Exploring the Incremental Improvements of YOLOv5 on Tracking and Identifying Great White Sharks in Cape Town
- Kuhlane, Luxolo L, Brown, Dane L, Boby, Alden
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Boby, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464107 , vital:76476 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37963-5_98"
- Description: The information on great white sharks is used by scientists to help better understand the marine organisms and to mitigate any chances of extinction of great white sharks. Sharks play a very important role in the ocean, and their role in the oceans is under-appreciated by the general public, which results in negative attitudes towards sharks. The tracking and identification of sharks are done using manual labour, which is not very accurate and time-consuming. This paper uses a deep learning approach to help identify and track great white sharks in Cape Town. A popular object detecting system used in this paper is YOLO, which is implemented to help identify the great white shark. In conjunction with YOLO, the paper also uses ESRGAN to help upscale low-quality images from the datasets into more high-quality images before being put into the YOLO system. The main focus of this paper is to help train the system; this includes training the system to identify great white sharks in difficult conditions such as murky water or unclear deep-sea conditions.
- Full Text:
- Date Issued: 2023
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Boby, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464107 , vital:76476 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37963-5_98"
- Description: The information on great white sharks is used by scientists to help better understand the marine organisms and to mitigate any chances of extinction of great white sharks. Sharks play a very important role in the ocean, and their role in the oceans is under-appreciated by the general public, which results in negative attitudes towards sharks. The tracking and identification of sharks are done using manual labour, which is not very accurate and time-consuming. This paper uses a deep learning approach to help identify and track great white sharks in Cape Town. A popular object detecting system used in this paper is YOLO, which is implemented to help identify the great white shark. In conjunction with YOLO, the paper also uses ESRGAN to help upscale low-quality images from the datasets into more high-quality images before being put into the YOLO system. The main focus of this paper is to help train the system; this includes training the system to identify great white sharks in difficult conditions such as murky water or unclear deep-sea conditions.
- Full Text:
- Date Issued: 2023
Improving licence plate detection using generative adversarial networks
- Authors: Boby, Alden , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464145 , vital:76480 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-04881-4_47"
- Description: The information on a licence plate is used for traffic law enforcement, access control, surveillance and parking lot management. Existing li-cence plate recognition systems work with clear images taken under controlled conditions. In real-world licence plate recognition scenarios, images are not as straightforward as the ‘toy’ datasets used to bench-mark existing systems. Real-world data is often noisy as it may contain occlusion and poor lighting, obscuring the information on a licence plate. Cleaning input data before using it for licence plate recognition is a complex problem, and existing literature addressing the issue is still limited. This paper uses two deep learning techniques to improve li-cence plate visibility towards more accurate licence plate recognition. A one-stage object detector popularly known as YOLO is implemented for locating licence plates under challenging situations. Super-resolution generative adversarial networks are considered for image upscaling and reconstruction to improve the clarity of low-quality input. The main focus involves training these systems on datasets that include difficult to detect licence plates, enabling better performance in unfavourable conditions and environments.
- Full Text:
- Date Issued: 2022
- Authors: Boby, Alden , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464145 , vital:76480 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-04881-4_47"
- Description: The information on a licence plate is used for traffic law enforcement, access control, surveillance and parking lot management. Existing li-cence plate recognition systems work with clear images taken under controlled conditions. In real-world licence plate recognition scenarios, images are not as straightforward as the ‘toy’ datasets used to bench-mark existing systems. Real-world data is often noisy as it may contain occlusion and poor lighting, obscuring the information on a licence plate. Cleaning input data before using it for licence plate recognition is a complex problem, and existing literature addressing the issue is still limited. This paper uses two deep learning techniques to improve li-cence plate visibility towards more accurate licence plate recognition. A one-stage object detector popularly known as YOLO is implemented for locating licence plates under challenging situations. Super-resolution generative adversarial networks are considered for image upscaling and reconstruction to improve the clarity of low-quality input. The main focus involves training these systems on datasets that include difficult to detect licence plates, enabling better performance in unfavourable conditions and environments.
- Full Text:
- Date Issued: 2022
Exploring The Incremental Improvements of YOLOv7 on Bull Sharks in Mozambique
- Kuhlane, Luxolo L, Brown, Dane L, Brown, Alden
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Brown, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464118 , vital:76478 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/368455814_Exploring_The_Incremental_Improvements_of_YOLOv7_on_Bull_Sharks_in_Mozambique/links/63e8d321dea6121757a4ba7f/Exploring-The-Incremental-Improvements-of-YOLOv7-on-Bull-Sharks-in-Mozambique.pdf?origin=journalDetailand_tp=eyJwYWdlIjoiam91cm5hbERldGFpbCJ9"
- Description: Scientists use bull shark data to better understand marine organisms and to reduce the likelihood of bull shark extinction. Sharks play an important role in the ocean, and their importance is underappreciated by the general public, leading to negative attitudes toward sharks. The tracking and identification of sharks is done by hand, which is inefficient and time-consuming. This paper employs a deep learning approach to assist in the identification and tracking of bull sharks in Mozambique. YOLO is a popular object detection system used in this paper to aid in the identification of the great white shark. In addition to YOLO, the paper employs ESRGAN to help upscale low-quality images from the datasets into higher-quality images before they are fed into the YOLO system. The primary goal of this paper is to assist in training the system to identify bull sharks in difficult conditions such as murky water or unclear deep-sea conditions.
- Full Text:
- Date Issued: 2023
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Brown, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464118 , vital:76478 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/368455814_Exploring_The_Incremental_Improvements_of_YOLOv7_on_Bull_Sharks_in_Mozambique/links/63e8d321dea6121757a4ba7f/Exploring-The-Incremental-Improvements-of-YOLOv7-on-Bull-Sharks-in-Mozambique.pdf?origin=journalDetailand_tp=eyJwYWdlIjoiam91cm5hbERldGFpbCJ9"
- Description: Scientists use bull shark data to better understand marine organisms and to reduce the likelihood of bull shark extinction. Sharks play an important role in the ocean, and their importance is underappreciated by the general public, leading to negative attitudes toward sharks. The tracking and identification of sharks is done by hand, which is inefficient and time-consuming. This paper employs a deep learning approach to assist in the identification and tracking of bull sharks in Mozambique. YOLO is a popular object detection system used in this paper to aid in the identification of the great white shark. In addition to YOLO, the paper employs ESRGAN to help upscale low-quality images from the datasets into higher-quality images before they are fed into the YOLO system. The primary goal of this paper is to assist in training the system to identify bull sharks in difficult conditions such as murky water or unclear deep-sea conditions.
- Full Text:
- Date Issued: 2023
Early Plant Disease Detection using Infrared and Mobile Photographs in Natural Environment
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464085 , vital:76474 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37717-4_21"
- Description: Plant disease identification is a critical aspect of plant health management. Identifying plant diseases is challenging since they manifest themselves in various forms and tend to occur when the plant is still in its juvenile stage. Plant disease also has cascading effects on food security, livelihoods and the environment’s safety, so early detection is vital. This work demonstrates the effectiveness of mobile and multispectral images captured in viable and Near Infrared (NIR) ranges to identify plant diseases under realistic environmental conditions. The data sets were classified using popular CNN models Xception, DenseNet121 and ResNet50V2, resulting in greater than 92% training and 74% test accuracy for all the data collected using various Kolari vision lenses. Moreover, an openly available balanced data set was used to compare the effect of the data set balance and unbalanced characteristics on the classification accuracy. The result showed that balanced data sets do not impact the outcome.
- Full Text:
- Date Issued: 2023
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464085 , vital:76474 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37717-4_21"
- Description: Plant disease identification is a critical aspect of plant health management. Identifying plant diseases is challenging since they manifest themselves in various forms and tend to occur when the plant is still in its juvenile stage. Plant disease also has cascading effects on food security, livelihoods and the environment’s safety, so early detection is vital. This work demonstrates the effectiveness of mobile and multispectral images captured in viable and Near Infrared (NIR) ranges to identify plant diseases under realistic environmental conditions. The data sets were classified using popular CNN models Xception, DenseNet121 and ResNet50V2, resulting in greater than 92% training and 74% test accuracy for all the data collected using various Kolari vision lenses. Moreover, an openly available balanced data set was used to compare the effect of the data set balance and unbalanced characteristics on the classification accuracy. The result showed that balanced data sets do not impact the outcome.
- Full Text:
- Date Issued: 2023
Darknet Traffic Detection Using Histogram-Based Gradient Boosting
- Brown, Dane L, Sepula, Chikondi
- Authors: Brown, Dane L , Sepula, Chikondi
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464063 , vital:76472 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_59"
- Description: The network security sector has observed a rise in severe attacks emanating from the darknet or encrypted networks in recent years. Network intrusion detection systems (NIDS) capable of detecting darknet or encrypted traffic must be developed to increase system security. Machine learning algorithms can effectively detect darknet activities when trained on encrypted and conventional network data. However, the performance of the system may be influenced, among other things, by the choice of machine learning models, data preparation techniques, and feature selection methodologies. The histogram-based gradient boosting strategy known as categorical boosting (CatBoost) was tested to see how well it could find darknet traffic. The performance of the model was examined using feature selection strategies such as correlation coefficient, variance threshold, SelectKBest, and recursive feature removal (RFE). Following the categorization of traffic as “darknet” or “regular”, a multi-class classification was used to determine the software application associated with the traffic. Further study was carried out on well-known machine learning methods such as random forests (RF), decision trees (DT), linear support vector classifier (SVC Linear), and long-short term memory (LST) (LSTM). The proposed model achieved good results with 98.51% binary classification accuracy and 88% multi-class classification accuracy.
- Full Text:
- Date Issued: 2023
- Authors: Brown, Dane L , Sepula, Chikondi
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464063 , vital:76472 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_59"
- Description: The network security sector has observed a rise in severe attacks emanating from the darknet or encrypted networks in recent years. Network intrusion detection systems (NIDS) capable of detecting darknet or encrypted traffic must be developed to increase system security. Machine learning algorithms can effectively detect darknet activities when trained on encrypted and conventional network data. However, the performance of the system may be influenced, among other things, by the choice of machine learning models, data preparation techniques, and feature selection methodologies. The histogram-based gradient boosting strategy known as categorical boosting (CatBoost) was tested to see how well it could find darknet traffic. The performance of the model was examined using feature selection strategies such as correlation coefficient, variance threshold, SelectKBest, and recursive feature removal (RFE). Following the categorization of traffic as “darknet” or “regular”, a multi-class classification was used to determine the software application associated with the traffic. Further study was carried out on well-known machine learning methods such as random forests (RF), decision trees (DT), linear support vector classifier (SVC Linear), and long-short term memory (LST) (LSTM). The proposed model achieved good results with 98.51% binary classification accuracy and 88% multi-class classification accuracy.
- Full Text:
- Date Issued: 2023
Efficient Plant Disease Detection and Classification for Android
- Brown, Dane L, Mazibuko, Sifisokuhle
- Authors: Brown, Dane L , Mazibuko, Sifisokuhle
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464096 , vital:76475 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_39"
- Description: This paper investigates the feasibility of using a CNN model to diagnose plant diseases in the wild. Plant diseases are a major risk to ecosystems, human and animal health, and the quality of life overall. They may reduce farm productivity drastically, leaving farmers with financial losses and food insecurity. Small-scale farmers and producers cannot pay for an expert to look at their plants for plant diseases because it would cost too much. A mobile solution is thus built for the Android platform that utilises a unified deep learning model to diagnose plant diseases and provide farmers with treatment information. The literature-recommended CNN architectures were first analysed on the PlantVillage dataset, and the best-performing model was trained for integration into the application. While training on the tomato subset of the PlantVillage dataset, the VGG16 and InceptionV3 networks achieved a higher F1-score of 94.49% than the MobileNetsV3Large and EfficientNetB0 networks (without parameter tuning). The VGG model achieved 94.43% accuracy and 0.24 loss on the RGB PlantVillage dataset, outperforming the segmented and greyscaled datasets, and was therefore chosen for use in the application. When tested on complex data collected in the wild, the VGG16 model trained on the RGB dataset yielded an accuracy of 63.02%. Thus, this research revealed the discrepancy between simple and real-world data, as well as the viability of present methodologies for future research.
- Full Text:
- Date Issued: 2023
- Authors: Brown, Dane L , Mazibuko, Sifisokuhle
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464096 , vital:76475 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_39"
- Description: This paper investigates the feasibility of using a CNN model to diagnose plant diseases in the wild. Plant diseases are a major risk to ecosystems, human and animal health, and the quality of life overall. They may reduce farm productivity drastically, leaving farmers with financial losses and food insecurity. Small-scale farmers and producers cannot pay for an expert to look at their plants for plant diseases because it would cost too much. A mobile solution is thus built for the Android platform that utilises a unified deep learning model to diagnose plant diseases and provide farmers with treatment information. The literature-recommended CNN architectures were first analysed on the PlantVillage dataset, and the best-performing model was trained for integration into the application. While training on the tomato subset of the PlantVillage dataset, the VGG16 and InceptionV3 networks achieved a higher F1-score of 94.49% than the MobileNetsV3Large and EfficientNetB0 networks (without parameter tuning). The VGG model achieved 94.43% accuracy and 0.24 loss on the RGB PlantVillage dataset, outperforming the segmented and greyscaled datasets, and was therefore chosen for use in the application. When tested on complex data collected in the wild, the VGG16 model trained on the RGB dataset yielded an accuracy of 63.02%. Thus, this research revealed the discrepancy between simple and real-world data, as well as the viability of present methodologies for future research.
- Full Text:
- Date Issued: 2023
Learning Movement Patterns for Improving the Skills of Beginner Level Players in Competitive MOBAs
- Brown, Dane L, Bischof, Jonah
- Authors: Brown, Dane L , Bischof, Jonah
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464161 , vital:76482 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_45"
- Description: League of Legends is a massively multiplayer online battle arena (MOBA)—a form of online competitive game in which teams of five players battle to demolish the opponent’s base. Expert players are aware of when to target, how to maximise their gold, and how to make choices. These are some of the talents that distinguish them from novices. The Riot API enables the retrieval of current League of Legends game data. This data is used to construct machine learning models that can benefit amateur players. Kills and goals can assist seasoned players understand how to take advantage of micro- and macro-teams. By understanding how professional players differ from novices, we may build tools to assist novices’ decision-making. 19 of 20 games for training a random forest (RF) and decision tree (DT) regressor produced encouraging results. An unseen game was utilised to evaluate the impartiality of the findings. RF and DT correctly predicted the locations of all game events in Experiment 1 with MSEs of 9.5 and 10.6. The purpose of the previous experiment was to fine-tune when novice players deviate from professional player behaviour and establish a solid commencement for battles. Based on this discrepancy, the system provided the player with reliable recommendations on which quadrant they should be in and which event/objective they should complete. This has shown to be a beneficial method for modelling player behaviour in future research.
- Full Text:
- Date Issued: 2023
- Authors: Brown, Dane L , Bischof, Jonah
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464161 , vital:76482 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_45"
- Description: League of Legends is a massively multiplayer online battle arena (MOBA)—a form of online competitive game in which teams of five players battle to demolish the opponent’s base. Expert players are aware of when to target, how to maximise their gold, and how to make choices. These are some of the talents that distinguish them from novices. The Riot API enables the retrieval of current League of Legends game data. This data is used to construct machine learning models that can benefit amateur players. Kills and goals can assist seasoned players understand how to take advantage of micro- and macro-teams. By understanding how professional players differ from novices, we may build tools to assist novices’ decision-making. 19 of 20 games for training a random forest (RF) and decision tree (DT) regressor produced encouraging results. An unseen game was utilised to evaluate the impartiality of the findings. RF and DT correctly predicted the locations of all game events in Experiment 1 with MSEs of 9.5 and 10.6. The purpose of the previous experiment was to fine-tune when novice players deviate from professional player behaviour and establish a solid commencement for battles. Based on this discrepancy, the system provided the player with reliable recommendations on which quadrant they should be in and which event/objective they should complete. This has shown to be a beneficial method for modelling player behaviour in future research.
- Full Text:
- Date Issued: 2023
Deep Learning Approach to Image Deblurring and Image Super-Resolution using DeblurGAN and SRGAN
- Kuhlane, Luxolo L, Brown, Dane L, Connan, James, Boby, Alden, Marais, Marc
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
An evaluation of hand-based algorithms for sign language recognition
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465124 , vital:76575 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856310"
- Description: Sign language recognition is an evolving research field in computer vision, assisting communication between hearing disabled people. Hand gestures contain the majority of the information when signing. Focusing on feature extraction methods to obtain the information stored in hand data in sign language recognition may improve classification accuracy. Pose estimation is a popular method for extracting body and hand landmarks. We implement and compare different feature extraction and segmentation algorithms, focusing on the hands only on the LSA64 dataset. To extract hand landmark coordinates, MediaPipe Holistic is implemented on the sign images. Classification is performed using poplar CNN architectures, namely ResNet and a Pruned VGG network. A separate 1D-CNN is utilised to classify hand landmark coordinates extracted using MediaPipe. The best performance was achieved on the unprocessed raw images using a Pruned VGG network with an accuracy of 95.50%. However, the more computationally efficient model using the hand landmark data and 1D-CNN for classification achieved an accuracy of 94.91%.
- Full Text:
- Date Issued: 2022
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465124 , vital:76575 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856310"
- Description: Sign language recognition is an evolving research field in computer vision, assisting communication between hearing disabled people. Hand gestures contain the majority of the information when signing. Focusing on feature extraction methods to obtain the information stored in hand data in sign language recognition may improve classification accuracy. Pose estimation is a popular method for extracting body and hand landmarks. We implement and compare different feature extraction and segmentation algorithms, focusing on the hands only on the LSA64 dataset. To extract hand landmark coordinates, MediaPipe Holistic is implemented on the sign images. Classification is performed using poplar CNN architectures, namely ResNet and a Pruned VGG network. A separate 1D-CNN is utilised to classify hand landmark coordinates extracted using MediaPipe. The best performance was achieved on the unprocessed raw images using a Pruned VGG network with an accuracy of 95.50%. However, the more computationally efficient model using the hand landmark data and 1D-CNN for classification achieved an accuracy of 94.91%.
- Full Text:
- Date Issued: 2022
A Robust Portable Environment for First-Year Computer Science Students
- Brown, Dane L, Connan, James
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
Deep face-iris recognition using robust image segmentation and hyperparameter tuning
- Authors: Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465145 , vital:76577 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-16-3728-5_19"
- Description: Biometrics are increasingly being used for tasks that involve sensitive or financial data. Hitherto, security on devices such as smartphones has not been a priority. Furthermore, users tend to ignore the security features in favour of more rapid access to the device. A bimodal system is proposed that enhances security by utilizing face and iris biometrics from a single image. The motivation behind this is the ability to acquire both biometrics simultaneously in one shot. The system’s biometric components: face, iris(es) and their fusion are evaluated. They are also compared to related studies. The best results were yielded by a proposed lightweight Convolutional Neural Network architecture, outperforming tuned VGG-16, Xception, SVM and the related works. The system shows advancements to ‘at-a-distance’ biometric recognition for limited and high computational capacity computing devices. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling additional accuracy gains. Highlights include near-perfect fivefold cross-validation accuracy on the IITD-Iris dataset when performing identification. Verification tests were carried out on the challenging CASIA-Iris-Distance dataset and performed well on few training samples. The proposed system is practical for small or large amounts of training data and shows great promise for at-a-distance recognition and biometric fusion.
- Full Text:
- Date Issued: 2022
- Authors: Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465145 , vital:76577 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-16-3728-5_19"
- Description: Biometrics are increasingly being used for tasks that involve sensitive or financial data. Hitherto, security on devices such as smartphones has not been a priority. Furthermore, users tend to ignore the security features in favour of more rapid access to the device. A bimodal system is proposed that enhances security by utilizing face and iris biometrics from a single image. The motivation behind this is the ability to acquire both biometrics simultaneously in one shot. The system’s biometric components: face, iris(es) and their fusion are evaluated. They are also compared to related studies. The best results were yielded by a proposed lightweight Convolutional Neural Network architecture, outperforming tuned VGG-16, Xception, SVM and the related works. The system shows advancements to ‘at-a-distance’ biometric recognition for limited and high computational capacity computing devices. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling additional accuracy gains. Highlights include near-perfect fivefold cross-validation accuracy on the IITD-Iris dataset when performing identification. Verification tests were carried out on the challenging CASIA-Iris-Distance dataset and performed well on few training samples. The proposed system is practical for small or large amounts of training data and shows great promise for at-a-distance recognition and biometric fusion.
- Full Text:
- Date Issued: 2022
Plant disease detection using deep learning on natural environment images
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465212 , vital:76583 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9855925"
- Description: Improving agriculture is one of the major concerns today, as it helps reduce global hunger. In past years, many technological advancements have been introduced to enhance harvest quality and quantity by controlling and preventing weeds, pests, and diseases. Several studies have focused on identifying diseases in plants, as it helps to make decisions on spraying fungicides and fertilizers. State-of-the-art systems typically combine image processing and deep learning methods to identify conditions with visible symptoms. However, they use already available data sets or images taken in controlled environments. This study was conducted on two data sets of ten plants collected in a natural environment. The first dataset contained RGB Visible images, while the second contained Near-Infrared (NIR) images of healthy and diseased leaves. The visible image dataset showed higher training and validation accuracies than the NIR image dataset with ResNet, Inception, VGG and MobileNet architectures. For the visible image and NIR dataset, ResNet-50V2 outperformed other models with validation accuracies of 98.35% and 94.01%, respectively.
- Full Text:
- Date Issued: 2022
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465212 , vital:76583 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9855925"
- Description: Improving agriculture is one of the major concerns today, as it helps reduce global hunger. In past years, many technological advancements have been introduced to enhance harvest quality and quantity by controlling and preventing weeds, pests, and diseases. Several studies have focused on identifying diseases in plants, as it helps to make decisions on spraying fungicides and fertilizers. State-of-the-art systems typically combine image processing and deep learning methods to identify conditions with visible symptoms. However, they use already available data sets or images taken in controlled environments. This study was conducted on two data sets of ten plants collected in a natural environment. The first dataset contained RGB Visible images, while the second contained Near-Infrared (NIR) images of healthy and diseased leaves. The visible image dataset showed higher training and validation accuracies than the NIR image dataset with ResNet, Inception, VGG and MobileNet architectures. For the visible image and NIR dataset, ResNet-50V2 outperformed other models with validation accuracies of 98.35% and 94.01%, respectively.
- Full Text:
- Date Issued: 2022
An Evaluation of YOLO-Based Algorithms for Hand Detection in the Kitchen
- Van Staden, Joshua, Brown, Dane L
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465134 , vital:76576 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519307"
- Description: Convolutional Neural Networks have offered an accurate method with which to run object detection on images. Specifically, the YOLO family of object detection algorithms have proven to be relatively fast and accurate. Since its inception, the different variants of this algorithm have been tested on different datasets. In this paper, we evaluate the performances of these algorithms on the recent Epic Kitchens-100 dataset. This dataset provides egocentric footage of people interacting with various objects in the kitchen. Most prominently shown in the footage is an egocentric view of the participants' hands. We aim to use the YOLOv3 algorithm to detect these hands within the footage provided in this dataset. In particular, we examine the YOLOv3 algorithm using two different backbones: MobileNet-lite and VGG16. We trained them on a mixture of samples from the Egohands and Epic Kitchens-100 datasets. In a separate experiment, average precision was measured on an unseen Epic Kitchens-100 subset. We found that the models are relatively simple and lead to lower scores on the Epic Kitchens 100 dataset. This is attributed to the high background noise on the Epic Kitchens 100 dataset. Nonetheless, the VGG16 architecture was found to have a higher Average Precision (AP) and is, therefore, more suited for retrospective analysis. None of the models was suitable for real-time analysis due to complex egocentric data.
- Full Text:
- Date Issued: 2021
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465134 , vital:76576 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519307"
- Description: Convolutional Neural Networks have offered an accurate method with which to run object detection on images. Specifically, the YOLO family of object detection algorithms have proven to be relatively fast and accurate. Since its inception, the different variants of this algorithm have been tested on different datasets. In this paper, we evaluate the performances of these algorithms on the recent Epic Kitchens-100 dataset. This dataset provides egocentric footage of people interacting with various objects in the kitchen. Most prominently shown in the footage is an egocentric view of the participants' hands. We aim to use the YOLOv3 algorithm to detect these hands within the footage provided in this dataset. In particular, we examine the YOLOv3 algorithm using two different backbones: MobileNet-lite and VGG16. We trained them on a mixture of samples from the Egohands and Epic Kitchens-100 datasets. In a separate experiment, average precision was measured on an unseen Epic Kitchens-100 subset. We found that the models are relatively simple and lead to lower scores on the Epic Kitchens 100 dataset. This is attributed to the high background noise on the Epic Kitchens 100 dataset. Nonetheless, the VGG16 architecture was found to have a higher Average Precision (AP) and is, therefore, more suited for retrospective analysis. None of the models was suitable for real-time analysis due to complex egocentric data.
- Full Text:
- Date Issued: 2021
Investigating popular CNN architectures for plant disease detection
- Poole, Louise C, Brown, Dane L
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465168 , vital:76579 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description: Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
- Full Text:
- Date Issued: 2021
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465168 , vital:76579 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description: Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
- Full Text:
- Date Issued: 2021
Investigating signer-independent sign language recognition on the lsa64 dataset
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden, Kuhlane, Luxolo L
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
Mobile attendance based on face detection and recognition using OpenVINO
- Authors: Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465201 , vital:76582 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9395836"
- Description: The OpenVINO toolkit enables versatile computer vision with an Intel® Movidius ™ Neural Compute Stick 2 connected to a Raspberry Pi. This small portable platform provides new opportunities for innovative solutions in computer vision applications and beyond. This paper investigates its feasibility for mobile attendance systems for settings such as classrooms or other scenarios that require headcount or roll call. Related studies of face-based systems are explored, while the advantages of the proposed system are highlighted. Although there are some positioning constraints, the proof-of-concept system processes an approximate average of five faces per second. That means it can take attendance in a lecture room of 90 students in about 18 seconds. A recognition accuracy of 98.1% with an f-score of 96.9% was yielded on a private classroom dataset captured with a modest RPi camera. These promising results were achieved using a tiny ResNet-18 architecture, producing significantly better results than MobileNet. Furthermore, it outperformed the recognition accuracy of other ‘lightweight’ methods used in the literature that do not run off embedded devices on publicly available datasets.
- Full Text:
- Date Issued: 2021
- Authors: Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465201 , vital:76582 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9395836"
- Description: The OpenVINO toolkit enables versatile computer vision with an Intel® Movidius ™ Neural Compute Stick 2 connected to a Raspberry Pi. This small portable platform provides new opportunities for innovative solutions in computer vision applications and beyond. This paper investigates its feasibility for mobile attendance systems for settings such as classrooms or other scenarios that require headcount or roll call. Related studies of face-based systems are explored, while the advantages of the proposed system are highlighted. Although there are some positioning constraints, the proof-of-concept system processes an approximate average of five faces per second. That means it can take attendance in a lecture room of 90 students in about 18 seconds. A recognition accuracy of 98.1% with an f-score of 96.9% was yielded on a private classroom dataset captured with a modest RPi camera. These promising results were achieved using a tiny ResNet-18 architecture, producing significantly better results than MobileNet. Furthermore, it outperformed the recognition accuracy of other ‘lightweight’ methods used in the literature that do not run off embedded devices on publicly available datasets.
- Full Text:
- Date Issued: 2021
Investigating the Effects of Image Correction Through Affine Transformations on Licence Plate Recognition
- Boby, Alden, Brown, Dane L, Connan, James, Marais, Marc
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465190 , vital:76581 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856380"
- Description: Licence plate recognition has many real-world applications, which fall under security and surveillance. Deep learning for licence plate recognition has been adopted to improve existing image-based processing techniques in recent years. Object detectors are a popular choice for approaching this task. All object detectors are some form of a convolutional neural network. The You Only Look Once framework and Region-Based Convolutional Neural Networks are popular models within this field. A novel architecture called the Warped Planar Object Detector is a recent development by Zou et al. that takes inspiration from YOLO and Spatial Network Transformers. This paper aims to compare the performance of the Warped Planar Object Detector and YOLO on licence plate recognition by training both models with the same data and then directing their output to an Enhanced Super-Resolution Generative Adversarial Network to upscale the output image, then lastly using an Optical Character Recognition engine to classify characters detected from the images.
- Full Text:
- Date Issued: 2022
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465190 , vital:76581 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856380"
- Description: Licence plate recognition has many real-world applications, which fall under security and surveillance. Deep learning for licence plate recognition has been adopted to improve existing image-based processing techniques in recent years. Object detectors are a popular choice for approaching this task. All object detectors are some form of a convolutional neural network. The You Only Look Once framework and Region-Based Convolutional Neural Networks are popular models within this field. A novel architecture called the Warped Planar Object Detector is a recent development by Zou et al. that takes inspiration from YOLO and Spatial Network Transformers. This paper aims to compare the performance of the Warped Planar Object Detector and YOLO on licence plate recognition by training both models with the same data and then directing their output to an Enhanced Super-Resolution Generative Adversarial Network to upscale the output image, then lastly using an Optical Character Recognition engine to classify characters detected from the images.
- Full Text:
- Date Issued: 2022
Using Technology to Teach a New Generation
- Connan, James, Brown, Dane L, Watkins, Caroline
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
- Authors: Connan, James , Brown, Dane L , Watkins, Caroline
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465223 , vital:76584 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_8"
- Description: Introductory programming courses attract students from diverse backgrounds in terms of ability, motivation and experience. This paper introduces two technological tools, Thonny and Runestone Academy, that can be used to enhance introductory courses. These tools enable instructors to track the progress of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.
- Full Text:
- Date Issued: 2021
An Evaluation of Machine Learning Methods for Classifying Bot Traffic in Software Defined Networks
- Van Staden, Joshua, Brown, Dane L
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463357 , vital:76402 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-7874-6_72"
- Description: Internet security is an ever-expanding field. Cyber-attacks occur very frequently, and so detecting them is an important aspect of preserving services. Machine learning offers a helpful tool with which to detect cyber attacks. However, it is impossible to deploy a machine-learning algorithm to detect attacks in a non-centralized network. Software Defined Networks (SDNs) offer a centralized view of a network, allowing machine learning algorithms to detect malicious activity within a network. The InSDN dataset is a recently-released dataset that contains a set of sniffed packets within a virtual SDN. These sniffed packets correspond to various attacks, including DDoS attacks, Probing and Password-Guessing, among others. This study aims to evaluate various machine learning models against this new dataset. Specifically, we aim to evaluate their classification ability and runtimes when trained on fewer features. The machine learning models tested include a Neural Network, Support Vector Machine, Random Forest, Multilayer Perceptron, Logistic Regression, and K-Nearest Neighbours. Cluster-based algorithms such as the K-Nearest Neighbour and Random Forest proved to be the best performers. Linear-based algorithms such as the Multilayer Perceptron performed the worst. This suggests a good level of clustering in the top few features with little space for linear separability. The reduction of features significantly reduced training time, particularly in the better-performing models.
- Full Text:
- Date Issued: 2023
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463357 , vital:76402 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-7874-6_72"
- Description: Internet security is an ever-expanding field. Cyber-attacks occur very frequently, and so detecting them is an important aspect of preserving services. Machine learning offers a helpful tool with which to detect cyber attacks. However, it is impossible to deploy a machine-learning algorithm to detect attacks in a non-centralized network. Software Defined Networks (SDNs) offer a centralized view of a network, allowing machine learning algorithms to detect malicious activity within a network. The InSDN dataset is a recently-released dataset that contains a set of sniffed packets within a virtual SDN. These sniffed packets correspond to various attacks, including DDoS attacks, Probing and Password-Guessing, among others. This study aims to evaluate various machine learning models against this new dataset. Specifically, we aim to evaluate their classification ability and runtimes when trained on fewer features. The machine learning models tested include a Neural Network, Support Vector Machine, Random Forest, Multilayer Perceptron, Logistic Regression, and K-Nearest Neighbours. Cluster-based algorithms such as the K-Nearest Neighbour and Random Forest proved to be the best performers. Linear-based algorithms such as the Multilayer Perceptron performed the worst. This suggests a good level of clustering in the top few features with little space for linear separability. The reduction of features significantly reduced training time, particularly in the better-performing models.
- Full Text:
- Date Issued: 2023
Improved palmprint segmentation for robust identification and verification
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/460576 , vital:75966 , xlink:href="https://doi.org/10.1109/SITIS.2019.00013"
- Description: This paper introduces an improved approach to palmprint segmentation. The approach enables both contact and contactless palmprints to be segmented regardless of constraining finger positions or whether fingers are even depicted within the image. It is compared with related systems, as well as more comprehensive identification tests, that show consistent results across other datasets. Experiments include contact and contactless palmprint images. The proposed system achieves highly accurate classification results, and highlights the importance of effective image segmentation. The proposed system is practical as it is effective with small or large amounts of training data.
- Full Text:
- Date Issued: 2019
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/460576 , vital:75966 , xlink:href="https://doi.org/10.1109/SITIS.2019.00013"
- Description: This paper introduces an improved approach to palmprint segmentation. The approach enables both contact and contactless palmprints to be segmented regardless of constraining finger positions or whether fingers are even depicted within the image. It is compared with related systems, as well as more comprehensive identification tests, that show consistent results across other datasets. Experiments include contact and contactless palmprint images. The proposed system achieves highly accurate classification results, and highlights the importance of effective image segmentation. The proposed system is practical as it is effective with small or large amounts of training data.
- Full Text:
- Date Issued: 2019