Investigating unimodal isolated signer-independent sign language recognition
- Authors: Marais, Marc Jason
- Date: 2024-04-04
- Subjects: Convolutional neural network , Sign language recognition , Human activity recognition , Pattern recognition systems , Neural networks (Computer science)
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/435343 , vital:73149
- Description: Sign language serves as the mode of communication for the Deaf and Hard of Hearing community, embodying a rich linguistic and cultural heritage. Recent Sign Language Recognition (SLR) system developments aim to facilitate seamless communication between the Deaf community and the broader society. However, most existing systems are limited by signer-dependent models, hindering their adaptability to diverse signing styles and signers, thus impeding their practical implementation in real-world scenarios. This research explores various unimodal approaches, both pose-based and vision-based, for isolated signer-independent SLR using RGB video input on the LSA64 and AUTSL datasets. The unimodal RGB-only input strategy provides a realistic SLR setting where alternative data sources are either unavailable or necessitate specialised equipment. Through systematic testing scenarios, isolated signer-independent SLR experiments are conducted on both datasets, primarily focusing on AUTSL – a signer-independent dataset. The vision-based R(2+1)D-18 model emerged as the top performer, achieving 90.64% accuracy on the unseen AUTSL dataset test split, closely followed by the pose-based Spatio- Temporal Graph Convolutional Network (ST-GCN) model with an accuracy of 89.95%. Furthermore, these models achieved comparable accuracies at a significantly lower computational demand. Notably, the pose-based approach demonstrates robust generalisation to substantial background and signer variation. Moreover, the pose-based approach demands significantly less computational power and training time than vision-based approaches. The proposed unimodal pose-based and vision-based systems were concluded to both be effective at classifying sign classes in the LSA64 and AUTSL datasets. , Thesis (MSc) -- Faculty of Science, Ichthyology and Fisheries Science, 2024
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A multispectral and machine learning approach to early stress classification in plants
- Authors: Poole, Louise Carmen
- Date: 2022-04-06
- Subjects: Machine learning , Neural networks (Computer science) , Multispectral imaging , Image processing , Plant stress detection
- Language: English
- Type: Master's thesis , text
- Identifier: http://hdl.handle.net/10962/232410 , vital:49989
- Description: Crop loss and failure can impact both a country’s economy and food security, often to devastating effects. As such, the importance of successfully detecting plant stresses early in their development is essential to minimize spread and damage to crop production. Identification of the stress and the stress-causing agent is the most critical and challenging step in plant and crop protection. With the development of and increase in ease of access to new equipment and technology in recent years, the use of spectroscopy in the early detection of plant diseases has become notably popular. This thesis narrows down the most suitable multispectral imaging techniques and machine learning algorithms for early stress detection. Datasets were collected of visible images and multispectral images. Dehydration was selected as the plant stress type for the main experiments, and data was collected from six plant species typically used in agriculture. Key contributions of this thesis include multispectral and visible datasets showing plant dehydration as well as a separate preliminary dataset on plant disease. Promising results on dehydration showed statistically significant accuracy improvements in the multispectral imaging compared to visible imaging for early stress detection, with multispectral input obtaining a 92.50% accuracy over visible input’s 77.50% on general plant species. The system was effective at stress detection on known plant species, with multispectral imaging introducing greater improvement to early stress detection than advanced stress detection. Furthermore, strong species discrimination was achieved when exclusively testing either early or advanced dehydration against healthy species. , Thesis (MSc) -- Faculty of Science, Ichthyology & Fisheries Sciences, 2022
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Statistical and Mathematical Learning: an application to fraud detection and prevention
- Authors: Hamlomo, Sisipho
- Date: 2022-04-06
- Subjects: Credit card fraud , Bootstrap (Statistics) , Support vector machines , Neural networks (Computer science) , Decision trees , Machine learning , Cross-validation , Imbalanced data
- Language: English
- Type: Master's thesis , text
- Identifier: http://hdl.handle.net/10962/233795 , vital:50128
- Description: Credit card fraud is an ever-growing problem. There has been a rapid increase in the rate of fraudulent activities in recent years resulting in a considerable loss to several organizations, companies, and government agencies. Many researchers have focused on detecting fraudulent behaviours early using advanced machine learning techniques. However, credit card fraud detection is not a straightforward task since fraudulent behaviours usually differ for each attempt and the dataset is highly imbalanced, that is, the frequency of non-fraudulent cases outnumbers the frequency of fraudulent cases. In the case of the European credit card dataset, we have a ratio of approximately one fraudulent case to five hundred and seventy-eight non-fraudulent cases. Different methods were implemented to overcome this problem, namely random undersampling, one-sided sampling, SMOTE combined with Tomek links and parameter tuning. Predictive classifiers, namely logistic regression, decision trees, k-nearest neighbour, support vector machine and multilayer perceptrons, are applied to predict if a transaction is fraudulent or non-fraudulent. The model's performance is evaluated based on recall, precision, F1-score, the area under receiver operating characteristics curve, geometric mean and Matthew correlation coefficient. The results showed that the logistic regression classifier performed better than other classifiers except when the dataset was oversampled. , Thesis (MSc) -- Faculty of Science, Statistics, 2022
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The development of an ionospheric storm-time index for the South African region
- Authors: Tshisaphungo, Mpho
- Date: 2021-04
- Subjects: Ionospheric storms -- South Africa , Global Positioning System , Neural networks (Computer science) , Regression analysis , Ionosondes , Auroral electrojet , Geomagnetic indexes , Magnetic storms -- South Africa
- Language: English
- Type: thesis , text , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/178409 , vital:42937 , 10.21504/10962/178409
- Description: This thesis presents the development of a regional ionospheric storm-time model which forms the foundation of an index to provide a quick view of the ionospheric storm effects over South African mid-latitude region. The model is based on the foF2 measurements from four South African ionosonde stations. The data coverage for the model development over Grahamstown (33.3◦S, 26.5◦E), Hermanus (34.42◦S, 19.22◦E), Louisvale (28.50◦S, 21.20◦E), and Madimbo (22.39◦S, 30.88◦E) is 1996-2016, 2009-2016, 2000-2016, and 2000-2016 respectively. Data from the Global Positioning System (GPS) and radio occultation (RO) technique were used during validation. As the measure of either positive or negative storm effect, the variation of the critical frequency of the F2 layer (foF2) from the monthly median values (denoted as _foF2) is modeled. The modeling of _foF2 is based on only storm time data with the criteria of Dst 6 -50 nT and Kp > 4. The modeling methods used in the study were artificial neural network (ANN), linear regression (LR) and polynomial functions. The approach taken was to first test the modeling techniques on a single station before expanding the study to cover the regional aspect. The single station modeling was developed based on ionosonde data over Grahamstown. The inputs for the model which related to seasonal variation, diurnal variation, geomagnetic activity and solar activity were considered. For the geomagnetic activity, three indices namely; the symmetric disturbance in the horizontal component of the Earth’s magnetic field (SYM − H), the Auroral Electrojet (AE) index and local geomagnetic index A, were included as inputs. The performance of a single station model revealed that, of the three geomagnetic indices, SYM − H index has the largest contribution of 41% and 54% based on ANN and LR techniques respectively. The average correlation coefficients (R) for both ANN and LR models was 0.8, when validated during the selected storms falling within the period of model development. When validated using storms that fall outside the period of model development, the model gave R values of 0.6 and 0.5 for ANN and LR respectively. In addition, the GPS total electron content (TEC) derived measurements were used to estimate foF2 data. This is because there are more GPS receivers than ionosonde locations and the utilisation of this data increases the spatial coverage of the regional model. The estimation of foF2 from GPS TEC was done at GPS-ionosonde co-locations using polynomial functions. The average R values of 0.69 and 0.65 were obtained between actual and derived _foF2 over the co-locations and other GPS stations respectively. Validation of GPS TEC derived foF2 with RO data over regions out of ionospheric pierce points coverage with respect to ionosonde locations gave R greater than 0.9 for the selected storm period of 4-8 August 2011. The regional storm-time model was then developed based on the ANN technique using the four South African ionosonde stations. The maximum and minimum R values of 0.6 and 0.5 were obtained over ionosonde and GPS locations respectively. This model forms the basis towards the regional ionospheric storm-time index. , Thesis (PhD) -- Faculty of Science, Physics and Electronics, 2021
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Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1
- Authors: Sheik Amamuddy, Olivier Serge André
- Date: 2020
- Subjects: Machine learning , Molecules -- Models , Data mining , Neural networks (Computer science) , Antiretroviral agents , Protease inhibitors , Drug resistance , Multidrug resistance , Molecular dynamics , Renin-angiotensin system , HIV (Viruses) -- South Africa , HIV (Viruses) -- Social aspects -- South Africa , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/115964 , vital:34282
- Description: Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis.
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Technology in conservation: towards a system for in-field drone detection of invasive vegetation
- Authors: James, Katherine Margaret Frances
- Date: 2020
- Subjects: Drone aircraft in remote sensing , Neural networks (Computer science) , Drone aircraft in remote sensing -- Case studies , Machine learning , Computer vision , Environmental monitoring -- Remote sensing , Invasive plants -- Monitoring
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/143408 , vital:38244
- Description: Remote sensing can assist in monitoring the spread of invasive vegetation. The adoption of camera-carrying unmanned aerial vehicles, commonly referred to as drones, as remote sensing tools has yielded images of higher spatial resolution than traditional techniques. Drones also have the potential to interact with the environment through the delivery of bio-control or herbicide, as seen with their adoption in precision agriculture. Unlike in agricultural applications, however, invasive plants do not have a predictable position relative to each other within the environment. To facilitate the adoption of drones as an environmental monitoring and management tool, drones need to be able to intelligently distinguish between invasive and non-invasive vegetation on the fly. In this thesis, we present the augmentation of a commercially available drone with a deep machine learning model to investigate the viability of differentiating between an invasive shrub and other vegetation. As a case study, this was applied to the shrub genus Hakea, originating in Australia and invasive in several countries including South Africa. However, for this research, the methodology is important, rather than the chosen target plant. A dataset was collected using the available drone and manually annotated to facilitate the supervised training of the model. Two approaches were explored, namely, classification and semantic segmentation. For each of these, several models were trained and evaluated to find the optimal one. The chosen model was then interfaced with the drone via an Android application on a mobile device and its performance was preliminarily evaluated in the field. Based on these findings, refinements were made and thereafter a thorough field evaluation was performed to determine the best conditions for model operation. Results from the classification task show that deep learning models are capable of distinguishing between target and other shrubs in ideal candidate windows. However, classification in this manner is restricted by the proposal of such candidate windows. End-to-end image segmentation using deep learning overcomes this problem, classifying the image in a pixel-wise manner. Furthermore, the use of appropriate loss functions was found to improve model performance. Field tests show that illumination and shadow pose challenges to the model, but that good recall can be achieved when the conditions are ideal. False positive detection remains an issue that could be improved. This approach shows the potential for drones as an environmental monitoring and management tool when coupled with deep machine learning techniques and outlines potential problems that may be encountered.
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Modelling Ionospheric vertical drifts over the African low latitude region
- Authors: Dubazane, Makhosonke Berthwell
- Date: 2018
- Subjects: Ionospheric drift , Magnetometers , Functions, Orthogonal , Neural networks (Computer science) , Ionospheric electron density -- Africa , Communication and Navigation Outage Forecasting Systems (C/NOFS)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63356 , vital:28396
- Description: Low/equatorial latitudes vertical plasma drifts and electric fields govern the formation and changes of ionospheric density structures which affect space-based systems such as communications, navigation and positioning. Dynamical and electrodynamical processes play important roles in plasma distribution at different altitudes. Because of the high variability of E × B drift in low latitude regions, coupled with various processes that sometimes originate from high latitudes especially during geomagnetic storm conditions, it is challenging to develop accurate vertical drift models. This is despite the fact that there are very few instruments dedicated to provide electric field and hence E × B drift data in low/equatorial latitude regions. To this effect, there exists no ground-based instrument for direct measurements of E×B drift data in the African sector. This study presents the first time investigation aimed at modelling the long-term variability of low latitude vertical E × B drift over the African sector using a combination of Communication and Navigation Outage Forecasting Systems (C/NOFS) and ground-based magnetometer observations/measurements during 2008-2013. Because the approach is based on the estimation of equatorial electrojet from ground-based magnetometer observations, the developed models are only valid for local daytime. Three modelling techniques have been considered. The application of Empirical Orthogonal Functions and partial least squares has been performed on vertical E × B drift modelling for the first time. The artificial neural networks that have the advantage of learning underlying changes between a set of inputs and known output were also used in vertical E × B drift modelling. Due to lack of E×B drift data over the African sector, the developed models were validated using satellite data and the climatological Scherliess-Fejer model incorporated within the International Reference Ionosphere model. Maximum correlation coefficient of ∼ 0.8 was achieved when validating the developed models with C/NOFS E × B drift observations that were not used in any model development. For most of the time, the climatological model overestimates the local daytime vertical E × B drift velocities. The methods and approach presented in this study provide a background for constructing vertical E ×B drift databases in longitude sectors that do not have radar instrumentation. This will in turn make it possible to study day-to-day variability of vertical E×B drift and hopefully lead to the development of regional and global models that will incorporate local time information in different longitude sectors.
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Tomographic imaging of East African equatorial ionosphere and study of equatorial plasma bubbles
- Authors: Giday, Nigussie Mezgebe
- Date: 2018
- Subjects: Ionosphere -- Africa, Central , Tomography -- Africa, Central , Global Positioning System , Neural networks (Computer science) , Space environment , Multi-Instrument Data Analysis System (MIDAS) , Equatorial plasma bubbles
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/63980 , vital:28516
- Description: In spite of the fact that the African ionospheric equatorial region has the largest ground footprint along the geomagnetic equator, it has not been well studied due to the absence of adequate ground-based instruments. This thesis presents research on both tomographic imaging of the African equatorial ionosphere and the study of the ionospheric irregularities/equatorial plasma bubbles (EPBs) under varying geomagnetic conditions. The Multi-Instrument Data Analysis System (MIDAS), an inversion algorithm, was investigated for its validity and ability as a tool to reconstruct multi-scaled ionospheric structures for different geomagnetic conditions. This was done for the narrow East African longitude sector with data from the available ground Global Positioning Sys-tem (GPS) receivers. The MIDAS results were compared to the results of two models, namely the IRI and GIM. MIDAS results compared more favourably with the observation vertical total electron content (VTEC), with a computed maximum correlation coefficient (r) of 0.99 and minimum root-mean-square error (RMSE) of 2.91 TECU, than did the results of the IRI-2012 and GIM models with maximum r of 0.93 and 0.99, and minimum RMSE of 13.03 TECU and 6.52 TECU, respectively, over all the test stations and validation days. The ability of MIDAS to reconstruct storm-time TEC was also compared with the results produced by the use of a Artificial Neural Net-work (ANN) for the African low- and mid-latitude regions. In terms of latitude, on average,MIDAS performed 13.44 % better than ANN in the African mid-latitudes, while MIDAS under performed in low-latitudes. This thesis also reports on the effects of moderate geomagnetic conditions on the evolution of EPBs and/or ionospheric irregularities during their season of occurrence using data from (or measurements by) space- and ground-based instruments for the east African equatorial sector. The study showed that the strength of daytime equatorial electrojet (EEJ), the steepness of the TEC peak-to-trough gradient and/or the meridional/transequatorial thermospheric winds sometimes have collective/interwoven effects, while at other times one mechanism dominates. In summary, this research offered tomographic results that outperform the results of the commonly used (“standard”) global models (i.e. IRI and GIM) for a longitude sector of importance to space weather, which has not been adequately studied due to a lack of sufficient instrumentation.
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