Factors associated with knowledge and practice of optimal breastfeeding among mothers in Dubai.

Although breastfeeding has been the primary source of nutrition for infants, many women do not choose to practise breastfeeding due to lack of knowledge, inability to do so or personal choices. This study aimed at determining the breastfeeding practices and examining the sociodemographic factors associated with optimal breastfeeding among mothers attending child welfare clinic at Dubai Health Authority. A structured questionnaire was designed consisting of sociodemographic data, knowledge, attitude and practice towards breastfeeding. The main reasons for not breastfeeding the baby within 30 min after delivery were having had a caesarean section, followed by separation of the baby from the mother. We found better practice among homemakers, non-United Arab Emirates (UAE) and married women, those with less monthly income, and those with standard delivery. There is a need for better education on optimal breastfeeding, especially in UAE national pregnant women, who have had caesarean sections, or having babies admitted in the intensive care unit.

Feature Subset Selection for Malware Detection in Smart IoT Platforms.

Malicious software ("malware") has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.