(Ministry of Higher Education and Scientific Research, Iraq)*corresponding author
AbstractThe implementation of an intelligent system for network control and monitoring that is built on an Internet of Things (IoT) is a focus of this line of research, with the end objective of improving the level of precision inside the network and its applications. You did indeed read it correctly; the system that is being referred to here is a deep neural network. The manner that it is constructed makes it possible for the layer that cannot be seen to contain more data. The application of element-modified deep learning and network buffer capacity control helps to improve the overall service quality that is provided by each sensor node. One method that can be applied to the process of instructing a machine to pay more attention includes deep learning in its various incarnations. The team was able to do calculations with a precision of 96.68 percent and the quickest execution time, thanks to the usage of wireless sensors. Using a sensor-based technique that has a brief implementation period, this piece has a degree of accuracy of 97.69 % when it comes to detecting and classifying proxies, and it does so using a method that is very efficient. On the other hand, our research represents a significant leap forward in comparison to earlier studies due to the fact that we were able to accurately identify and categorize a wide variety of invasions and real-time proxies.
KeywordsDeep learning; Intelligent-IoT; DNN; WSN
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DOIhttps://doi.org/10.31763/ijrcs.v2i4.769 |
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