Recent Submissions

  • Unsupervised Fine-tuning of Speaker Diarisation Pipelines using Silhouette Coefficients 

    Van Wyk, Lucas; Davel, Marelie, H; Van Heerden, Charl (SACAIR, 2021)
    We investigate the use of silhouette coefficients in cluster analysis for speaker diarisation, with the dual purpose of unsupervised fine-tuning during domain adaptation and determining the number of speakers in an audio ...
  • Tracking translation invariance in CNNs 

    Myburgh, Johannes C.; Mouton, Coenraad; Davel, Marelie H. (Southern African Conference for Artificial Intelligence Research, 2020)
    Although Convolutional Neural Networks (CNNs) are widely used, their translation invariance (ability to deal with translated inputs) is still subject to some controversy. We explore this question using translation-sensitivity ...
  • Stride and translation invariance in CNNs 

    Mouton, Coenraad; Myburgh, Johannes C.; Davel, Marelie H. (Southern African Conference for Artificial Intelligence Research, 2020)
    Convolutional Neural Networks have become the standard for image classification tasks, however, these architectures are not invariant to translations of the input image. This lack of invariance is attributed to the use of ...
  • Pairwise networks for feature ranking of a geomagnetic storm model 

    Beukes, Jacques Pieter; Davel, Marelie Hattingh; Lotz, Stefan (South African Institute of Computer Scientists and Information Technologists, 2020)
    Feedforward neural networks provide the basis for complex regression models that produce accurate predictions in a variety of applications. However, they generally do not explicitly provide any information about the utility ...
  • Using summary layers to probe neural network behaviour 

    Davel, Marelie Hattingh (South African Institute of Computer Scientists and Information Technologists, 2020)
    No framework exists that can explain and predict the generalisation ability of deep neural networks in general circumstances. In fact, this question has not been answered for some of the least complicated of neural network ...
  • Benign interpolation of noise in deep learning 

    Davel, Marelie Hattingh; Barnard, Etienne; Theunissen, Marthinus Wilhelmus (South African Institute of Computer Scientists and Information Technologists, 2020)
    The understanding of generalisation in machine learning is in a state of flux, in part due to the ability of deep learning models to interpolate noisy training data and still perform appropriately on out-of-sample data, ...
  • Pre-interpolation loss behavior in neural networks 

    Venter, Arthur Edgar William; Theunissen, Marthinus Wilhelm; Davel, Marelie Hattingh (Springer, 2020)
    When training neural networks as classifiers, it is common to observe an increase in average test loss while still maintaining or improving the overall classification accuracy on the same dataset. In spite of the ubiquity ...
  • The South African directory enquiries (SADE) name corpus 

    Thirion, Jan Willem Frederick; Van Heerden, Charl Johannes; Giwa, Oluwapelumi; Davel, Marelie Hattingh (Springer, 2020)
    We present the design and development of a South African directory enquiries (DE) corpus. It contains audio and orthographic transcriptions of a wide range of South African names produced by first language speakers of four ...
  • Optimising word embeddings for recognised multilingual speech 

    Barnard, Etienne; Heyns, Nuette (Southern African Conference for Artificial Intelligence Research, 2020)
    Word embeddings are widely used in natural language processing (NLP) tasks. Most work on word embeddings focuses on monolingual languages with large available datasets. For embeddings to be useful in a multilingual ...
  • Classifying recognised speech with deep neural networks 

    Strydom, Rhyno A; Barnard, Etienne (Southern African Conference for Artificial Intelligence Research, 2020)
    We investigate whether word embeddings using deep neural networks can assist in the analysis of text produced by a speechrecognition system. In particular, we develop algorithms to identify which words are incorrectly ...
  • Exploring neural network training dynamics through binary node activations 

    Haasbroek, Daniël G.; Davel, Marelie H. (Southern African Conference for Artificial Intelligence Research, 2020)
    Each node in a neural network is trained to activate for a specific region in the input domain. Any training samples that fall within this domain are therefore implicitly clustered together. Recent work has highlighted ...
  • Using a meta-model to compensate for training-evaluation mismatches 

    Lamprecht, Dylan; Barnard, Etienne (Southern African Conference for Artificial Intelligence Research, 2020)
    One of the fundamental assumptions of machine learning is that learnt models are applied to data that is identically distributed to the training data. This assumption is often not realistic: for example, data collected ...
  • Chitosan composite biomaterials for bone tissue engineering: a review 

    Fourie, Jaundrie; Du Preez, Louis; Taute, Francois; De Beer, Deon (Springer, 2020)
    The bone is a highly dynamic tissue with the remarkable ability to remodel and is in a continuous cycle of resorption and renewal as a result of internal mediators and external mechanical demands. Researchers have doubled ...
  • Genetic fuzzy rule extraction for optimised sizing and control of hybrid renewable energy hydrogen systems 

    Human, G.; Van Schoor, G.; Uren, K.R. (Elsevier, 2020)
    A major challenge related to the design of a hybrid renewable energy hydrogen system is which energy sources to include and at what capacity, for regionally different potentials of renewable energy and hydrogen demand. In ...
  • Prevalence of dyslipidaemia among type 2 diabetes mellitus patients in the Western Cape, South Africa 

    Omodanisi, Elizabeth I.; Ntwampe, Seteno K.O.; Tomose, Yibanathi; Okeleye, Benjamin I.; Aboua, Yapo G. (MDPI, 2020)
    Dyslipidaemia, an irregular aggregate of lipids in the blood is common in diabetes and cardiovascular disease sufferers. A cross-sectional study on the prevalence of dyslipidaemia was performed among type 2 diabetes mellitus ...
  • Performance evaluation and kinetic modeling of down-flow high-rate anaerobic bioreactors for poultry slaughterhouse wastewater treatment 

    Njoya, Mahomet; Ntwampe, Seteno Karabo Obed; Basitere, Moses; Lim, Jun Wei (Springer, 2020)
    In this study, the treatment of poultry slaughterhouse wastewater (PSW) was evaluated using two new down-flow high-rate anaerobic bioreactor systems (HRABS), including the down-flow expanded granular bed reactor (DEGBR) ...
  • Equilibrium, kinetic, and thermodynamic studies of lead ion adsorption from mine wastewater onto MoS2-clinoptilolite composite 

    Pandey, S.; Fosso-Kankeu, E.; Waanders, F.; Spiro, M.J.; Kumar, N. (Elsevier, 2020)
    This work explored the potential of clinoptilolite, molybdenum sulphide (MoS2), and MoS2-clinoptilolite composite in lead (Pb) removal from aqueous medium and industrial mining wastewater. MoS2-clinoptilolite composite was ...
  • On the influence of methanol addition on the performances of PEM fuel cells operated at subzero temperatures 

    Ivanova, Nataliya A.; Grigoriev, Sergey A.; Spasov, Dmitry D.; Kamyshinsky, Roman A.; Peters, Georgy S. (Elsevier, 2020)
    The storage and the operation of proton exchange membrane fuel cells (PEM FCs) at subzero temperatures result in the ageing and degradation of membrane-electrode assemblies (MEAs). In this study, we investigated the effect ...
  • Investigation of structural and optical properties of biosynthesized Zincite (ZnO) nanoparticles (NPs) via an aqueous extract of Rosmarinus officinalis (rosemary) leaves 

    Noukelag, S.K.; Ntwampe, S.K.O.; Mohamed, H.E.A.; Moussa, B.; Razanamahandry, L.C. (Cambridge Univ Press, 2020)
    Biosynthesized Zincite nanoparticles have been successfully demonstrated by a completely green process mediated aqueous extract of rosemary leaves acting as both reducing and stabilizing agents and zinc nitrate hexahydrate ...
  • Development of a low purity aluminum alloy (Al6082) anodization process and its application as a platinum-based catalyst in catalytic hydrogen combustion 

    Kozhukhova, A.E.; Du Preez, S.P.; Shuro, I.; Bessarabov, D.G. (Elsevier, 2020)
    In this study, we present a process to prepare a Pt/anodized aluminum oxide (AAO) catalyst for catalytic hydrogen combustion applications using low cost and low aluminum (Al) purity Al6082 alloy (97.5% Al) as the substrate. ...

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