In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression‐based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.

Recently, various advanced technologies have been employed to build smart cities. Smart cities aim at improving the quality of life through the delivery of better services. One of the current services that are essential for any smart city, is the availability of enough parking spaces to ensure smooth and easy traffic flow. This research proposes a new framework for solving the problem of parking lot allocation, which emphasizes the equitable allocation of people based on the overall count of people in each parking space. The allocation process is performed while considering the available parking lots in each parking space. To accomplish the desired goal, this research will develop a set of seven algorithms to reduce the gap in the number of people between parking spaces. Many experiments carried out on 2430 different cases to cover several aspects such as the execution time and the gap calculations, were used to explore the performance of the developed algorithm. Analyzing the obtained results indicates a good performance behavior of the developed algorithms. Also, it shows that the developed algorithms can solve the studied problem in terms of gap and time calculations. The MR algorithm gained excellent performance results compared to one of the best algorithms in the literature. The MR algorithm has a percentage of 96.1 %, an average gap of 0.02, and a good execution time of 0.007 s.

The breakdown of vital communication infrastructures is one of the most common characteristics of all disasters. It can cause severe communication problems such as time delays and data loss, which cause deterioration in system performance. New techniques are needed to cope with such situations, many of which have been made possible due to the ongoing evolution of artificial intelligence technologies. In this study, we consider the case of a network consisting of several router allocation problems in situations of high priority and emergency data allocation. A novel network component called the scheduler is introduced and window constraints for routers are imposed. To solve the studied problem, four different algorithms are developed in this work. These algorithms were then applied in a particular scenario consisting of several routers and 2200 instances. In terms of the gap and running time, the proposed algorithms provide acceptable results. The best performances were achieved using the critical packet algorithm for 80% of instances with an average gap value of 0.009 and an average time of 0.209 s.

Introduction: The fear of emergence of newer strains of SARS-CoV-2 as well as concerns of waning of protection after doses of COVID-19 vaccine has created a degree of global uncertainty surrounding the pandemic. Some of the emerging strains of SARS-CoV-2 have shown potential for causing serious disease and death, a threat that has been ameliorated by ensuring the vaccine coverage in populations. Still, the vaccine coverage remains unsatisfactory in certain populations. Hence, understanding and working on the factors which affect acceptance of the vaccine amongst the public can be considered a priority for public health as much as ensuring availability of the vaccines. Objective: This research work aims to build and validate a scale to assess the public attitude towards COVID vaccination. The proposed scale has been named as COVID Vaccination Attitude Scale (C-VAS). Materials and Methods: A three-stage process was used to develop the C-VAS which includes (1) item generation (deductive and inductive approach); (2) item–refinement (pre-testing and pilot testing, exploratory factor analysis (EFA); and (3) scale validation (confirmatory factor analysis, CFA). The sample size used for this research was 840. In order to overcome the issue of common method bias, the data was collected in two phases. The sample n1 (411) was used for EFA and the sample n2 (429) was employed for undertaking CFA. Common method bias was assessed to check if variations in responses are caused by the instrument instead of the actual dispositions of the respondents. Items of the scale were taken by reviewing the extant literature about vaccination, from the relevant established theories such as health belief model and by interviewing with domain experts. The content validity of the scale was determined. Results: EFA extracted five factors, labelled as “Perceived Benefits”, “Perceived Barriers”, “Perceived Severity”, “Health Motivation” and “Perceived Risk”. To further validate the factor–item structure CFA was performed. Conclusion: The measurement model was assessed by applying CFA to examine the reliability, accuracy and validity of the scale. Development of this scale can help in understanding factors that affect vaccine acceptability behavior. This can be used in promoting COVID vaccine coverage in countries and societies which still have low vaccination rates especially due to lack of acceptance of the vaccine. This scale also has the potential to understand public behavior in relation to similar future outbreaks and the acceptance of the mitigatory vaccines.

Nowadays, developing environmental solutions to ensure the preservation and sustainability of natural resources is one of the core research topics for providing a better life quality. Using renewable energy sources, such as solar energy, is one of the solutions that can reduce the overuse of natural resources. This research aims to boost the efficiency of solar energy plants by proposing a novel approach to optimize the total flying time of battery-based drone systems to enhance the performance of solar plant systems. The contribution of the proposed approach is to solve scheduling problems based on timing constraints to monitor the solar plant. The main objective of the proposed approach is to maximize the drone’s minimum total flying time, which will increase the availability and reliability of the solar plant monitoring system. Time to empty values is calculated based on battery degradation rates. This problem is proven to be NP-hard. Four categories of enhanced algorithms were developed to solve drones’ scheduling problems in handling various tasks within multiple errands in the extent of solar parks in the monitored power plant to achieve the desired objective. Experimental results of the presented algorithms showed that the $M2S$ algorithm has a stable performance behavior in all conducted experiments.