• MalFe—malware feature engineering generation platform 

  Singh, Avinash; Ikuesan, Richard Adeyemi; Venter, H.S. (Hein) (MDPI, 2023-10-08)

  The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have ...
• A review and comparative study of cancer detection using machine learning : SBERT and SimCSE application 

  Mokoatle, Mpho; Marivate, Vukosi; Mapiye, Darlington; Bornman, Maria S. (Riana); Hayes, Vanessa M. (BMC, 2023-03-23)

  BACKGROUND : Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the ...
• Machine learning approaches identify chemical features for stage-specific antimalarial compounds 

  Van Heerden, Ashleigh; Turon, Gemma; Duran-Frigola, Miquel; Pillay, Nelishia; Birkholtz, Lyn-Marie (American Chemical Society, 2023-11-07)

  Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite Plasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a ...
• Max-SAT-based synthesis of optimal and Nash equilibrium strategies for multi-agent systems 

  Timm, Nils; Botha, Josua; Jordaan, Steve (Elsevier, 2023-06)

  We present techniques for verifying strategic abilities of multi-agent systems via SAT-based and Max-SAT-based bounded model checking. In our approach we focus on systems of agents that pursue goals with regard to the ...
• Decision-maker’s preference-driven dynamic multi-objective optimization 

  Adekoya, Adekunle Rotimi; Helbig, Marde (MDPI, 2023-10)

  Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two ...
• Smart face masks for Covid-19 pandemic management : a concise review of emerging architectures, challenges and future research directions 

  Fagbola, Temitayo Matthew; Fagbola, Funmilola Ikeolu; Aroba, Oluwasegun Julius; Doshi, Ruchi; Hiran, Kamal Kant; Thakur, Surendra Colin (Institute of Electrical and Electronics Engineers, 2023-01)

  Smart sensing technology has been playing tremendous roles in digital healthcare management over time with great impacts. Lately, smart sensing has awoken the world by the advent of smart face masks (SFMs) in the global ...
• Machine-learning forensics : state of the art in the use of machine-learning techniques for digital forensic investigations within smart environments 

  Tageldin, Laila; Venter, H.S. (Hein) (MDPI, 2023-09)

  Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, ...
• Understanding issues and challenges of DFR implementation in SDN platform 

  Munkhondya, Howard; Ikuesan, Richard Adeyemi; Singh, Avinash; Venter, H.S. (Hein) (Elsevier, 2023)

  Software-Defined Networking (SDN) is an evolutionary networking paradigm that offers simplified and agile network configuration and management capabilities. However, embracing this new and futuristic paradigm requires the ...
• The effect of deep learning methods on deepfake audio detection for digital investigation 

  Mcuba, Mvelo; Singh, Avinash; Ikuesan, Richard Adeyemi; Venter, H.S. (Hein) (Elsevier, 2023)

  Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and ...
• An application of CNN to classify barchan dunes into asymmetry classes 

  Van der Merwe, Barend Jacobus; Pillay, Nelishia; Coetzee, Serena Martha (Elsevier, 2022-06)

  Barchan morphometric data have been used as proxies of meteorological and topographical data in environments where this data is lacking (such as other planetary bodies), gaining insights into barchan dune field dynamics ...
• Discriminatory Gleason grade group signatures of prostate cancer : an application of machine learning methods 

  Mokoatle, Mpho; Mapiye, Darlington; Marivate, Vukosi; Hayes, Vanessa M.; Bornman, Maria S. (Riana) (Public Library of Science, 2022-06-09)

  One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological ...
• Smart microgrid energy market : evaluating distributed ledger technologies for remote and constrained microgrid deployments 

  Ledwaba, Lehlogonolo P.I.; Hancke, Gerhard P.; Isaac, Sherrin J.; Venter, H.S. (Hein) (MDPI, 2021-03-18)

  The increasing strain on ageing generation infrastructure has seen more frequent instances of scheduled and unscheduled blackouts, rising reliability on fossil fuel based energy alternatives and a slow down in efforts ...
• A comparative study of nonlinear regression and autoregressive techniques in hybrid with particle swarm optimization for time-series forecasting 

  Kuranga, Cry; Pillay, Nelishia (Elsevier, 2022-03)

  Usually, real-world time-series forecasting problems are dynamic. If such time-series are characterized by mere concept shifts, a passive approach to learning become ideal to continuously adapt the model parameters whenever ...
• Why is this an anomaly? Explaining anomalies using sequential explanations 

  Mokoena, Tshepiso; Celik, Turgay; Marivate, Vukosi (Elsevier, 2022-01)

  In most applications, anomaly detection operates in an unsupervised mode by looking for outliers hoping that they are anomalies. Unfortunately, most anomaly detectors do not come with explanations about which features make ...
• Feature selection using Benford’s law to support detection of malicious social media bots 

  Mbona, Innocent; Eloff, Jan H.P. (Elsevier, 2022-01)

  The increased amount of high-dimensional imbalanced data in online social networks challenges existing feature selection methods. Although feature selection methods such as principal component analysis (PCA) are effective ...
• On computer simulations, with particular regard to their application in contemporary astrophysics 

  Gruner, Stefan; Bartelmann, Matthias (Cosmos and History Publishing Co-op, 2020-10)

  In this survey contribution, which extends [GBa19], we consider and discuss computer simulations from a variety of perspectives with particular attention to computer simulations in astrophysics and cosmology. We begin by ...
• Model checking safety and liveness via k-induction and witness refinement with constraint generation 

  Timm, Nils; Gruner, Stefan; Nxumalo, Madoda; Botha, Josua (Elsevier, 2020-12)

  In this article, we revise our constraint-based abstraction refinement technique for checking temporal logic properties of concurrent software systems. Our technique employs predicate abstraction and SAT-based three-valued ...
• Visualising basins of attraction for the cross-entropy and the squared error neural network loss functions 

  Bosman, Anna Sergeevna; Engelbrecht, Andries; Helbig, Marde (Elsevier, 2020-08)

  Quantification of the stationary points and the associated basins of attraction of neural network loss surfaces is an important step towards a better understanding of neural network loss surfaces at large. This work proposes ...
• Nonlinear regression in dynamic environments using particle swarm optimization 

  Kuranga, Cry; Pillay, Nelishia (Springer, 2020-11)

  This paper extends a PSO-based nonlinear regression technique to dynamic environments whereby the induced model dynamically adjusts when an environmental change is detected. As such, this work hybridizes a PSO designed for ...
• A study of bi-space search for solving the one-dimensional bin packing problem 

  Beckedahl, Derrick; Pillay, Nelishia (Springer, 2020-10)

  Traditionally search techniques explore a single space to solve the problem at hand. This paper investigates performing search across more than one space which we refer to as bi-space search. As a proof of concept we ...

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