Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user’s intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology.

The maritime industry, essential for international logistics and trade, increasingly requires precise and reliable control systems for surface vessels, especially during critical operations such as docking and maneuvering in tight areas. This research presents a data-driven framework for predicting ship control commands, specifically focusing on the number of turns and rudder angle, by using hybrid models that combine deep learning and ensemble machine learning techniques. CNNs are used for feature extraction, while recurrent architectures like GRU and LSTM are used to handle the time-series data generated by a three-DOF vessel model operating in a simulated port environment. To improve prediction performance, these deep learning models are combined with Random Forest and Extra Trees regressors. Comparative evaluations show that the GRU+ExtraTree model delivers superior accuracy and responsiveness, particularly during sudden changes in control signals, due to its ability to capture temporal dependencies. The proposed method shows promising potential for real-time trajectory tracking and autonomous docking applications in maritime navigation.

The purpose of this study is to improve the classification of electroencephalogram (EEG) signals for motor imagery-based brain–computer interfaces (MI-BCIs), thus enabling more reliable user interactions in assistive technologies such as autonomous wheelchairs. The study contributes by systematically investigating and comparing the performance of both simple (Transformer, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM)) and hybrid deep learning architectures (Transformer + CNN, Transformer + LSTM, CNN + LSTM) for EEG signal classification using multiple evaluation metrics, including accuracy, ROC AUC, confusion matrices, and prediction time. The findings demonstrate that the Transformer + CNN hybrid model consistently outperforms all other architectures, thereby achieving perfect classification accuracy (1.00), zero misclassifications, flawless AUC scores (1.000), and a competitive inference time (1.17 s), thus making it highly suitable for real-time MI-BCI applications. In contrast, simple models, particularly LSTM, show lower accuracies and reduced stabilities. Overall, this study highlights the advantage of combining attention mechanisms and convolutional architectures to effectively capture both spatial and contextual patterns in nonstationary EEG signals, thus establishing Transformer-based hybrid models as a promising approach for robust, real-time EEG classifications in next-generation MI-BCI systems.