No abstract

Human Immunodeficiency Virus (HIV) continues to be a leading cause of mortality and reduces manpower throughout the world. HIV transmission from mother to child is still a global challenge in health research. According to UNAIDS, in every 7 girls, 6 are found to be newly infected among adolescents whereby 15-24 years are likely to be living with HIV which is the maternal age and likely to transfer to the child. Machine learning methods have been used to predict HIV/AIDS transmission from mother to child but left behind some important considerations including the use of patient-level information and techniques in balancing the dataset which may impact models’ performance. A robust prediction model for mother-to-child HIV/AIDS transmission is vital to alleviate HIV/AIDS detrimental effects. The Random Forest Machine Learning method was employed based on features from the individual medical history of HIV-positive mothers. A total of 680 balanced data tuples were used for model development using the ratio of 75:25 for training and testing the dataset. The Random Forest model outperformed the most commonly used learning algorithms achieving the performance of 99% accuracy, recall and F1-score of 0.99 and an error of 0.01, thus improving the prediction rate.

Several studies indicate that there are no enough people in the market with data science skills and even those graduates in ICT from universities do not possess skills required by employers. Thus, researchers have suggested the urgency for universities to review their curricular as the world is heading towards data era. The aim of this research was to analyze the current skill-gaps needs from stakeholders and opportunities to establish data science postgraduate programme that reflects the current technological trends and market demands at the University of Dar es Salaam (UDSM). A questionnaire was administered to 85 identified organizations to solicit information on the needs for data scientists and existing skill gaps. A total of 61 filled questionnaires response were received out of the 85 that were administered to selected organizations indicating a turn out rate of over 70%. Overall the analyzed data articulated a compelling evidence for the local industry growing need for data scientist. The survey that was conducted was followed up by conduct of various workshops and meetings to solicit inputs from different experts and stakeholders on different versions of the developed curriculum. Finally, a new programme in MSc in data Science was approved and established from April 2018 at UDSM. Despite its late approval and without formal advertisement on the public media, the programme attracted a large number of applicants for 2018/19 academic year, compared to other several postgraduate programmes in ICT offered at UDSM. .

ABSTRACT

The usage of social media has exponentially grown in recent years leaving the users with no limitations on misusing the platforms through abusive contents as deemed fit to them. This exacerbates abusive words exposure to innocent users, especially in social media forums, including children. In an attempt to alleviate the problem of abusive words proliferation on social media, researchers have proposed different methods to help deal with variants of the abusive words; however, obfuscated abusive words detection still poses challenges. A method that utilizes a combination of rule based approach and character percentage matching techniques is proposed to improve the detection rate for obfuscated abusive words. The evaluation results achieved F1 score percentage ratio of 0.97 and accuracy percentage ratio of 0.96 which were above the significance ratio of 0.5. Hence, the proposed approach is highly effective for obfuscated abusive words detection and prevention. Keywords:     Rule based approach, Character percentage matching techniques, Obfuscated abuse, Abuse detection, Abusive words, Social media