The Journal of Bigdata
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• v.8 no.2
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• pp.177-187
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• 2023

Semi-supervised learning (SSL) is an effective approach to training models using a small amount of labeled data and a larger amount of unlabeled data. However, many papers in the field use a fixed threshold when applying pseudo-labels without considering the feature-wise differences among images of different classes. In this paper, we propose a SSL method for synthetic aperture radar (SAR) image classification that applies different thresholds for each class instead of using a single fixed threshold for all classes. We propose a threshold learning module into the model, considering the differences in feature distributions among classes, to dynamically learn thresholds for each class. We compare the application of a SSL SAR image classification method using different thresholds and examined the advantages of employing class-specific thresholds.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.311-317
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• 2009

The traditional network anomaly detection systems execute the threshold-based detection without considering dynamic network environments, which causes false positive and limits an effective resource utilization. To overcome the drawbacks, we present the adaptive network anomaly detection model based on artificial immune system (AIS) in centralized network. AIS is inspired from human immune system that has learning, adaptation and memory. In our proposed model, the interaction between dendritic cell and T-cell of human immune system is adopted. We design the main components, such as central node and router node, and define functions of them. The central node analyzes the anomaly information received from the related router nodes, decides response policy and sends the policy to corresponding nodes. The router node consists of detector module and responder module. The detector module perceives the anomaly depending on learning data and the responder module settles the anomaly according to the policy received from central node. Finally we evaluate the possibility of the proposed detection model through simulation.