Adol-Safety: A Framework for Empowering Parents to be Aware of Social Network Threats Affecting Adolescents
- Mjoli, Phumelela, Shibeshi, Z
- Authors: Mjoli, Phumelela , Shibeshi, Z
- Date: 2020
- Subjects: Social networks Social media|
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/12016 , vital:39127
- Description: The use of social networks has grown so much that adolescents have become active members of various social networks such as Twitter, Facebook, and Instagram, etc. The gradual change in how people choose to communicate, socialize and share ideas today has influenced adolescents to an extent that they find themselves wanting to engage more on social networks than they really should due to peer pressure. Whenever a person joins social networks or browses the Internet, they by default are exposed and become vulnerable to many cyber threats. Cyber threats are driven by users that have negative intentions on the Internet or social networks. Adolescents are no exception to these cyber threats. The findings of this research reveal that threats such as cyberbullying, harassment, and online predators to name a few are often designed to abuse and affect adolescents). Therefore, this research aims to prevent such threats from prevailing by empowering parents to be aware of the threats that affect their adolescents in an online environment, which typically includes social networks. To achieve this, this research starts by investigating the cyber threats that affect adolescents and then explores ways that can be used to empower parents. A framework is developed to handle this. The framework includes strategies that parents can adopt and ways in which safety on social networks can be increased, as well as guidelines that can be followed in order to prevent cyber threats. The framework also aims to enhance a parent-child relationship that can help in preventing social network threats. Lastly, the framework is implemented as a knowledgesharing website that can be used by parents to receive and give an insight into social network threats that influence adolescents on social networks.
- Full Text:
- Date Issued: 2020
- Authors: Mjoli, Phumelela , Shibeshi, Z
- Date: 2020
- Subjects: Social networks Social media|
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/12016 , vital:39127
- Description: The use of social networks has grown so much that adolescents have become active members of various social networks such as Twitter, Facebook, and Instagram, etc. The gradual change in how people choose to communicate, socialize and share ideas today has influenced adolescents to an extent that they find themselves wanting to engage more on social networks than they really should due to peer pressure. Whenever a person joins social networks or browses the Internet, they by default are exposed and become vulnerable to many cyber threats. Cyber threats are driven by users that have negative intentions on the Internet or social networks. Adolescents are no exception to these cyber threats. The findings of this research reveal that threats such as cyberbullying, harassment, and online predators to name a few are often designed to abuse and affect adolescents). Therefore, this research aims to prevent such threats from prevailing by empowering parents to be aware of the threats that affect their adolescents in an online environment, which typically includes social networks. To achieve this, this research starts by investigating the cyber threats that affect adolescents and then explores ways that can be used to empower parents. A framework is developed to handle this. The framework includes strategies that parents can adopt and ways in which safety on social networks can be increased, as well as guidelines that can be followed in order to prevent cyber threats. The framework also aims to enhance a parent-child relationship that can help in preventing social network threats. Lastly, the framework is implemented as a knowledgesharing website that can be used by parents to receive and give an insight into social network threats that influence adolescents on social networks.
- Full Text:
- Date Issued: 2020
Developing a Machine Learning Algorithm for Outdoor Scene Image Segmentation
- Authors: Zangwa, Yamkela
- Date: 2020
- Subjects: Computational intelligence Computer science
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/12087 , vital:39150
- Description: Image segmentation is one of the major problems in image processing, computer vision and machine learning fields. The main reason for image segmentation existence is to reduce the gap between computer vision and human vision by training computers with different data. Outdoor image segmentation and classification has become very important in the field of computer vision with its applications in woodland-surveillance, defence and security. The task of assigning an input image to one class from a fixed set of categories seem to be a major problem in image segmentation. The main question that has been addressed in this research is how outdoor image classification algorithms can be improved using Region-based Convolutional Neural Network (R-CNN) architecture. There has been no one segmentation method that works best on any given problem. To determine the best segmentation method for a certain dataset, various tests have to be done in order to achieve the best performance. However deep learning models have often achieved increasing success due to the availability of massive datasets and the expanding model depth and parameterisation. In this research Convolutional Neural Network architecture is used in trying to improve the implementation of outdoor scene image segmentation algorithms, empirical research method was used to answer questions about existing image segmentation algorithms and the techniques used to achieve the best performance. Outdoor scene images were trained on a pre-trained region-based convolutional neural network with Visual Geometric Group-16 (VGG-16) architecture. A pre-trained R-CNN model was retrained on five different sample data, the samples had different sizes. Sample size increased from sample one to five, to increase the size on the last two samples the data was duplicated. 21 test images were used to evaluate all the models. Researchers has shown that deep learning methods perform better in image segmentation because of the increase and availability of datasets. The duplication of images did not yield the best results; however, the model performed well on the first three samples.
- Full Text:
- Date Issued: 2020
- Authors: Zangwa, Yamkela
- Date: 2020
- Subjects: Computational intelligence Computer science
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/12087 , vital:39150
- Description: Image segmentation is one of the major problems in image processing, computer vision and machine learning fields. The main reason for image segmentation existence is to reduce the gap between computer vision and human vision by training computers with different data. Outdoor image segmentation and classification has become very important in the field of computer vision with its applications in woodland-surveillance, defence and security. The task of assigning an input image to one class from a fixed set of categories seem to be a major problem in image segmentation. The main question that has been addressed in this research is how outdoor image classification algorithms can be improved using Region-based Convolutional Neural Network (R-CNN) architecture. There has been no one segmentation method that works best on any given problem. To determine the best segmentation method for a certain dataset, various tests have to be done in order to achieve the best performance. However deep learning models have often achieved increasing success due to the availability of massive datasets and the expanding model depth and parameterisation. In this research Convolutional Neural Network architecture is used in trying to improve the implementation of outdoor scene image segmentation algorithms, empirical research method was used to answer questions about existing image segmentation algorithms and the techniques used to achieve the best performance. Outdoor scene images were trained on a pre-trained region-based convolutional neural network with Visual Geometric Group-16 (VGG-16) architecture. A pre-trained R-CNN model was retrained on five different sample data, the samples had different sizes. Sample size increased from sample one to five, to increase the size on the last two samples the data was duplicated. 21 test images were used to evaluate all the models. Researchers has shown that deep learning methods perform better in image segmentation because of the increase and availability of datasets. The duplication of images did not yield the best results; however, the model performed well on the first three samples.
- Full Text:
- Date Issued: 2020
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