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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]

Biomedical imaging devices, in general, have been made and used a lot lately to examine the insides of the body during diagnostic and analytic procedures. Biomedical imaging gives accurate information about metabolites, which can be used to find and classify diseases because it is not invasive. For the study of cervical cancer (CC), the pap smear is a crucial type of biological imaging. CC is a crucial reason to enhance the rate of women's mortalities. Proper screening of pap smear images is critical for assisting in the early detection and analysis of CC. Computer‐aided systems for cancerous cell recognition need well established artificial intelligence (AI) methods. In this study, we introduce an automated Cervical Precancerous Lesion Classification using Quantum Invasive Weed Optimization with Deep Learning (CPLC‐QIWODL) on biomedical pap smear images. The presented CPLC‐QIWODL technique examines the pap smear images for cervical cancer classification. To do so, the presented CPLC‐QIWODL technique pre‐processes the biomedical images using a Gabor filtering (GF) approach. Moreover, the CPLC‐QIWODL technique uses a deep convolutional neural network‐based SqueezeNet system for feature extraction. Furthermore, the hyperparameter tuning of the SqueezeNet methodology takes place using the QIWO technique, showing the novelty of the work. Finally, to classify CC, the deep variational autoencoder (DVAE) model is applied. The experimental result analysis of the CPLC‐QIWODL technique is tested using a benchmark medical image database. Extensive comparative results demonstrated the enhanced outcomes of the CPLC‐QIWODL technique over other existing algorithms, with a maximum accuracy of 99.07%. [ABSTRACT FROM AUTHOR]