Using action research for curriculum development and improving the learning experience : a case study
- Authors: Oksiutycz, A. , Azionya, C.
- Date: 2017
- Subjects: Authentic learning , Projects-based teaching , Deep learning
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/240346 , uj:24726 , Citation: Oksiutycz, A. & Azionya, C. 2017. Using action research for curriculum development and improving the learning experience : a case study.
- Description: Abstract: Please refer to full text to view abstract
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- Authors: Oksiutycz, A. , Azionya, C.
- Date: 2017
- Subjects: Authentic learning , Projects-based teaching , Deep learning
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/240346 , uj:24726 , Citation: Oksiutycz, A. & Azionya, C. 2017. Using action research for curriculum development and improving the learning experience : a case study.
- Description: Abstract: Please refer to full text to view abstract
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Outcomes-based education and deep learning in first year social work in South Africa : two case examples
- Authors: Collins, Kathleen Jane
- Date: 2015-06-04
- Subjects: Deep learning , Narrative construction , Outcomes-based education , Social work education
- Type: Article
- Identifier: uj:5557 , ISSN 00208728 , http://hdl.handle.net/10210/14144
- Description: South African social work education changed from norm-based to outcomes-based education soon after the first democratic government came into power in 1994 and a new Bachelor of Social Work has been in existence since 2007. The article argues in support of deep learning principles and presents narrative constructions from two differently advantaged departments of social work, illustrating how lecturers and students there have adapted to outcomes-based education. Conclusions indicate that statutory requirements and institutional pressures militate against the development of deep learning. The urgency to incorporate transformative learning in meeting professional standards is placed in the international context.
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- Authors: Collins, Kathleen Jane
- Date: 2015-06-04
- Subjects: Deep learning , Narrative construction , Outcomes-based education , Social work education
- Type: Article
- Identifier: uj:5557 , ISSN 00208728 , http://hdl.handle.net/10210/14144
- Description: South African social work education changed from norm-based to outcomes-based education soon after the first democratic government came into power in 1994 and a new Bachelor of Social Work has been in existence since 2007. The article argues in support of deep learning principles and presents narrative constructions from two differently advantaged departments of social work, illustrating how lecturers and students there have adapted to outcomes-based education. Conclusions indicate that statutory requirements and institutional pressures militate against the development of deep learning. The urgency to incorporate transformative learning in meeting professional standards is placed in the international context.
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A survey of machine learning methods applied to anomaly detection on drinking-water quality data
- Dogo, Eustace M., Nwulu, Nnamdi I., Twala, Bhekisipho, Aigbavboa, Clinton
- Authors: Dogo, Eustace M. , Nwulu, Nnamdi I. , Twala, Bhekisipho , Aigbavboa, Clinton
- Date: 2019
- Subjects: Machine learning , Anomaly detection , Deep learning
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/406768 , uj:34213 , Citation: Dogo, E.M. et al. 2019. A survey of machine learning methods applied to anomaly detection on drinking-water quality data.
- Description: Abstract: Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), application of ELM is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data.
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- Authors: Dogo, Eustace M. , Nwulu, Nnamdi I. , Twala, Bhekisipho , Aigbavboa, Clinton
- Date: 2019
- Subjects: Machine learning , Anomaly detection , Deep learning
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/406768 , uj:34213 , Citation: Dogo, E.M. et al. 2019. A survey of machine learning methods applied to anomaly detection on drinking-water quality data.
- Description: Abstract: Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), application of ELM is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data.
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Improved sparse autoencoder based artificial neural network approach for prediction of heart disease
- Mienye, Ibomoiye Domor, Sun, Yanxia, Wang, Zenghui
- Authors: Mienye, Ibomoiye Domor , Sun, Yanxia , Wang, Zenghui
- Date: 2020
- Subjects: Sparse autoencoder , Deep learning , Unsupervised learning
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/415954 , uj:35166 , Citation: Wang, Z., Sun, Y., Mienye, I.D. Improved sparse autoencoder based artificial neural network approach for prediction of heart disease. DOI: https://doi.org/10.1016/j.imu.2020.100307
- Description: Abstract: , In this paper a two stage method is proposed to effectively predict heart disease. The first stage involves training an improved sparse autoencoder (SAE), an unsupervised neural network, to learn the best representation of the training data. The second stage involves using an artificial neural network (ANN) to predict the health status based on the learned records. The SAE was optimized so as to train an efficient model. The experimental result shows that the proposed method improves the performance of the ANN classifier, and is more robust as compared to other methods and similar scholarly works.
- Full Text:
Improved sparse autoencoder based artificial neural network approach for prediction of heart disease
- Authors: Mienye, Ibomoiye Domor , Sun, Yanxia , Wang, Zenghui
- Date: 2020
- Subjects: Sparse autoencoder , Deep learning , Unsupervised learning
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/415954 , uj:35166 , Citation: Wang, Z., Sun, Y., Mienye, I.D. Improved sparse autoencoder based artificial neural network approach for prediction of heart disease. DOI: https://doi.org/10.1016/j.imu.2020.100307
- Description: Abstract: , In this paper a two stage method is proposed to effectively predict heart disease. The first stage involves training an improved sparse autoencoder (SAE), an unsupervised neural network, to learn the best representation of the training data. The second stage involves using an artificial neural network (ANN) to predict the health status based on the learned records. The SAE was optimized so as to train an efficient model. The experimental result shows that the proposed method improves the performance of the ANN classifier, and is more robust as compared to other methods and similar scholarly works.
- Full Text:
Improved predictive sparse decomposition method with DenseNet for prediction of lung cancer
- Mienye, Ibomoiye Domor, Sun, Yanxia, Wang, Zenghui
- Authors: Mienye, Ibomoiye Domor , Sun, Yanxia , Wang, Zenghui
- Date: 2020
- Subjects: Convolutional neural network , Deep learning , DenseNet
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/459656 , uj:40874 , Citation: Mienye, I.D., Sun, Y. & Wang, Z. 2020. Improved predictive sparse decomposition method with DenseNet for prediction of lung cancer.
- Description: Abstract: Lung cancer is the second most common form of cancer in both men and women. It is responsible for at least 25% of all cancer-related deaths in the United States alone. Accurate and early diagnosis of this form of cancer can increase the rate of survival. Computed tomography (CT) imaging is one of the most accurate techniques for diagnosing the disease. In order to improve the classification accuracy of pulmonary lesions indicating lung cancer, this paper presents an improved method for training a densely connected convolutional network (DenseNet). The optimized setting ensures that code prediction error and reconstruction error within hidden layers are simultaneously minimized. To achieve this and improve the classification accuracy of the DenseNet, we propose an improved predictive sparse decomposition (PSD) approach for extracting sparse features from the medical images. The sparse decomposition is achieved by using a linear combination of basis functions over the L2 norm. The effect of dropout and hidden layer expansion on the classification accuracy of the DenseNet is also investigated. CT scans of human lung samples are obtained from The Cancer Imaging Archive (TCIA) hosted by the University of Arkansas for Medical Sciences (UAMS). The proposed method outperforms seven other neural network architectures and machine learning algorithms with a classification accuracy of 95%.
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- Authors: Mienye, Ibomoiye Domor , Sun, Yanxia , Wang, Zenghui
- Date: 2020
- Subjects: Convolutional neural network , Deep learning , DenseNet
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/459656 , uj:40874 , Citation: Mienye, I.D., Sun, Y. & Wang, Z. 2020. Improved predictive sparse decomposition method with DenseNet for prediction of lung cancer.
- Description: Abstract: Lung cancer is the second most common form of cancer in both men and women. It is responsible for at least 25% of all cancer-related deaths in the United States alone. Accurate and early diagnosis of this form of cancer can increase the rate of survival. Computed tomography (CT) imaging is one of the most accurate techniques for diagnosing the disease. In order to improve the classification accuracy of pulmonary lesions indicating lung cancer, this paper presents an improved method for training a densely connected convolutional network (DenseNet). The optimized setting ensures that code prediction error and reconstruction error within hidden layers are simultaneously minimized. To achieve this and improve the classification accuracy of the DenseNet, we propose an improved predictive sparse decomposition (PSD) approach for extracting sparse features from the medical images. The sparse decomposition is achieved by using a linear combination of basis functions over the L2 norm. The effect of dropout and hidden layer expansion on the classification accuracy of the DenseNet is also investigated. CT scans of human lung samples are obtained from The Cancer Imaging Archive (TCIA) hosted by the University of Arkansas for Medical Sciences (UAMS). The proposed method outperforms seven other neural network architectures and machine learning algorithms with a classification accuracy of 95%.
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