Deshen Moodley

2022

Price CS, Moodley D, Pillay A, Rens G. An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making. South African Computer Journal. 2022;34(1). doi:https://doi.org/10.18489/sacj.v34i1.857.

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An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making

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 and Deshen Moodley and Anban Pillay and 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},
}

Wanyana T, Nzomo M, Price CS, Moodley D. Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation. In: Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE. INSTICC: SciTePress; 2022. doi:https://doi.org/10.5220/0011046100003188.

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Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation.pdf

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 and Mbithe Nzomo and C. Sue Price and 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},
}

Pillay K, Moodley D. Exploring Graph Neural Networks for Stock Market Prediction on the JSE. Communications in Computer and Information Science. 2022;1551. doi:10.1007/978-3-030-95070-5_7.

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Stock markets are dynamic systems that exhibit complex intra-share and inter-share temporal dependencies. Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic systems with complex spatial and temporal dependencies such as city traffic networks. In this research, we apply three state-of-the-art ST-GNN architectures, i.e. Graph WaveNet, MTGNN and StemGNN, to predict the closing price of shares listed on the Johannesburg Stock Exchange (JSE) and attempt to capture complex inter-share dependencies. The results show that ST-GNN architectures, specifically Graph WaveNet, produce superior performance relative to an LSTM and are potentially capable of capturing complex intra-share and inter-share temporal dependencies in the JSE. We found that Graph WaveNet outperforms the other approaches over short-term and medium-term horizons. This work is one of the first studies to apply these ST-GNNs to share price prediction.

@article{443,
  author = {Kialan Pillay and Deshen Moodley},
  title = {Exploring Graph Neural Networks for Stock Market Prediction on the JSE},
  abstract = {Stock markets are dynamic systems that exhibit complex intra-share and inter-share temporal dependencies. Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic systems with complex spatial and temporal dependencies such as city traffic networks. In this research, we apply three state-of-the-art ST-GNN architectures, i.e. Graph WaveNet, MTGNN and StemGNN, to predict the closing price of shares listed on the Johannesburg Stock Exchange (JSE) and attempt to capture complex inter-share dependencies. The results show that ST-GNN architectures, specifically Graph WaveNet, produce superior performance relative to an LSTM and are potentially capable of capturing complex intra-share and inter-share temporal dependencies in the JSE. We found that Graph WaveNet outperforms the other approaches over short-term and medium-term horizons. This work is one of the first studies to apply these ST-GNNs to share price prediction.},
  year = {2022},
  journal = {Communications in Computer and Information Science},
  volume = {1551},
  pages = {95-110},
  publisher = {Springer},
  address = {Cham},
  isbn = {978-3-030-95070-5},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-95070-5_7},
  doi = {10.1007/978-3-030-95070-5_7},
}

Drake R, Moodley D. INVEST: Ontology Driven Bayesian Networks for Investment Decision Making on the JSE. In: Second Southern African Conference for AI Research (SACAIR 2022). Online; 2022. https://protect-za.mimecast.com/s/OFYSCpgo02fL1l9gtDHUkY.

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This research proposes an architecture and prototype implementation of a knowledge-based system for automating share evaluation and investment decision making on the Johannesburg Stock Exchange (JSE). The knowledge acquired from an analysis of the investment domain for a value investing approach is represented in an ontology. A Bayesian network, developed using the ontology, is used to capture the complex causal relations between different factors that influence the quality and value of individual shares. The system was found to adequately represent the decision-making process of investment professionals and provided superior returns to selected benchmark JSE indices from 2012 to 2018.

@{442,
  author = {Rachel Drake and Deshen Moodley},
  title = {INVEST: Ontology Driven Bayesian Networks for Investment Decision Making on the JSE},
  abstract = {This research proposes an architecture and prototype implementation of a knowledge-based system for automating share evaluation and investment decision making on the Johannesburg Stock Exchange (JSE). The knowledge acquired from an analysis of the investment domain for a value investing approach is represented in an ontology. A Bayesian network, developed using the ontology, is used to capture the complex causal relations between different factors that influence the quality and value of individual shares. The system was found to adequately represent the decision-making process of investment professionals and provided superior returns to selected benchmark JSE indices from 2012 to 2018.},
  year = {2022},
  journal = {Second Southern African Conference for AI Research (SACAIR 2022)},
  pages = {252-273},
  month = {06/12/2021-10/12/2021},
  address = {Online},
  isbn = {978-0-620-94410-6},
  url = {https://protect-za.mimecast.com/s/OFYSCpgo02fL1l9gtDHUkY},
}

2021

Wanyana T, Moodley D. An Agent Architecture for Knowledge Discovery and Evolution. In: KI 2021: Advances in Artificial Intelligence. volume 12873. Cham: Springer International Publishing; 2021. doi:https://doi.org/10.1007/978-3-030-87626-5_18.

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Wanyana-Moodley2021_Chapter_AnAgentArchitectureForKnowledg.pdf

The abductive theory of method (ATOM) was recently proposed to describe the process that scientists use for knowledge discovery. In this paper we propose an agent architecture for knowledge discovery and evolution (KDE) based on ATOM. The agent incorporates a combination of ontologies, rules and Bayesian networks for representing different aspects of its internal knowledge. The agent uses an external AI service to detect unexpected situations from incoming observations. It then uses rules to analyse the current situation and a Bayesian network for finding plausible explanations for unexpected situations. The architecture is evaluated and analysed on a use case application for monitoring daily household electricity consumption patterns.

@inbook{425,
  author = {Tezira Wanyana and Deshen Moodley},
  title = {An Agent Architecture for Knowledge Discovery and Evolution},
  abstract = {The abductive theory of method (ATOM) was recently proposed to describe the process that scientists use for knowledge discovery. In this paper we propose an agent architecture for knowledge discovery and evolution (KDE) based on ATOM. The agent incorporates a combination of ontologies, rules and Bayesian networks for representing different aspects of its internal knowledge. The agent uses an external AI service to detect unexpected situations from incoming observations. It then uses rules to analyse the current situation and a Bayesian network for finding plausible explanations for unexpected situations. The architecture is evaluated and analysed on a use case application for monitoring daily household electricity consumption patterns.},
  year = {2021},
  journal = {KI 2021: Advances in Artificial Intelligence},
  edition = {volume 12873},
  pages = {241-256},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-030-87626-5},
  doi = {https://doi.org/10.1007/978-3-030-87626-5_18},
}

2020

Toussaint W, Moodley D. Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa. South African Computer Journal. 2020;32(2). doi:http://dx.doi.org/10.18489/sacj.v32i2.845 .

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Wiebke and Deshen SACJ.pdf

Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clustering validation measures are well established in the electricity domain, they are limited for selecting useful clusters. Based on an application case study in South Africa, we present an approach for formalising implicit expert knowledge as external evaluation measures to create customer archetypes that capture variability in residential electricity consumption behaviour. By combining internal and external validation measures in a structured manner, we were able to evaluate clustering structures based on the utility they present for our application. We validate the selected clusters in a use case where we successfully reconstruct customer archetypes previously developed by experts. Our approach shows promise for transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.

@article{408,
  author = {Wiebke Toussaint and Deshen Moodley},
  title = {Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa},
  abstract = {Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clustering validation measures are well established in the electricity domain, they are limited for selecting useful clusters. Based on an application case study in South Africa, we present an approach for formalising implicit expert knowledge as external evaluation measures to create customer archetypes that capture variability in residential electricity consumption behaviour. By combining internal and external validation measures in a structured manner, we were able to evaluate clustering structures based on the utility they present for our application. We validate the selected clusters in a use case where we successfully reconstruct customer archetypes previously developed by experts. Our approach shows promise for transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.},
  year = {2020},
  journal = {South African Computer Journal},
  volume = {32},
  pages = {1-34},
  issue = {2},
  publisher = {SACJ},
  address = {Online},
  isbn = {ISSN 2313-7835},
  url = {http://www.scielo.org.za/scielo.php?pid=S2313-78352020000200003&amp;script=sci_arttext&amp;tlng=en},
  doi = {http://dx.doi.org/10.18489/sacj.v32i2.845},
}

Kouassi K, Moodley D. An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data. In: First Southern African Conference for AI Research (SACAIR 2020). Virtual: Springer; 2020. doi:https://doi.org/10.1007/978-3-030-66151-9_8.

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Kouassi-Moodley2020_Chapter_AnAnalysisOfDeepNeuralNetworks_0.pdf

Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series datasets and did not deal with model update. In this research we replicated the TreNet experiments on the same datasets using a walk-forward validation method and tested our best model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four datasets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all datasets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.

@{407,
  author = {Kouame Kouassi and Deshen Moodley},
  title = {An Analysis of Deep Neural Networks for Predicting Trends in Time Series Data},
  abstract = {Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series datasets and did not deal with model update. In this research we replicated the TreNet experiments on the same datasets using a walk-forward validation method and tested our best model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four datasets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all datasets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.},
  year = {2020},
  journal = {First Southern African Conference for AI Research (SACAIR 2020)},
  pages = {119-140},
  month = {22/02/2021},
  publisher = {Springer},
  address = {Virtual},
  isbn = {978-3-030-66151-9},
  url = {https://link.springer.com/book/10.1007/978-3-030-66151-9},
  doi = {https://doi.org/10.1007/978-3-030-66151-9_8},
}

Wanyana T, Moodley D, Meyer T. An Ontology for Supporting Knowledge Discovery and Evolution. In: First Southern African Conference for Artificial Intelligence Research. Virtual: SACAIR2020; 2020. https://2020.sacair.org.za/wp-content/uploads/2021/02/SACAIR_Proceedings-MainBook_Finv4_compressed.pdf?_ga=2.116601743.849395099.1621802506-572599210.1621419278.

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An Ontology for Supporting Knowledge Discovery and Evolution.pdf

Knowledge Discovery and Evolution (KDE) is of interest to a broad array of researchers from both Philosophy of Science (PoS) and Artificial Intelligence (AI), in particular, Knowledge Representation and Reasoning (KR), Machine Learning and Data Mining (ML-DM) and the Agent Based Systems (ABS) communities. In PoS, Haig recently pro- posed a so-called broad theory of scientific method that uses abduction for generating theories to explain phenomena. He refers to this method of scientific inquiry as the Abductive Theory of Method (ATOM). In this paper, we analyse ATOM, align it with KR and ML-DM perspectives and propose an algorithm and an ontology for supporting agent based knowledge discovery and evolution based on ATOM. We illustrate the use of the algorithm and the ontology on a use case application for electricity consumption behaviour in residential households.

@{405,
  author = {Tezira Wanyana and Deshen Moodley and Tommie Meyer},
  title = {An Ontology for Supporting Knowledge Discovery and Evolution},
  abstract = {Knowledge Discovery and Evolution (KDE) is of interest to a broad array of researchers from both Philosophy of Science (PoS) and Artificial Intelligence (AI), in particular, Knowledge Representation and Reasoning (KR), Machine Learning and Data Mining (ML-DM) and the Agent Based Systems (ABS) communities. In PoS, Haig recently pro- posed a so-called broad theory of scientific method that uses abduction for generating theories to explain phenomena. He refers to this method of scientific inquiry as the Abductive Theory of Method (ATOM). In this paper, we analyse ATOM, align it with KR and ML-DM perspectives and propose an algorithm and an ontology for supporting agent based knowledge discovery and evolution based on ATOM. We illustrate the use of the algorithm and the ontology on a use case application for electricity consumption behaviour in residential households.},
  year = {2020},
  journal = {First Southern African Conference for Artificial Intelligence Research},
  pages = {206-221},
  month = {22/02/2021},
  publisher = {SACAIR2020},
  address = {Virtual},
  isbn = {978-0-620-89373-2},
  url = {https://2020.sacair.org.za/wp-content/uploads/2021/02/SACAIR_Proceedings-MainBook_Finv4_compressed.pdf?_ga=2.116601743.849395099.1621802506-572599210.1621419278},
}

Moodley D, Meyer T. Artificial Intelligence – Where it is heading and what we should do about it . 2020. https://link.springer.com/article/10.1007/s42354-020-0269-5.

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Moodley-Meyer2020_Article_ArtificialIntelligenceWhereItI (1).pdf

Artificial Intelligence (AI) is already shaping our everyday lives. While there is enormous potential for harnessing AI to solve complex industrial and social problems and to create new and innovative products and solutions, many organisations are still grappling to understand the relevance and future impact of AI on their activities and what they should be doing about it.

@misc{381,
  author = {Deshen Moodley and Tommie Meyer},
  title = {Artificial Intelligence – Where it is heading and what we should do about it},
  abstract = {Artificial Intelligence (AI) is already shaping our everyday lives. While there is enormous potential for harnessing AI to solve complex industrial and social problems and to create new and innovative products and solutions, many organisations are still grappling to understand the relevance and future impact of AI on their activities and what they should be doing about it.},
  year = {2020},
  url = {https://link.springer.com/article/10.1007/s42354-020-0269-5},
}

Toussaint W, Moodley D. Identifying optimal clustering structures for residential energy consumption patterns using competency questions. In: SAICSIT ’20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020. Virtual: ACM Digital Library; 2020. https://dl.acm.org/doi/proceedings/10.1145/3410886.

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Touissant & Moodley.pdf

Traditional cluster analysis metrics rank clustering structures in terms of compactness and distinctness of clusters. However, in real world applications this is usually insufficient for selecting the optimal clustering structure. Domain experts and visual analysis are often relied on during evaluation, which results in a selection process that tends to be adhoc, subjective and difficult to reproduce. This work proposes the use of competency questions and a cluster scoring matrix to formalise expert knowledge and application requirements for qualitative evaluation of clustering structures. We show how a qualitative ranking of clustering structures can be integrated with traditional metrics to guide cluster evaluation and selection for generating representative energy consumption profiles that characterise residential electricity demand in South Africa. The approach is shown to be highly effective for identifying usable and expressive consumption profiles within this specific application context, and certainly has wider potential for efficient, transparent and repeatable cluster selection in real-world applications.

@{380,
  author = {Wiebke Toussaint and Deshen Moodley},
  title = {Identifying optimal clustering structures for residential energy consumption patterns using competency questions},
  abstract = {Traditional cluster analysis metrics rank clustering structures in terms of compactness and distinctness of clusters. However, in real world applications this is usually insufficient for selecting the optimal clustering structure. Domain experts and visual analysis are often relied on during evaluation, which results in a selection process that tends to be adhoc, subjective and difficult to reproduce. This work proposes the use of competency questions and a cluster scoring matrix to formalise expert knowledge and application requirements for qualitative evaluation of clustering structures. We show how a qualitative ranking of clustering structures can be integrated with traditional metrics to guide cluster evaluation and selection for generating representative energy consumption profiles that characterise residential electricity demand in South Africa. The approach is shown to be highly effective for identifying usable and expressive consumption profiles within this specific application context, and certainly has wider potential for efficient, transparent and repeatable cluster selection in real-world applications.},
  year = {2020},
  journal = {SAICSIT &#039;20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020},
  pages = {66-73},
  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},
}

Clark A, Pillay A, Moodley D. A system for pose analysis and selection in virtual reality environments. In: SAICSIT ’20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020. Virtual: ACM Digital Library; 2020. https://dl.acm.org/doi/proceedings/10.1145/3410886.

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Clark, Pillay & Moodley.pdf

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 and Anban Pillay and 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 &#039;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},
}

2019

Toussaint W, Moodley D. Comparison of clustering techniques for residential load profiles in South Africa. In: Forum for Artificial Intelligence Research. CEUR; 2019. http://ceur-ws.org/Vol-2540/FAIR2019_paper_55.pdf.

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FAIR2019_paper_55.pdf

This work compares techniques for clustering metered residential energy consumption data to construct representative daily load profiles in South Africa. The input data captures a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine their effect on producing a good clustering structure. A Combined Index is developed as a relative score to ease the comparison of experiments across different metrics. The study shows that normalisation, specifically unit norm and the zero-one scaler, produce the best clusters. Pre-binning appears to improve clustering structures as a whole, but its effect on individual experiments remains unclear. Like several previous studies, the k-means algorithm produces the best results. To our knowledge this is the first work that rigorously compares state of the art cluster analysis techniques in the residential energy domain in a developing country context.

@{249,
  author = {Wiebke Toussaint and Deshen Moodley},
  title = {Comparison of clustering techniques for residential load profiles in South Africa},
  abstract = {This work compares techniques for clustering metered residential energy consumption data to construct representative daily load profiles in South Africa. The input data captures a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine their effect on producing a good clustering structure. A Combined Index is developed as a relative score to ease the comparison of experiments across different metrics. The study shows that normalisation, specifically unit norm and the zero-one scaler, produce the best clusters. Pre-binning appears to improve clustering structures as a whole, but its effect on individual experiments remains unclear. Like several previous studies, the k-means algorithm produces the best results. To our knowledge this is the first work that rigorously compares state of the art cluster analysis techniques in the residential energy domain in a developing country context.},
  year = {2019},
  journal = {Forum for Artificial Intelligence Research},
  pages = {117 -132},
  month = {03/12 - 06/12},
  publisher = {CEUR},
  isbn = {1613-0073},
  url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_55.pdf},
}