The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

An inscribed hexagonal fractal slotted patch antenna with some additional geometry and slots is proposed for the optimization in this paper. This research work is concerned with the optimization of this slotted fractal antenna with the help of the curve-fitting method in conjunction with the modified version of Lightning Attachment Procedure Optimization (MLAPO) technique. The data required for the curve-fitting technique and for the optimization technique have been obtained by varying some of the parameters of the proposed antenna. Different equations are developed to know the relations between these parameters of the proposed antenna. The MLAPO technique is applied thereafter to calculate the different optimized geometrical parameters to optimize the bandwidth for the proposed antenna. The optimized geometrical parameters are verified with the help of a parametric variation to justify the reliable optimization. The bandwidth obtained by the MLAPO technique has been found to be better than that obtained by PSO and normal LAPO algorithm. The prototype of the optimized antenna is fabricated and the experimental results are found to be compatible with the results obtained by simulation. The proposed optimized antenna may be utilized in various applications in C and X bands.