Babu Sena Paul

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.

Missing values or data is one popular characteristic of real-world datasets, especially healthcare data. This could be frustrating when using machine learning algorithms on such datasets, simply because most machine learning models perform poorly in the presence of missing values. The aim of this study is to compare the performance of seven imputation techniques, namely Mean imputation, Median Imputation, Last Observation carried Forward (LOCF) imputation, K-Nearest Neighbor (KNN) imputation, Interpolation imputation, Missforest imputation, and Multiple imputation by Chained Equations (MICE), on three healthcare datasets. Some percentage of missing values - 10\%, 15\%, 20\% and 25\% - were introduced into the dataset, and the imputation techniques were employed to impute these missing values. The comparison of their performance was evaluated by using root mean squared error (RMSE) and mean absolute error (MAE). The results show that Missforest imputation performs the best followed by MICE imputation. Additionally, we try to determine whether it is better to perform feature selection before imputation or vice versa by using the following metrics - the recall, precision, f1-score and accuracy. Due to the fact that there are few literature on this and some debate on the subject among researchers, we hope that the results from this experiment will encourage data scientists and researchers to perform imputation first before feature selection when dealing with data containing missing values.

The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. The selection of the incorrect qualification increases the risk of dropping out, changing qualifications, or not finishing the qualification enrolled in the minimum time. This study harnesses a real-world dataset comprising student records across four engineering disciplines from the 2016 and 2017 academic years at a public South African university. The study examines the relative importance of features in models for predicting student performance and determining whether students are better suited for extended or mainstream programmes. The study employs a three-step methodology, encompassing data pre-processing, feature importance selection, and model training with evaluation, to predict student performance by addressing issues such as dataset imbalance, biases, and ethical considerations. By relying exclusively on high school performance data, predictions are based solely on students’ abilities, fostering fairness and minimising biases in predictive tasks. The results show that removing demographic features like ethnicity or nationality reduces bias. The study’s findings also highlight the significance of the following features: mathematics, physical sciences, and admission point scores when predicting student performance. The models are evaluated, demonstrating their ability to provide accurate predictions. The study’s results highlight varying performance among models and their key contributions, underscoring the potential to transform academic advising and enhance student decision-making. These models can be incorporated into the academic advising recommender system, thereby improving the quality of academic guidance.

In 2015, the United Nations Member States developed a collective blueprint for sustainability and development. The 2030 Plan includes the 17 UN-SDGs, which are an immediate call for action from all countries in the form of a global collaboration. To date, a number of countries have made significant strides in achieving the goals. One solution is the transition from a linear economy to a circular economy. Together with this, new 4IR innovative technologies has helped many countries in their transition to a circular economy as well as achieving the SDGs. Countries and organizations have also adopted environmental, social, and governance reporting as another technique, and have become mandatory in some regions and organizations. The economy of South Africa is beset by poverty and inequality, considerable unemployment, carbon-intensive, water insecurity and slow GDP growth. Furthermore, the COVID-19 epidemic has caused the economic crisis to worsen further and emphasizes the need for a new development strategy to spur economic recovery. In this paper, we will compare South Africa’s sustainability and circular economic road map to the rest of the world, and we suggest solutions and policies that can be put in place for the future benefit of the country.

The United Nations Member States created a common roadmap for sustainability and development in 2015. The UN-SDGs are included in the 2030 Plan as an immediate call to action from all nations in the form of global partnerships. To date, a handful of countries have achieved substantial progress toward the targets. The climate–water–energy–food nexus is being advocated as a conceptual method for achieving sustainable development. According to research, frameworks for adopting nexus thinking have not been the best solution to clearly or sufficiently include thoughts on sustainability. Therefore, there is much room for other solutions; these are in the form of newer Fourth Industrial Revolution digital technologies, as well as transitioning from a linear economy to a circular economy. In this paper, we come to understand these two models and their linkages between climate, water, energy, and food; their application and challenges, and, finally, the effects on the UN-SDGs. It was found that both circular economy and newer Fourth Industrial Revolution digital technologies can positively support the nexus as well as directly address the UN-SDGs, specifically SDGs 7, 8, 9, 11, 12, and 13.

We are living in an age when data centers are expanding, require abundant spaces, and are an integral part in the urban communities, using massive amounts of environmental resources, and remains in the foreseeable future as the primary driver of the global energy consumption. This demand is disruptive and at times of both peril and opportunity due to impacts such as the COVID-19 pandemic, which is altering the demand of digital infrastructure around the world. With the global call for zero carbon emissions, there needs to be solutions put in place for the de-carbonization of data centers. New innovations are made available, which will have an economic, social, and environmental impact on data centers. Concepts such as circular economy and fourth industrial revolution technologies are useful procedural tools that can be used to systematically analyze data centers, control their mining and critical raw materials, can be utilized in the transition towards a sustainable and circular data center, by objectively assessing the environmental and economic impacts, and evaluating alternative options. In this paper, we will look at the current research and practice, the impact on the United Nations Sustainable Development goals, and look at future strides being taken towards more sustainable and circular data centers. We had discovered that decreasing the environmental effect and energy consumption of data centers is not sufficient. When it comes to data center architecture, both embodied and operational emissions are critical. Data centers also have a vital societal role in our daily lives, enabling us to share data and freely communicate via social media, transacting on the blockchain with cryptocurrencies, free online education, and job creation. As a result, sustainability and efficiency measures have expanded in a variety of ways, including circularity and its associated tools, as well as newer technologies.

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.

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.

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.

A set of trusted information in the form of material passports is necessary in order to understand the circular value of systems and materials in the built environment. Innovations such as digital technologies and material passports are useful circular economy transitional tools in managing the materials flows and decarbonizing the built environment. Material ppassports are datasets and reliable information consisting of the entire value chain—specifications of the materials used, and specific supply chains involved from the sources to producers, distributors and consumers or users; and technical facts to improve the re-use or recycling of materials and to enhance their residual value. This paper presents the material passports in the built environment for the transition towards a circular economy as well as the latest trends in research and practice.