Background/Objectives: Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder, which causes memory loss and loss of cognitive functioning, along with behavioral changes. Early detection is important to delay disease progression, timely intervention and to increase patients’ and caregivers’ quality of life (QoL). One of the major and primary challenges for preventing any disease is to identify the disease at the initial stage through a quick and reliable detection process. Different researchers across the world are still working relentlessly, coming up with significant solutions. Artificial intelligence-based solutions are putting great importance on identifying the disease efficiently, where deep learning with medical imaging is highly being utilized to develop disease detection frameworks. In this work, a novel and optimized detection framework has been proposed that comes with remarkable performance that can classify the level of Alzheimer’s accurately and efficiently. Methods: A powerful vision transformer model (ViT-B16) with three efficient Convolutional Neural Network (CNN) models (VGG19, ResNet152V2, and EfficientNetV2B3) has been trained with a benchmark dataset, ‘OASIS’, that comes with a high volume of brain Magnetic Resonance Images (MRI). Results: A weighted average ensemble technique with a Grasshopper optimization algorithm has been designed and utilized to ensure maximum performance with high accuracy of 97.31%, precision of 97.32, recall of 97.35, and F1 score of 0.97. Conclusions: The work has been compared with other existing state-of-the-art techniques, where it comes with high efficiency, sensitivity, and reliability. The framework can be utilized in IoMT infrastructure where one can access smart and remote diagnosis services.

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies.

Technology is being developed in different fields to reduce the human effort where systems are getting smart and autonomous. Different autonomous systems can perform tasks more accurately and efficiently than humans, reducing human interaction with the machine. Patients and disabled persons typically use wheelchairs, which are common but important objects. In a traditional wheelchair, human effort is required to operate. In some cases, patients are unable to manually operate the wheelchair by moving the wheel, necessitating additional support or assistance to push from the backside. Technologies are being used to make wheelchairs smart and autonomous now, where any external assistance is not required anymore. In this work, a framework for IoT-based smart heelchairs has been proposed, where multifunctional designs with advanced operating features have been developed. The wheelchair an be controlled with hand gestures, where the user can operate the chair by moving hand position. Smart features like obstacle detection, GPS, fall detection, home appliance control, IoT-integrated features like IoMT, and a remote alert system have been installed. The device can detect any obstacle and can move automatically to avoid it. GPS location helps to figure out the current location of thewheelchair. The user can control or operate the home appliance, like turning the light or fan on/ off remotely from the wheelchair. IoMT applications help to take the user’s health data and send it to healthcare for a checkup. In case of any urgency, the wheelchair can communicate with the emergency contact to acknowledge the status of the user. The proposed device can serve as an effective solution, providing a seamless experience with smart operating features and multiple advanced functionalities.

Electric vehicle (EV) adoption has surged in recent years, underscoring the need for accurate forecasting models to inform policy and industry planning. Traditional forecasting approaches often struggle to capture the complex, nonlinear patterns in EV adoption data, motivating the use of deep learning methods. This paper proposes a novel multi-branch Long Short-Term Memory (LSTM) architecture enhanced with attention mechanisms to forecast EV adoption trends. We review the state-of-the-art in EV adoption forecasting, including classical time-series models and modern deep learning techniques. We then analyze current multi-branch LSTM and attention-based models, highlighting their advantages in capturing multifaceted data influences. A critical comparison of alternative methods is presented along with their performance metrics and limitations. Building on identified gaps—such as scalability and integration of heterogeneous data—we outline the scope and methodology for the proposed model. The multi-branch LSTM with attention is expected to improve forecasting accuracy and interpretability by simultaneously learning from multiple data streams (e.g., historical adoption, economic indicators) and focusing on the most relevant temporal patterns. Figures and tables are included to illustrate EV adoption trends and comparative performance of forecasting models.