Most organizations use IT projects as vehicles to implement the IT strategy which contributes to achieving organizational strategic objectives and goals. Agile project management has been a potential solution to deliver successful IT projects instead of the traditional waterfall approach. This potential has resulted in organizations adopting agile project management to deliver IT projects on time and realise benefits. However, auditors especially those whose experience lies with more traditional and system development life cycle controls are struggling with how to audit agile projects. The problem addressed in this article is lack of an audit framework for auditing agile projects to ensure IT project success. The purpose of this article is to present the proposed conceptual framework for auditing agile projects that are implemented using Scrum methodology. The framework introduces audit processes in each process of the Scrum methodology. The quantitative research method used online survey questionnaire to validate the conceptual framework amongst IT professionals in South Africa. The data were analyzed using SPSS 26.0. The research revealed that there was a significant correlation between the identified audit processes and agile project success. This research emphasizes on the need to take into account auditing agile project from its initiation to closure. This article contributes to the body of knowledge with regard to project auditing. Auditors can use the proposed framework to audit agile projects which are implemented using Scrum methodology to ensure successful completion of IT projects.

Presumptive treatment and self-medication for malaria have been used in limited-resource countries. However, these approaches have been considered unreliable due to the unnecessary use of malaria medication. This study aims to demonstrate supervised machine learning models in diagnosing malaria using patient symptoms and demographic features. Malaria diagnosis dataset extracted in two regions of Tanzania: Morogoro and Kilimanjaro. Important features were selected to improve model performance and reduce processing time. Machine learning classifiers with the k-fold cross-validation method were used to train and validate the model. The dataset developed a machine learning model for malaria diagnosis using patient symptoms and demographic features. A malaria diagnosis dataset of 2556 patients’ records with 36 features was used. It was observed that the ranking of features differs among regions and when combined dataset. Significant features were selected, residence area, fever, age, general body malaise, visit date, and headache. Random Forest was the best classifier with an accuracy of 95% in Kilimanjaro, 87% in Morogoro and 82% in the combined dataset. Based on clinical symptoms and demographic features, a regional-specific malaria predictive model was developed to demonstrate relevant machine learning classifiers. Important features are useful in making the disease prediction.

The recorded cases of corona virus (COVID-19) pandemic disease are millions and its mortality rate was maximized during the period from April 2020 to January 2022. Misinformation arose regarding this threat, which spread through social media platforms, and especially Twitter, often spreading confusion, social turmoil, and panic to the public. To identify such misinformation, a machine learning model is needed to detect whether the given information is true (true information) or not (misinformation). The aim of this paper is to present a machine-learning model for detecting COVID-19 misinformation in the Swahili language in tweets. The five machine learning algorithms that were trained for detecting Swahili language misinformation related to COVID-19 are Logistic Regression (LR), Support Vector Machine (SVM), Bagging Ensemble (BE), Multinomial Naïve Bayes (MNB), and Random Forest (RF). The study used the qualitative research method because non-numerical data, i.e. text, were used. Python programming language was used for data analysis due to its powerful libraries such as pandas and numpy. Four metrics were used to evaluate the model performance. The results revealed that SVM achieved the highest accuracy of 83.67% followed by LR with 82.47%. MNB achieved the best precision of 92.00% and in terms of recall and F1-score, RF, and SVM achieved the best results with 84.82% and 81.45%, respectively. This study will enable the public to easily identify Swahili language misinformation related to COVID-19 that is circulated on Twitter social media platform.

The adoption of project management as a discipline in both public and private sector organisations is still growing. The growth has been evidenced by organisations continually aligning project activities with business strategy to achieve their strategic goals and create value for their organisations. Some of these initiatives are managed as IT projects. In 2016 the global information technology (IT) spend is forecast to total US$3.54 trillion. Despite this growth in IT investment, IT projects still fail at an alarming rate. These failed IT projects have resulted in organisations not achieving their strategic objectives and goals, in addition to costing them huge amounts of money. Many factors contribute to IT projects failing, such as a poorly defined project scope, insufficient human resources, cost overruns, poor communication among project stakeholders, project auditing practices not being followed, inadequate practices in project risk management and lack of correct auditing of processes in IT projects. The majority of project audits are based on ad hoc management requests rather than systematic auditing processes throughout the project life cycle. Project managers are still challenged on managing IT projects to successful delivery. The problem addressed in this article is the lack of IT project auditing processes throughout the project life cycle to ensure IT project success. A conceptual framework is proposed for IT project auditing assurance throughout the project life cycle. The framework introduces high-level and detailed IT project assurance processes in each assurance review gate. These processes can be tailored to ensure the successful delivery of an IT project. The benefits of this article are its contribution to the body of knowledge with regard to project auditing. Secondly, practitioners and project managers can use this framework to audit IT projects to ensure successful completion of IT projects.

Malaria remains an important cause of death, especially in sub-Saharan Africa with about 228 million malaria cases worldwide and an estimated 405,000 deaths in 2019. Currently, malaria is diagnosed in the health facility using a microscope (BS) or rapid malaria diagnostic test (MRDT) and with area where these tools are inadequate the presumptive treatment is performed. Apart from that self-diagnosis and treatment is also practiced in some of the households. With the high-rate self-medication on malaria drugs, this study aimed at computing the most significant features using feature selection methods for best prediction of malaria in Tanzania that can be used in developing a machine learning model for malaria diagnosis. A malaria symptoms and clinical diagnosis dataset were extracted from patients’ files from four (4) identified health facilities in the regions of Kilimanjaro and Morogoro. These regions were selected to represent the high endemic areas (Morogoro) and low endemic areas (Kilimanjaro) in the country. The dataset contained 2556 instances and 36 variables. The random forest classifier a tree based was used to select the most important features for malaria prediction. Regional based features were obtained to facilitate accurate prediction. The feature ranking as indicated that fever is universally the most influential feature for predicting malaria followed by general body malaise, vomiting and headache. However, these features are ranked differently across the regional datasets. Subsequently, six predictive models, using important features selected by feature selection method, were used to evaluate the features performance. The features identified complies with malaria diagnosis and treatment guideline provided with WHO and Tanzania Mainland. The compliance is observed so as to produce a prediction model that will fit in the current health care provision system in Tanzania.