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Articles 1 - 16 of 16
Full-Text Articles in Theory and Algorithms
Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig
Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig
Research outputs 2014 to 2021
Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used …
A Genetic Algorithm-Based Feature Selection, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
A Genetic Algorithm-Based Feature Selection, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Research outputs 2014 to 2021
This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature …
Genetic Algorithm With Logistic Regression For Prediction Of Progression To Alzheimer's Disease, Piers Johnson, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S. Macaulay, Kathryn A. Ellis, Cassandra Szoeke, Ralph N. Martins, Christopher Rowe, Colin L. Masters, David Ames, Ping Zhang
Genetic Algorithm With Logistic Regression For Prediction Of Progression To Alzheimer's Disease, Piers Johnson, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S. Macaulay, Kathryn A. Ellis, Cassandra Szoeke, Ralph N. Martins, Christopher Rowe, Colin L. Masters, David Ames, Ping Zhang
Research outputs 2014 to 2021
Assessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search …
Zernike Moments And Genetic Algorithm : Tutorial And Application, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Zernike Moments And Genetic Algorithm : Tutorial And Application, Oluleye H. Babatunde, Leisa Armstrong, Jinsong Leng, Dean Diepeveen
Research outputs 2014 to 2021
Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic …
Novel Image-Dependent Quality Assessment Measures, Asaad Hashim, Zahir Hussain
Novel Image-Dependent Quality Assessment Measures, Asaad Hashim, Zahir Hussain
Research outputs 2014 to 2021
The image is a 2D signal whose pixels are highly correlated in a 2D manner. Hence, using pixel by pixel error what we called previously Mean-Square Error, (MSE) is not an efficient way to compare two similar images (e.g., an original image and a compressed version of it). Due to this correlation, image comparison needs a correlative quality measure. It is clear that correlation between two signals gives an idea about the relation between samples of the two signals. Generally speaking, correlation is a measure of similarity between the two signals. An important step in image similarity was introduced by …
Usefulness Of Infeasible Solutions In Evolutionary Search: An Empirical And Mathematical Study, Lyndon While, Philip Hingston
Usefulness Of Infeasible Solutions In Evolutionary Search: An Empirical And Mathematical Study, Lyndon While, Philip Hingston
Research outputs 2013
When evolutionary algorithms are used to solve constrained optimization problems, the question arises how best to deal with infeasible solutions in the search space. A recent theoretical analysis of two simple test problems argued that allowing infeasible solutions to persist in the population can either help or hinder the search process, depending on the structure of the fitness landscape. We report new empirical and mathematical analyses that provide a different interpretation of the previous theoretical predictions: that the important effect is on the probability of finding the global optimum, rather than on the time complexity of the algorithm. We also …
High-Dimensional Objective-Based Data Farming, Zeng Fanchao, James Decraene, Malcolm Low, Wentong Cai, Suiping Zhou, Philip F. Hingston
High-Dimensional Objective-Based Data Farming, Zeng Fanchao, James Decraene, Malcolm Low, Wentong Cai, Suiping Zhou, Philip F. Hingston
Research outputs 2011
In objective-based data farming, decision variables of the Red Team are evolved using evolutionary algorithms such that a series of rigorous Red Team strategies can be generated to assess the Blue Team's operational tactics. Typically, less than 10 decision variables (out of 1000+) are selected by subject matter experts (SMEs) based on their past experience and intuition. While this approach can significantly improve the computing efficiency of the data farming process, it limits the chance of discovering “surprises” and moreover, data farming may be used only to verify SMEs' assumptions. A straightforward solution is simply to evolve all Red Team …
Morphology Independent Dynamic Locomotion Control For Virtual Characters, Adrian Boeing
Morphology Independent Dynamic Locomotion Control For Virtual Characters, Adrian Boeing
Research outputs pre 2011
Physically based animation of virtual characters is an attractive technology for computer games. It enables characters to dynamically react to interactions with the environment. Existing dynamic simulation controllers are often complex to understand and manipulate, and so are of limited use for animators. This paper presents an extended spline-based control strategy similar to splines used in standard keyframe animation techniques. Unlike existing dynamic control strategies, this allows animators to modify the control system parameters in a manner similar to traditional kinematic animation techniques. A genetic algorithm is employed to produce the initial control parameters for the desired gait, and extend …
A Study Of The Parallelisation Of Multiobjective Evolutionary Algorithms In A Cluster Environment, Sadeesha Gamhewa
A Study Of The Parallelisation Of Multiobjective Evolutionary Algorithms In A Cluster Environment, Sadeesha Gamhewa
Theses : Honours
The two main issues relating to the use of Multiobjective Evolutionary Algorithms (MOEAs) are the efficiency and effectiveness of the algorithms. As a result of the multiobjective and multi dimensional nature of MOEAs, the overall execution time that is taken to solve real world problems with MOEAs can be significant. Therefore, a few studies have recently been completed to address these performance issues by the use of parallelisation methods. The most widely known parallel Multiobjective Evolutionary Algorithm (pMOEA) models are the Master-slave, the Island, and the Diffusion models. The Master-slave and the Island models are generally implemented using message passing …
Video Compression Using Wavelets And Hierarchical Motion Estimation, Andrew Peter Byrne
Video Compression Using Wavelets And Hierarchical Motion Estimation, Andrew Peter Byrne
Theses : Honours
This thesis investigates the benefits and the significant compression that can be obtained from data that has been decomposed using a wavelet transform. A video compression algorithm was developed that employs the wavelet transform and a hierarchical motion estimation algorithm which itself utilises benefits of the wavelet transform. Using MATLAB, a popular software tool for matrix based computation and analysis, several functions were developed which together formed the video compression algorithm. A variety of tests were conducted on a sample video sequence to ascertain the strengths and weaknesses of the techniques employed. The results, although not the same as the …
Vhdl Design And Simulation For Embedded Zerotree Wavelet Quantisation, Hung Huynh
Vhdl Design And Simulation For Embedded Zerotree Wavelet Quantisation, Hung Huynh
Theses : Honours
This thesis discusses a highly effective still image compression algorithm – The Embedded Zerotree Wavelets coding technique, as it is called. This technique is simple but achieves a remarkable result. The image is wavelet-transformed, symbolically coded and successive quantised, therefore the compression and transmission/storage saving can be achieved by utilising the structure of zerotree. The algorithm was first proposed by Jerome M. Shapiro in 1993, however to minimise the memory usage and speeding up the EZW processor, a Depth First Search method is used to transverse across the image rather than Breadth First Search method as initially discussed in Shapiro's …
A 2d Dwt Architecture Suitable For The Embedded Zerotree Wavelet Algorithm, James Martinez
A 2d Dwt Architecture Suitable For The Embedded Zerotree Wavelet Algorithm, James Martinez
Theses : Honours
Digital Imaging has had an enormous impact on industrial applications such as the Internet and video-phone systems. However, demand for industrial applications is growing enormously. In particular, internet application users are, growing at a near exponential rate. The sharp increase in applications using digital images has caused much emphasis on the fields of image coding, storage, processing and communications. New techniques are continuously developed with the main aim of increasing efficiency. Image coding is in particular a field of great commercial interest. A digital image requires a large amount of data to be created. This large amount of data causes …
An Adaptive Hierarchical Fuzzy Logic System For Modelling And Prediction Of Financial Systems, Mark Kingham
An Adaptive Hierarchical Fuzzy Logic System For Modelling And Prediction Of Financial Systems, Mark Kingham
Theses: Doctorates and Masters
In this thesis, an intelligent fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates is developed. The proposed system uses a Hierarchical Fuzzy Logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases. A fuzzy logic system is developed to model and predict three month quarterly interest rate fluctuations. The system is further trained to model and predict interest rates for six month and one year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to …
Development Of A Model For Smart Card Based Access Control In Multi-User, Multi-Resource, Multi-Level Access Systems, David Shaw
Theses: Doctorates and Masters
The primary focus of this research is an examination of the issues involved in the granting of access in an environment characterised by multiple users, multiple resources and multiple levels of access permission. Increasing levels of complexity in automotive systems provides opportunities for improving the integration and efficiency of the services provided to the operator. The vehicle lease / hire environment provided a basis for evaluating conditional access to distributed, mobile assets where the principal medium for operating in this environment is the Smart Card. The application of Smart Cards to existing vehicle management systems requires control of access to …
Simulator For The Performance Analysis Of Cpm Schemes In An Indoor Wireless Environment, Ronald Chua
Simulator For The Performance Analysis Of Cpm Schemes In An Indoor Wireless Environment, Ronald Chua
Theses : Honours
A software simulator for characterising Continuous Phase Modulation (CPM) schemes in an indoor multipath environment has been developed using SIMULINK and MATLAB. The simulator is capable of simulating a wide range of CPM schemes to determine bandwidth efficiency and robustness to additive white Gaussian noise (AWGN) and Rician fading. Initial trials of the simulator indicate that the simulator is functioning correctly. Eventually, the simulator will be used to determine the most suitable modulation scheme for the development of an actual indoor wireless system.
An Analysis And Implementation Of Linear Derivation Strategies, Winston M. Tabada
An Analysis And Implementation Of Linear Derivation Strategies, Winston M. Tabada
Theses: Doctorates and Masters
This study examines the efficacy of six linear derivation strategies: (i) s-linear resolution, (ii) the ME procedure; (iii) t-linear resolution, (iv) SL -resolution, (v) the GC procedure, and (vi) SLM. The analysis is focused on the different restrictions and operations employed in each derivation strategy. The selection function, restrictive ancestor resolution, compulsory ancestor resolution on literals having atoms which are or become identical, compulsory merging operations, reuse of truncated literals, spreading of FALSE literals, no-tautologies resection, no two non-B-literals having identical atoms restriction, and the use of semantic information to trim irrelevant derivations from the search tree are the major …