Background: In Ethiopia, child malnutrition is one of the most serious public health problem and the highest in the world. The overall prevalence of underweight among children under five years old in Ethiopia was 25% and 7% are severely and moderate underweight in 2014 respectively. Malnutrition in sub-Saharan Africa contributes to high rates of childhood morbidity and mortality. However, little information on the nutritional status of children is available from informal settlements. The primary aim of this study was then to determine the determinants of children malnutrition in Ethiopia using Bayesian generalized linear model. The overall prevalence of underweight among children in Ethiopia was 36.4%.

Methods: Data was obtained from 2011 Ethiopian Demographic and Health Survey (EDHS). Bayesian Generalized regression model was used to identify the effects of selected socioeconomic, demographic, health and environmental covariates. Bayesian approach with Markov Chain Monte Carlo (MCMC) technique was used.

Results: The analysis result revealed that out of the 11,654 number cases examined in this study 32.8% of male children were underweight and 33.6% of female children were underweight. It was found that the covariates succeeding birth interval, sex of child, child by choice not by chance, vaccination and cough were the most important determinants of children nutritional status in Ethiopia.

Conclusion: Inference is the fully Bayesian and classical generalized linear model based on recent Markov chain Monte Carlo techniques. Some of the socioeconomic, demographic and environmental determinants included in the study were found to be statistically significant. The result was suggested that for reducing childhood malnutrition, due emphasis should be given in improving the knowledge and practice of parents on appropriate young child feeding practice and frequent growth monitoring together with appropriate and timely interventions.

There are acceptable standards for designing biometric security systems that capture unique biological features and characteristics of individuals. Such standards guarantee global best practice especially with regards to the enrollment process. However, while most designers focus on the time-space efficiency of the biometric system, security of the system should be of utmost concern while the integrity of the data is not compromised. Note that a typical biometric system could be hacked at various points during the enrollment process. Therefore, this paper presents eight models for encoding of multimedia data in biometric security systems that have been proposed. These models were designed and simulated using Jet Brains IntelliJ IDEA as the development environment. These models are expected to make the enrollment process secure with reduced space consumption while keeping the time complexity optimally small. The results from the simulation were empirically analyzed with respect to algorithm steps, execution time, space, security level and quality. It was noticed that it is possible to achieve highest level of data integrity and security – though at the expense of increase in computation complexity. For instance, encryption is responsible for 100% rise in the execution time (that is from 0.69ms to 1.47, 1.48 and 1.32ms). While the models could be realized via any programming language of choice, the simulation source code written in Java programming language could be adopted as software framework by designers and developers of biometric security systems.