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                <text>Objective&#13;
In developing countries, malnutrition is a noteworthy concern related to the well-being of people, and this study aimed to determine the factors that affect malnutrition among children below 5 years in Ghana.&#13;
Design&#13;
The study used a secondary data source, specifically the Ghanaian Multiple Indicator Cluster Survey Six (MICS 6), which was conducted by the Ghana Statistical Service in 2017–2018. The MICS data are hierarchical, as children are categorised within households, and households are further grouped within a higher cluster, violating the independence assumption that must be addressed in the analyses. This study used a Bayesian multilevel ordinal logistic regression to model, identify and analyse the factors linked to child malnutrition in Ghana.&#13;
Setting&#13;
The setting of the study was the household level across the previous 10 administrative regions in Ghana.&#13;
Participants&#13;
Data for 8875 children …</text>
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                <text>In the academic environment, players have overstretched University faculty with less available time. The task of reading and deleting electronic mail (e-mail) spam tends to consume or steal the little available time they have at their disposal. Due to the spam issue, automated processes or methods for separating spam from valid emails are becoming important. Due to the unstructured nature of the material, additional features, and a vast number of documents, the process of automatically classifying spam email presents significant difficulties. Increasing usage of the e-mail spam directly affects the performance of these spam classifications with regards to the quality and speed based on the challenges stated above. Most of the recent algorithms consider only relevant features or characteristics for the classification of the e-mails as spam or legitimate. The main objective of this work was to use a machine-learning …</text>
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