As we stand at the cusp of the fourth industrial revolution, digital technologies such as artificial intelligence, machine learning, the Internet of Things, Big Data, Blockchain, Robotics, 3D technologies, and many more have become the means and solutions to many of the world’s problems. Most recently, these technologies have assisted in the global fight of the COVID-19 pandemic and other societal problems. Together with these innovative techniques, the concept of circular economy and its relevant tools such as life cycle costing, life cycle impact assessment, materials passports, and circularity measurements have been implemented in a number of sectors in different countries for the transition from a linear “take, make, and dispose” model towards a more circular model, which has shown positive results for the environment and economy. In this article, with the help of implementation, prototyping, and case studies, we explore how these technological advancements and innovative techniques are used in different sectors such as information and communications technology, the built environment, mining and manufacturing, education, healthcare, the public sectors, and others to provide an opportunity to understand and resolve the agreed upon framework in 2015 by 193 countries, that is, the 17 United Nations Sustainable Development Goals.

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

Missing values are a common feature of real-world datasets, particularly in healthcare data. This can be challenging when applying machine learning algorithms, as most models perform poorly in the presence of incomplete data. The goal of this study is to evaluate the performance of seven imputation techniques: Mean Imputation, Median Imputation, Last Observation Carried Forward (LOCF), K-Nearest Neighbor (KNN) Imputation, Interpolation, MissForest, and Multiple Imputation by Chained Equations (MICE) on three healthcare datasets. Various levels of missing data were introduced—10%, 15%, 20%, and 25%—and the imputation techniques were used to fill in the gaps. The methods were compared using root mean squared error (RMSE) and mean absolute error (MAE). The results indicate that MissForest imputation performed best, followed by MICE. Additionally, we examined whether feature selection should be performed before or after imputation, using recall, precision, F1-score, and accuracy as evaluation metrics. The result suggests that performing imputation before feature selection is better. Since there is limited research on the order of imputation and feature selection, and ongoing debate among researchers, we hope the findings of this study will encourage data scientists and researchers to prioritize imputation before feature selection when working with datasets containing missing values.

Increased road traffic congestion is due to different factors, such as population and economic growth, in different cities globally. On the other hand, many households afford personal vehicles, contributing to the high volume of cars. The primary purpose of this study is to perform a comparative analysis of ensemble methods using road traffic congestion data. Ensemble methods are capable of enhancing the performance of weak classifiers. The comparative analysis was conducted using a real-world dataset and bagging, boosting, stacking and random forest ensemble models to compare the predictive performance of the methods. The ensemble prediction models are developed to predict road traffic congestion. The models are evaluated using the following performance metrics: accuracy, precision, recall, f1-score, and the misclassification cost viewed as a penalty for errors incurred during the classification process. The combination of AdaBoost with decision trees exhibited the best performance in terms of all performance metrics. Additionally, the results showed that the variables that included travel time, traffic volume, and average speed helped predict vehicle traffic flow on the roads. Thus, the model was developed to benefit transport planners, researchers, and transport stakeholders to allocate resources accordingly. Furthermore, adopting this model would benefit commuters and businesses in tandem with other interventions proffered by the transport authorities.

Equity traders are always looking for tools that will help them maximise returns and minimise risk, be it fundamental or technical analysis techniques. This research integrates tools used by equity traders and uses them together with machine learning and deep learning techniques. The presented work introduces a South African-based sentiment classifier to extract sentiment from new headlines and tweets. The experimental work uses four machine learning models for fundamental analysis and six long short-term memory model architectures, including a developed encoder-decoder long short-term memory model for technical analysis. Data used in the experiments is mined and collected from news sites, tweets from Twitter and Yahoo Finance. The results from 2 experiments show an accuracy of 96% in predicting one of the major telecommunication companies listed on the JSE closing price movement while using the linear discriminant analysis model and an RMSE of 0.023 in predicting a significant telecommunication company closing price using encoder-decoder long short-term memory. These findings reveal that the sentiment feature contains an essential fundamental value, and technical indicators also help move closer to predicting the closing price.