The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-w...
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The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI. To overwhelm this problem, a band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification (PCNNC-AVOACS-EEG) is proposed in this article. Initially, the input EEG signals are taken from BCI competition IV, dataset 1. Then the input EEG signals are pre-processed by contrast-limited adaptive histogram equalization filtering. These pre-processed EEG signals are extracted by hexadecimal local adaptive binary pattern (HLABP) method. This HLABP method extracts the features of alpha and beta bands from the EEG segments. Each EEG channel's band power data are utilized as features for a PCNNC to exactly classify the EEG into 3 classes: two MI states and idle state. The AVOA is applied within the band power feature PCNNC for channel selection, wherein channel selection aids to enhance the categorization accuracy on test set that is a vital indicator for real-time BCI applications. The proposed method is activated in python. From the experiment, the proposed technique attains 17.91%, 20.46% and 18.146% higher accuracy;14.105%, 15.295% and 5.291% higher area under the curve and 70%, 60% and 65.714% lower computation time compared with the existing approaches.
Mobile Ad-hoc networks (MANETs) are used in a wide range of applications because of their unique capabilities. The routing in MANET is regarded as a main challenge on account of its permanent movement and nodes' r...
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Mobile Ad-hoc networks (MANETs) are used in a wide range of applications because of their unique capabilities. The routing in MANET is regarded as a main challenge on account of its permanent movement and nodes' randomness that can cause a continual change of network topology, thus finding correct paths among the nodes is the fundamental task of routing protocols. There is a rise in protocols that depend on cross-layer communication across several layers to increase the MANET performance. In this manuscript, a Hybrid Clustering Approach (SG-MFOA) using a Multipath Cross-Layer Design in a MANET network is proposed. Here, multiple routes are selected for data packet transmission with the help of hybrid routing. Furthermore, a cross-layer metric is acquired depending upon Expected Transmission Time (ETT), Residual Energy and Load Balancing Factor. It is necessary to select a Cluster Head (CH) depending on this trade for proficient routing. So, the cluster head is optimally chosen to utilize SG-MFOA. Thus, the multipath route selection is performed by the multi-objective functions containing bandwidth, congestion delay, transmission delay and to ensure the prolonging of network lifetime, queue delay at each connection Access Category from the available routes to a destination. When compared with the existing methods, like a proficient self-attention-based conditional variational auto-encoder generative adversarial networks-based multipath cross-layer design routing paradigm for MANET (SCVAGAN-MCDR-MANET), Energy Aware Cross Layer Routing Protocol to Increase Route Reliability in MANETs (EACLRP-RR-MANET), Cross-layer Adaptive Fuzzy-based Ad hoc On-Demand Distance Vector Routing Protocol for MANET (CLAF-DVRP-MANET), respectively.
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