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
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