This paper proposes an effective and robust method for image alignment and recovery on a set of linearly correlated data via Frobenius and L2,1 norms. The most popular and successful approach is to model the robust PCA problem as a low-rank matrix recovery problem in the presence of sparse corruption. The existing algorithms still lack in dealing with the potential impact of outliers and heavy sparse noises for image alignment and recovery. Thus, the new algorithm tackles the potential impact of outliers and heavy sparse noises via using novel ideas of affine transformations and Frobenius and L2,1 norms. To attain this, affine transformations and Frobenius and L2,1 norms are incorporated in the decomposition process. As such, the new algorithm is more resilient to errors, outliers, and occlusions. To solve the convex optimization involved, an alternating iterative process is also considered to alleviate the complexity. Conducted simulations on the recovery of face images and handwritten digits demonstrate the effectiveness of the new approach compared with the main state-of-the-art works.
In this study, predictive models are proposed to accurately estimate the confirmed cases and deaths due to of Corona virus 2019 (COVID-19) in Africa. The study proposed the predictive models to determine the spatial and temporal pattern of COVID 19 in Africa. The result of the study has shown that the spatial and temporal pattern of the pandemic is varying across in the study area. The result has shown that cubic model is best outperforming compared to the other six families of exponentials (
. The adopted cubic algorithm is more robust in predicting the confirmed cases and deaths due to COVID 19. The cubic algorithm is more superior to the state of the art of the works based on the world health organization data. This also entails the best way to mitigate the expansion of COVID 19 is through persistent and strict self-isolation. This pandemic will sustain to grow up, and peak to the highest for which a strong care and public health interventions practically implemented. It is highly recommended for Africans must go beyond theory preparations implementations practically through the public interventions.
In this work, a new robust regularized shrinkage regression method is proposed to recover and align high-dimensional images via affine transformation and Tikhonov regularization. To be more resilient with occlusions and illuminations, outliers, and heavy sparse noises, the new proposed approach incorporates novel ideas affine transformations and Tikhonov regularization into high-dimensional images. The highly corrupted, distorted, or misaligned images can be adjusted through the use of affine transformations and Tikhonov regularization term to ensure a trustful image decomposition. These novel ideas are very essential, especially in pruning out the potential impacts of annoying effects in high-dimensional images. Then, finding optimal variables through a set of affine transformations and Tikhonov regularization term is first casted as mathematical and statistical convex optimization programming techniques. Afterward, a fast alternating direction method for multipliers (ADMM) algorithm is applied, and the new equations are established to update the parameters involved and the affine transformations iteratively in the form of the round-robin manner. Moreover, the convergence of these new updating equations is scrutinized as well, and the proposed method has less time computation as compared to the state-of-the-art works. Conducted simulations have shown that the new robust method surpasses to the baselines for image alignment and recovery relying on some public datasets.
Background
World Health Organization recommends exclusive breastfeeding (EBF) for the first 6 months of life. EBF has sustainable long-term health benefits for both infants and mothers. Despite its benefits, the practice of EBF in Ethiopia is lower than the internationally recommended one. This study aimed at identifying factors influencing EBF practice among under-6 month infants in Ethiopia.
Methods
This study used data drawn from the 2019 Ethiopian Mini Demographic and Health Survey (2019 EMDHS) data. A multivariable logistic regression model was employed to investigate factors significantly associated with EBF practice among under-6 month infants in Ethiopia. An adjusted odds ratio with 95% confidence interval was used to measure the association of factors with EBF practice.
Results
A total of 566 infants under the age of 6 months were included in the study. The prevalence of exclusive breastfeeding practice was 83% (95% CI: 79.70–86%). Urban residences (AOR: 0.40, 95% CI: 0.22–0.73), mothers having secondary education (AOR: 1.54, 95% CI: 1.29–1.84) and higher education (AOR: 3.18, 95% CI: 0.68–15.02), mothers having ANC visits of 1 to 3 times (AOR: 1.52, 95% CI: 1.24–1.88) and ANC visits of 4 and more times (AOR: 4.27, 95% CI: 1.06–17.25), family size of more than 5 (AOR: 0.45, 95% CI: 0.26–0.88), caesarean births (AOR: 0.63, 95% CI: 0.42–0.95), and deliveries at health facilities (AOR: 2.51, 95% CI: 1.12–5.63) were factors significantly associated with EBF practice among under-6 month infants.
Conclusion
In this study, EBF practice among under-6 month infants was significantly associated with place of residence, maternal educational level, ANC visits, family size, mode of delivery, and place of delivery. Therefore, encouraging ANC visit and promotion of institutional (health facility) delivery are recommended. Furthermore, special attention has to be given to mothers with no or less education to make them better aware of the EBF and its benefits to enhance exclusive breastfeeding practice.
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