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Latest Research Publications:
Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.
@article{488, author = {C. Sue Price, Deshen Moodley, Anban Pillay, Gavin Rens}, title = {An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making}, abstract = {Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.}, year = {2022}, journal = {South African Computer Journal}, volume = {34}, pages = {152-191}, issue = {1}, url = {https://sacj.cs.uct.ac.za/index.php/sacj/article/view/857}, doi = {https://doi.org/10.18489/sacj.v34i1.857}, }
Depth cameras provide a natural and intuitive user interaction mechanism in virtual reality environments by using hand gestures as the primary user input. However, building robust VR systems that use depth cameras are challenging. Gesture recognition accuracy is affected by occlusion, variation in hand orientation and misclassification of similar hand gestures. This research explores the limits of the Leap Motion depth camera for static hand pose recognition in virtual reality applications. We propose a system for analysing static hand poses and for systematically identifying a pose set that can achieve a near-perfect recognition accuracy. The system consists of a hand pose taxonomy, a pose notation, a machine learning classifier and an algorithm to identify a reliable pose set that can achieve near perfect accuracy levels. We used this system to construct a benchmark hand pose data set containing 2550 static hand pose instances, and show how the algorithm can be used to systematically derive a set of poses that can produce an accuracy of 99% using a Support Vector Machine classifier.
@{379, author = {Andrew Clark, Anban Pillay, Deshen Moodley}, title = {A system for pose analysis and selection in virtual reality environments}, abstract = {Depth cameras provide a natural and intuitive user interaction mechanism in virtual reality environments by using hand gestures as the primary user input. However, building robust VR systems that use depth cameras are challenging. Gesture recognition accuracy is affected by occlusion, variation in hand orientation and misclassification of similar hand gestures. This research explores the limits of the Leap Motion depth camera for static hand pose recognition in virtual reality applications. We propose a system for analysing static hand poses and for systematically identifying a pose set that can achieve a near-perfect recognition accuracy. The system consists of a hand pose taxonomy, a pose notation, a machine learning classifier and an algorithm to identify a reliable pose set that can achieve near perfect accuracy levels. We used this system to construct a benchmark hand pose data set containing 2550 static hand pose instances, and show how the algorithm can be used to systematically derive a set of poses that can produce an accuracy of 99% using a Support Vector Machine classifier.}, year = {2020}, journal = {SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020}, pages = {210-216}, month = {14/09/2020}, publisher = {ACM Digital Library}, address = {Virtual}, isbn = {978-1-4503-8847-4}, url = {https://dl.acm.org/doi/proceedings/10.1145/3410886}, }
A dynamic Bayesian decision network was developed to model the preharvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was validated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a prerun scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain.
@{244, author = {C. Sue Price, Deshen Moodley, Anban Pillay}, title = {Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers}, abstract = {A dynamic Bayesian decision network was developed to model the preharvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was validated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a prerun scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain.}, year = {2019}, journal = {Forum for Artificial Intelligence Research (FAIR2019)}, pages = {145-160}, month = {4/12-6/12}, publisher = {CEUR}, address = {Cape Town}, url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_53.pdf}, }
Aim/Purpose
The aim of this project was to explore models for stimulating health
informatics innovation and capacity development in South Africa.
Background
There is generally a critical lack of health informatics innovation and capacity in South Africa and sub-Saharan Africa. This is despite the wide anticipation that digital health systems will play a fundamental role in strengthening health systems and improving service delivery
Methodology
We established a program over four years to train Masters and Doctoral students and conducted research projects across a wide range of biomedical and health informatics technologies at a leading South African university. We also developed a Health Architecture Laboratory Innovation and Development Ecosystem (HeAL-IDE) designed to be a long-lasting and potentially reproducible output of the project.
Contribution
We were able to demonstrate a successful model for building innovation and capacity in a sustainable way. Key outputs included: (i)a successful partnership model; (ii) a sustainable HeAL-IDE; (iii) research papers; (iv) a world-class software product and several
demonstrators; and (iv) highly trained staff.
Findings
Our main findings are that: (i) it is possible to create a local ecosystem for innovation and capacity building that creates value for the partners (a university and a private non-profit company); (ii) the ecosystem is able to create valuable outputs that would be much less likely to have been developed singly by each partner, and; (iii) the ecosystem could serve as a powerful model for adoption in other settings.
Recommendations for Practitioners
Non-profit companies and non-governmental organizations implementing health information systems in South Africa and other low resource settings have an opportunity to partner with local universities for purposes of internal capacity development and assisting with the research, reflection and innovation aspects of their projects and programmes.
Recommendation for Researchers
Applied health informatics researchers working in low resource settings could productively partner with local implementing organizations in order to gain a better understanding of the challenges and requirements at field sites and to accelerate the testing and deployment of health information technology solutions.
Impact on Society
This research demonstrates a model that can deliver valuable software products for public health.
Future Research
It would be useful to implement the model in other settings and research whether the model is more generally useful
@{252, author = {Deshen Moodley, Anban Pillay, Chris Seebregts}, title = {Establishing a Health Informatics Research Laboratory in South Africa}, abstract = {Aim/Purpose The aim of this project was to explore models for stimulating health informatics innovation and capacity development in South Africa. Background There is generally a critical lack of health informatics innovation and capacity in South Africa and sub-Saharan Africa. This is despite the wide anticipation that digital health systems will play a fundamental role in strengthening health systems and improving service delivery Methodology We established a program over four years to train Masters and Doctoral students and conducted research projects across a wide range of biomedical and health informatics technologies at a leading South African university. We also developed a Health Architecture Laboratory Innovation and Development Ecosystem (HeAL-IDE) designed to be a long-lasting and potentially reproducible output of the project. Contribution We were able to demonstrate a successful model for building innovation and capacity in a sustainable way. Key outputs included: (i)a successful partnership model; (ii) a sustainable HeAL-IDE; (iii) research papers; (iv) a world-class software product and several demonstrators; and (iv) highly trained staff. Findings Our main findings are that: (i) it is possible to create a local ecosystem for innovation and capacity building that creates value for the partners (a university and a private non-profit company); (ii) the ecosystem is able to create valuable outputs that would be much less likely to have been developed singly by each partner, and; (iii) the ecosystem could serve as a powerful model for adoption in other settings. Recommendations for Practitioners Non-profit companies and non-governmental organizations implementing health information systems in South Africa and other low resource settings have an opportunity to partner with local universities for purposes of internal capacity development and assisting with the research, reflection and innovation aspects of their projects and programmes. Recommendation for Researchers Applied health informatics researchers working in low resource settings could productively partner with local implementing organizations in order to gain a better understanding of the challenges and requirements at field sites and to accelerate the testing and deployment of health information technology solutions. Impact on Society This research demonstrates a model that can deliver valuable software products for public health. Future Research It would be useful to implement the model in other settings and research whether the model is more generally useful}, year = {2018}, journal = {Digital Re-imagination Colloquium 2018}, pages = {16 - 24}, month = {13/03 - 15/03}, publisher = {NEMISA}, isbn = {978-0-6399275-0-3}, url = {http://uir.unisa.ac.za/bitstream/handle/10500/25615/Digital%20Skills%20Proceedings%202018.pdf?sequence=1&isAllowed=y}, }
This paper proposes an improved Generalized Regression Neural Network (KGRNN) for the diagnosis of type II diabetes. Dia- betes, a widespread chronic disease, is a metabolic disorder that develops when the body does not make enough insulin or is unable to use insulin effectively. Type II diabetes is the most common type and accounts for an estimated 90% of cases. The novel KGRNN technique reported in this study uses an enhanced K-Means clustering technique (CVE-K-Means) to produce cluster centers (centroids) that are used to train the network. The technique was applied to the Pima Indian diabetes dataset, a widely used benchmark dataset for Diabetes diagnosis. The technique outper- forms the best known GRNN techniques for Type II diabetes diagnosis in terms of classification accuracy and computational time and obtained a classification accuracy of 86% with 83% sensitivity and 87% specificity. The Area Under the Receiver Operating Characteristic Curve (ROC) of 87% was obtained.
@inbook{195, author = {Moeketsi Ndaba, Anban Pillay, Absalom Ezugwu}, title = {An Improved Generalized Regression Neural Network for Type II Diabetes Classification}, abstract = {This paper proposes an improved Generalized Regression Neural Network (KGRNN) for the diagnosis of type II diabetes. Dia- betes, a widespread chronic disease, is a metabolic disorder that develops when the body does not make enough insulin or is unable to use insulin effectively. Type II diabetes is the most common type and accounts for an estimated 90% of cases. The novel KGRNN technique reported in this study uses an enhanced K-Means clustering technique (CVE-K-Means) to produce cluster centers (centroids) that are used to train the network. The technique was applied to the Pima Indian diabetes dataset, a widely used benchmark dataset for Diabetes diagnosis. The technique outper- forms the best known GRNN techniques for Type II diabetes diagnosis in terms of classification accuracy and computational time and obtained a classification accuracy of 86% with 83% sensitivity and 87% specificity. The Area Under the Receiver Operating Characteristic Curve (ROC) of 87% was obtained.}, year = {2018}, journal = {ICCSA 2018, LNCS 10963}, edition = {10963}, pages = {659-671}, publisher = {Springer International Publishing AG}, isbn = {3319951718}, }
Latest Research Publications:

Latest Research Publications:

Latest Research Publications:

Latest Research Publications:
Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.
@article{488, author = {C. Sue Price, Deshen Moodley, Anban Pillay, Gavin Rens}, title = {An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making}, abstract = {Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.}, year = {2022}, journal = {South African Computer Journal}, volume = {34}, pages = {152-191}, issue = {1}, url = {https://sacj.cs.uct.ac.za/index.php/sacj/article/view/857}, doi = {https://doi.org/10.18489/sacj.v34i1.857}, }
We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual
@inbook{478, author = {Tezira Wanyana, Mbithe Nzomo, C. Sue Price, Deshen Moodley}, title = {Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation}, abstract = {We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual}, year = {2022}, journal = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE}, pages = {81-92}, publisher = {SciTePress}, address = {INSTICC}, isbn = {978-989-758-566-1}, doi = {https://doi.org/10.5220/0011046100003188}, }
Universities supply free Wi-Fi to registered students on campus to access learning materials. Many issues could reduce the quality of students' Wi-Fi use, e.g., devices using different Wi-Fi standards than those used on campus, and numerous students accessing Wi-Fi through a single access point simultaneously. Understanding where, when and how students use Wi-Fi on campus can help IT administrators to provide an adequate Wi-Fi service. This pre-COVID study adopted a mixed method approach. Questionnaires were completed by a representative sample of 373 students on the Westville campus of the University of KwaZulu-Natal, South Africa. Two Information Communication Services (lCS) staff members were interviewed to gain insights into how the Wi-Fi was set up, and their perspectives on how students utilise the Wi-Fi. The questionnaire data were analysed statistically, and interview results were used to explain results. The students' most used venues, and the places they encountered poor and best Wi-Fi signal quality, are presented, along with the durations of use and problems encountered. When analysing these results by students' residence type, each category showed a different pattern of use. These results can help IT administrators understand where to improve the Wi-Fi quality, if necessary. It is particularly useful if some categories of students return to campus, while others do not, e.g. as COVID-19 lockdowns ease.
@{424, author = {V. Mbonye, C. Sue Price}, title = {Students’ use of on-campus wireless networks: Analysis by residence type}, abstract = {Universities supply free Wi-Fi to registered students on campus to access learning materials. Many issues could reduce the quality of students' Wi-Fi use, e.g., devices using different Wi-Fi standards than those used on campus, and numerous students accessing Wi-Fi through a single access point simultaneously. Understanding where, when and how students use Wi-Fi on campus can help IT administrators to provide an adequate Wi-Fi service. This pre-COVID study adopted a mixed method approach. Questionnaires were completed by a representative sample of 373 students on the Westville campus of the University of KwaZulu-Natal, South Africa. Two Information Communication Services (lCS) staff members were interviewed to gain insights into how the Wi-Fi was set up, and their perspectives on how students utilise the Wi-Fi. The questionnaire data were analysed statistically, and interview results were used to explain results. The students' most used venues, and the places they encountered poor and best Wi-Fi signal quality, are presented, along with the durations of use and problems encountered. When analysing these results by students' residence type, each category showed a different pattern of use. These results can help IT administrators understand where to improve the Wi-Fi quality, if necessary. It is particularly useful if some categories of students return to campus, while others do not, e.g. as COVID-19 lockdowns ease.}, year = {2021}, journal = {2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)}, month = {5-6/08}, publisher = {IEEE}, address = {Durban, South Africa}, isbn = {978-1-7281-8591-0}, url = {https://ieeexplore.ieee.org/document/9519327}, doi = {https://doi.org/10.1109/icABCD51485.2021.9519327}, }
A dynamic Bayesian decision network was developed to model the preharvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was validated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a prerun scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain.
@{244, author = {C. Sue Price, Deshen Moodley, Anban Pillay}, title = {Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers}, abstract = {A dynamic Bayesian decision network was developed to model the preharvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was validated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a prerun scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain.}, year = {2019}, journal = {Forum for Artificial Intelligence Research (FAIR2019)}, pages = {145-160}, month = {4/12-6/12}, publisher = {CEUR}, address = {Cape Town}, url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_53.pdf}, }
Understanding how satisfied users are with services is very important in the delivery of quality services and in improving them. While studies have investigated perceptions of Wi-Fi among students, there is still a gap in understanding the overall perception of quality of service in terms of the different factors that may affect Wi-Fi service quality. Brady & Cronin Jr’s service quality model proposes that outcome quality, physical environment quality and interaction quality affect service quality. Sub-constructs for the independent variables were generated, and Likert-scale items developed for each sub-construct, based on the literature. 373 questionnaires were administered to University of KwaZulu-Natal (UKZN) Westville campus students. Factor analysis was to confirm the sub-constructs. Multiple regression analysis was used to test the model’s ability to predict Wi-Fi service quality.
Of the three independent constructs, the outcome quality mean had the highest value (4.53), and it was similar to how the students rated service quality (4.52). All the constructs were rated at above the neutral score of 4. In the factor analysis, two physical environment quality items were excluded, and one service quality item was categorised with the expertise sub-construct of interaction quality. Using multiple regression analysis, the model showed that the independent constructs predict service quality with an R2 of 59.5%. However, when models for individual most-used locations (the library and lecture venues) were conducted, the R2 improved. The model can be used to understand users’ perceptions of outcome quality, physical environment quality and interaction quality which influence the quality of Wi-Fi performance, and evaluate the Wi-Fi performance quality of different locations.
@{217, author = {V. Mbonye, C. Sue Price}, title = {A model to evaluate the quality of Wi-Fi perfomance: Case study at UKZN Westville campus}, abstract = {Understanding how satisfied users are with services is very important in the delivery of quality services and in improving them. While studies have investigated perceptions of Wi-Fi among students, there is still a gap in understanding the overall perception of quality of service in terms of the different factors that may affect Wi-Fi service quality. Brady & Cronin Jr’s service quality model proposes that outcome quality, physical environment quality and interaction quality affect service quality. Sub-constructs for the independent variables were generated, and Likert-scale items developed for each sub-construct, based on the literature. 373 questionnaires were administered to University of KwaZulu-Natal (UKZN) Westville campus students. Factor analysis was to confirm the sub-constructs. Multiple regression analysis was used to test the model’s ability to predict Wi-Fi service quality. Of the three independent constructs, the outcome quality mean had the highest value (4.53), and it was similar to how the students rated service quality (4.52). All the constructs were rated at above the neutral score of 4. In the factor analysis, two physical environment quality items were excluded, and one service quality item was categorised with the expertise sub-construct of interaction quality. Using multiple regression analysis, the model showed that the independent constructs predict service quality with an R2 of 59.5%. However, when models for individual most-used locations (the library and lecture venues) were conducted, the R2 improved. The model can be used to understand users’ perceptions of outcome quality, physical environment quality and interaction quality which influence the quality of Wi-Fi performance, and evaluate the Wi-Fi performance quality of different locations.}, year = {2019}, journal = {2nd International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD 2019)}, pages = {291-297}, month = {05/08 - 06/08}, publisher = {IEEE}, address = {Danvers MA}, isbn = {978-1-5386-9235-6}, }

Under the supervision of Dr Hussein Suleman, I conducted research on information systems for my Honours degree, and we successfully implemented the first South African National Heritage Portal Prototype using metadata aggregation. Furthermore, my Masters degree research focuses on machine learning, specifically the topic of "Automated Machine Learning for Solving the Combined Algorithm Search and Hyperparameter Optimisation Problem in Trend Prediction".
Latest Research Publications:

Latest Research Publications:

She has a PhD in Philosophy in the domains of mathematical logic and the philosophy of science. Her thesis focused on formulating a mathematical model-theoretic analysis of the structure of scientific theories. Currently, she works on themes in the philosophy of technology relating to human-technology relations, and in AI ethics on themes in machine ethics, the ethics of social robotics, and data ethics. She also does research on technology-related policy making, and focuses on generating culturally sensitive policies for trustworthy AI technologies, while aiming for global regulation. In the philosophy of science, her work is centred on debates in scientific realism, the structure of scientific theories, and the status of machine learning-based methodologies in the discovery/justification debate in the philosophy of science. Her research in both the ethics of artificial intelligence and the philosophy of science includes application of non-classical formal logics to selected problems.
Prof Ruttkamp-Bloem is a corresponding member of the International Academy for the Philosophy of Science. She has been the elected South African representative at the International Union of the History and Philosophy of Science and Technology (IUHPST) since 2014. She is an associate editor of the Journal for Science and Engineering Ethics, a member of the editorial board of Springer’s respected Synthese Library Book Series, and a member of the editorial board of Acta Baltica: Historiae et Philosophiae Scientiarum. She is the founder of the CAIR/UP ‘Artificial Intelligence for Society’ Symposium Series and the ‘South African Logic and Philosophy of Science’ Colloquium Series.
She currently has 6 current PhD, 2 MA, and 2 honours students and has delivered 5 PhD, 9 MA, and 14 Honours students. She is the author of a book in the Springer Synthese Library Series, 15 articles, and 5 book chapters. She has collaborated on 5 intergovernmental policy documents. She has been interviewed on various media platforms such as the Deutsche Welle (Germany), Tortoise Media (UK) and Business Day TV (South Africa), and has recently written an OpEd for the Mail and Guardian on the technology driving inequality, as well as a piece for Acteurs Publics in France.
In her capacity as an AI ethics policy researcher, Prof Ruttkamp-Bloem is a member of the African Union Development Agency (AUDA)-NEPAD Consultative Roundtable on Ethics in Africa, a member of the African Commission Human and People’s Rights Committee (ACHPR) task team working on the Resolution 473 study on Human and Peoples’ Rights and AI, Robotics and other New and Emerging Technologies in Africa, and the rapporteur for the UNESCO World Commission for Ethics of Scientific Knowledge and Technology (COMEST). She was the chairperson of the Bureau of the UNESCO Ad Hoc Expert Group (AHEG) on the ethics of artificial intelligence tasked to draft the Recommendation for a global instrument on the ethics of AI which was adopted by UNESCO member states after various periods of negotiation in November 2021. She is a current member of the AHEG working on implementing the Recommendation.
In addition, Prof Ruttkamp-Bloem is the South African representative at the Responsible AI Network Africa (RAIN), which is a joint venture of the Technical University Munich and the Kwame Nkrumah University of Science and Technology in Ghana. She is a member of the advisory board of the Wallenberg AI, Autonomous Systems and Software Programme (Human Sciences) hosted by Umeå University in Sweden; of the advisory board of the Global AI Ethics Institute; and, as country advisor for South Africa, of the advisory board of the International Group of Artificial Intelligence (IGOAI). She is also a member of the advisory board of the international Z-Inspection® network. The Z-Inspection® assessment method for Trustworthy AI is an approach based on the Ethics Guidelines for Trustworthy AI by the European Commission High-Level Expert Group on Artificial Intelligence. She is also on the advisory board for SAP GE in Germany. Prof Ruttkamp-Bloem is a collaborator on the IEEE’s (Institute of Electrical and Electronics Engineers) Strong Sustainability by Design and Accountable Sustainability by Design reports as part of their Planet Positive 2030 Campaign. She is also a collaborating fellow at the International Research Center for AI Ethics and Governance, Chinese Academy of Sciences. She is the co-convener of the Ethics Working Group at their AI for Atoms Technical Meeting in 2021 at the International Atomic Energy Agency (IAEA) as well as at future IAEA-ITU events. Finally, she is a member of the GAIA (Global AI Association) Think Tank on Compassionate AI.
Latest Research Publications:
In the face of the fact that AI ethics guidelines currently, on the whole, seem to have no significant impact on AI practices, the quest of AI ethics to ensure trustworthy AI is in danger of becoming nothing more than a nice ideal. Serious work is to be done to ensure AI ethics guidelines are actionable. To this end, in this paper, I argue that AI ethics should be approached 1) in a multi-disciplinary manner focused on concrete research in the discipline of the ethics of AI and 2) as a dynamic system on the basis of virtue ethics in order to work towards enabling all AI actors to take responsibility for their own actions and to hold others accountable for theirs. In conclusion, the paper emphasises the importance of understanding AI ethics as playing out on a continuum of interconnected interests across academia, civil society, public policy-making and the private sector, and a novel notion of ‘AI ethics capital’ is put on the table as outcome of actionable AI ethics and essential ingredient for sustainable trustworthy AI.
@article{384, author = {Emma Ruttkamp-Bloem}, title = {The Quest for Actionable AI Ethics}, abstract = {In the face of the fact that AI ethics guidelines currently, on the whole, seem to have no significant impact on AI practices, the quest of AI ethics to ensure trustworthy AI is in danger of becoming nothing more than a nice ideal. Serious work is to be done to ensure AI ethics guidelines are actionable. To this end, in this paper, I argue that AI ethics should be approached 1) in a multi-disciplinary manner focused on concrete research in the discipline of the ethics of AI and 2) as a dynamic system on the basis of virtue ethics in order to work towards enabling all AI actors to take responsibility for their own actions and to hold others accountable for theirs. In conclusion, the paper emphasises the importance of understanding AI ethics as playing out on a continuum of interconnected interests across academia, civil society, public policy-making and the private sector, and a novel notion of ‘AI ethics capital’ is put on the table as outcome of actionable AI ethics and essential ingredient for sustainable trustworthy AI.}, year = {2020}, journal = {Communications in Computer and Information Science}, volume = {1342}, pages = {34-52}, publisher = {Springer}, isbn = {978-3-030-66151-9}, url = {https://link.springer.com/chapter/10.1007/978-3-030-66151-9_3}, doi = {https://doi.org/10.1007/978-3-030-66151-9_3}, }
This Introduction has two foci: the first is a discussion of the motivation for and the aims of the 2014 conference on New Thinking about Scientific Realism in Cape Town South Africa, and the second is a brief contextualization of the contributed articles in this special issue of Synthese in the framework of the conference. Each focus is discussed in a separate section.
@article{416, author = {Stathis Psillos, Emma Ruttkamp-Bloem}, title = {Scientific realism: quo vadis? Introduction: new thinking about scientific realism}, abstract = {This Introduction has two foci: the first is a discussion of the motivation for and the aims of the 2014 conference on New Thinking about Scientific Realism in Cape Town South Africa, and the second is a brief contextualization of the contributed articles in this special issue of Synthese in the framework of the conference. Each focus is discussed in a separate section.}, year = {2017}, journal = {Synthese}, volume = {194}, pages = {3187-3201}, issue = {4}, publisher = {Springer}, isbn = {0039-7857 , 1573-0964}, doi = {10.1007/s11229-017-1493-x}, }
"Naturalised realism" is presented as a version of realism which is more compatible with the history of science than convergent or explanationist forms of realism. The account is unpacked according to four theses : 1) Whether realism is warranted with regards to a particular theory depends on the kind and quality of evidence available for that theory; 2) Reference is about causal interaction with the world; 3) Most of science happens somewhere in between instrumentalism and scientific realism on a continuum of stances towards the status of theories; 4) The degree to which realism is warranted has something to do with the degree to which theories successfully refer, rather than with the truth of theories.
@article{417, author = {Emma Ruttkamp-Bloem}, title = {Repositioning realism}, abstract = {"Naturalised realism" is presented as a version of realism which is more compatible with the history of science than convergent or explanationist forms of realism. The account is unpacked according to four theses : 1) Whether realism is warranted with regards to a particular theory depends on the kind and quality of evidence available for that theory; 2) Reference is about causal interaction with the world; 3) Most of science happens somewhere in between instrumentalism and scientific realism on a continuum of stances towards the status of theories; 4) The degree to which realism is warranted has something to do with the degree to which theories successfully refer, rather than with the truth of theories.}, year = {2015}, journal = {Philosphia Scientiæ}, volume = {19}, pages = {85-98}, issue = {1}, publisher = {Université Nancy 2}, isbn = {1281-2463}, doi = {10.4000/philosophiascientiae.1042}, }

Latest Research Publications:
Aim/Purpose
The aim of this project was to explore models for stimulating health
informatics innovation and capacity development in South Africa.
Background
There is generally a critical lack of health informatics innovation and capacity in South Africa and sub-Saharan Africa. This is despite the wide anticipation that digital health systems will play a fundamental role in strengthening health systems and improving service delivery
Methodology
We established a program over four years to train Masters and Doctoral students and conducted research projects across a wide range of biomedical and health informatics technologies at a leading South African university. We also developed a Health Architecture Laboratory Innovation and Development Ecosystem (HeAL-IDE) designed to be a long-lasting and potentially reproducible output of the project.
Contribution
We were able to demonstrate a successful model for building innovation and capacity in a sustainable way. Key outputs included: (i)a successful partnership model; (ii) a sustainable HeAL-IDE; (iii) research papers; (iv) a world-class software product and several
demonstrators; and (iv) highly trained staff.
Findings
Our main findings are that: (i) it is possible to create a local ecosystem for innovation and capacity building that creates value for the partners (a university and a private non-profit company); (ii) the ecosystem is able to create valuable outputs that would be much less likely to have been developed singly by each partner, and; (iii) the ecosystem could serve as a powerful model for adoption in other settings.
Recommendations for Practitioners
Non-profit companies and non-governmental organizations implementing health information systems in South Africa and other low resource settings have an opportunity to partner with local universities for purposes of internal capacity development and assisting with the research, reflection and innovation aspects of their projects and programmes.
Recommendation for Researchers
Applied health informatics researchers working in low resource settings could productively partner with local implementing organizations in order to gain a better understanding of the challenges and requirements at field sites and to accelerate the testing and deployment of health information technology solutions.
Impact on Society
This research demonstrates a model that can deliver valuable software products for public health.
Future Research
It would be useful to implement the model in other settings and research whether the model is more generally useful
@{252, author = {Deshen Moodley, Anban Pillay, Chris Seebregts}, title = {Establishing a Health Informatics Research Laboratory in South Africa}, abstract = {Aim/Purpose The aim of this project was to explore models for stimulating health informatics innovation and capacity development in South Africa. Background There is generally a critical lack of health informatics innovation and capacity in South Africa and sub-Saharan Africa. This is despite the wide anticipation that digital health systems will play a fundamental role in strengthening health systems and improving service delivery Methodology We established a program over four years to train Masters and Doctoral students and conducted research projects across a wide range of biomedical and health informatics technologies at a leading South African university. We also developed a Health Architecture Laboratory Innovation and Development Ecosystem (HeAL-IDE) designed to be a long-lasting and potentially reproducible output of the project. Contribution We were able to demonstrate a successful model for building innovation and capacity in a sustainable way. Key outputs included: (i)a successful partnership model; (ii) a sustainable HeAL-IDE; (iii) research papers; (iv) a world-class software product and several demonstrators; and (iv) highly trained staff. Findings Our main findings are that: (i) it is possible to create a local ecosystem for innovation and capacity building that creates value for the partners (a university and a private non-profit company); (ii) the ecosystem is able to create valuable outputs that would be much less likely to have been developed singly by each partner, and; (iii) the ecosystem could serve as a powerful model for adoption in other settings. Recommendations for Practitioners Non-profit companies and non-governmental organizations implementing health information systems in South Africa and other low resource settings have an opportunity to partner with local universities for purposes of internal capacity development and assisting with the research, reflection and innovation aspects of their projects and programmes. Recommendation for Researchers Applied health informatics researchers working in low resource settings could productively partner with local implementing organizations in order to gain a better understanding of the challenges and requirements at field sites and to accelerate the testing and deployment of health information technology solutions. Impact on Society This research demonstrates a model that can deliver valuable software products for public health. Future Research It would be useful to implement the model in other settings and research whether the model is more generally useful}, year = {2018}, journal = {Digital Re-imagination Colloquium 2018}, pages = {16 - 24}, month = {13/03 - 15/03}, publisher = {NEMISA}, isbn = {978-0-6399275-0-3}, url = {http://uir.unisa.ac.za/bitstream/handle/10500/25615/Digital%20Skills%20Proceedings%202018.pdf?sequence=1&isAllowed=y}, }
• Several different paradigms and standards exist for creating digital health architectures that
are mostly complementary, but sometimes contradictory.
• The potential benefits of using EA
approaches and tools are that they help to ensure the appropriate use of standards for
interoperability and data storage and exchange, and encourage the creation of reusable
software components and metadata.
@article{162, author = {Chris Seebregts, Anban Pillay, Ryan Crichton, S. Singh, Deshen Moodley}, title = {14 Enterprise Architectures for Digital Health}, abstract = {• Several different paradigms and standards exist for creating digital health architectures that are mostly complementary, but sometimes contradictory. • The potential benefits of using EA approaches and tools are that they help to ensure the appropriate use of standards for interoperability and data storage and exchange, and encourage the creation of reusable software components and metadata.}, year = {2017}, journal = {Global Health Informatics: Principles of eHealth and mHealth to Improve Quality of Care}, pages = {173-182}, publisher = {MIT Press}, isbn = {978-0262533201}, url = {https://books.google.co.za/books?id=8p-rDgAAQBAJ&pg=PA173&lpg=PA173&dq=14+Enterprise+Architectures+for+Digital+Health&source=bl&ots=i6SQzaXiPp&sig=zDLJ6lIqt3Xox3Lt5LNCuMkUoJ4&hl=en&sa=X&ved=0ahUKEwivtK6jxPDYAhVkL8AKHXbNDY0Q6AEINDAB#v=onepage&q=14%20Enterp}, }
Poor adherence to prescribed treatment is a major factor contributing to tuberculosis patients developing drug resistance and failing treatment. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that vary between regions and communities. Decision network models can potentially be used to predict treatment adherence behaviour. However, determining the network structure (identifying the factors and their causal relations) and the conditional probabilities is a challenging task. To resolve the former we developed an ontology supported by current scientific literature to categorise and clarify the similarity and granularity of factors
@{158, author = {Olukunle Ogundele, Deshen Moodley, Chris Seebregts, Anban Pillay}, title = {Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa}, abstract = {Poor adherence to prescribed treatment is a major factor contributing to tuberculosis patients developing drug resistance and failing treatment. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that vary between regions and communities. Decision network models can potentially be used to predict treatment adherence behaviour. However, determining the network structure (identifying the factors and their causal relations) and the conditional probabilities is a challenging task. To resolve the former we developed an ontology supported by current scientific literature to categorise and clarify the similarity and granularity of factors}, year = {2014}, journal = {4th International Symposium (FHIES 2014) and 6th International Workshop (SEHC 2014)}, pages = {81-95}, month = {17/07-18/07}, }
The development of drug resistance is a major factor imped- ing the efficacy of antiretroviral treatment of South Africa’s HIV infected population. While genotype resistance testing is the standard method to determine resistance, access to these tests is limited in low-resource set- tings. In this paper we investigate machine learning techniques for drug resistance prediction from routine treatment and laboratory data to help clinicians select patients for confirmatory genotype testing. The tech- niques, including binary relevance, HOMER, MLkNN, predictive clus- tering trees (PCT), RAkEL and ensemble of classifier chains were tested on a dataset of 252 medical records of patients enrolled in an HIV treat- ment failure clinic in rural KwaZulu-Natal in South Africa. The PCT method performed best with a discriminant power of 1.56 for two drugs, above 1.0 for three others and a mean true positive rate of 0.68. These methods show potential for application where access to genotyping is limited.
@{97, author = {Pascal Brandt, Deshen Moodley, Anban Pillay, Chris Seebregts, T. de Oliveira}, title = {An Investigation of Classification Algorithms for Predicting HIV Drug Resistance Without Genotype Resistance Testing}, abstract = {The development of drug resistance is a major factor imped- ing the efficacy of antiretroviral treatment of South Africa’s HIV infected population. While genotype resistance testing is the standard method to determine resistance, access to these tests is limited in low-resource set- tings. In this paper we investigate machine learning techniques for drug resistance prediction from routine treatment and laboratory data to help clinicians select patients for confirmatory genotype testing. The tech- niques, including binary relevance, HOMER, MLkNN, predictive clus- tering trees (PCT), RAkEL and ensemble of classifier chains were tested on a dataset of 252 medical records of patients enrolled in an HIV treat- ment failure clinic in rural KwaZulu-Natal in South Africa. The PCT method performed best with a discriminant power of 1.56 for two drugs, above 1.0 for three others and a mean true positive rate of 0.68. These methods show potential for application where access to genotyping is limited.}, year = {2014}, journal = {Third International Symposium on Foundations of Health Information Engineering and Systems}, pages = {236-253}, month = {21/08-23/08}, isbn = {978-3-642-53955-8}, url = {http://link.springer.com/chapter/10.1007/978-3-642-53956-5_16}, }
eHealth governance and regulation are necessary in low resource African countries to ensure effective and equitable use of health information technology and to realize national eHealth goals such as interoperability, adoption of standards and data integration. eHealth regulatory frameworks are under-developed in low resource settings, which hampers the progression towards coherent and effective national health information systems. Ontologies have the potential to clarify issues around interoperability and the effectiveness of different standards to deal with different aspects of interoperability. Ontologies can facilitate drafting, reusing, implementing and compliance testing of eHealth regulations. In this regard, we have developed an OWL ontology to capture key concepts and relations concerning interoperability and standards. The ontology includes an operational definition for interoperability and is an initial step towards the development of a knowledge representation modeling platform for eHealth regulation and governance.
@{92, author = {Deshen Moodley, Chris Seebregts, Anban Pillay, Tommie Meyer}, title = {An Ontology for Regulating eHealth Interoperability in Developing African Countries}, abstract = {eHealth governance and regulation are necessary in low resource African countries to ensure effective and equitable use of health information technology and to realize national eHealth goals such as interoperability, adoption of standards and data integration. eHealth regulatory frameworks are under-developed in low resource settings, which hampers the progression towards coherent and effective national health information systems. Ontologies have the potential to clarify issues around interoperability and the effectiveness of different standards to deal with different aspects of interoperability. Ontologies can facilitate drafting, reusing, implementing and compliance testing of eHealth regulations. In this regard, we have developed an OWL ontology to capture key concepts and relations concerning interoperability and standards. The ontology includes an operational definition for interoperability and is an initial step towards the development of a knowledge representation modeling platform for eHealth regulation and governance.}, year = {2014}, journal = {Foundations of Health Information Engineering and Systems, Revised and Selected Papers, Lecture Notes in Computer Science Volume 7789}, pages = {107-124}, month = {15/09}, isbn = {978-3-642-53955-8}, }

FULL DETAIL OF TALKS:
1) Segun, S.T. (4 September 2019). “Making Afro-ethics Computational”. Paper presented at 2019 SAHUDA Conference, Themed, Time, Thought, Materiality: Africa and the Fourth Industrial Revolution, University of Johannesburg, Johannesburg, South Africa;
2) Segun, S.T. (9 November 2018). “Conditions for an Explicit Ethical Agent”. Paper presented at the Forum for Artificial Intelligence Research (FAIR 2018). Hermanus, South Africa;
3) Segun, S.T. (26 October 2018). “Teaching Machines to Be Moral”. Paper presented as part of the series of academic public lectures tagged University of Johannesburg Talks. Johannesburg, South Africa;
4) Segun, S.T. (20 October 2018). “Rethinking Privilege and Otherness”. Paper presented at the Postgraduate Philosophical Society Conference. University of Pretoria, South Africa;
5) Segun, S.T. (11 May 2018) “Constructing an Afro-ethical Framework for Autonomous Intelligence Systems”. Paper presented at the AI for Social Good Working Group. University of Pretoria, South Africa;
6) Segun, S.T. (20 October 2018). “Rethinking Privilege and Otherness”. Paper presented at the Postgraduate Philosophical Society Conference. University of Pretoria, South Africa;
7) Segun, S.T. (11 May 2018) “Constructing an Afro-ethical Framework for Autonomous Intelligence Systems”. Paper presented at the AI for Social Good Working Group. University of Pretoria, South Africa;
8) Segun, S.T. (14 August 2017). “Is There a Language of Philosophy?” Paper presented at the Contemporary Language, Logic, and Metaphysics Conference (CLLMC). University of Witwatersrand, South Africa.
FULL DETAIL OF ANY PUBLICATION:
BOOK CHAPTER:
1) Segun, S. T. (2019). Neurophilosophy and the Problem of Consciousness: An Equiphenomenalist Perspective. In New Conversations on the Problems of Identity, Consciousness and Mind. J.O. Chimakonam, U. Egbai, S.T. Segun, A.D. Attoe (pp. 33-65). Springer, Cham.
PEER-REVIEWED/REFEREED JOURNAL:
1) Segun, S.T. (2020). From Machine Ethics to Computational Ethics. 'AI & Society: Knowledge, Culture and Communication'. Karamjit S. Gill (ed). Springer [Scopus Accredited].
IN PROGRESS:
1) Segun, S.T. (2020). Computational Possibilities of Non-classical Ethics.
LINKS TO RESEARCH:
Research gate: https://www.researchgate.net/profile/Samuel_Segun2;
Google Scholar: https://scholar.google.com/citations?user=p33vnrcAAAAJ&hl=en;
Academia.edu: https://johannesburg.academia.edu/samuelsegun;
Orcid: https://orcid.org/0000-0002-1017-0906.
Latest Research Publications: