Abstract The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios.

Abstract Covid-19 is a very dangerous disease as a result of the rapid and unprecedented spread of any previous disease. It is truly a crisis that threatens the world since its first appearance in December 2019 until our time. Due to the lack of a vaccine that has proved sufficiently effective so far, the rapid and more accurate diagnosis of this disease is extremely necessary to enable the medical staff to identify infected cases and isolate them from the rest to prevent further loss of life. In this paper, Covid-19 diagnostic strategy (CDS) as a new classification strategy that consists of two basic phases: Feature selection phase (FSP) and diagnosis phase (DP) has been introduced. During the first phase called FSP, the best set of features in laboratory test findings for Covid-19 patients will be selected using enhanced gray wolf optimization (EGWO). EGWO combines both types of selection techniques called wrapper and filter. Accordingly, EGWO includes two stages called filter stage (FS) and wrapper stage (WS). While FS uses many different filter methods, WS uses a wrapper method called binary gray wolf optimization (BGWO). The second phase called DP aims to give fast and more accurate diagnosis using a hybrid diagnosis methodology (HDM) based on the selected features from FSP. In fact, the HDM consists of two phases called weighting patient phase (WP2) and diagnostic patient phase (DP2). WP2 aims to calculate the belonging degree of each patient in the testing dataset to class category using naïve Bayes (NB) as a weight method. On the other hand, K-nearest neighbor (KNN) will be used in DP2 based on the weights of patients in the testing dataset as a new training dataset to give rapid and more accurate detection. The suggested CDS outperforms other strategies according to accuracy, precision, recall (or sensitivity) and F-measure calculations that are equal to 99%, 88%, 90% and 91%, respectively, as showed in experimental results.