Automated Machine Learning for Predicting Trends in Time Series Data by Kouassi, Kouame

“…However, manual ASHO is expensive and often results in a sub-optimal or mediocre model because it needs extensive experimentation as well as domain specific and Machine Learning (ML) expert knowledge. …”
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Unravelling black box machine learning methods using biplots by Rowan, Adriaan

“…The goal of this dissertation is to expand on the theory and use of biplots to visualise the impact of the various input factors on the output of the machine learning black box. Biplots are used because they give an optimal two-dimensional representation of the data set on which the machine learning model is based.The biplot allows every point on the biplot plane to be converted back to the original ��-dimensions – in the same format as is used by the machine learning model. …”
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An interactive R shiny application for learning multivariate data analysis and time series modelling

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Forecasting energy consumption on mines using machine learning techniques by Esterhuizen, Lohani

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Machine Learning Multimodal Framework for Fake News Detection and Mitigation by GabAllah, Nada A

“…This research aims to develop a comprehensive machine learning framework for detecting fake news on social media by incorporating multiple modalities of data. …”
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A Machine Learning Approach to Predicting the Employability of a Graduate by Modibane, Masego

“…The aim for the dissertation is to build a machine learning classification model that can predict a students likelihood to become employed, based on their student data (for example, their GPA, degree/s held etc). …”
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Predictive Maintenance in Rail Transportation: An Explainable Machine Learning Approach by Mabaso, Nelisa Zamantungwa Sphumelele

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Modelling highly imbalanced credit card fraud detection data using statistical learning by Moodley, Revesa

“…The best supervised learning models are RF and XGBoost. The tree models were generally well suited to the imbalanced dataset, whilst LR performed the worst, even when applying regularisation. …”
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Interpretable machine learning in natural language processing for misinformation data

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Predicting household poverty with machine learning methods: the case of Malawi by Chinyama, Francis

“…Area under the Curve, Receiver Operating Characteristic curve, recall, precision, F1 score, Matthews Correlation Coefficient and Cohen's Kappa scores confirmed the classification models' reliability. The study, therefore, established that poverty can be predicted by open-source machine learning algorithms using a substantially reduced number of features with accuracy comparable to using the full feature set. …”
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Machine learning approaches towards tuning ALICE TRD simulations by Ramraj, Nikhiel

“…It is shown that the optimal parameters suggested by our deep learning models through inference of their performance metrics are not clear and in agreement with that suggested by naive pulse height inspections.…”
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User Profiling through Zero-Permission Sensors and Machine Learning by ElHussiny, Ahmed

“…We additionally test the minimum timespan needed to accurately profile a user, and test three machine learning models. We find that it is indeed possible to accurately predict the biological gender of a user, given 1-day intervals, and using a support vector machine.…”
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An RFI simulation pipeline to help teach interferometry and machine learning by Agbetsiafa, Insight Enya Aku

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Efficacy of machine learning and lidar data for crop type mapping by Prins, Adriaan

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The impact of news on the South African sovereign bond market

Subjects: “…Machine-learning…”
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Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa by Briers, C. J.

Subjects: “…Machine learning…”
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Seedling and juvenile tree detection using UAV photogrammetry and machine learning by Kok, Simeon James

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Money Laundering Alert Prioritisation Using Machine Learning and Benford’s Law by Spies, Luka

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Investigating automated bird detection from webcams using machine learning by Mirugwe, Alex

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