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The brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) related to the Internet of Things (IoT) have shown incredible growth with technological advancements, offering a greater number of applications all over the world. Sensor nodes are deployed closer to the base station (BS) in some scenarios and held accountable for transmitting data from neighboring and own nodes against the BS and depletes energy. In the network, this problem is referred to as the "hotspot issue" and it may be fixed using procedures that use unequal clustering. In this study, a novel approach for IoT-assisted wireless sensor networks called the Fire Hawk Optimization-based Unequal Clustering Scheme for Hotspot Mitigation (FHOUCS-HSM) is developed. In the presented FHOUCS-HSM model, a first-order radio energy model has been utilized. The FHOUCS-HSM technique follows the foraging characteristics of whistling kites, black kites, and brown falcons. The design of the FHO algorithm for unequal clustering shows the novelty of the work. In addition to this, the FHOUCS-HSM model computes a fitness function for the use of uneven cluster size and the selection of cluster heads (CH). The comparative research highlights the advantages that the FHOUCS-HSM technique has over many models that are already in use. [ABSTRACT FROM AUTHOR]