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.

The usage of embedded system in the traffic control unit is not new. Many advanced intelligent systems are being used to reduce the human effort, and wasting time. As a result, road congestion and accident rate are being decreased. But in some critical spots like blind turn in the hill areas where the turn is too much sharp that it’s impossible to identify the existence of coming vehicles and the result may be very dangerous which need to be improved more with advance traffic controlling system. In this paper, we are going to propose a laser based embedded system which can efficiently control the traffic in the blind turn areas through giving proper signals in any situation. A driver can easily identify both the types and numbers of the vehicles coming from the other side by getting signals from the signal terminal. The proposed system is flexible and cost effective so that it can play a vital role by being widely used in the near future.

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.

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.