A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique.

COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.

Equity distribution of quality evaluation reports to doctors in health care organizations.

There are volumes of patient reports generated in any healthcare organization daily. The reports can be very lengthy or of few pages. Maintaining records of patients is essential for ensuring quality medical care. Doctors, apart from their routine activities, are also responsible to sort, examine and archive the generated reports. However, this process consumes doctors' time, who are already hard-pressed for time. The objective of this study is to search for a method that can assign reports to doctors to ensure equitable and fair distribution of the overall workload. As a part of the solution, a mathematical model will be proposed to perform different developed heuristics. An experimental evaluation using different classes with a total of 2,450 different instances will be tested to measure the performance of the developed heuristics in terms of, elapsed time and gap value calculations. The clustering heuristics which is based on two groups is the best heuristic with 96.1% for the small instances and 98% for the big scale instances. The contribution of this work is based on employing dispatching rules with several variants; randomization approach, clustering methods; probabilistic method, and iterative methods approach to assign all given reports to doctors while ensuring the equitable distribution of the paper workload.

Public Attitude Towards COVID-19 Vaccination: Validation of COVID-Vaccination Attitude Scale (C-VAS).

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.