Open Access
ARTICLE
Huaxiang Song*
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.039315
Abstract Recently, the convolutional neural network (CNN) has been dominant in studies on interpreting remote sensing images (RSI). However, it
appears that training optimization strategies have received less attention in
relevant research. To evaluate this problem, the author proposes a novel algorithm named the Fast Training CNN (FST-CNN). To verify the algorithm’s
effectiveness, twenty methods, including six classic models and thirty architectures from previous studies, are included in a performance comparison.
The overall accuracy (OA) trained by the FST-CNN algorithm on the same
model architecture and dataset is treated as an evaluation baseline. Results
show that there is a maximal OA… More >
Open Access
ARTICLE
Mingguang Yu1,2, Xia Zhang1,2,*
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2023.038798
Abstract As cloud system architectures evolve continuously, the interactions among distributed components in various roles become increasingly
complex. This complexity makes it difficult to detect anomalies in cloud
systems. The system status can no longer be determined through individual
key performance indicators (KPIs) but through joint judgments based on synergistic relationships among distributed components. Furthermore, anomalies
in modern cloud systems are usually not sudden crashes but rather gradual, chronic, localized failures or quality degradations in a weakly available
state. Therefore, accurately modeling cloud systems and mining the hidden
system state is crucial. To address this challenge, we propose an anomaly
detection… More >
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