In numerous applications, such as in the areas of law enforcement and medical imaging systems, together with perceptual transparency, it is required to reverse the modified media back to the original without any deformation, after the secret data are regained for some legal deliberations. This paper proposes a completely reversible watermarking system with enhanced imperceptibility, robustness, and capacity for colour medical images which can effectively check the unlawful utilisation of the medical images with no affect to medical information and its visual quality. The proposed algorithm depends on transforming non-overlapping blocks of the host image using wavelet transform and is completely blind. The proposed technique attains high values of peak signal to noise ratio (PSNR) of watermarked image and high values of normalised correlation (NC) of the extracted watermark and recovered original image. State-of-the-art comparison shows that the proposed scheme has advantages over the existing techniques. This scheme may be proficient in providing a broad range of applications that aims at security and privacy protection in the medical field. The scheme can also be helpful for the purpose of identification of fundus images for storage in distributed medical databases and tele-ophthalmological applications.

The purpose of smart parking systems is to optimize the use of parking spaces in urban areas by combining sensors, cameras, and software. The systems detect parking spots automatically and notify users in real-time, instead of requiring manual searches for parking.They contribute to the reduction of traffic congestion, fuel consumption, and environmental pollution by reducing time spent searching for parking. This paper proposes a vision-based smart parking system that utilizes deep learning techniques for real-time detection and monitoring of parking space occupancy. The system employs YOLOv7 for object detection, integrated with a Flask-based web interface and OpenCV for image preprocessing. The SQLite database makes it possible for a responsive web application to deliver timely, accurate information with low latency to users. The experimental results presented in this paper indicate that the system achieved an overall average detection accuracy of 94.23% across diverse environmental conditions, which reflects the project’s scalability and dependability. Furthermore, our approach offers a low-cost, easy to implement solution with minimal dependence on hardware-based infrastructure that can be applied in a wide range of land use in urban contexts. Furthermore, the project aligns with securing critical infrastructure in next generation networks, promoting urban mobility, enhancing intelligent transportation systems, and ensuring resilience of smart urban services that are resource-efficient and accessible. This AI-enhanced framework presents an opportunity to protect and optimize critical urban infrastructure using AI-based technology, while also using machine learning in its real-time decisions.