Due to the increased number of cars, outdoor parking is one of the critical problems. Moreover, the management of the parking system is also considered a difficult task. Humans, on the other hand, were acclimated to efficiently parking their automobiles by providing them with the precise location of parking in advance of their arrival. As a result of human inefficiency, it was unsuccessful and ultimately increased the compliance cost. As a result of the development of the notion of the Internet of Things. A lot of systems were installed regarding smart parking systems that are decreasing the cost but also contain a huge impact on the reduction of emissions from cars. While it is possible to integrate Internet of Things (IoT) devices into automobiles, such an approach will necessitates the deployment of additional infrastructure, which will raise the cost, and also it is not feasible within current infrastructure configurations. Then there's the fact that CCTV technology is widely available and also small enough to fit into any parking area without being noticeable. In this paper, Convolution Neural Network (CNN) based smart parking system is designed and implemented. The CNN is used to detect vacant and occupied parking spaces through CCTV cameras and provide feedback to the passengers. Furthermore, the proposed approach is using CNR and PKLot datasets for ensuring the effectiveness of the model. This was developed to solve the issues of time, cost, and accuracy with the existing systems. As a result, the proposed model provides excellent results in terms of accuracy. Moreover, it is cost-effective and saves time.

Emergency response time has always been one of the most important factors in saving lives, yet many healthcare systems struggle with delays and resource inefficiencies. This paper proposes the use of cloud-based digital twin technology, integrated with an Internet of Things (IoT) hub, to enhance patient care and optimize emergency department workflows. The system utilizes an Azure IoT virtual hub and an Azure Digital Twins model for real-time data transmission and processing of bed availability and capacity in a hospital emergency department. Azure Digital Twins can create virtual models of any physical environment, such as an emergency department, which is essential for creating an automated decision-making system using advanced and modern technologies. Unlike previous solutions that lack coherence in resource allocation systems or real-time decision-making mechanisms, our system dynamically updates emergency department bed availability in real-time through embedded system devices in hospitals, and makes automated decisions based on both patient location and resource availability. The system is implemented using a mobile application and validated using a case study. The case study includes data from multiple emergency departments in hospitals within a specific area. The system provides the best options for emergency patients based on the hospital location and bed availability.

It is essential to assign the correct triage level to patients as soon as they arrive in the emergency department in order to save lives, especially during peak demand. However, many healthcare systems estimate the triage levels by manual eyes-on evaluation, which can be inconsistent and time consuming. This study creates a full Digital Twin-based architecture for patient monitoring and automated triage level recommendation using IoT sensors, AI, and cloud-based services. The system can monitor all patients’ vital signs through embedded sensors. The readings are used to update the Digital Twin instances that represent the present condition of the patients. This data is then used for triage prediction using a pretrained model that can predict the patients’ triage levels. The training of the model utilized the synthetic minority over-sampling technique, combined with Tomek links to lessen the degree of data imbalance. Additionally, Lagrange element optimization was applied to select those features of the most informative nature. The final triage level is predicted using the Tabular Prior-Data Fitted Network, a transformer-based model tailored for tabular data classification. This combination achieved an overall accuracy of 87.27%. The proposed system demonstrates the potential of integrating digital twins and AI to improve decision support in emergency healthcare environments.

The complexity involved in predicting the progression of Alzheimer’s Disease (AD) occurs because of differences in neuroimaging data and the many inconsistencies in biomarker data and cognitive data alike. The current research examined an AI model that uses deep learning, organized data preprocessing, and post-experiment interpretations. A tracing examination of the provided computing material established that the experiments were not conducted using actual ADNI or OASIS-3 data but rather fabricated ADNI/OASIS-type data; therefore, the partition of OASIS was considered an independent synthetic test in the context of external data testing and not clinical validation. The complete dataset used in the research involved 603 records of MCI participants and used only good-quality MRI recordings. In the research, MRI data were combined with demographic, cognitive, APOE4-related, PET, and biomarker data. The output of the experiment was based on post-experiment explanation tools identified as SHAP and Grad-CAM. The internal AUROC of the gated model reached 0.676; sensitivity and specificity were equal to 0.524 and 0.690; the F1 score was equal to 0.489. The external domain testing results were AUROC 0.675; AUPRC 0.571; sensitivity 0.593; specificity 0.656; F1 score 0.551. The logistic regression model slightly surpassed the gated model in terms of internal AUROC; the simple multimodal concatenated model slightly surpassed the gated model in terms of external AUROC and AUPRC; therefore, the predictive edge cannot be claimed. The preprocessing benchmark yielded a throughput of approximately 708–738 records/s and a batch-8 inference latency of approximately 1.25 ms/record. These results support technical feasibility within the tested workload but do not establish large-scale big-data scalability or clinical applicability.

In metropolitan communication infrastructures a revolutionary technique is emerge known as terrestrial optical wireless communication (OWC), which makes a high-rise building connection is possible. Even with this solution, there are many other problems like the influence of haze and fog in the propagation channel which obstruct and scatter OWC propagation light and consequently led to a big attenuation, due to propagate in temporal, angular and spatial of the light signal. Not to mention the minimum visibility that discourages the implementation of the pointing errors (PE) and tracking system. This present work aims to analyze the interrelation between multiple scattering (dense fog, heavy fog, light fog, heavy haze and light haze) and receiver PE under modified duo-binary return-to-zero (MDRZ) system. We found that PE caused by beam swag is the main controlling factor and industriously minimize the link margin, signal-to-noise ratio (SNR), and raise the bit error rate (BER) when there is an increasing the turbulence strength and the track length. We recommended to guarantee transmitter– receiver alignment by installing a variable field of view (FOV) receiver (a tracking system) to overcome the scattering impact of the fog that make render urban laser communication effective in the presence of PE.