Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliersrobust principal component analysis (ORPCA) by imposing the joint constraints on the objective function. ORPCA relaxes the orthogonal constraints and penalizes the regression coefficient, thus, it selects important features and ignores the same features that exist in other principal components. It is commonly known that square Frobenius norm is sensitive to outliers. To overcome this issue, we have devised an alternative way to derive objective function. Experimental results on four publicly available benchmark datasets show the effectiveness of joint feature selection and provide better performance as compared to state-of-the-art dimensionality-reduction methods.
Principal component analysis (PCA) is widely used methods for dimensionality reduction and Lots of variants have been proposed to improve the robustness of algorithm, however, these methods suffer from the fact that PCA is linear combination which makes it difficult to interpret complex nonlinear data, and sensitive to outliers or cannot extract features consistently, i.e., collectively; PCA may still require measuring all input features. 2DPCA based on 1 − norm has been recently used for robust dimensionality reduction in the image domain but still sensitive to noise. In this paper, we introduce robust formation of 2DPCA by centering the data using the optimized mean for two-dimensional joint sparse as well as effectively combining the robustness of 2DPCA and the sparsity-inducing lasso regularization. Optimal mean helps to improve the robustness of joint sparse PCA further. The distance in spatial dimension is measure in F-norm and sum of different datapoint uses 1-norm. 2DR-JSPCA imposes joint sparse constraints on its objective function whereas additional plenty term help to deal with outliers efficiently. Both theoretical and empirical results on six publicly available benchmark datasets shows that Optimal mean 2DR-JSPCA provides better performance for dimensionality reduction as compare to nonsparse (2DPCA and 2DPCA-L1) and sparse (SPCA, JSPCA).
Minimizing the gap and ensuring agreement between patients’ perceptions and expectations is an indication of a better quality of hospital services. This study aimed to examine the agreement between patients’ perceptions and expectations of the quality of hospital services. A cross-sectional design was adopted, and quantitative methods were employed for data collection. The SERVAQUAL tool was used. The sample size was 415 participants. This study was conducted in Jordanian teaching hospitals. The study population was patients who used outpatient clinics in these hospitals. The study found that there is very low agreement between patients’ expectation and their perceptions. Overall, the perceived service quality was significantly lower than the expected service quality across all of the dimensions used to measure the service quality gap (reliability, responsiveness, assurance, empathy, and tangibles). The results suggest regional variation, where patients who sought care at hospitals in Amman have a four-fold higher perception of the quality of services than patients who visited Irbid hospitals. Also, patients who are more highly educated (Diploma, Bachelor, or Higher Studies) have a higher perception than patients who have less than secondary education. Age and gender were found to have no significant association with patients’ perceptions. The findings of this study suggest that there is a gap between patients’ perceptions and expectations. Thus, there is a need to close this gap by improving patient satisfaction with the quality of services.
The new coronavirus disease 2019 (COVID-19) is a major global concern. Due to the number of asymptomatic cases that go untested, the actual proportion of those who have been infected is likely to be higher than the reported prevalence. Thus, investigating the exact proportion of those who developed antibodies against the virus through serological surveys is crucial to identify the immune status of the population and direct public health decisions accordingly. ObjectivesThe aim of this study is to estimate the seroprevalence of SARS-CoV-2 in the community and to describe the epidemiological characteristics of the discovered cases. MethodsBetween July and October 2020, a cross-sectional sero-survey was conducted including a total of 15,873 serum samples collected from seven regions within the kingdom. Using a multistage convenient sampling, people were invited to participate in an interviewer-administrated questionnaire. Afterward, blood samples were collected and seroprevalence was determined using the SARS-CoV-2 virus IgG/IgM antibody detection kits (ELISA). A p-value of <0.05 and 95% CI were used to report the significance. ResultsThe overall seroprevalence of SARS-CoV-2 in the sample was 17.0%, and Makkah region constituted the highest number of reactive cases (33.3%). There was a significant association between all comorbidities and having symptoms except for diabetes. In addition, age, education, nationality, and region were all significant predeterminants of sero-result. Also, contact with a confirmed or suspected case increased the risk of being seropositive by nearly 1.5 times. ConclusionThis study estimated the national seroprevalence of SARS-CoV-2 in Saudi Arabia to be 17%. At the time of this study, most of the population did not have the SARS-CoV-2 specific antibodies. This suggests that the population is still below the threshold of herd immunity and emphasizes the importance of mass vaccination programs and abiding by recommended prevention precautions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.