Preventing and controlling grape diseases is essential for a good grape harvest. With the help of "single shot multibox detectors", "faster region based convolutional neural networks", & "You only look once-X," the study improved grape leaf disease detection accuracy with effective attention mechanisms, which includes convolutional block attention module, squeeze & excitation networks, & efficient channel attention. The various attention techniques helped to emphasize important features while reducing the impact of irrelevant ones, which ultimately improved the precision of the models and allowed for real-time performance. As a result of examining the optimal models from the three types, it was found that the Faster (R-CNN) model had a lower precision value, while You only look once-X and SSD with various attention techniques required the fewest parameters with the highest precision, with the best real-time performance. In addition to providing insights into grape diseases & symptoms in automated agricultural production, this study provided valuable insights into grape leaf disease detection.
Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both cost-effective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data.
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