Using Artificial Intelligence to Predict Customer Satisfaction with E-Payment Systems during the COVID-19 Pandemic

This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID-19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict customer satisfaction during COVID-19 pandemic. The proposed system contemplates customer resistance (CR), access to technology (AT), privacy (PV), costs (CT), and speed of efficiency (SE) as the input variables and customer satisfaction (CS) as the output variable. Various statistical tests are utilized to determine the efficiency of input variables in the obtained data. The statistical tests are multicollinearity tests, reliability and validity, ordinal least square (OLS), fixed effect, and random development. As a result, we can determine each input variable’s direct and indirect impact on the CS. Under OLS, fixed effect, and unexpected effect, the SE, CT, PV, AT, and CR considerably impact EP. The EP has been shown to have substantial positive indirect implications. Under OLS, fixed effect, and random effect, the CT, PV, and CR are found to have a significant positive impact on CS. In addition, the AT has a substantial impact on CS in a fixed effect indirect effect. The results of HyFIS were compared to those of the adaptive network-based fuzzy inference system (ANFIS). The results reveal that HyFIS outperforms ANFIS in predicting CS based on the error criterion.

Fear of COVID-19 among Healthcare Workers of a Tertiary Care Cardiac Facility Before- and After-Vaccination and Serology

Objective: This study aimed to assess the changes in the perceptions and practices during the coronavirus disease-2019 (COVID-19) era before and after vaccination and antibodies titer among the healthcare workers (HCWs) at a tertiary care cardiac center. Materials and Methods: This descriptive study included HCWs working at a tertiary care cardiac center in Karachi, Pakistan. A predefined structured questionnaire was used to assess the sense of security, practice, and perception of the HCWs before vaccination, after vaccination, and after knowing the antibodies titer. Results: Out of 151 HCWs, 70.2% (106) were male, and a majority, 65.6% (99), were ≤35 years old with an overall mean age of 34.92 ± 7.64 years. Nearly half of the individuals, (n=74;49%), were doctors, 10 individuals (6.6%) were non-clinical staff, and reaming were nursing staff. The mean day since COVID-19 vaccination was 89.6 ± 40.07 before COVID-19 infection. Antibodies titer levels were >250 U/mL in 108 cases (71.5%) and ≤100 U/mL in 18 cases (11.8%). A significant increase in perception score was observed after serology with a mean of 61.04 ± 25.23 vs 53.86 ± 28.96;(p=0.008) compared to the post-vaccination perception score. A significant declining trend has been witnessed in mean practice scores, with a pre-vaccination mean of 69.93 ± 27.12, post-vaccination mean of 59.47 ± 30.61 (p<0.001). And post-serology mean of 55.1 ± 27.1 (p<0.001). Conclusion: An increase in the sense of security and leniency in adherence to personal protective measures has been observed among HCWs after vaccination and after knowing the antibodies titer (English) [ FROM AUTHOR] Amaç: Bu çalışmada, üçüncü basamak bir kalp merkezindeki sağlık çalışanları arasında koronavirüs hastalığı-2019 (COVID-19) döneminde aşılama öncesi ve sonrası algı ve uygulamalardaki değişikliklerinin ve antikor titrelerinin değerlendirilmesi amaçlanmıştır. Gereç ve Yöntem: Bu tanımlayıcı çalışma, Pakistan, Karaçi’deki üçüncü basamak bir kalp merkezindeki sağlık çalışanlarını içermektedir. Sağlık çalışanlarının aşılamadan önce, aşılamadan sonra ve antikor titresini öğrendikten sonra güvenlik hissi, uygulama ve algılarını değerlendirmek için önceden tanımlanmış yapılandırılmış bir anket kullanılmıştır. Bulgular: Yüz elli bir sağlık çalışanının %70.2 (n=106) erkek ve katılımcıların çoğunluğu, %65.6 (n=99) 35 yaşında ya da daha geç yaştaydı ve ortalama yaş 34.92 ± 7.64 yıl olarak saptandı. Neredeyse yarısı, (n=74;%49) hekim ve %6.6 (n=10) klinik dışı personel, geri kalan kişiler hasta bakım personeli görevindeydi. Önceki COVID-19 enfeksiyonu, doğası gereği 10 kişide (%6.6) ciddi, 1 kişide (%0.7) kritik olmak üzere 62 kişide (%41.1) rapor edilmiştir. COVID-19 aşılamasından bu yana geçen ortalama gün sayısı 89.6 ± 40.07 ve 11 kişide (%7.3) aşılama sonrası COVID-19 bildirildi. Antikor titre seviyeleri 108 kişide (%71.5) >250 U/mL ve 18 kişide ise (%11.9) ≤100 U/mL ve altında saptandı. Aşılama sonrası algı puanı ile karşılaştırıldığında algı skorunda seroloji sonrası ortalama 61.04 ± 25.23 ile 53.86 ± 28.96 arasında anlamlı bir artış gözlendi (p=0.008). Aşılama öncesi ortalama 69.93 ± 27.12, aşılama sonrası ortalama 59.47 ± 30.61 (p<0.001) ve seroloji sonrası 55.1 ± 27.1 olmak üzere (p<0.001) olan ortalama uygulama puanlarında önemli bir düşüş eğilimi görülmüştür (Turkish) [ FROM AUTHOR] Copyright of Turkish Journal of Immunology is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 Antibodies in Vaccinated Healthcare Workers: The Security Currency.

Background The coronavirus disease 2019 (COVID-19) vaccinations have brought new hope to the world and have a significant psychosocial impact on communities as well as healthcare systems around the globe. This study aimed to assess the antibody titer level among healthcare workers after at least six weeks of the second dose of the COVID-19 vaccine. Methods Participants of the study were healthcare workers of a tertiary care cardiac center including doctors, nursing staff, paramedics, and office staff. All participants were fully vaccinated with recommended double dose of available vaccine at least six weeks before the study. A blood sample of five milliliters was collected from all the participants by a trained phlebotomist at a local laboratory, and COVID-19 antibodies titer level was assessed using Food and Drug Administration (FDA) approved kit with a standard range of 1.0. This qualitative assay detects IgG and IgM as total antibodies targeted against nucleocapsid antigen performed on a fully automated cobas® 6000 analyzer (F. Hoffmann-La Roche Ltd, Basel, Switzerland) using electrochemiluminescence technology. COVID-19 antibodies titer levels were categorized as ≤100, 101-250, and >250. Results A total of 151 healthcare workers were included, of which 70.2% (106) were male. The history of COVID-19 infection before vaccination was found in 41.1% (62). The mean duration since the last dose of the vaccine was 89.6±40.07 days. In total 71.5% (108) had antibodies titer level of >250, which were mostly found in participants of younger age and who had previous COVID-19 infection. However, antibodies titer level of >250 were observed in 84% (21/25) at 61 to 90 days of vaccination, which declined to 80% (20/25) after 91 to 120 days and to 57.1% (32/56) after >120 days of vaccination. Conclusions Good antibodies titer levels were observed in vaccinated healthcare workers, especially in those who were younger and had previous COVID-19 infection.

Migratory birds as the vehicle of transmission of multi drug resistant extended spectrum ß lactamase producing Escherichia fergusonii, an emerging zoonotic pathogen.

The acquisition of multi-drug resistance (MDR) genes by pathogenic bacterial bugs and their dispersal to different food webs has become a silent pandemic. The multiplied use of different antibacterial therapeutics during COVID-19 pandemic has accelerated the process among emerging pathogens. Wild migratory birds play an important role in the spread of MDR pathogens and MDR gene flow due to the consumption of contaminated food and water. Escherichia fergusonii is an emerging pathogen of family Enterobacteriaceae and commonly causes disease in human and animals. The present study focused on the isolation of E. fergusonii from blood, saliva, and intestine of selected migratory birds of the Hazara Division. The sensitivity of isolated strains was assessed against ten different antibiotics. The isolation frequency of E. fergusonii was 69%. In blood samples, a high rate of resistance was observed against ceftriaxone (80%) followed by ampicillin (76%) whereas, in oral and intestinal samples, ceftriaxone resistant strains were 56% and 57% while ampicillin resistance was 49% and 52% respectively. The overall ceftriaxone and ampicillin-resistant cases in all three sample sources were 71% and 65% respectively. In comparison to oral and intestinal samples, high numbers of ceftriaxone-resistant strains were isolated from the blood of mallard while ampicillin-resistant strains were observed in blood samples of cattle egrets. 16S rRNA-based confirmed strains of E. fergusonii were processed for detection of CTX-M and TEM-1 gene through Polymerase chain reaction (PCR) after DNA extraction. Hundred percent ceftriaxone resistant isolates possessed CTX-M and all ampicillin-resistant strains harbored TEM-1 genes. Amplified products were sequenced by using the Sanger sequencing method and the resulted sequences were checked for similarity in the nucleotide Database through the BLAST program. TEM-1 gene showed 99% and the CTX-M gene showed 98% similar sequences in the Database. The 16S rRNA sequence and nucleotide sequences for TEM-1 and CTX-M genes were submitted to Gene Bank with accession numbers LC521304, LC521306, LC521307 respectively. We posit to combat MDR gene flow among the bacterial pathogens across different geographical locations, regular surveillance of new zoonotic pathogens must be conducted.

Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.