The most commonly used models in computer vision applications is a form of neural network called convolutional neural networks. Neural networks are a type of machine learning model that takes inspiration from the structure and functioning of the human brain. Convolutional neural networks refer to a type of neural network model that is especially adept at computer vision tasks such as image classification, object detection and video analysis owing to the use of convolutional layers. Convolutional neural networks can consist of millions of parameters, the majority of which are stored in the filters that the networks use during convolution operations. Thus, one major problem hampering more widespread adoption of convolutional neural network models in practice is the size of these models. The storage required to deploy these models is not trivial, leading to a need for methods to compress these models without materially degrading their predictive capabilities. One such method is filter selection and pruning, which refers to methods that assess the filters in a convolutional neural network and remove the least important filters to reduce the size of the model. This project proposes the use of a genetic algorithm to optimise the process of filter selection, allowing multiple filter selection methods to be applied concurrently. The proposed algorithm allows filters to be pruned adaptively, with the removal methods and number of filters removed being optimised for the network being pruned.

When applying the proposed algorithm, we achieve 90.91% model compression at the cost of a 0.13% point accuracy drop for a network trained on audio data. When applied to the classic Fashion-MNIST data set, 91.37% compression is achieved with a corresponding 0.39% point drop in accuracy. We also achieved 86.06% compression while increasing accuracy by 2.37% points on a model trained on the CIFAR-10 data set. These results show the utility of the algorithm and its ability to compress networks adaptively with different architectures trained on different data sets. This study reveals that genetic algorithms can be applied successfully to prune filters from convolutional neural networks and provides the underpinnings for a comprehensive genetic algorithm capable of pruning filters from any given convolutional neural network architecture.

This study focused on understanding how users interact with different categories of essential services offered through the MoyaApp platform, a Datafree subsidiary, particularly on grants, education, jobs, and other information services such as weather and electricity. The researcher used data mining techniques such as temporal association rule mining and other statistical methods to analyze user interests and usage patterns.

The findings revealed that many low-income users initially registered on MoyaApp to access grant services; users gradually explored other essential services over time and became regular platform users.

The researcher proposed a few recommendations to improve the benefits MoyaApp provides to low-income communities: Firstly, MoyaApp should consider expanding the jobs category to cater to users with varying levels of education. Secondly, targeting grant users with information services like weather and electricity encourages engagement. Once users are regular users of the platform, they are more likely to use more beneficial services such as education and jobs, which leads to improved socio-economic status. Thirdly, the results of this study can be used to develop a recommendation engine to suggest relevant essential services to low-income users.

In conclusion, this research assignment demonstrated that providing zero rated internet services or, more accurately, reverse billing data to low income communities can be an effective strategy to enhance access to essential services and bridge the digital divide.

Given this problem with fracture diagnostics, there is an opportunity to use artificial intelligence (AI) to assist with the diagnosis of fractures. Successful implementation of an AI system that correctly locates and classifies fractures would lead to more accurate prognosis and treatment advice.

The selected fracture classification system for this research assignment is the Arbeitsgemeinschaftf fur Osteosynthesefragen / Orthopaedic Trauma Association (AO/OTA) classification [90]. The object detection models selected in this research to evaluate whether AI can be used for accurate location and classification of fractures according to the AO/OTA classification are the faster region-based convolutional neural network (Faster R-CNN) [115], you only look once version 8 nano (YOLOv8n) [54], you only look once version 8 large (YOLOv8l) [54], and RetinaNet [76].

A secondary problem that this research assignment addresses is that of data scarcity. Deep learning algorithms require large amounts of data to achieve exceptional performance. The target dataset in this research assignment, the distal radius dataset (DIRAD), only consists of 776 images, where roughly half of the images contain fractures. The technique applied to overcome the data scarcity problem is transfer learning. With trans fer learning, the object detection models are pretrained on larger datasets iv such as the common objects in context (COCO) [77] and the Graz Paediatric Wrist Digital X-rays (GRAZPEDWRI-DX) dataset [95] before being trained on the target dataset.

This research assignment shows that pretraining of object detectors on larger datasets leads to superior performance on scarce datasets. Furthermore, when pretraining an object detection model on a large dataset from a similar domain to perform a similar task, such as GRAZPEDWRI DX, it leads to even better results. The pretraining of the Faster R-CNN, YOLOv8n, YOLOv8l, and RetinaNet on the GRAZPEDWRI-DX improved mean average precision at an intersection over union of 50 (mAP50) by an average of 33.6% compared the same models trained from randomly initialised weights. The best performing model, namely the YOLOv8l, achieved a mAP50 of 59.7% on the DIRAD dataset.

In this assignment, the non-dominated sorting genetic algorithm II (NSGA-II) and the strength pareto evolutionary algorithm 2 (SPEA2) algorithms for the TSPDi problem were adapted, with modifications and enhancements to optimise their performance. A custom population initialisation function was added to both algorithms, improving the starting point for the evolutionary process. In addition, a heuristic mutation method was developed that produces feasible high quality solutions. To create a more varied solution pool, a mechanism for selecting unique solutions for both the parent and archive populations was implemented to ensure that no duplicate solutions occurred. This approach was especially successful in keeping a wide range of solutions during extended iterations. Empirical results showed that NSGA-II is better than SPEA2 in scenarios with larger datasets and many delivery nodes, while SPEA2 has a slight advantage in smaller datasets with fewer delivery nodes.

Further analysis was performed to compare the performance of the algorithms with those of Ernst [29] and Moremi [52]. Delivery time was the most important factor in the comparison, as it was the objective optimised by Ernst [29] and Moremi [52]. The results showed that the new Multi-Objective Evolutionary Algorithms (MOEA) performed similarly to the single objective function on the smaller datasets (i.e. 10 and 20 nodes) in terms of the delivery time metric; however, in most cases they did not perform better. For larger data sets (i.e. 50 to 500 nodes), MOEAs outperformed all algorithms developed by Moremi [52] and were more competitive compared to algorithms developed by Ernst [29], surpassing them in performance on most large data sets. For the truck distance metric, the MOAEs outperformed most of the single-objective evolutionary algorithms (EAs) for smaller and larger datasets. This was expected since single-objective EAs were not designed to optimize time.

The first part of this project entailed the development of a framework to represent CNN architectures, which drew inspiration from the concept of neuroevolution of augmenting topologies (NEAT). This developed representation framework was used to define the composition and layout of CNN architectures. Next, a genetic algorithm was adapted to fit within the framework, thus enabling the evolution of CNN blocks using various evolutionary operators, including mutation, speciation and crossover. The representation framework and genetic algorithm were combined to evolve a population of 100 CNNblocks over 30 generations. Throughout the evolution process, the search was guided by the measured quality of the blocks, defined by a fitness function that was designed to balance complexity and performance. Five repetitions of the experiment were performed and compared to randomly generated blocks to assess the overall success of this approach. Additionally, the performance of the evolved blocks was evaluated against manually designed blocks such as ResNet and GoogLeNet’s Inception.

The results of the comparison between the genetic algorithm and random procedures demonstrated the effectiveness of the genetic algorithm in producing highly optimal solutions based on the fitness evaluation. The results showing the distribution of the population evolutionary operators also explained how the subprocedures can be used to effectively control the search. Furthermore, the result obtained using a small sample of the best performing evolved blocks proved to be highly competitive when compared to manually designed counterparts, namely ResNet and Inception. This study validates the concept of using evolutionary algorithms for neural network block generation and emphasises their ability to rival manually designed networks. The findings suggest that evolutionary computation successfully automates the discovery of competitive blocks within CNN architectures, offering new avenues for neuroevolution and overcoming limitations in the manual design processes.

Element-to-mineral conversion (EMC) refers to the method of using bulk rock compositional data to calculate mineral grade quantities. EMC is a chemical mass balancing technique that utilises the bulk rock chemistry, , and the minerals’ compositional data, , to solve for modal mineralogy, . Chemical mass balances are expressed as a set of simultaneous equations that can be solved using the least-squares approach (LS-EMC). LS-EMC can only be applied if the number of unknowns (minerals) is less than or equal to the number of known variables (elements). It is often the case that there are more minerals than elements. However, minerals can be grouped such that the number of resultant mineral sets is equal to the number of elements. Although this method of grouping minerals is sufficient for geometallurgical models, it is insufficient for mineral processing models which require exact quantities for individual minerals.

This study sought to investigate alternative data-science-based methods to LS-EMC. Data science is an interdisciplinary field that focuses on the application of computational statistical methods, such as machine learning, for the extraction of knowledge from data. Three tree-based machine learning (ML) algorithms, namely, Decision Tree, Random Forest, Extra Trees, were trained to predict mineral grade quantities using positional and geochemical data. The dataset used in the investigation consisted of 135 observations sourced from a geological study conducted on the Kalahari Manganese Deposit (KMD) (Blignaut, 2017). LS-EMC was also applied, and the mineral grade estimates obtained by this method were compared to ML models’ output. The R2 statistic was used to quantify how well the LS-EMC and ML-EMC output agreed with the modal mineralogy measurements obtained through quantitative x-ray diffraction (QXRD). In comparison to the other the techniques, the modal mineralogy results from the Extra Trees regressor correlated the most with the QXRD measurements, achieving R2 scores > 0.5 for six out of the eight mineral groups. Furthermore, the Extra Trees algorithm outperformed the other two tree-based models in a test designed to see which ML algorithm provided the most reliable mineral quantity predictions for ungrouped minerals. The results of this study support the conclusion that tree based machine learning algorithms can be used to improve upon the shortcomings of LS-EMC.

In this research assignment, the coordinates of customer nodes are randomly changed to simulate a dynamic environment while a truck and drone system performs deliveries. This problem is re ferred to as the dynamic travelling salesperson problem with drone with interception (DTSPDi). This research assignment solves the problem using the ant colony system (ACS) [30], MAX-MIN ant system (MMAS) [87] and a modified ACS that transfers pheromone knowledge to the next time slice. The research assignment builds on the algorithm designed by Moremi [64] for the travelling salesperson problem with drone with interception (TSPDi). The three algorithms use 30 datasets of different sizes and spatial patterns for input. The result from the benchmarking was that ACS-KT outperformed both algorithms in both the time and distance dimensions. Interestingly, a lower wait time does not mean a lower time or distance for a route. There was also no correlation between drone and truck distances. Therefore, it seems that ACS-KT is better at handling dynamic environmental changes for the DTSPDi problem.

The essence of this research lies in its pursuit to critically review, analyze, and evaluate various clustering models, focusing on their suitability and adaptability in handling big data, characterized by the four Vs, i.e. velocity, variety, volume, and veracity. The aim is to discern the operational dynamics of diverse clustering models, considering the findings of prior literature, which have demonstrated varying degrees of performance of these models based on selected metrics.

The methodology is firmly rooted in the execution of a series of experiments on chosen clustering methods, metrics, and datasets. This empirical method is crucial to extrapolate how each model fares across di↵erent metrics and datasets, o↵ering a comparative perspective on their performance. Subsequent to the experimental phase, an extensive analysis was conducted, breaking down the selected approaches into their algorithmic components. This decomposition is pivotal to identify the origins of gains, losses, or tradeo↵s in performance, allowing for an in-depth understanding of why certain models outperform others concerning given metrics and datasets.

Insights from this research highlighted the scalability and e ciency of models like parallel k-means and mini batch k-means, both theoretically and empirically, marking them as exemplary for large-scale applications. Conversely, it unveiled the computational constraints of models like selective sampling based scalable sparse subspace clustering (S5C) and purity weighted consensus clustering (PWCC), showing their limitations in scaling to big data.

Acknowledging the limitations imposed by the resource constraints of Google Collab Pro+, the study presents the constraints faced during the evaluation process.

The culmination of this project is marked by a comprehensive performance summary, o↵ering key insights into the strengths and weaknesses of the approached models and pro↵ering informed advice on the contextual utilization of each model. It lays the foundation for a centralized database for clustering research, aiming to fill existing knowledge gaps and facilitate optimal model discovery tailored to specific needs and infrastructural capabilities.

In conclusion, this research stands as an exploration and analysis in the field of big data clustering, to uncover the potentials and bottlenecks of various models, and o↵ers valuable insights and recommendations, all while reconciling theoretical complexities with empirical validations.

Machine learning can be used to automate the bioacoustic identification of species, which would reduce analysis costs and time. Unfortunately, many machine learning algorithms require large amounts of data to perform reliably. Few-shot learning is a loosely defined structure in machine learning that aims to solve the limited data problem with unique approaches.

This assignment explores the viability of accurate, image-based classification models when subject to low data volumes. Audio data is converted to spectrograms and used in image analysis. A Siamese framework, which has roots in convolutional neural networks (CNN), is the foundation of the few-shot learning approach. Within this CNN-based framework, contrastive-loss and triplet loss architectures, data augmentation techniques, transfer learning methods, and reduced image resolution datasets are investigated.

The results indicate that the triplet-loss architecture produces the most accurate models, with excellent precision, recall, and F1-score statistics. The triplet-loss models prefer lower resolution images, which reduce computation time and cost. Importantly, the performance of the triplet-loss models is not affected by low data volumes. On the other hand, contrastive-loss models show significant performance degradation on lower data volumes.

Overall, the triplet-loss “base CNN” model is the recommended network. This network achieves an accuracy of 99.08% and F1-score of 0.995. The Siamese framework has demonstrated a strong ability to identify the bioacoustic signature of the Hainan gibbon. Recommendations are provided for further research in this domain.

Digitization is a way of converting PDF documents into format which can be analyzed using a computer. There are two common ways to digitize documents namely manual and automatic. Manual digitization include copying and pasting information from documents to a database or retyping information contained in the document into a database. This process is laborious, time consuming, prone to errors and costly. This project explored and presented an automated way of digitizing documents using object detection model for document layout analysis and optical character recognition for extracting alphanumeric characters from images.

Object detection model was developed by fine-tuning faster R-CNN pre-trained model available in Detectron2 framework. This process involved leveraging a blend of manually annotated images and synthetically generated annotations. The results demonstrated model R-101 (a variant of R101-FPN) as having a balanced performance based on accuracy and inference time. The values of mAR, mAP and inference time for model R-101 are 74.3%, 71.0% and 0.371 seconds/image, respectively. This object detection model was used to identify and provide ROI coordinates and labels to optical character recognition algorithm.

Various optical character recognition algorithms were evaluated and compared across various image qualities. PaddleOCR outperformed the other three algorithms, achieving a word recognition rate of 96%. Nevertheless the performance of these algorithms was lower on blurred images as compare to other image qualities. Spelling check and correction was conducted to improve recognition rate of paddleOCR outputs by a further 1.2%.

An interactive application which can be accessed online via a web link or offline in a desktop was developed for exploring the dataset. This application allows for creating scenarios using multiple slicers to visualize a word cloud of common words and frequency of characteristics (e.g soil type, moisture condition and particle size) used to describe each scenario. Semantic search algorithm was fine-tuned using sentence transformers to allow users to query the dataset using natural language and a separate desktop application was developed to facilitate this. Evaluating semantic search algorithm revealed precision, recall and F1 score of 68.3%, 65.7% and 67.0%, respectively.

Suggestions for further work include performing exhaustive data analysis to discover insights and hidden patterns, training language model for improving spelling correction, collecting more documents for developing a large geological and engineering dataset as well as training a question and answering machine learning model to make data and insights more discoverable.

The financial time series data collected for this research assignment were 10-year minute ticker data. The two foreign exchange rate data sets, the USD/ZAR and ZAR/JPY foreign exchange rates were used. The other three data sets collected were the S&P 500 index, the FTSE 100 index, and the Brent crude oil index. The first step was to analyse the quality of the data sets. After the quality had been assessed, a trading strategy and financial trading model were used to combine the 20-period moving average, the relative strength index, and the average directional index for the labelling process. Twelve machine-learning models were developed to forecast the financial time series data sets. These were the baseline logistic regression, support vector ma chine, k-nearest neighbour, decision tree, random forest, Elman recurrent neural network, Jordan recurrent neural network, Jordan-Elman recurrent neural network, long short-term memory neural network, time-delay neural network, resilient back propagation feed-forward neural network, and particle swarm optimisation feed-forward neural network.

The results from the experiments indicate that the support vector machine performed the best out of all the machine learning models considered. The baseline logistic regression model outperformed all the other machine learning models. The random forest and resilient back propagation feed-forward neural network models performed third and fourth best. These two models had higher recall scores than most models, but their accuracy scores were significantly lower than the baseline and support vector machine models. The recurrent neural network models had very poor performance. Specifically, the Elman and Jordan-Elman models had the poorest performance of the models investigated. It was determined that the non neural network machine learning models were less computationally complex, and were less dependent on a balanced data set than the neural network models.

In this research assignment, online news articles are collected from nine South African news websites. Topic modelling is then applied to each of the collected data sets with the goal to group the collected news articles related to specific infrastructure issues together, e.g., group all news articles about potholes, or group all news articles about sewage spills, and then represent each group of news articles as a topic. A summary for each topic is then generated by making use of a large language model. Lastly, a dashboard is designed to effectively visualise the topics, and the summaries generated for these topics. This dashboard can then be used as a tool by SAICE to identify, and monitor prevalent infrastructure issues in various regions of South Africa, while also providing SAICE with additional data for the compilation of IRCs.

This research assignment concludes that it is feasible to apply topic modelling to South African news data sets for the extraction of infrastructure-related topics. It is furthermore concluded that topic modelling can help address the lack of data in compiling the SAICE IRCs. Lastly, it is concluded that it is feasible to generate summaries for the extracted topics using large language models, although the generated topic summaries can be improved upon.

A thorough literature review was conducted to gain an in-depth understanding of the topic and to survey existing solutions in the field.

The study involved a thorough analysis of datasets to understand the inherent structures of the series. Evaluation metrics and various algorithms were used to assess the effectiveness of time-series forecasting techniques.

The research assignment found that deep learning techniques and ensemble theory can successfully be applied to forecast sales in the liquor industry. A stacked ensemble approach was effective in improving the overall performance.

The findings have the potential to significantly improve current implementations of time-series forecasting, while reducing the computational complexity and expenses associated with granular forecasting models.

The research assignment concludes that deep learning and ensemble models offer a promising avenue for efficient and accurate sales forecasting in the liquor industry, being more time-efficient and computationally less complex than traditional methods.

The results of the investigation indicate that the optimal deep learning solution is a two-model pipeline that employs a you only look once (YOLO) object detection model and a densely connected convolutional neural network (DenseNet) classification model. The DenseNet classification model classifies the trauma implants localised by the YOLO object detection model. The proposed pipeline achieves a mean average precision (intersection of union threshold of 0.5) of 0.967 for implant localisation and an accuracy of 73.7% for implant classification. The results of the study provide proof that deep learning models are capable of identifying trauma implants. Additionally, the study offers a deep learning solution that can be utilised in future research related to identifying trauma implants.

In this research assignment, a methodology is developed to evaluate whether the features used by a CNN to classify skin lesions correspond to the ABC-criteria and the 7-point skin lesion malignancy checklist. A CNN model is developed, trained, and tested to assess the application of the formulated methodology. The association between the ABC-criteria and the 7-point skin lesion malignancy check list features and melanoma in the test dataset is investigated using statistical methods to establish a ground truth. The association between ABC-criteria and the 7-point skin lesion malignancy checklist features and the features extracted by the CNN is determined using t-distributed stochastic neighbour embedding (t-SNE) and statistical tests. The importance of colour is evaluated by testing the performance of the CNN on a grayscale dataset. The association of dataset issues with the extracted features is examined using statistical tests, and misclassifications are investigated based on features and dataset issues. Local interpretable model-agnostic explanations (LIME) is employed to explain misclassifications and correctly classified images, providing insights into the decision-making process of the CNN.

The InceptionResNetV2 model with a leaky ReLU activation was selected to evaluate the formulated methodology. The correlation tests between the ABC-criteria and the 7-point skin lesion malignancy checklist features and the melanoma diagnosis in the curated test dataset showed a strong association between all the features and melanoma except for vascular structures, brown, red and black. These results were reflected in the evaluation of the association between the features used by the CNN and the ABC-criteria and the 7-point malignancy checklist since there was a strong association between the extracted features and the ABC-criteria and the 7-point malignancy checklist features except for vascular structures, brown, red and black. The decrease in performance of the InceptionResNetV2 model on the grayscale dataset indicated that colour is a feature that the CNN uses to detect melanoma. The CNN demonstrated robustness to dataset issues but showed sensitivity to the presence of hair and immersion fluid, suggesting the need for further preprocessing of the images. Overall, it was concluded that the developed methodology can determine whether a CNN uses the features in the ABC-criteria and the 7 point malignancy checklist to classify skin lesions as malignant or benign. The developed methodology showed that the CNN uses the features of the ABC-criteria and the 7-point malignancy checklist to determine whether a skin lesion is malignant or benign.