Doctoral Degrees (Computer Science)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7113

Browse

Now showing 1 - 20 of 38

An investigation into the use of genetic programming for the induction of novice procedural programming solution algorithms in intelligent programming tutors.
(2004) Pillay, Nelishia.; Sartori-Angus, Alan G.
Intelligent programming tutors have proven to be an economically viable and effective means of assisting novice programmers overcome learning difficulties. However, the large-scale use of intelligent programming tutors has been impeded by the high developmental costs associated with building intelligent programming tutors. The research presented in this thesis forms part of a larger initiative aimed at reducing these costs by building a generic architecture for the development of intelligent programming tutors. One of the facilities that must be provided by the generic architecture is the automatic generation of solutions to programming problems. The study presented in the thesis examines the use of genetic programming as means of inducing solution algorithms to novice programming problems. The scope of the thesis is limited to novice procedural programming paradigm problems requiring the use of arithmetic, string manipulation, conditional, iterative and recursive programming structures. The methodology employed in the study is proof-by-demonstration. A genetic programming system for the induction of novice procedural solution algorithms was implemented and tested on randomly chosen novice procedural programming problems. The study has identified the standard and advanced genetic programming features needed for the successful generation of novice procedural solution algorithms. The outcomes of this study include the derivation of an internal representation language for representing procedural solution algorithms and a high-level programming problem specification format for describing procedural problems, in the generic architecture. One of the limitations of genetic programming is its susceptibility to converge prematurely to local optima and not find a solution in some cases. The study has identified fitness function biases against certain structural components that are needed to find a solution, as an additional cause of premature convergence in this domain. It presents an iterative structure-based algorithm as a solution to this problem. This thesis has contributed to both the fields of genetic programming and intelligent programming tutors. While genetic programming has been successfully implemented in various domains, it is usually applied to a single problem within that domain. In this study the genetic programming system must be capable of solving a number of different programming problems in different application domains. In addition to this, the study has also identified a means of overcoming premature convergence caused by fitness function biases in a genetic programming system for the induction of novice procedural programming algorithms. Furthermore, although a number of studies have addressed the student modelling and pedagogical aspects of intelligent programming tutors, none have examined the automatic generation of problem solutions as a means of reducing developmental costs. Finally, this study has contributed to the ongoing research being conducted by the artificial intelligence in education community, to test the effectiveness of using machine learning techniques in the development of different aspects of intelligent tutoring systems.
The design and simulation of routing protocols for mobile ad hoc networks.
(2000) Kabeto, Mieso Denko.; Goddard, Wayne.
This thesis addresses a novel type of network known as a mobile ad hoc network. A mobile ad hoc network is a collection of entirely mobile nodes that can establish communication in the absence of any fixed infrastructure. Envisioned applications of these networks include virtual classrooms, emergency relief operations, military tactical communications, sensor networks and community networking. Mobile ad hoc networking poses several new challenges in the design of network protocols. This thesis focuses on the routing problem. The main challenges in the design of a routing protocol for mobile ad hoc networks result from them having limited resources and there being frequent topological changes that occur unpredictably. Moreover, there is no fixed infrastructure that supports routing. The conventional routing protocols are not generally suitable for mobile ad hoc networks, as they cannot react quickly to the changing network topology, cause excessive communication and computation, or converge very slowly creating routing loops. In this thesis we propose two classes of routing schemes for mobile ad hoc networks. The first class is known as Limited Flooding Protocol. The protocol is fully reactive and does not require the computation of routing tables. It uses some basic principles of flooding, but reduces the communication overhead by restricting packet propagation through the network. Several variations of limited flooding are considered including deterministic, randomised and priority-based mechanisms. The main advantage of this protocol is that it can be used in networks with unpredictable topological changes and highly mobile nodes, since maintaining routing table at the intermediate nodes is not required. The second class of routing protocols is based on hierarchical clustering architecture and is intended for use in a relatively low mobility environment. The basic idea of this protocol is to partition the entire network into smaller units known as clusters and define routing mechanisms both within and between clusters using a hierarchical architecture. The main advantage of this architecture is reduction of storage requirements of routing information, communication overhead and computational overhead at each node. Discrete-event simulation is used for modelling and performance evaluation. Various options and variations of the protocols are examined in the…[Page 2 of abstract is missing.]
Modelling with mathematica.
(1994) Murrell, Hugh Crozier.; Sartori-Angus, Alan G.
In this thesis a number of mathematical models are investigated with the aid of the modelling package Mathematica. Some of the models are of a mechanical nature and some of the models are laboratories that have been constructed for the purpose of assisting researchers in a particular field. In the early sections of the thesis mechanical models are investigated. After the equations of motion for the model have been presented, Mathematica is employed to generate solutions which are then used to drive animations of the model. The frames of the animations are graphical snapshots of the model in motion. Mathematica proves to be an ideal tool for this type of modelling since it combines algebraic, numeric and graphics capabilities on one platform. In the later sections of this thesis, Mathematica laboratories are created for investigating models in two different fields. The first laboratory is a collection of routines for performing Phase-Plane analysis of planar autonomous systems of ordinary differential equations. A model of a mathematical concept called a bifurcation is investigated and an animation of this mathematical event is produced. The second laboratory is intended to help researchers in the tomography field. A standard filtered back-projection algorithm for reconstructing images from their projections is implemented. In the final section of the thesis an indication of how the tomography laboratory could be used is presented. Wavelet theory is used to construct a new filter that could be used in filtered back-projection tomography.
Bi-modal biometrics authentication on iris and signature.
(2010) Viriri, Serestina.; Tapamo, Jules-Raymond.
Multi-modal biometrics is one of the most promising avenues to address the performance problems in biometrics-based personal authentication systems. While uni-modal biometric systems have bolstered personal authentication better than traditional security methods, the main challenges remain the restricted degrees of freedom, non-universality and spoof attacks of the traits. In this research work, we investigate the performance improvement in bi-modal biometrics authentication systems based on the physiological trait, the iris, and behavioral trait, the signature. We investigate a model to detect the largest non-occluded rectangular part of the iris as a region of interest (ROI) from which iris features are extracted by a cumulative-sums-based grey change analysis algorithm and Gabor Filters. In order to address the space complexity of biometric systems, we proposed two majority vote-based algorithms which compute prototype iris features codes as the reliable specimen templates. Experiments obtained a success rate of 99.6%. A text-based directional signature verification algorithm is investigated. The algorithm verifies signatures, even when they are composed of symbols and special unconstrained cursive characters which are superimposed and embellished. The experimental results show that the proposed approach has an improved true positive rate of 94.95%. A user-specific weighting technique, the user-score-based, which is based on the different degrees of importance for the iris and signature traits of an individual, is proposed. Then, an intelligent dual ν-support vector machine (2ν-SVM) based fusion algorithm is used to integrate the weighted match scores of the iris and signature modalities at the matching score level. The bi-modal biometrics system obtained a false rejection rate (FRR) of 0.008, and a false acceptance rate (FAR) of 0.001.
A practical investigation of meteor-burst communications.
(1991) Melville, Stuart William.; Sartori-Angus, Alan G.; Oellermann, Ortrud Ruth.
This study considers the meteor-burst communication (MBC) environment at three levels. At the lowest level, the trails themselves are studied and analysed. Then individual links are studied in order to determine the data throughput and wait time that might be expected at various data rates. Finally, at the top level, MBC networks are studied in order to provide information on the effects of routing strategies, topologies, and connectivity in such networks. A significant amount of theoretical work has been done in the classification of meteor trails, and the analysis of the throughput potential of the channel. At the same time the issues of wait time on MBC links, and MBC network strategies, have been largely ignored. The work presented here is based on data captured on actual monitoring links, and is intended to provide both an observational comparison to theoretical predictions in the well-researched areas, and a source of base information for the others. Chapter 1 of this thesis gives an overview of the field of meteor-burst communications. Prior work in the field is discussed, as are the advantages and disadvantages of the channel, and current application areas. Chapter 2 describes work done on the classification of observed meteor trails into distinctive 'families'. The rule-based system designed for this task is discussed as well as the eventual classification schema produced, which is far more comprehensive and consistent than previously proposed schemas. Chapter 3 deals with the throughput potential of the channel, based on the observed trails. A comparison to predicted results, both as regards fixed and adaptive data-rates, is made with some notable differences between predicted v results and observed results highlighted. The trail families with the largest contribution to the throughput capacity of the channel are identified. Chapter 4 deals with wait time in meteor-burst communications. The data rates at which wait time is minimised in the links used are found, and compared to the rates at which throughput was optimised. These are found to be very different, as indeed are the contributions of the various trail families at these rates. Chapter 5 describes a software system designed to analyse the effect of routing strategies in MBC networks, and presents initial results derived from this system. Certain features of the channel, in particular its sporadic nature, are shown to have significant effects on network performance. Chapter 6 continues the presentation of network results, specifically concentrating on the effect of topologies and connectivity within MBC networks. Chapter 7 concludes the thesis, highlighting suggested areas for further research as well as summarising the more important results presented.
The investigation into an algorithm based on wavelet basis functions for the spatial and frequency decomposition of arbitrary signals.
(1994) Goldstein, Hilton.; Sartori-Angus, Alan G.
The research was directed toward the viability of an O(n) algorithm which could decompose an arbitrary signal (sound, vibration etc.) into its time-frequency space. The well known Fourier Transform uses sine and cosine functions (having infinite support on t) as orthonormal basis functions to decompose a signal i(t) in the time domain to F(w) in the frequency . domain, where the Fourier coefficients F(w) are the contributions of each frequency in the original signal. Due to the non-local support of these basis functions, a signal containing a sharp localised transient does not have localised coefficients, but rather coefficients that decay slowly. Another problem is that the coefficients F(w) do not convey any time information. The windowed Fourier Transform, or short-time Fourier Transform, does attempt to resolve the latter, but has had limited success. Wavelets are basis functions, usually mutually orthonormal, having finite support in t and are therefore spatially local. Using non-orthogonal wavelets, the Dominant Scale Transform (DST) designed by the author, decomposes a signal into its approximate time-frequency space. The associated Dominant Scale Algorithm (DSA) has O(n) complexity and is integer-based. These two characteristics make the DSA extremely efficient. The thesis also investigates the problem of converting a music signal into it's equivalent music score. The old problem of speech recognition is also examined. The results obtained from the DST are shown to be consistent with those of other authors who have utilised other methods. The resulting DST coefficients are shown to render the DST particularly useful in speech segmentation (silence regions, voiced speech regions, and frication). Moreover, the Spectrogram Dominant Scale Transform (SDST), formulated from the DST, was shown to approximate the Fourier coefficients over fixed time intervals within vowel regions of human speech.
RAT online : design, delivery and evaluation of constructivist computer supported martial arts learning environments.
(2011) Yates, Steven.; Amory, Alan M.
This thesis describes the evaluation of several computer supported martial arts learning environments. These learning environments were designed, developed and implemented for practitioners of Rough and Tumble (RAT), a South African martial art, originally as a result of an increasing number of RAT practitioners relocating to other countries and yet wishing to continue their learning and practise of RAT. This project revolves mainly around the effectiveness evaluation of whether RAT martial arts knowledge, skills and attitudes can be learned in computer supported learning environments. The research is situated within design research and has pragmatic goals to provide a computer supported learning environment for the learning of RAT. Furthermore the design research was conducted to derive design principles for future design and development efforts. A brief account of the literature is provided, covering three main learning paradigms, with a focus on behaviourism and constructivism, followed by a description of issues in the computer supported learning field, an explanation of various definitions of martial arts and how the term is delimited in this study, and an overview of various evaluation paradigms. This account revealed inadequacies of the theories and terminology described pertaining to this study, resulting in the combined use of various underlying theoretical approaches to guide this research. These approaches include the eclectic-mixed methods-pragmatic paradigm as the overarching framework, a social constructivist learning approach, cognitive flexibility theory, Bloom’s Taxonomy, the RAT approach to martial arts learning and teaching, and a mixed methods research design. Two main components were developed as solutions, which included the development of a computer martial arts resource, the RAT CD-ROM, and four online courses, the RAT Online courses. Data were collected using a number of research instruments, such as questionnaires, interviews, observations, records, expert reviews and learner artefacts in an attempt to understand the data from multiple viewpoints and develop a more reliable depiction of evaluation events. The data were analysed using mainly qualitative coding in software, expert rating diagrams, basic frequency statistics, and martial arts assessments of physical performances. These analyses revealed that although there is significant work involved in mixed methods research and there are issues such as participants not meeting task deadlines, technology failures, software usability issues, and small participant numbers, the research approach has contributed to the pragmatic goal of providing computer supported learning environments to RAT practitioners, who otherwise would not have been able to participate in RAT. In addition a number of design principles for the creation of RAT computer supported learning environments were derived from this research, including the use of social constructivism, cognitive flexibility theory, Bloom’s Taxonomy, multiple contextual training, and using computers as learning and knowledge construction tools. These underlying theoretical principles translate to more practical procedural principles, such as amongst others, to design computer supported learning environments incorporating tools to enable knowledge construction and collaboration, provide learning designs that are complex and authentic, encourage multiple representations of learner knowledge, take on a mentor role as online course facilitator, and to build problem solving activities into the learning design.
Ontology driven multi-agent systems : an architecture for sensor web applications.
(2009) Moodley, Deshendran.; Tapamo, Jules-Raymond.; Kinyua, Johnson D. M.
Advances in sensor technology and space science have resulted in the availability of vast quantities of high quality earth observation data. This data can be used for monitoring the earth and to enhance our understanding of natural processes. Sensor Web researchers are working on constructing a worldwide computing infrastructure that enables dynamic sharing and analysis of complex heterogeneous earth observation data sets. Key challenges that are currently being investigated include data integration; service discovery, reuse and composition; semantic interoperability; and system dynamism. Two emerging technologies that have shown promise in dealing with these challenges are ontologies and software agents. This research investigates how these technologies can be integrated into an Ontology Driven Multi-Agent System (ODMAS) for the Sensor Web. The research proposes an ODMAS framework and an implemented middleware platform, i.e. the Sensor Web Agent Platform (SWAP). SWAP deals with ontology construction, ontology use, and agent based design, implementation and deployment. It provides a semantic infrastructure, an abstract architecture, an internal agent architecture and a Multi-Agent System (MAS) middleware platform. Distinguishing features include: the incorporation of Bayesian Networks to represent and reason about uncertain knowledge; ontologies to describe system entities such as agent services, interaction protocols and agent workflows; and a flexible adapter based MAS platform that facilitates agent development, execution and deployment. SWAP aims to guide and ease the design, development and deployment of dynamic alerting and monitoring applications. The efficacy of SWAP is demonstrated by two satellite image processing applications, viz. wildfire detection and monitoring informal settlement. This approach can provide significant benefits to a wide range of Sensor Web users. These include: developers for deploying agents and agent based applications; end users for accessing, managing and visualising information provided by real time monitoring applications, and scientists who can use the Sensor Web as a scientific computing platform to facilitate knowledge sharing and discovery. An Ontology Driven Multi-Agent Sensor Web has the potential to forever change the way in which geospatial data and knowledge is accessed and used. This research describes this far reaching vision, identifies key challenges and provides a first step towards the vision.
Studies in particle swarm optimization technique for global optimization.
(2013) Martins, Arasomwan Akugbe.; Adewumi, Aderemi Oluyinka.
Abstract available in the digital copy.
Formalisms for agents reasoning with stochastic actions and perceptions.
(2014) Rens, Gavin Brian.; Meyer, Thomas Andreas.; Lakemeyer, Gerhard.
The thesis reports on the development of a sequence of logics (formal languages based on mathematical logic) to deal with a class of uncertainty that agents may encounter. More accurately, the logics are meant to be used for allowing robots or software agents to reason about the uncertainty they have about the effects of their actions and the noisiness of their observations. The approach is to take the well-established formalism called the partially observable Markov decision process (POMDP) as an underlying formalism and then design a modal logic based on POMDP theory to allow an agent to reason with a knowledge-base (including knowledge about the uncertainties). First, three logics are designed, each one adding one or more important features for reasoning in the class of domains of interest (i.e., domains where stochastic action and sensing are considered). The final logic, called the Stochastic Decision Logic (SDL) combines the three logics into a coherent formalism, adding three important notions for reasoning about stochastic decision-theoretic domains: (i) representation of and reasoning about degrees of belief in a statement, given stochastic knowledge, (ii) representation of and reasoning about the expected future rewards of a sequence of actions and (iii) the progression or update of an agent’s epistemic, stochastic knowledge. For all the logics developed in this thesis, entailment is defined, that is, whether a sentence logically follows from a knowledge-base. Decision procedures for determining entailment are developed, and they are all proved sound, complete and terminating. The decision procedures all employ tableau calculi to deal with the traditional logical aspects, and systems of equations and inequalities to deal with the probabilistic aspects. Besides promoting the compact representation of POMDP models, and the power that logic brings to the automation of reasoning, the Stochastic Decision Logic is novel and significant in that it allows the agent to determine whether or not a set of sentences is entailed by an arbitrarily precise specification of a POMDP model, where this is not possible with standard POMDPs. The research conducted for this thesis has resulted in several publications and has been presented at several workshops, symposia and conferences.
Improved roach-based algorithms for global optimization problems.
(2014) Obagbuwa, Ibidun Christiana.; Adewumi, Aderemi Oluyinka.
Optimization of systems plays an important role in various fields including mathematics, economics, engineering and life sciences. A lot of real world optimization problems exist across field of endeavours such as engineering design, space planning, networking, data analysis, logistic management, financial planning, risk management, and a host of others. These problems are constantly increasing in size and complexity, necessitating the need for improved techniques. Many conventional approaches have failed to solve complex problems effectively due to increasingly large solution space. This has led to the development of evolutionary algorithms that draw inspiration from the process of natural evolution. It is believed that nature provides inspirations that can lead to innovative models or techniques for solving complex optimization problems. Among the class of paradigm based on this inspiration is Swarm Intelligence (SI). SI is one of the recent developments of evolutionary computation. A SI paradigm is comprised of algorithms inspired by the social behaviour of animals and insects. SI-based algorithms have attracted interest, gained popularity and attention because of their flexibility and versatility. SIbased algorithms have been found to be efficient in solving real world optimization problems. Examples of SI algorithms include Ant Colony Optimization (ACO) inspired by the pheromone trail-following behaviour of ant species; Particle Swarm Optimization (PSO) inspired by flocking and swarming behaviour of insects and animals; and Bee Colony Optimization (BCO) inspired by bees’ food foraging. Recent emerging techniques in SI includes Roach-based Algorithms (RBA) motivated by cockroaches social behaviour. Two recently introduced RBA algorithms are Roach Infestation Optimization (RIO) and Cockroach Swarm Optimization (CSO) which have been applied to some optimization problems to achieve competitive results when compared to PSO. This study is motivated by the promising results of RBA, which have shown that the algorithms have potentials to be efficient tools for solving optimization problems. Extensive studies of existing RBA were carried out in this work revealing the shortcomings such as slow convergence and entrapment in local minima. The aim of this study is to overcome the identified drawbacks. We investigate RBA variants that are introduced in this work by introducing parameters such as constriction factor and sigmoid function that have proved effective for similar evolutionary algorithms in the literature. In addition components such as vigilance, cannibalism and hunger are incorporated into existing RBAs. These components are constructed by the use of some known techniques such as simple Euler, partial differential equation, crossover and mutation methods to speed up convergence and enhance the stability, exploitation and exploration of RBA. Specifically, a stochastic constriction factor was introduced to the existing CSO algorithm to improve its performance and enhance its ability to solve optimization problems involving thousands of variables. A CSO algorithm that was originally designed with three components namely chase-swarming, dispersion and ruthlessness is extended in this work with hunger component to improve its searching ability and diversity. Also, predator-prey evolution using crossover and mutation techniques were introduced into the CSO algorithm to create an adaptive search in each iteration thereby making the algorithm more efficient. In creating a discrete version of a CSO algorithm that can be used to evaluate optimization problems with any discrete range value, we introduced the sigmoid function. Furthermore, a dynamic step-size adaptation with simple Euler method was introduced to the existing RIO algorithm enhancing swarm stability and improving local and global searching abilities. The existing RIO model was also re-designed with the inclusion of vigilance and cannibalism components. The improved RBA were tested on established global optimization benchmark problems and results obtained compared with those from the literature. The improved RBA introduced in this work show better improvements over existing ones.
Detection and characterisation of vessels in retinal images.
(2015) Mapayi, Temitope.; Viriri, Serestina.; Tapamo, Jules-Raymond.
As retinopathies such as diabetic retinopathy (DR) and retinopathy of prematurity (ROP) continue to be the major causes of blindness globally, regular retinal examinations of patients can assist in the early detection of the retinopathies. The manual detection of retinal vessels is a very tedious and time consuming task as it requires about two hours to manually detect vessels in each retinal image. Automatic vessel segmentation has been helpful in achieving speed, improved diagnosis and progress monitoring of these diseases but has been challenging due to complexities such as the varying width of the retinal vessels from very large to very small, low contrast of thin vessels with respect to background and noise due to nonhomogeneous illumination in the retinal images. Although several supervised and unsupervised segmentation methods have been proposed in the literature, the segmentation of thinner vessels, connectivity loss of the vessels and time complexity remain the major challenges. In order to address these problems, this research work investigated di erent unsupervised segmentation approaches to be used in the robust detection of large and thin retinal vessels in a timely e cient manner. Firstly, this thesis conducted a study on the use of di erent global thresholding techniques combined with di erent pre-processing and post-processing techniques. Two histogram-based global thresholding techniques namely, Otsu and Isodata were able to detect large retinal vessels but fail to segment the thin vessels because these thin vessels have very low contrast and are di cult to distinguish from the background tissues using the histogram of the retinal images. Two new multi-scale approaches of computing global threshold based on inverse di erence moment and sum-entropy combined with phase congruence are investigated to improve the detection of vessels. One of the findings of this study is that the multi-scale approaches of computing global threshold combined with phase congruence based techniques improved on the detection of large vessels and some of the thin vessels. They, however, failed to maintain the width of the detected vessels. The reduction in the width of the detected large and thin vessels results in low sensitivity rates while relatively good accuracy rates were maintained. Another study on the use of fuzzy c-means and GLCM sum entropy combined on phase congruence for vessel segmentation showed that fuzzy c-means combined with phase congruence achieved a higher average accuracy rates of 0.9431 and 0.9346 but a longer running time of 27.1 seconds when compared with the multi-scale based sum entropy thresholding combined with phase congruence with the average accuracy rates of 0.9416 and 0.9318 with a running time of 10.3 seconds. The longer running time of the fuzzy c-means over the sum entropy thresholding is, however, attributed to the iterative nature of fuzzy c-means. When compared with the literature, both methods achieved considerable faster running time. This thesis investigated two novel local adaptive thresholding techniques for the segmentation of large and thin retinal vessels. The two novel local adaptive thresholding techniques applied two di erent Haralick texture features namely, local homogeneity and energy. Although these two texture features have been applied for supervised image segmentation in the literature, their novelty in this thesis lies in that they are applied using an unsupervised image segmentation approach. Each of these local adaptive thresholding techniques locally applies a multi-scale approach on each of the texture information considering the pixel of interest in relationship with its spacial neighbourhood to compute the local adaptive threshold. The localised multi-scale approach of computing the thresholds handled the challenge of the vessels' width variation. Experiments showed significant improvements in the average accuracy and average sensitivity rates of these techniques when compared with the previously discussed global thresholding methods and state of the art. The two novel local adaptive thresholding techniques achieved a higher reduction of false vessels around the border of the optic disc when compared with some of the previous techniques in the literature. These techniques also achieved a highly improved computational time of 1.9 to 3.9 seconds to segment the vessels in each retinal image when compared with the state of the art. Hence, these two novel local adaptive thresholding techniques are proposed for the segmentation of the vessels in the retinal images. This thesis further investigated the combination of di erence image and kmeans clustering technique for the segmentation of large and thin vessels in retinal images. The pre-processing phase computed a di erence image and k-means clustering technique was used for the vessel detection. While investigating this vessel segmentation method, this thesis established the need for a difference image that preserves the vessel details of the retinal image. Investigating the di erent low pass filters, median filter yielded the best di erence image required by k-means clustering for the segmentation of the retinal vessels. Experiments showed that the median filter based di erence images combined with k-means clustering technique achieved higher average accuracy and average sensitivity rates when compared with the previously discussed global thresholding methods and the state of the art. The median filter based di erence images combined with k-means clustering technique (that is, DIMDF) also achieved a higher reduction of false vessels around the border of the optic disc when compared with some previous techniques in the literature. These methods also achieved a highly improved computational time of 3.4 to 4 seconds when compared with the literature. Hence, the median filter based di erence images combined with k-means clustering technique are proposed for the segmentation of the vessels in retinal images. The characterisation of the detected vessels using tortuosity measure was also investigated in this research. Although several vessel tortuosity methods have been discussed in the literature, there is still need for an improved method that e ciently detects vessel tortuosity. The experimental study conducted in this research showed that the detection of the stationary points helps in detecting the change of direction and twists in the vessels. The combination of the vessel twist frequency obtained using the stationary points and distance metric for the computation of normalised and nonnormalised tortuosity index (TI) measure was investigated. Experimental results showed that the non-normalised TI measure had a stronger correlation with the expert's ground truth when compared with the distance metric and normalised TI measures. Hence, a non-normalised TI measure that combines the vessel twist frequency based on the stationary points and distance metric is proposed for the measurement of vessel tortuosity.
Practical reasoning for defeasable description logics.
(2016) Moodley, Kodylan.; Meyer, Thomas Andreas.
Description Logics (DLs) are a family of logic-based languages for formalising ontologies. They have useful computational properties allowing the development of automated reasoning engines to infer implicit knowledge from ontologies. However, classical DLs do not tolerate exceptions to speci ed knowledge. This led to the prominent research area of nonmonotonic or defeasible reasoning for DLs, where most techniques were adapted from seminal works for propositional and rst-order logic. Despite the topic's attention in the literature, there remains no consensus on what \sensible" defeasible reasoning means for DLs. Furthermore, there are solid foundations for several approaches and yet no serious implementations and practical tools. In this thesis we address the aforementioned issues in a broad sense. We identify the preferential approach, by Kraus, Lehmann and Magidor (KLM) in propositional logic, as a suitable abstract framework for de ning and studying the precepts of sensible defeasible reasoning. We give a generalisation of KLM's precepts, and their arguments motivating them, to the DL case. We also provide several preferential algorithms for defeasible entailment in DLs; evaluate these algorithms, and the main alternatives in the literature, against the agreed upon precepts; extensively test the performance of these algorithms; and ultimately consolidate our implementation in a software tool called Defeasible-Inference Platform (DIP). We found some useful entailment regimes within the preferential context that satisfy all the KLM properties, and some that have scalable performance in real world ontologies even without extensive optimisation.
Intelligent instance selection techniques for support vector machine speed optimization with application to e-fraud detection.
(2017) Akinyelu, Ayobami Andronicus.; Adewumi, Aderemi Oluyinka.
Decision-making is a very important aspect of many businesses. There are grievous penalties involved in wrong decisions, including financial loss, damage of company reputation and reduction in company productivity. Hence, it is of dire importance that managers make the right decisions. Machine Learning (ML) simplifies the process of decision making: it helps to discover useful patterns from historical data, which can be used for meaningful decision-making. The ability to make strategic and meaningful decisions is dependent on the reliability of data. Currently, many organizations are overwhelmed with vast amounts of data, and unfortunately, ML algorithms cannot effectively handle large datasets. This thesis therefore proposes seven filter-based and five wrapper-based intelligent instance selection techniques for optimizing the speed and predictive accuracy of ML algorithms, with a particular focus on Support Vector Machine (SVM). Also, this thesis proposes a novel fitness function for instance selection. The primary difference between the filter-based and wrapper-based technique is in their method of selection. The filter-based techniques utilizes the proposed fitness function for selection, while the wrapper-based technique utilizes SVM algorithm for selection. The proposed techniques are obtained by fusing SVM algorithm with the following Nature Inspired algorithms: flower pollination algorithm, social spider algorithm, firefly algorithm, cuckoo search algorithm and bat algorithm. Also, two of the filter-based techniques are boundary detection algorithms, inspired by edge detection in image processing and edge selection in ant colony optimization. Two different sets of experiments were performed in order to evaluate the performance of the proposed techniques (wrapper-based and filter-based). All experiments were performed on four datasets containing three popular e-fraud types: credit card fraud, email spam and phishing email. In addition, experiments were performed on 20 datasets provided by the well-known UCI data repository. The results show that the proposed filter-based techniques excellently improved SVM training speed in 100% (24 out of 24) of the datasets used for evaluation, without significantly affecting SVM classification quality. Moreover, experimental results also show that the wrapper-based techniques consistently improved SVM predictive accuracy in 78% (18 out of 23) of the datasets used for evaluation and simultaneously improved SVM training speed in all cases. Furthermore, two different statistical tests were conducted to further validate the credibility of the results: Freidman’s test and Holm’s post-hoc test. The statistical test results reveal that the proposed filter-based and wrapper-based techniques are significantly faster, compared to standard SVM and some existing instance selection techniques, in all cases. Moreover, statistical test results also reveal that Cuckoo Search Instance Selection Algorithm outperform all the proposed techniques, in terms of speed. Overall, the proposed techniques have proven to be fast and accurate ML-based e-fraud detection techniques, with improved training speed, predictive accuracy and storage reduction. In real life application, such as video surveillance and intrusion detection systems, that require a classifier to be trained very quickly for speedy classification of new target concepts, the filter-based techniques provide the best solutions; while the wrapper-based techniques are better suited for applications, such as email filters, that are very sensitive to slight changes in predictive accuracy.
A knowledge-based system for automated discovery of ecological interactions in flower-visiting data.
(2017) Coetzer, Willem Gabriël.; Moodley, Deshendran.; Gerber, Aurona Jacoba.
Studies on the community ecology of flower-visiting insects, which can be inferred to pollinate flowers, are important in agriculture and nature conservation. Many scientific observations of flower-visiting insects are associated with digitized records of insect specimens preserved in natural history collections. Specimen annotations include heterogeneous and incomplete, in situ field documentation of ecologically significant relationships between individual organisms (i.e. insects and plants), which are nevertheless potentially valuable. A wealth of unrepresented biodiversity and ecological knowledge can be unlocked from such detailed data by augmenting the data with expert knowledge encoded in knowledge models. An analysis of the knowledge representation requirements of flower-visiting community ecologists is presented, as well as an implementation and evaluation of a prototype knowledge-based system for automated semantic enrichment, semantic mediation and interpretation of flower-visiting data. A novel component of the system is a semantic architecture which incorporates knowledge models validated by experts. The system combines ontologies and a Bayesian network to enrich, integrate and interpret flower- visiting data, specifically to discover ecological interactions in the data. The system’s effectiveness, to acquire and represent expert knowledge and simulate the inferencing ability of expert flower-visiting ecologists, is evaluated and discussed. The knowledge-based system will allow a novice ecologist to use standardised semantics to construct interaction networks automatically and objectively. This could be useful, inter alia, when comparing interaction networks for different periods of time at the same place or different places at the same time. While the system architecture encompasses three levels of biological organization, data provenance can be traced back to occurrences of individual organisms preserved as evidence in natural history collections. The potential impact of the semantic architecture could be significant in the field of biodiversity and ecosystem informatics because ecological interactions are important in applied ecological studies, e.g. in freshwater biomonitoring or animal migration.
An ontology-driven approach for structuring scientific knowledge for predicting treatment adherence behaviour: a case study of tuberculosis in Sub-Saharan African communities.
(2016) Ogundele, Olukunle Ayodeji.; Moodley, Deshendran.; Pillay, Anban Woolaganathan.; Seebregts, Christopher.
Poor adherence to prescribed treatment is a complex phenomenon and has been identified as a major contributor to patients developing drug resistance and failing treatment in sub-Saharan African countries. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that may vary drastically between communities in different regions. Computer based predictive models can be used to identify individuals and communities at risk of non-adherence and aid in supporting resource allocation and intervention planning in disease control programs. However, constructing effective predictive models is challenging, and requires detailed expert knowledge to identify factors and determine their influence on treatment adherence in specific communities. While many clinical studies and abstract conceptual models exist in the literature, there is no known concrete, unambiguous and comprehensive computer based conceptual model that categorises factors that influence treatment adherence behaviour. The aim of this research was to develop an ontology-driven approach for structuring knowledge of factors that influence treatment adherence behaviour and for constructing adherence risk prediction models for specific communities. Tuberculosis treatment adherence in sub-Saharan Africa was used as a case study to explore and validate the approach. The approach provides guidance for knowledge acquisition, for building a comprehensive conceptual model, its formalisation into an OWL ontology, and generation of probabilistic risk prediction models. The ontology was evaluated for its comprehensiveness and correctness, and its effectiveness for constructing Bayesian decision networks for predicting adherence risk. The approach introduces a novel knowledge acquisition step that guides the capturing of influencing factors from peer-reviewed clinical studies and the scientific literature. Furthermore, the ontology takes an evidence based approach by explicitly relating each factor to published clinical studies, an important consideration for health practitioners. The approach was shown to be effective in constructing a flexible and extendable ontology and automatically generating the structure of a Bayesian decision network, a crucial step towards automated, computer based prediction of adherence risk for individuals in specific communities.
Multi-level parallelization for accurate and fast medical image retrieval image retrieval.
(2016) Chikamai, Keith Sasala.; Viriri, Serestina.; Tapamo, Jules-Raymond.
Breast cancer is the most prevalent form of cancer diagnosed in women. Mammograms offer the best option in detecting the disease early, which allows early treatment and by implication, a favorable prognosis. Content-based Medical Image Retrieval (CBMIR) technique is increasingly gaining research attention as a Computer Aided Diagnosis (CAD)) approach for breast cancer diagnosis. Such systems work by availing mammogram images that are pathologically similar to a given query example, which are used to support the diagnostic decision by referential basis. In most cases, the query is of the form “return k images similar to the specified query image”. Similarity in the Content-based Image Retrieval (CBIR) context is based on the content of images, rather than text or keywords. The essence of CBIR systems is to enable indexing of pictorial content in databases and eliminating the drawbacks of manual annotation. CBMIR is a relatively young technology that is yet to gain widespread use. One major challenge for CBMIR systems is bridging the “semantic gap” in the description of image content. Semantic gap describes the discord in the notion of similarity between the descriptions of humans and CBMIR systems. Low accuracy concerns inhibit the full adoption of CBMIR systems into regular practice, with research focusing on improving the accuracy of CBMIR systems. Nonetheless, the area is still an open problem. As a contribution towards improving the accuracy of CBMIR for mammogram images, this work proposes a novel feature modeling technique for CBMIR systems based on classifier scores and standard statistical calculations on the same. A set of gradient-based filters are first used to highlight possible calcification objects; an Entropy-based thresholding technique is then used to segment the calcifications from the background. Experimental results show that the proposed model achieves a 100% detection rate, which shows the effectiveness of combining the likelihood maps from various filters in detecting calcification objects. Feature extraction considers established textural and geometric features, which are calculated from the detected calcification objects; these are then used to generate secondary features using the Support Vector Machine and Quadratic Discriminant Analysis classifier. The model is validated through a range of benchmarks, and is shown to perform competitively in comparison to similar works. Specifically, it scores 95%, 82%, 78%, and 98% on the accuracy, positive predictive value, sensitivity and specificity benchmarks respectively. Parallel computing is applied to the task of feature extraction to show its viability in reducing the cost of extraction features. This research considers two technologies for implementation: distributed computing using the message passing interface (MPI) and multicore computing using OpenMP threads. Both technologies involve the division of tasks to facilitate sharing of the computational burden in order to reduce the overall time cost. Communication cost is one penalty implied with parallel systems and a significant design target where efficiency of parallel models is concerned. This research focuses on mitigating the communication overhead for increasing the efficacy of parallel computation; it proposes an adaptive task assignment model dependent on network bandwidth for the parallel extraction of features. Experimental results report speedup values of between 4:7x and 10:4x, and efficiency values of between 0:11 and 0:62. There is a positive increase in both the speedup and efficiency values with an increase in the database size. The proposed adaptive assignment of tasks positively impacts on the speedup and efficiency performance of the parallel model. All experiments are based on the mammographic image analysis society (MIAS) database, which is a publicly available database that has been widely used in related works. The results achieved for both the mammogram pathology-based retrieval model as well as its computational efficiency met the objectives set for the research. In the domain of breast cancer applications, the models proposed in this work should positively contribute to the improvement of retrieval results of computer aided diagnosis/detection systems, where applicable. The improved accuracy will lead to higher acceptability of such systems by radiologists, which will enhance the quality of diagnosis both by reducing the decision-making time as well as improving the accuracy of the entire diagnostic process.
Hierarchical age estimation using enhanced facial features.
(2018) Angulu, Raphael.; Tapamo, Jules-Raymond.; Adewumi, Aderemi Oluyinka.
Ageing is a stochastic, inevitable and uncontrollable process that constantly affect shape, texture and general appearance of the human face. Humans can easily determine ones’ gender, identity and ethnicity with highest accuracy as compared to age. This makes development of automatic age estimation techniques that surpass human performance an attractive yet challenging task. Automatic age estimation requires extraction of robust and reliable age discriminative features. Local binary patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age discriminative features. Although local ternary patterns (LTP) is insensitive to noise, it uses a single static threshold for all images regardless of varied image conditions. Local directional patterns (LDP) uses k directional responses to encode image gradient and disregards not only central pixel in the local neighborhood but also 8 􀀀 k directional responses. Every pixel in an image carry subtle information. Discarding 8 􀀀 k directional responses lead to lose of discriminative texture features. This study proposes two variations of LDP operator for texture extraction. Significantorientation response LDP (SOR-LDP) encodes image gradient by grouping eight directional responses into four pairs. Each pair represents orientation of an edge with respect to central reference pixel. Values in each pair are compared and the bit corresponding to the maximum value in the pair is set to 1 while the other is set to 0. The resultant binary code is converted to decimal and assigned to the central pixel as its’ SOR-LDP code. Texture features are contained in the histogram of SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the difference between neighboring pixels and central pixel in 3 3 image region. These differential values are convolved with Kirsch edge detectors to obtain directional responses. These responses are normalized and used as probability of an edge occurring towards a respective direction. An adaptive threshold is applied to derive LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms of negative and positive LTDP encoded images are concatenated to obtain texture feature. Regardless of there being evidence of spatial frequency processing in primary visual cortex, biologically inspired features (BIF) that model visual cortex uses only scale and orientation selectivity in feature extraction. Furthermore, these BIF are extracted using holistic (global) pooling across scale and orientations leading to lose of substantive information. This study proposes multi-frequency BIF (MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol, this study investigated performance of proposed feature extractors in age estimation in a hierarchical way by performing age-group classification using Multi-layer Perceptron (MLP) followed by within age-group exact age regression using support vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were used to evaluate performance of proposed face descriptors. Experimental results on FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform state-of-the-art feature descriptors in age estimation. Experimental results show that performing gender discrimination before age-group and age estimation further improves age estimation accuracies. Shape, appearance, wrinkle and texture features are simultaneously extracted by visual system in primates for the brain to process and understand an image or a scene. However, age estimation systems in the literature use a single feature for age estimation. A single feature is not sufficient enough to capture subtle age discriminative traits due to stochastic and personalized nature of ageing. This study propose fusion of different facial features to enhance their discriminative power. Experimental results show that fusing shape, texture, wrinkle and appearance result into robust age discriminative features that achieve lower MAE compared to single feature performance.
A semantic sensor web framework for proactive environmental monitoring and control.
(2017) Adeleke, Jude Adekunle.; Moodley, Deshendran.; Rens, Gavin Brian.; Adewumi, Aderemi Oluyinka.
Observing and monitoring of the natural and built environments is crucial for main- taining and preserving human life. Environmental monitoring applications typically incorporate some sensor technology to continually observe specific features of inter- est in the physical environment and transmitting data emanating from these sensors to a computing system for analysis. Semantic Sensor Web technology supports se- mantic enrichment of sensor data and provides expressive analytic techniques for data fusion, situation detection and situation analysis. Despite the promising successes of the Semantic Sensor Web technology, current Semantic Sensor Web frameworks are typically focused at developing applications for detecting and reacting to situations detected from current or past observations. While these reactive applications provide a quick response to detected situations to minimize adverse effects, they are limited when it comes to anticipating future adverse situations and determining proactive control actions to prevent or mitigate these situations. Most current Semantic Sensor Web frameworks lack two essential mechanisms required to achieve proactive control, namely, mechanisms for antici- pating the future and coherent mechanisms for consistent decision processing and planning. Designing and developing proactive monitoring and control Semantic Sensor Web applications is challenging. It requires incorporating and integrating different tech- niques for supporting situation detection, situation prediction, decision making and planning in a coherent framework. This research proposes a coherent Semantic Sen- sor Web framework for proactive monitoring and control. It incorporates ontology to facilitate situation detection from streaming sensor observations, statistical ma- chine learning for situation prediction and Markov Decision Processes for decision making and planning. The efficacy and use of the framework is evaluated through the development of two different prototype applications. The first application is for proactive monitoring and control of indoor air quality to avoid poor air quality situations. The second is for proactive monitoring and control of electricity usage in blocks of residential houses to prevent strain on the national grid. These appli- cations show the effectiveness of the proposed framework for developing Semantic Sensor Web applications that proactively avert unwanted environmental situations before they occur.
Leaf recognition for accurate plant classification.
(2017) Kala, Jules Raymond.; Viriri, Serestina.; Moodley, Deshendran.
Plants are the most important living organisms on our planet because they are sources of energy and protect our planet against global warming. Botanists were the first scientist to design techniques for plant species recognition using leaves. Although many techniques for plant recognition using leaf images have been proposed in the literature, the precision and the quality of feature descriptors for shape, texture, and color remain the major challenges. This thesis investigates the precision of geometric shape features extraction and improved the determination of the Minimum Bounding Rectangle (MBR). The comparison of the proposed improved MBR determination method to Chaudhuri's method is performed using Mean Absolute Error (MAE) generated by each method on each edge point of the MBR. On the top left point of the determined MBR, Chaudhuri's method has the MAE value of 26.37 and the proposed method has the MAE value of 8.14. This thesis also investigates the use of the Convexity Measure of Polygons for the characterization of the degree of convexity of a given leaf shape. Promising results are obtained when using the Convexity Measure of Polygons combined with other geometric features to characterize leave images, and a classification rate of 92% was obtained with a Multilayer Perceptron Neural Network classifier. After observing the limitations of the Convexity Measure of Polygons, a new shape feature called Convexity Moments of Polygons is presented in this thesis. This new feature has the invariant properties of the Convexity Measure of Polygons, but is more precise because it uses more than one value to characterize the degree of convexity of a given shape. Promising results are obtained when using the Convexity Moments of Polygons combined with other geometric features to characterize the leaf images and a classification rate of 95% was obtained with the Multilayer Perceptron Neural Network classifier. Leaf boundaries carry valuable information that can be used to distinguish between plant species. In this thesis, a new boundary-based shape characterization method called Sinuosity Coefficients is proposed. This method has been used in many fields of science like Geography to describe rivers meandering. The Sinuosity Coefficients is scale and translation invariant. Promising results are obtained when using Sinuosity Coefficients combined with other geometric features to characterize the leaf images, a classification rate of 80% was obtained with the Multilayer Perceptron Neural Network classifier. Finally, this thesis implements a model for plant classification using leaf images, where an input leaf image is described using the Convexity Moments, the Sinuosity Coefficients and the geometric features to generate a feature vector for the recognition of plant species using a Radial Basis Neural Network. With the model designed and implemented the overall classification rate of 97% was obtained.

Browse

Browsing Doctoral Degrees (Computer Science) by Date Accessioned