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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},
}