A Practical Use for AI-Generated Images
- Boby, Alden, Brown, Dane L, Connan, James
- Authors: Boby, Alden , Brown, Dane L , Connan, James
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463345 , vital:76401 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-43838-7_12"
- Description: Collecting data for research can be costly and time-consuming, and available methods to speed up the process are limited. This research paper compares real data and AI-generated images for training an object detection model. The study aimed to assess how the utilisation of AI-generated images influences the performance of an object detection model. The study used a popular object detection model, YOLO, and trained it on a dataset with real car images as well as a synthetic dataset generated with a state-of-the-art diffusion model. The results showed that while the model trained on real data performed better on real-world images, the model trained on AI-generated images, in some cases, showed improved performance on certain images and was good enough to function as a licence plate detector on its own. The study highlights the potential of using AI-generated images for data augmentation in object detection models and sheds light on the trade-off between real and synthetic data in the training process. The findings of this study can inform future research in object detection and help practitioners make informed decisions when choosing between real and synthetic data for training object detection models.
- Full Text:
- Date Issued: 2023
- Authors: Boby, Alden , Brown, Dane L , Connan, James
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463345 , vital:76401 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-43838-7_12"
- Description: Collecting data for research can be costly and time-consuming, and available methods to speed up the process are limited. This research paper compares real data and AI-generated images for training an object detection model. The study aimed to assess how the utilisation of AI-generated images influences the performance of an object detection model. The study used a popular object detection model, YOLO, and trained it on a dataset with real car images as well as a synthetic dataset generated with a state-of-the-art diffusion model. The results showed that while the model trained on real data performed better on real-world images, the model trained on AI-generated images, in some cases, showed improved performance on certain images and was good enough to function as a licence plate detector on its own. The study highlights the potential of using AI-generated images for data augmentation in object detection models and sheds light on the trade-off between real and synthetic data in the training process. The findings of this study can inform future research in object detection and help practitioners make informed decisions when choosing between real and synthetic data for training object detection models.
- Full Text:
- Date Issued: 2023
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
Best practices in the use and exchange of microorganism biological control genetic resources
- Mason, Peter G, Hill, Martin P, Smith, David, Silvestri, Luciano C, Weyl, Philip S R, Brodeur, Jacques, Vitorino, Marcello Diniz
- Authors: Mason, Peter G , Hill, Martin P , Smith, David , Silvestri, Luciano C , Weyl, Philip S R , Brodeur, Jacques , Vitorino, Marcello Diniz
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/417927 , vital:71495 , xlink:href="https://doi.org/10.1007/s10526-023-10197-3"
- Description: The Nagoya Protocol actions the third objective of the Convention on Biological Diversity and provides a framework to effectively implement the fair and equitable sharing of benefits arising out of the use of genetic resources. This includes microorganisms used as biological control agents. Thus biological control practitioners must comply with access and benefit-sharing regulations that are implemented by countries providing microbial biological control agents. A review of best practices and guidance for the use and exchange of microorganisms used for biological control has been prepared by the IOBC Global Commission on Biological Control and Access and Benefit-Sharing to demonstrate commitment to comply with access and benefit-sharing requirements, and to reassure the international community that biological control is a very successful and environmentally safe pest management strategy that uses biological resources responsibly and sustainably. We propose that best practices include the following elements: collaboration to facilitate information exchange about the availability of microbial biological control agents and where they may be sourced; freely sharing available knowledge in databases about successes and failures; collaborative research with provider countries to develop capacity; and production technology transfer to provide economic opportunities. We recommend the use of model concept agreements for accessing microorganisms for scientific research and non-commercial release into nature where access and benefit-sharing regulations exist and where regulations are not restrictive or do not exist. We also recommend a model agreement for deposition of microbial biological control agents into culture collections.
- Full Text:
- Date Issued: 2023
- Authors: Mason, Peter G , Hill, Martin P , Smith, David , Silvestri, Luciano C , Weyl, Philip S R , Brodeur, Jacques , Vitorino, Marcello Diniz
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/417927 , vital:71495 , xlink:href="https://doi.org/10.1007/s10526-023-10197-3"
- Description: The Nagoya Protocol actions the third objective of the Convention on Biological Diversity and provides a framework to effectively implement the fair and equitable sharing of benefits arising out of the use of genetic resources. This includes microorganisms used as biological control agents. Thus biological control practitioners must comply with access and benefit-sharing regulations that are implemented by countries providing microbial biological control agents. A review of best practices and guidance for the use and exchange of microorganisms used for biological control has been prepared by the IOBC Global Commission on Biological Control and Access and Benefit-Sharing to demonstrate commitment to comply with access and benefit-sharing requirements, and to reassure the international community that biological control is a very successful and environmentally safe pest management strategy that uses biological resources responsibly and sustainably. We propose that best practices include the following elements: collaboration to facilitate information exchange about the availability of microbial biological control agents and where they may be sourced; freely sharing available knowledge in databases about successes and failures; collaborative research with provider countries to develop capacity; and production technology transfer to provide economic opportunities. We recommend the use of model concept agreements for accessing microorganisms for scientific research and non-commercial release into nature where access and benefit-sharing regulations exist and where regulations are not restrictive or do not exist. We also recommend a model agreement for deposition of microbial biological control agents into culture collections.
- 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
Diversification of the African legless skinks in the subfamily Acontinae (Family Scincidae)
- Zhao, Zhongning, Conradie, Werner C, Pietersen, Darren W, Jordaan, Adriaan, Nicolau, Gary K, Edwards, Shelley, Riekert, Stephanus, Heideman, Neil
- Authors: Zhao, Zhongning , Conradie, Werner C , Pietersen, Darren W , Jordaan, Adriaan , Nicolau, Gary K , Edwards, Shelley , Riekert, Stephanus , Heideman, Neil
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/461495 , vital:76207 , xlink:href="https://doi.org/10.1016/j.ympev.2023.107747"
- Description: Cladogenic diversification is often explained by referring to climatic oscillations and geomorphic shifts that cause allopatric speciation. In this regard, southern Africa retains a high level of landscape heterogeneity in vegetation, geology, and rainfall patterns. The legless skink subfamily Acontinae occurs broadly across the southern African subcontinent and therefore provides an ideal model group for investigating biogeographic patterns associated with the region. A robust phylogenetic study of the Acontinae with comprehensive coverage and adequate sampling of each taxon has been lacking up until now, resulting in unresolved questions regarding the subfamily’s biogeography and evolution. In this study, we used multi-locus genetic markers (three mitochondrial and two nuclear) with comprehensive taxon coverage (all currently recognized Acontinae species) and adequate sampling (multiple specimens for most taxa) of each taxon to infer a phylogeny for the subfamily.
- Full Text:
- Date Issued: 2023
- Authors: Zhao, Zhongning , Conradie, Werner C , Pietersen, Darren W , Jordaan, Adriaan , Nicolau, Gary K , Edwards, Shelley , Riekert, Stephanus , Heideman, Neil
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/461495 , vital:76207 , xlink:href="https://doi.org/10.1016/j.ympev.2023.107747"
- Description: Cladogenic diversification is often explained by referring to climatic oscillations and geomorphic shifts that cause allopatric speciation. In this regard, southern Africa retains a high level of landscape heterogeneity in vegetation, geology, and rainfall patterns. The legless skink subfamily Acontinae occurs broadly across the southern African subcontinent and therefore provides an ideal model group for investigating biogeographic patterns associated with the region. A robust phylogenetic study of the Acontinae with comprehensive coverage and adequate sampling of each taxon has been lacking up until now, resulting in unresolved questions regarding the subfamily’s biogeography and evolution. In this study, we used multi-locus genetic markers (three mitochondrial and two nuclear) with comprehensive taxon coverage (all currently recognized Acontinae species) and adequate sampling (multiple specimens for most taxa) of each taxon to infer a phylogeny for the subfamily.
- 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
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
Enhanced plant species and early water stress detection using visible and near-infrared spectra
- Brown, Dane L, Poole, Louise C
- Authors: Brown, Dane L , Poole, Louise C
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463384 , vital:76404 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-9819-5_55"
- Description: This paper reports on recent successful work aimed at preventing crop loss and failure before visible symptoms are present. Food security is critical, especially after the COVID-19 pandemic. Detecting early-stage plant stresses in agriculture is essential in minimizing crop damage and maximizing yield. Identification of both the stress type and cause is a non-trivial multitask classification problem. However, the application of spectroscopy to early plant diseases and stress detection has become viable with recent advancements in technology. Suitable frequencies of the electromagnetic spectrum and machine learning algorithms were thus first investigated. This guided data collection in two sessions by capturing standard visible images in contrast with images from multiple spectra (VIS-IR). These images consisted of six plant species that were carefully monitored from healthy to dehydrated stages. Promising results were achieved using VIS-IR compared to standard visible images on three deep learning architectures. Statistically, significant accuracy improvements were shown for VIS-IR for early dehydration detection, where ResNet-44 modelling of VIS-IR input yielded 92.5% accuracy compared to 77.5% on visible input on general plant species. Moreover, ResNet-44 achieved good species separation.
- Full Text:
- Date Issued: 2023
- Authors: Brown, Dane L , Poole, Louise C
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463384 , vital:76404 , xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-9819-5_55"
- Description: This paper reports on recent successful work aimed at preventing crop loss and failure before visible symptoms are present. Food security is critical, especially after the COVID-19 pandemic. Detecting early-stage plant stresses in agriculture is essential in minimizing crop damage and maximizing yield. Identification of both the stress type and cause is a non-trivial multitask classification problem. However, the application of spectroscopy to early plant diseases and stress detection has become viable with recent advancements in technology. Suitable frequencies of the electromagnetic spectrum and machine learning algorithms were thus first investigated. This guided data collection in two sessions by capturing standard visible images in contrast with images from multiple spectra (VIS-IR). These images consisted of six plant species that were carefully monitored from healthy to dehydrated stages. Promising results were achieved using VIS-IR compared to standard visible images on three deep learning architectures. Statistically, significant accuracy improvements were shown for VIS-IR for early dehydration detection, where ResNet-44 modelling of VIS-IR input yielded 92.5% accuracy compared to 77.5% on visible input on general plant species. Moreover, ResNet-44 achieved good species separation.
- Full Text:
- Date Issued: 2023
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
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
Impact of Access and Benefit Sharing implementation on biological control genetic resources
- Mason, Peter G, Barratt, Barbara I P, Mc Kay, Fernando, Klapwijk, Johannette N, Silvestri, Luciano C, Hill, Martin P, Hinz, Hariet L, Sheppard, Andy, Brodeur, Jacques, Vitorino, Marcello Diniz, Weyl, Philip S R, Hoelmer, Kim A
- Authors: Mason, Peter G , Barratt, Barbara I P , Mc Kay, Fernando , Klapwijk, Johannette N , Silvestri, Luciano C , Hill, Martin P , Hinz, Hariet L , Sheppard, Andy , Brodeur, Jacques , Vitorino, Marcello Diniz , Weyl, Philip S R , Hoelmer, Kim A
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/418013 , vital:71501 , xlink:href="https://doi.org/10.1007/s10526-023-10176-8"
- Description: The third objective of the Convention on Biological Diversity, the fair and equitable sharing of benefits arising out of the use of genetic resources was further developed when the Nagoya Protocol on Access and Benefit Sharing came into effect in 2014. Interpretation of how this agreement is being implemented is wide-ranging and there are implications for biological control. A survey of biological control workers indicated that while some countries have facilitated access to biological control genetic resources, requirements in other countries have impeded biological control implementation. There was consensus that benefits to provider countries should be in the form of supporting local research communities. There was also agreement that the free use and exchange of biological control genetic resources has provided benefits to the global community, including to both providers and recipients of the agents. It is recommended that consideration of the free use and exchange principal should be a key element of Access and Benefit Sharing measures for the future.
- Full Text:
- Date Issued: 2023
- Authors: Mason, Peter G , Barratt, Barbara I P , Mc Kay, Fernando , Klapwijk, Johannette N , Silvestri, Luciano C , Hill, Martin P , Hinz, Hariet L , Sheppard, Andy , Brodeur, Jacques , Vitorino, Marcello Diniz , Weyl, Philip S R , Hoelmer, Kim A
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/418013 , vital:71501 , xlink:href="https://doi.org/10.1007/s10526-023-10176-8"
- Description: The third objective of the Convention on Biological Diversity, the fair and equitable sharing of benefits arising out of the use of genetic resources was further developed when the Nagoya Protocol on Access and Benefit Sharing came into effect in 2014. Interpretation of how this agreement is being implemented is wide-ranging and there are implications for biological control. A survey of biological control workers indicated that while some countries have facilitated access to biological control genetic resources, requirements in other countries have impeded biological control implementation. There was consensus that benefits to provider countries should be in the form of supporting local research communities. There was also agreement that the free use and exchange of biological control genetic resources has provided benefits to the global community, including to both providers and recipients of the agents. It is recommended that consideration of the free use and exchange principal should be a key element of Access and Benefit Sharing measures for the future.
- Full Text:
- Date Issued: 2023
International agreement for the use and exchange of classical biological control genetic resources: a practical proposal
- Mason, Peter G, Mc Kay, Fernando, Silvestri, Luciano C, Hill, Martin P, Weyl, Philip S R, Hinz, Hariet L, Brodeur, Jacques, Vitorino, Marcello Diniz, Barratt, Barbara I P
- Authors: Mason, Peter G , Mc Kay, Fernando , Silvestri, Luciano C , Hill, Martin P , Weyl, Philip S R , Hinz, Hariet L , Brodeur, Jacques , Vitorino, Marcello Diniz , Barratt, Barbara I P
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/418025 , vital:71502 , xlink:href="https://doi.org/10.1007/s10526-023-10177-7"
- Description: The Nagoya Protocol on Access and Benefit Sharing (ABS) was implemented to further develop the third objective of the Convention on Biological Diversity, the fair and equitable sharing of benefits arising out of the utilization of genetic resources. Interpretation of this agreement is wide-ranging and there is concern that if ABS measures are poorly implemented biological control and the resultant public good will be greatly impeded. The ethos of multilateral use and exchange of genetic resources used in classical biological control will be particularly affected. In the spirit of the fair and equitable sharing of benefits arising out of the utilization of genetic resources, we propose a simple practical solution in the form of an international agreement on the use and exchange of classical biological control genetic resources.
- Full Text:
- Date Issued: 2023
- Authors: Mason, Peter G , Mc Kay, Fernando , Silvestri, Luciano C , Hill, Martin P , Weyl, Philip S R , Hinz, Hariet L , Brodeur, Jacques , Vitorino, Marcello Diniz , Barratt, Barbara I P
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/418025 , vital:71502 , xlink:href="https://doi.org/10.1007/s10526-023-10177-7"
- Description: The Nagoya Protocol on Access and Benefit Sharing (ABS) was implemented to further develop the third objective of the Convention on Biological Diversity, the fair and equitable sharing of benefits arising out of the utilization of genetic resources. Interpretation of this agreement is wide-ranging and there is concern that if ABS measures are poorly implemented biological control and the resultant public good will be greatly impeded. The ethos of multilateral use and exchange of genetic resources used in classical biological control will be particularly affected. In the spirit of the fair and equitable sharing of benefits arising out of the utilization of genetic resources, we propose a simple practical solution in the form of an international agreement on the use and exchange of classical biological control genetic resources.
- 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
Nagoya Protocol and Africa’s willingness to share biological control agents, are we deterred by barriers instead of using opportunities to work together?
- Ivey, Philip J, Hill, Martin P, Voukeng, Sonia Nadege Kenfack, Weaver, Kim N
- Authors: Ivey, Philip J , Hill, Martin P , Voukeng, Sonia Nadege Kenfack , Weaver, Kim N
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/418040 , vital:71503 , xlink:href="https://doi.org/10.1007/s10526-023-10184-8"
- Description: Amongst members of the biological control community there is a range of perceptions regarding the Nagoya Protocol, at best it will hinder access to natural enemies of pests and invasive plants and at worst implementation of the Protocol will prevent access to these resources. In this preliminary study of Africa’s preparedness to implement the Nagoya Protocol and control access to potential biological control agents, we found that several countries have not yet established procedures and policies in this regard. Several factors including lack of awareness, insufficient relevant information and lack of capacity may cause delay in countries implementing access and benefit sharing legislation and processes. The lack of preparedness provides an opportunity for the research community to work with government officials to facilitate future access to natural enemies to act as biological control agents on invasive plants and agricultural pests. Collaboration between researchers, managers and bureaucrats in support of African countries could lead to collective action that develops policies and implements processes to foster exploration of African biodiversity. This collaboration could also foster the sharing of biological control agents that will benefit Africa through integrated pest management in agriculture, protection of human lives and livelihoods, and reduction of the impact of invasive alien species on biodiversity and environmental infrastructure.
- Full Text:
- Date Issued: 2023
- Authors: Ivey, Philip J , Hill, Martin P , Voukeng, Sonia Nadege Kenfack , Weaver, Kim N
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/418040 , vital:71503 , xlink:href="https://doi.org/10.1007/s10526-023-10184-8"
- Description: Amongst members of the biological control community there is a range of perceptions regarding the Nagoya Protocol, at best it will hinder access to natural enemies of pests and invasive plants and at worst implementation of the Protocol will prevent access to these resources. In this preliminary study of Africa’s preparedness to implement the Nagoya Protocol and control access to potential biological control agents, we found that several countries have not yet established procedures and policies in this regard. Several factors including lack of awareness, insufficient relevant information and lack of capacity may cause delay in countries implementing access and benefit sharing legislation and processes. The lack of preparedness provides an opportunity for the research community to work with government officials to facilitate future access to natural enemies to act as biological control agents on invasive plants and agricultural pests. Collaboration between researchers, managers and bureaucrats in support of African countries could lead to collective action that develops policies and implements processes to foster exploration of African biodiversity. This collaboration could also foster the sharing of biological control agents that will benefit Africa through integrated pest management in agriculture, protection of human lives and livelihoods, and reduction of the impact of invasive alien species on biodiversity and environmental infrastructure.
- Full Text:
- Date Issued: 2023
The complexities of trans women’s access to healthcare in South Africa: moving health systems beyond the gender binary towards gender equity
- Shabalala, Siyanda B, Campbell, Megan M
- Authors: Shabalala, Siyanda B , Campbell, Megan M
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/450741 , vital:74978 , xlink:href="https://doi.org/10.1186/s12939-023-02039-6"
- Description: Public health research highlights the influence of socio-political biases shaping obstacles to fair healthcare access based on gender. South Africa has shown commitment to resolving gender imbalances in healthcare, historically emphasizing cisgender women’s challenges. However, research gaps exist in exploring how public health systems perpetuate disparities among gender-diverse persons, like trans women, who face exclusion due to their deviation from cisgender norms in healthcare. Critical, intersectionality-informed health research carries the potential to reveal the diversity of gendered healthcare experiences and expose the systems and processes that marginalize trans patients.
- Full Text:
- Date Issued: 2023
- Authors: Shabalala, Siyanda B , Campbell, Megan M
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/450741 , vital:74978 , xlink:href="https://doi.org/10.1186/s12939-023-02039-6"
- Description: Public health research highlights the influence of socio-political biases shaping obstacles to fair healthcare access based on gender. South Africa has shown commitment to resolving gender imbalances in healthcare, historically emphasizing cisgender women’s challenges. However, research gaps exist in exploring how public health systems perpetuate disparities among gender-diverse persons, like trans women, who face exclusion due to their deviation from cisgender norms in healthcare. Critical, intersectionality-informed health research carries the potential to reveal the diversity of gendered healthcare experiences and expose the systems and processes that marginalize trans patients.
- Full Text:
- Date Issued: 2023
AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis
- Isamura, Bienfait K, Lobb, Kevin A
- Authors: Isamura, Bienfait K , Lobb, Kevin A
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/453143 , vital:75226 , xlink:href="https://link.springer.com/article/10.1186/s13321-022-00618-3"
- Description: Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to generate any Diels–Alder (DA) transition state geometry (TS) and analyze determined IRC paths in a (quasi-)automated fashion, given the product SMILES. Two modules of the package are devoted to performing, from IRC paths, reaction force analyses (RFA) and atomic (fragment) decompositions of the reaction force F and reaction force constant κ. The performance of the protocol has been assessed using a dataset of 2000 DA cycloadducts retrieved from the ZINC database. The sequential location of the corresponding TSs was achieved with a success rate of 95%. RFA plots confrmed the reaction force constant κ to be a good indicator of the (non)synchronicity of the associated DA reactions. Moreover, the atomic decomposition of κ allows for the rationalization of the (a)synchronicity of each DA reaction in terms of contributions stemming from pairs of interacting atoms. The source code of the AMADAR tool is available on GitHub [CMCDD/AMADAR(github. com)] and can be used directly with minor customizations, mostly regarding the local working environment of the user.
- Full Text:
- Date Issued: 2022
- Authors: Isamura, Bienfait K , Lobb, Kevin A
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/453143 , vital:75226 , xlink:href="https://link.springer.com/article/10.1186/s13321-022-00618-3"
- Description: Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to generate any Diels–Alder (DA) transition state geometry (TS) and analyze determined IRC paths in a (quasi-)automated fashion, given the product SMILES. Two modules of the package are devoted to performing, from IRC paths, reaction force analyses (RFA) and atomic (fragment) decompositions of the reaction force F and reaction force constant κ. The performance of the protocol has been assessed using a dataset of 2000 DA cycloadducts retrieved from the ZINC database. The sequential location of the corresponding TSs was achieved with a success rate of 95%. RFA plots confrmed the reaction force constant κ to be a good indicator of the (non)synchronicity of the associated DA reactions. Moreover, the atomic decomposition of κ allows for the rationalization of the (a)synchronicity of each DA reaction in terms of contributions stemming from pairs of interacting atoms. The source code of the AMADAR tool is available on GitHub [CMCDD/AMADAR(github. com)] and can be used directly with minor customizations, mostly regarding the local working environment of the user.
- Full Text:
- Date Issued: 2022
Best of both worlds: The thermal physiology of Hydrellia egeriae, a biological control agent for the submerged aquatic weed, Egeria densa in South Africa
- Smith, Rosali, Coetzee, Julie A, Hill, Martin P
- Authors: Smith, Rosali , Coetzee, Julie A , Hill, Martin P
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/417913 , vital:71494 , xlink:href="https://doi.org/10.1007/s10526-022-10142-w"
- Description: The submerged aquatic weed, Egeria densa Planch. (Hydrocharitaceae) or Brazilian waterweed, is a secondary invader of eutrophic freshwater systems in South Africa, following the successful management of floating aquatic weeds. In 2018, the leaf and stem-mining fly, Hydrellia egeriae Rodrigues-Júnior, Mathis and Hauser (Diptera: Ephydridae), was released against E. densa, the first agent released against a submerged aquatic weed in South Africa. During its life stages, the biological control agent is exposed to two environments, air and water. The thermal physiology of both life stages was investigated to optimize agent establishment through fine-tuned release strategies. The thermal physiological limits of H. egeriae encompassed its host plant’s optimal temperature range of 10 to 35 °C, with lower and upper critical temperatures of 2.6 to 47.0 °C, lower and upper lethal temperatures of − 5.6 and 40.6 °C for adults, and − 6.3 to 41.3 °C for larvae. Results from development time experiments and degree-day accumulation showed that the agent is capable of establishing at all E. densa sites in South Africa, with between 6.9 and 8.3 generations per year. However, cold temperatures (14 °C) prolonged the agent’s development time to three months, allowing it to only develop through one generation in winter. Predictions obtained from laboratory thermal physiology experiments corroborates field data, where the agent has established at all the sites it was released.
- Full Text:
- Date Issued: 2022
- Authors: Smith, Rosali , Coetzee, Julie A , Hill, Martin P
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/417913 , vital:71494 , xlink:href="https://doi.org/10.1007/s10526-022-10142-w"
- Description: The submerged aquatic weed, Egeria densa Planch. (Hydrocharitaceae) or Brazilian waterweed, is a secondary invader of eutrophic freshwater systems in South Africa, following the successful management of floating aquatic weeds. In 2018, the leaf and stem-mining fly, Hydrellia egeriae Rodrigues-Júnior, Mathis and Hauser (Diptera: Ephydridae), was released against E. densa, the first agent released against a submerged aquatic weed in South Africa. During its life stages, the biological control agent is exposed to two environments, air and water. The thermal physiology of both life stages was investigated to optimize agent establishment through fine-tuned release strategies. The thermal physiological limits of H. egeriae encompassed its host plant’s optimal temperature range of 10 to 35 °C, with lower and upper critical temperatures of 2.6 to 47.0 °C, lower and upper lethal temperatures of − 5.6 and 40.6 °C for adults, and − 6.3 to 41.3 °C for larvae. Results from development time experiments and degree-day accumulation showed that the agent is capable of establishing at all E. densa sites in South Africa, with between 6.9 and 8.3 generations per year. However, cold temperatures (14 °C) prolonged the agent’s development time to three months, allowing it to only develop through one generation in winter. Predictions obtained from laboratory thermal physiology experiments corroborates field data, where the agent has established at all the sites it was released.
- Full Text:
- Date Issued: 2022
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
Deep palmprint recognition with alignment and augmentation of limited training samples
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464074 , vital:76473 , xlink:href="https://doi.org/10.1007/s42979-021-00859-3"
- Description: This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.
- Full Text:
- Date Issued: 2022
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464074 , vital:76473 , xlink:href="https://doi.org/10.1007/s42979-021-00859-3"
- Description: This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.
- Full Text:
- Date Issued: 2022
Exploring the Incremental Improvements of YOLOv7 over YOLOv5 for Character 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/10962/463395 , vital:76405 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35644-5_5"
- Description: Technological advances are being applied to aspects of life to improve quality of living and efficiency. This speaks specifically to automation, especially in the industry. The growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. This paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. This paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET.
- 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/10962/463395 , vital:76405 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35644-5_5"
- Description: Technological advances are being applied to aspects of life to improve quality of living and efficiency. This speaks specifically to automation, especially in the industry. The growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. This paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. This paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET.
- Full Text:
- Date Issued: 2022