Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters ($1\times 1,3\times 3$, and $5\times 5$). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.

Unmanned aerial vehicles, commonly known as drones, face unique authentication challenges due to their distinct characteristics and operational environments. These challenges include identity management, secure key management, secure communication channels, and limited resources. To address these challenges, we propose a dual-layer authentication scheme utilizing Advanced Encryption Standard and Elliptic Curve Cryptography on Field-Programmable Gate Arrays. This scheme leverages the flexibility and programmability of Field-Programmable Gate Arrays incorporated in drones to enhance security. The integration of Advanced Encryption Standard on Field-Programmable Gate Arrays provides a robust solution to withstand various attacks such as impersonation, tampering, and replay attacks. Simulation results in Xilinx Vivado demonstrate the efficiency and effectiveness of the proposed scheme compared to existing methods.