A model for measuring and predicting stress for software developers using vital signs and activities
- Authors: Hibbers, Ilze
- Date: 2024-04
- Subjects: Machine learning , Neural networks (Computer science) , Computer software developers
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/63799 , vital:73614
- Description: Occupational stress is a well-recognised issue that affects individuals in various professions and industries. Reducing occupational stress has multiple benefits, such as improving employee's health and performance. This study proposes a model to measure and predict occupational stress using data collected in a real IT office environment. Different data sources, such as questionnaires, application software (RescueTime) and Fitbit smartwatches were used for collecting heart rate (HR), facial emotions, computer interactions, and application usage. The results of the Demand Control Support and Effort and Reward questionnaires indicated that the participants experienced high social support and an average level of workload. Participants also reported their daily perceived stress and workload level using a 5- point score. The perceived stress of the participants was overall neutral. There was no correlation found between HR, interactions, fear, and meetings. K-means and Bernoulli algorithms were applied to the dataset and two well-separated clusters were formed. The centroids indicated that higher heart rates were grouped either with meetings or had a higher difference in the center point values for interactions. Silhouette scores and 5-fold-validation were used to measure the accuracy of the clusters. However, these clusters were unable to predict the daily reported stress levels. Calculations were done on the computer usage data to measure interaction speeds and time spent working, in meetings, or away from the computer. These calculations were used as input into a decision tree with the reported daily stress levels. The results of the tree helped to identify which patterns lead to stressful days. The results indicated that days with high time pressure led to more reported stress. A new, more general tree was developed, which was able to predict 82 per cent of the daily stress reported. The main discovery of the research was that stress does not have a straightforward connection with computer interactions, facial emotions, or meetings. High interactions sometimes lead to stress and other times do not. So, predicting stress involves finding patterns and how data from different data sources interact with each other. Future work will revolve around validating the model in more office environments around South Africa. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
A model for measuring and predicting stress for software developers using vital signs and activities
- Authors: Hibbers, Ilze
- Date: 2024-04
- Subjects: Machine learning , Neural networks (Computer science) , Computer software developers
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/63799 , vital:73614
- Description: Occupational stress is a well-recognised issue that affects individuals in various professions and industries. Reducing occupational stress has multiple benefits, such as improving employee's health and performance. This study proposes a model to measure and predict occupational stress using data collected in a real IT office environment. Different data sources, such as questionnaires, application software (RescueTime) and Fitbit smartwatches were used for collecting heart rate (HR), facial emotions, computer interactions, and application usage. The results of the Demand Control Support and Effort and Reward questionnaires indicated that the participants experienced high social support and an average level of workload. Participants also reported their daily perceived stress and workload level using a 5- point score. The perceived stress of the participants was overall neutral. There was no correlation found between HR, interactions, fear, and meetings. K-means and Bernoulli algorithms were applied to the dataset and two well-separated clusters were formed. The centroids indicated that higher heart rates were grouped either with meetings or had a higher difference in the center point values for interactions. Silhouette scores and 5-fold-validation were used to measure the accuracy of the clusters. However, these clusters were unable to predict the daily reported stress levels. Calculations were done on the computer usage data to measure interaction speeds and time spent working, in meetings, or away from the computer. These calculations were used as input into a decision tree with the reported daily stress levels. The results of the tree helped to identify which patterns lead to stressful days. The results indicated that days with high time pressure led to more reported stress. A new, more general tree was developed, which was able to predict 82 per cent of the daily stress reported. The main discovery of the research was that stress does not have a straightforward connection with computer interactions, facial emotions, or meetings. High interactions sometimes lead to stress and other times do not. So, predicting stress involves finding patterns and how data from different data sources interact with each other. Future work will revolve around validating the model in more office environments around South Africa. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
Augmenting the Moore-Penrose generalised Inverse to train neural networks
- Authors: Fang, Bobby
- Date: 2024-04
- Subjects: Neural networks (Computer science) , Machine learning , Mathematical optimization -- Computer programs
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/63755 , vital:73595
- Description: An Extreme Learning Machine (ELM) is a non-iterative and fast feedforward neural network training algorithm which uses the Moore-Penrose generalised inverse of a matrix to compute the weights of the output layer of the neural network, using a random initialisation for the hidden layer. While ELM has been used to train feedforward neural networks, the effectiveness of the MP generalised to train recurrent neural networks is yet to be investigated. The primary aim of this research was to investigate how biases in the output layer and the MP generalised inverse can be used to train recurrent neural networks. To accomplish this, the Bias Augmented ELM (BA-ELM), which concatenated the hidden layer output matrix with a ones-column vector to simulate the biases in the output layer, was proposed. A variety of datasets generated from optimisation test functions, as well as using real-world regression and classification datasets, were used to validate BA-ELM. The results showed in specific circumstances that BA-ELM was able to perform better than ELM. Following this, Recurrent ELM (R-ELM) was proposed which uses a recurrent hidden layer instead of a feedforward hidden layer. Recurrent neural networks also rely on having functional feedback connections in the recurrent layer. A hybrid training algorithm, Recurrent Hybrid ELM (R-HELM), was proposed, which uses a gradient-based algorithm to optimise the recurrent layer and the MP generalised inverse to compute the output weights. The evaluation of R-ELM and R-HELM algorithms were carried out using three different recurrent architectures on two recurrent tasks derived from the Susceptible- Exposed-Infected-Removed (SEIR) epidemiology model. Various training hyperparameters were evaluated through hyperparameter investigations to investigate their effectiveness on the hybrid training algorithm. With optimal hyperparameters, the hybrid training algorithm was able to achieve better performance than the conventional gradient-based algorithm. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Fang, Bobby
- Date: 2024-04
- Subjects: Neural networks (Computer science) , Machine learning , Mathematical optimization -- Computer programs
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/63755 , vital:73595
- Description: An Extreme Learning Machine (ELM) is a non-iterative and fast feedforward neural network training algorithm which uses the Moore-Penrose generalised inverse of a matrix to compute the weights of the output layer of the neural network, using a random initialisation for the hidden layer. While ELM has been used to train feedforward neural networks, the effectiveness of the MP generalised to train recurrent neural networks is yet to be investigated. The primary aim of this research was to investigate how biases in the output layer and the MP generalised inverse can be used to train recurrent neural networks. To accomplish this, the Bias Augmented ELM (BA-ELM), which concatenated the hidden layer output matrix with a ones-column vector to simulate the biases in the output layer, was proposed. A variety of datasets generated from optimisation test functions, as well as using real-world regression and classification datasets, were used to validate BA-ELM. The results showed in specific circumstances that BA-ELM was able to perform better than ELM. Following this, Recurrent ELM (R-ELM) was proposed which uses a recurrent hidden layer instead of a feedforward hidden layer. Recurrent neural networks also rely on having functional feedback connections in the recurrent layer. A hybrid training algorithm, Recurrent Hybrid ELM (R-HELM), was proposed, which uses a gradient-based algorithm to optimise the recurrent layer and the MP generalised inverse to compute the output weights. The evaluation of R-ELM and R-HELM algorithms were carried out using three different recurrent architectures on two recurrent tasks derived from the Susceptible- Exposed-Infected-Removed (SEIR) epidemiology model. Various training hyperparameters were evaluated through hyperparameter investigations to investigate their effectiveness on the hybrid training algorithm. With optimal hyperparameters, the hybrid training algorithm was able to achieve better performance than the conventional gradient-based algorithm. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
Computer vision as a tool for tracking gastropod chemical trails
- Authors: Viviers, Andre
- Date: 2024-04
- Subjects: Computers , Electronic data processing , Machine learning
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64863 , vital:73934
- Description: The difficulties encountered in previous gastropod research with human intervention (Raw, Miranda, & Perissinotto, 2013) inspired this dissertation. More specifically the tedious task of human intervention in the tracking of gastropod chemical trails, which is a time-consuming and error-prone exercise. In this study, computer vision is proposed as an alternative to human intervention. A machine learning literature review was conducted to identify relevant methodologies and techniques for the research. Furthermore, it investigates data preprocessing techniques on a variety of different data types. This sets the stage for a deeper investigation of techniques used for pre-processing image and video data. Following that, another literature review delved deeper into the computer vision pipeline. The review is divided into two parts: data pre-processing and model training. First, it provides a deeper investigation into relevant data pre-processing techniques for use in constructing a dataset comprised of gastropod images. Following that, it delves into the complexities of training a computer vision model. The study then investigates convolutional neural networks, revealing the neural networks’ suitability in image/video processing. A convolutional neural network is selected as the foundation for the best-effort model. This serves as the foundation for the subsequent experimental research. The first part of the experimental work involves creating a labelled dataset from the video dataset provided by Raw et al. (2013). By employing data preprocessing techniques in a strategic manner, an unlabeled dataset is generated. Then a labelled dataset is generated using a simple K-Means clustering algorithm and manual labelling. Thereafter, a best-effort model is trained to detect gastropods within images using this dataset. After making the labelled dataset, the next step in the exploration is to build a prototype that can find gastropods and draw trace lines based on their movement. Five evaluation runs serve to gauge the prototype’s effectiveness. Videos with varying properties from the original dataset are purposefully chosen for each run. The prototype’s trace lines are compared to the original dataset’s human-drawn pathways. The versatility of the prototype is demonstrated in the final evaluation by generating fine-grained trace lines post-processing. This enables the plot to be adjusted to different parameters based on the characteristics that the resulting plot should have. Through the versatility and accuracy demonstrated by the evaluation runs, this research found that a gastropod tracking solution based on computer vision can alleviate human intervention. The dissertation concludes with a discourse on the lessons learned from the research study. These are presented as guidelines to aid future work in developing a gastropod tracking solution based on computer vision. , Thesis (MIT) -- Faculty of Engineering, the Built Environment, and Technology, School of Information Technology, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Viviers, Andre
- Date: 2024-04
- Subjects: Computers , Electronic data processing , Machine learning
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64863 , vital:73934
- Description: The difficulties encountered in previous gastropod research with human intervention (Raw, Miranda, & Perissinotto, 2013) inspired this dissertation. More specifically the tedious task of human intervention in the tracking of gastropod chemical trails, which is a time-consuming and error-prone exercise. In this study, computer vision is proposed as an alternative to human intervention. A machine learning literature review was conducted to identify relevant methodologies and techniques for the research. Furthermore, it investigates data preprocessing techniques on a variety of different data types. This sets the stage for a deeper investigation of techniques used for pre-processing image and video data. Following that, another literature review delved deeper into the computer vision pipeline. The review is divided into two parts: data pre-processing and model training. First, it provides a deeper investigation into relevant data pre-processing techniques for use in constructing a dataset comprised of gastropod images. Following that, it delves into the complexities of training a computer vision model. The study then investigates convolutional neural networks, revealing the neural networks’ suitability in image/video processing. A convolutional neural network is selected as the foundation for the best-effort model. This serves as the foundation for the subsequent experimental research. The first part of the experimental work involves creating a labelled dataset from the video dataset provided by Raw et al. (2013). By employing data preprocessing techniques in a strategic manner, an unlabeled dataset is generated. Then a labelled dataset is generated using a simple K-Means clustering algorithm and manual labelling. Thereafter, a best-effort model is trained to detect gastropods within images using this dataset. After making the labelled dataset, the next step in the exploration is to build a prototype that can find gastropods and draw trace lines based on their movement. Five evaluation runs serve to gauge the prototype’s effectiveness. Videos with varying properties from the original dataset are purposefully chosen for each run. The prototype’s trace lines are compared to the original dataset’s human-drawn pathways. The versatility of the prototype is demonstrated in the final evaluation by generating fine-grained trace lines post-processing. This enables the plot to be adjusted to different parameters based on the characteristics that the resulting plot should have. Through the versatility and accuracy demonstrated by the evaluation runs, this research found that a gastropod tracking solution based on computer vision can alleviate human intervention. The dissertation concludes with a discourse on the lessons learned from the research study. These are presented as guidelines to aid future work in developing a gastropod tracking solution based on computer vision. , Thesis (MIT) -- Faculty of Engineering, the Built Environment, and Technology, School of Information Technology, 2024
- Full Text:
- Date Issued: 2024-04
Supporting competitive robot game mission planning using machine learning
- Authors: Strydom, Elton
- Date: 2024-04
- Subjects: Machine learning , High performance computing , Robotics , LEGO Mindstorms toys Computer programming
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64841 , vital:73929
- Description: This dissertation presents a study aimed at supporting the strategic planning and execution of missions in competitive robot games, particularly in the FIRST LEGO® League (FLL), through the use of machine learning techniques. The primary objective is to formulate guidelines for evaluating mission strategies using machine learning techniques within the FLL landscape, thereby supporting participants in the mission strategy design journey within the FLL robot game. The research methodology encompasses a literature review, focusing on the current practices in the FLL mission strategy design process. This is followed by a literature review of machine learning techniques on a broad level pivoting towards evolutionary algorithms. The study then delves into the specifics of genetic algorithms, exploring their suitability and potential advantages for mission strategy evaluation in competitive robotic environments within the FLL robot game. A significant portion of the research involves the development and testing of a prototype system that applies a genetic algorithm to simulate and evaluate different mission strategies, providing a practical tool for FLL teams. During the development of the evaluation prototype, guidelines were formulated aligning with the primary research objective which is to formulate guidelines for evaluating mission strategies in robot games using machine learning techniques. Key findings of this study highlight the effectiveness of genetic algorithms in identifying optimal mission strategies. The prototype demonstrates the feasibility of using machine learning to provide real-time, feedback to participating teams, enabling more informed decision-making in the formulation of mission strategies. , Thesis (MIT) -- Faculty of Engineering, the Built Environment, and Technology, School of Information Technology, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Strydom, Elton
- Date: 2024-04
- Subjects: Machine learning , High performance computing , Robotics , LEGO Mindstorms toys Computer programming
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64841 , vital:73929
- Description: This dissertation presents a study aimed at supporting the strategic planning and execution of missions in competitive robot games, particularly in the FIRST LEGO® League (FLL), through the use of machine learning techniques. The primary objective is to formulate guidelines for evaluating mission strategies using machine learning techniques within the FLL landscape, thereby supporting participants in the mission strategy design journey within the FLL robot game. The research methodology encompasses a literature review, focusing on the current practices in the FLL mission strategy design process. This is followed by a literature review of machine learning techniques on a broad level pivoting towards evolutionary algorithms. The study then delves into the specifics of genetic algorithms, exploring their suitability and potential advantages for mission strategy evaluation in competitive robotic environments within the FLL robot game. A significant portion of the research involves the development and testing of a prototype system that applies a genetic algorithm to simulate and evaluate different mission strategies, providing a practical tool for FLL teams. During the development of the evaluation prototype, guidelines were formulated aligning with the primary research objective which is to formulate guidelines for evaluating mission strategies in robot games using machine learning techniques. Key findings of this study highlight the effectiveness of genetic algorithms in identifying optimal mission strategies. The prototype demonstrates the feasibility of using machine learning to provide real-time, feedback to participating teams, enabling more informed decision-making in the formulation of mission strategies. , Thesis (MIT) -- Faculty of Engineering, the Built Environment, and Technology, School of Information Technology, 2024
- Full Text:
- Date Issued: 2024-04
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