Self-adapting simulated artificial societies by Gower-Winter, Brandon

“…Despite their potential, ABM are limited by the fact their agents are rarely adaptive despite adaptability often touted as one of Agent-Based Modelling's greatest strengths. In this work we remedy this by investigating whether Machine Learning (ML) algorithms can be used as adaptive mechanisms for Agent-based Models simulating complex social phenomena. …”
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Applications of Gaussian Process Regression to the Pricing and Hedging of Exotic Derivatives by Muchabaiwa, Tinotenda Munashe

“…GPR is a supervised machine learning technique which makes use of a training set to train an algorithm so that it makes predictions. …”
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Development and Prediction of a Construction Price Index in Egypt by Ramadan, Mohamed

“…The trend in the prediction models for construction indices, construction materials and construction companies’ stock prices, is developing artificial intelligence and machine learning models. …”
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Spamming mobile botnet detection using computational intelligence

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Unsupervised classification of simulated black hole shadows

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The quantification of red wine phenolics using fluorescence spectroscopy with chemometrics by Dos Santos, Isabel Anne

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Improved estimates and understanding of interannual trends of CO₂ fluxes in the Southern Ocean by Gregor, Luke

“…Lastly CO₂ sampling platforms, namely ships, profiling floats and moorings, were tested in an idealised simulated model environment using a machine learning approach. …”
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Reliable Outdoor Ai-Based Autonomous Uav Localization and Trajectory Tracking Using Cellular Networks by Afifi, Ghada

“…Specifically, we propose two machine learning-based techniques, namely, reinforcement learning and the deep supervised learning-based approaches. …”
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Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar by Holtshausen, Damian Luke

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Viewpoint estimation in medical imaging by Hounkanrin, Mahouclo Anicet

“…We investigate methods to solve the viewpoint estimation problem for medical images, notably the determination of orientation parameters. Machine learning models, particularly convolutional neural networks (CNNs), are developed to predict a human subject's orientation in a radiograph. …”
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In vitro validation of chemo-transcriptomic fingerprints for the classification of the mode of action of antimalarial drugs

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Prediction of isobaric heat capacities of room temperature ionic liquids by Wilson, Tayla Lee

“…In this work, predictive models for CP have been developed using linear and nonlinear machine-learning methods. …”
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Using Siamese neural networks to identify individual animals by Madzingira, Tinotenda

“…The dissertation answers whether machine learning can determine if the animals in a pair of images come from the same individual animal. …”
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Cough endpoint detection in continuous audio recordings by Lamprecht, Christiaan Gerrit

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Exploring topological data analysis in gene expression data topology-driven biomarker discovery and clinical outcome prediction in oncology by Nyase, Ndivhuwo

“…Moreover, by integrating these topological features with machine learning models we aim to enhance the predictive accuracy for clinical outcomes. …”
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Simulating recombinant sequence date to evaluate and improve computational methods of multiple sequence alignment and recombinant identification by Swanepoel, Phillip

“…To improve the accuracy with which RDP5 identifies recombinant sequences, we simulated the evolution of recombining sequences to produce large datasets that could then be used to train a number of machine learning models to accurately differentiate recombinants from their parental sequences. …”
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End-to-end automated speech recognition using a character based small scale transformer architecture.

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Active Inference in Multi-Objective Dynamic Environments by Hodson, Rowan

“…Much work has been done to explore the framework's viability in modelling scenarios both related to neural process theory and more classical agent-based machine learning. …”
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Approximation approaches for training neural network problems with dynamic mini-batch sub-sampled losses

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Exploring the application of Natural Language Processing to scientific medical cannabis publications by de Beer, James Charles

“…The unsupervised methods were able to effectively cluster and model topic distributions between the data to group documents by topic, while the supervised learning methods were able to accurately train models based on these suggestions, thereby solving a real-world practical problem in data management and analysis.…”
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