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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
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 …
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 …
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 …
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 …
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 …
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 …