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- W4386985231 abstract "Smart grid, a modernization of the traditional electricity grid system, is created by integrating cutting-edge communication technologies and artificial intelligence. It offers a two-way data flow to address issues faced by conventional grid. The incorporation of cognitive radio into sensor networks enables effective communication for the transfer of data and intelligent control signals, enabling real-time monitoring. We employ Cognitive Radio (CR) technology to sensibly distribute unused spectrum for effective communication. We decided to include attention neural network-based models in our channel selection mechanism since they have undergone significant progress in recent years and have proven effective across a wide range of fields. In order to choose the best available spectrum with the least amount of delay, this study focuses on implementing a cutting-edge spectrum allocation technique known as the Fuzzy Long Short-Term Memory-based Crow Search Optimization Algorithm (FLSTM-CSOA). In order to improve classification and prediction, we propose the Fuzzy Attention Long Short-Term Memory-based Crow Search Optimization Algorithm (FALSTM-CSOA). Quality of service requirements such as BER, throughput, and latency are computed to analyze system performance. To show the usefulness and superiority of the proposed algorithm, the performance is compared with a few other existing algorithms." @default.
- W4386985231 created "2023-09-24" @default.
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- W4386985231 date "2023-01-01" @default.
- W4386985231 modified "2023-10-18" @default.
- W4386985231 title "Attention Neural Network-Based Channel Selection Algorithm for Cognitive Decision-Making and Control in Smart Grid" @default.
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- W4386985231 doi "https://doi.org/10.1007/978-981-99-1431-9_39" @default.
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