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Background: Staff that were redeployed from different departments to the intensive care unit (ICU) dealing with COVID-19 infected patients have had social, psychological, and physical effects as a result of the COVID-19 pandemic. Objective: To evaluated the challenges and experiences of the non-ICU nursing staff when they were redeployed during the current pandemic. Study Design: Place and Duration of Study: Riyadh Healthcare from 1st July 2021 to 31st December 2021 Methodology: Two hundred and thirty-eight respondents were enrolled. Results: 70.6% of redeployed staff claimed very satisfied with neutral behaviour. 80.7% of nurses had the choice before redeployment, and 33.6% of staff felt happy to be redeployed to the ICU. 26.9% were neutral, while only 16.8% of nurses reported feeling very unhappy. Orientation was attended by 81.5% of staff, 54.6 % found it uses full. 50% of participants said they had the same workload as their previous departments, but only 5% of staff complained about a lot more work. The attitude of ICU staff and appreciation of administration showed positive answers compared to complaints in this regard (44.4 and 51.5% positive answers respectively). 63.1% of staff was clear about their role when redeployed to ICU. 47.1% of staff was satisfied, 23.5% remained neutral and 5.9% were dissatisfied with the redeployment. 45.4% claimed their redeployment was useful from a patient care perspective. 83.2% of nurses found new acquaintances useful in ICU and get good support from them. Conclusion: Several factors can contribute to nurses' dissatisfaction with their redeployment from non-ICU areas, according to this study, including increased workload, poor orientation, a lack of hospital and ICU staff cooperation, and their preferences for where they should work. For the sake of avoiding such issues in the future, these regions must be organized properly. When faced with a pandemic of COVID-19, the healthcare system must be ready with creative solutions that can be applied in a practical situation. Keywords: Corvid-19, healthcare provider, redeployment, Intensive care Unit, Nursing Staff.

The purpose of this paper is to create a smart and effective tool for evaluating students in classroom objectively by overcoming human subjectivity resulting from lack of experience of instructors, and students’ over-trust in themselves. We had provided instructors in Sur university with the “Program for Student Assessment (PISA)” tool to assess it’s positive impact on academic performance, self-regulation, and improvement on their final exam scores. The study sample included in this study was the students enrolled at Sur University College at the time of data collection in the 2018/2019 semester. In the purpose of testing the efficiency of four models in predicting students’ final scores based on their mark in the first exam. The four tested algorithms were: Multiple Linear Regressions (MLP), K-mean cluster, Modular feed for-ward neural network and Radial Basis Function (RBF) (De Marchi and Wendland, Appl Math Lett 99:105996, 2020 [3], Niu et al, Water 11(1):88, 2019 12]). After comparing the four models’ effectiveness in predicting the final score, results show that RBF has the highest average classification rate, followed by neural network and K-mean cluster, while Multiple Linear Regressions was the worst at performance. RBF has been used to create the Instructor Program for Student Assessment (PISA).predicting student performance early will help students to improve their performance and help instructors modify their teaching style to fit their student’s needs.

In this paper the exploitation of infrastructure and take advantage of communication techniques wired and wireless to help drivers in finding ways does not contain any obstacles and reach to the target with commitment in speed of assessed road. We use Fusion or Polygamy Technology with Fuzzy Logic, Neural Network and Genetic Algorithm for extract Cut-Node (CN). We use Cut-Node (CN) in order to identify obstacles and avoid them such as changes that occur in the lighting, weather conditions, accidents, congestion and maintenance of roads. Cut-Node (CN) useful in understanding the surrounding environment, supporting safe driving, increases the flow and fastest path to arrive at the designated target (end point). Three models have been proposed to extract Cut-Node (CN) as follows: 1) Intuitionistic Fuzzy Set (IFS). 2) The Intuitionistic Fuzzy Set Data Base is representing imprecise data. 3) Intuitionistic Fuzzy Neural Network with Genetic Algorithm (IFNN-GA).