One indispensable element within security systems deployed at public venues such as airports, bus stops, train stations, and marketplaces is video surveillance. The evolution of more robust and efficient automated technological solutions for video surveillance is imperative. In light of the escalating global threat of terrorist attacks in recent years, any unattended object in public areas is treated as potentially suspicious. Ensuring the protection of individuals in these public spaces necessitates the implementation of safety measures. The intricacies of surveillance recordings introduce challenges when it comes to identifying abandoned or removed objects, owing to factors like occlusion, abrupt lighting changes, and other variables. This paper proposes a novel two-stage method for identifying and locating stationary objects in public settings. The first stage uses a sequential model to capture temporal features and detect potential abandoned objects within the monitored area. When the sequential model detects such an object, it triggers a subsequent phase. The second stage uses YOLOv8l model to precisely locate the detected objects. YOLOv8l is renowned for its ability to accurately pinpoint object locations within the surveillance scene. The proposed method achieves remarkable accuracy rates of 99.20% and 99.70% on combined PETS 2006 and ABODA datasets, respectively, effectively localizing the target object. This achievement not only underscores the model’s precision in accurately pinpointing the object’s position within the given context but also establishes its superiority over other existing models. By integrating these two stages, our method provides an effective solution for enhancing the detection of abandoned objects in public spaces, contributing to improved security and safety measures.

Image enhancement and reconstruction is an important field of research in digital image analysis. To increase the quality of low-contrast images, a variety of image-enhancing technologies are available. Among these image enhancement techniques, singular value decomposition and discrete wavelet transform are the popular approaches for enhancing low-quality images. In this study, we have developed a modified singular value decomposition approach to enhance low-contrast and low-resolution close-circuit television images. Low-quality and low-resolution images with singular values near zero are used as input in the proposed approach. On the selected data, a threshold value was determined in the singular values of the diagonal matrix. Finally, the proposed modified singular value decomposition technique was applied to enhance the value of images with low quality and resolution. The datasets include 90 facial images of different individuals with low quality and resolution from a university database. The performance of the proposed approach is assessed using image entropy, peak signal to noise ratio, mean square error, contrast measurement and time computation. During the experiments performed on the obtained dataset, it was found that the proposed modified singular value decomposition approach outperformed the existing singular value decomposition, discrete wavelet transform-singular value decomposition and stationary wavelet transform methods.

Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data.