Advanced process monitoring using wavelets and non-linear principal component analysis

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Quantum Machine Learning Applied to Astronomical Datasets by Slabbert, Donovan Michael

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Predicting mire distribution using species distribution models (SDMs) and GISc : a case study of the Prince Edward Islands (PEIs)

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Utilization of artificial neural networks to resolve chemical kinetics in turbulent fine structures of an advanced CFD combustion model by Laubscher, Ryno

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Design of a centrifugal compressor impeller for micro gas turbine application by Van der Merwe, Bosman Botha

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Computer vision-based automated cow lameness estimation using machine learning and synthetic data

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Hydraulic Data Preprocessing for Anomaly Based Intrusion Detection on SCADA Level of Water Treatment Systems by Mboweni, Ignitious

“…The best performance was achieved when the preprocessed dataset was randomly split into training and testing, yielding a significant improvement in accuracy, F1 score and time to detection for both algorithms used in the study, namely Fine Tree and Boosted Trees Ensemble.…”
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Learning data-derived vehicle motion models for use in localisation and mapping

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Seedling and juvenile tree detection using UAV photogrammetry and machine learning by Kok, Simeon James

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A computer vision framework towards automated scene understanding & analysis by Sarah-lee de Greeff

“…Most computer vision algorithms are deep learning based approaches but differ significantly in architecture. …”
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Force and velocity jump characteristics of University-level field hockey players by Van Jaarsveld, Amori

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Monitoring and diagnosis of process systems using kernel-based learning methods by Jemwa, Gorden Takawadiyi

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Disentangled representations in speech processing applications by Baas, Matthew

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Detecting Electrical Submersible Pump (ESP) Failures and Estimating Run Life Using Artificial Neural Networks by Sobhy, Mostafa Ahmed

“…The RUL prediction module achieves high accuracy (R² > 0.96; MAE < 10 days) through supervised algorithms specifically designed for the ESPs' dataset dynamics. …”
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Project Leanness Score: A Machine Learning Approach by Said, Julia

“…Fourth, benchmarking the leanness score representing the industry’s performance through collecting extensive project data from 30 construction projects. Fifth, training and validating models using machine learning algorithms such as regression, decision trees, and artificial neural networks to predict the schedule and budget performance of construction projects using the factors of the leanness score. …”
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Mixed-Criticality Scheduling Using Reinforcement Learning by ElSeadawy, Omar

“…Sensitivity analysis shows that training the model with randomized parameters yields a stable performance that is relatively robust to some changes in the generated instances. …”
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Wearable device localisation and its effect on activity recognition using machine learning

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Identifying predictive markers in complex samples of biogenic volatile compounds using GC×GC-TOFMS and machine learning

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Fast accurate diphone-based phoneme recognition by Du Preez, Marianne

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Evaluation of a high-pole number reluctance synchronous wind generator technology by Baziruwiha, Jean-Claude

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