Papers by Zainab Rahamneh
International Journal of Computer Applications, 2011
We propose and experimentally evaluate a software solution for automatic detection and classifica... more We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm"s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].
Journal of Software Engineering and Applications, 2011
A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These m... more A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These models suffer many problems when handling various types of project. The reason is; the nature of each project makes it difficult to build a model which can generalize. In this paper we propose the use of Genetic Programming (GP) as an evolutionary computation approach to handle the software reliability modeling problem. GP deals with one of the key issues in computer science which is called automatic programming. The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve problems. GP will be used to build a SRGM which can predict accumulated faults during the software testing process. We evaluate the GP developed model and compare its performance with other common growth models from the literature. Our experiments results show that the proposed GP model is superior compared to Yamada S-Shaped, Generalized Poisson, NHPP and Schneidewind reliability models.
Neural Computing and Applications
Digital image processing techniques and algorithms have become a great tool to support medical ex... more Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values. Keywords Harris hawks optimization Á Multilevel thresholding Á Image segmentation Á Otsu method Á Covid-19 Á CT images
International Journal of Computer Applications, 2021
Medical volume data such as MRI and CT images consist of a large number of voxels. Thus, the proc... more Medical volume data such as MRI and CT images consist of a large number of voxels. Thus, the process of displaying, storing and transmission of medical volume data is a big challenge in the biomedical field. Applying surface simplification techniques to reduce the size occupied by medical images is considered as one of the most common approachs to overcome this challenge. However, not all of the surface simplification techniques are accurate enough to be used in the medical fields. This paper aims to evaluate the impact and the accuracy of applying the Uniform Mesh Resampling (UMR) technique and the Quadric Edge Collapse Decimation (QECD) technique. Moreover, this study investigates Poisson Surface Reconstruction (PSR) technique and sets experimentally the optimal offsetting value of this technique. Two real medical benchmark datasets are used in this study to evaluate the experimental work. The outcomes indicate clearly that the use of QECD as a surface simplification technique achieves competitive results when used with medical volume data.
Evaluation of Particle Swarm Optimisation for Medical Image Segmentation
Advances in Intelligent Systems and Computing, 2016
Otsu’s criteria is a popular image segmentation approach that selects a threshold to maximise the... more Otsu’s criteria is a popular image segmentation approach that selects a threshold to maximise the inter-class variance of the distribution of intensity levels in the image. The algorithm finds the optimum threshold by performing an exhaustive search, but this is time-consuming, particularly for medical images employing 16-bit quantisation. This paper investigates particle swarm optimisation (PSO), Darwinian PSO and Fractional Order Darwinian PSO to speed up the algorithm. We evaluate the algorithms in medical imaging applications concerned with volume reconstruction, with a particular focus on addressing artefacts due to immobilisation masks, commonly worn by patients undergoing radiotherapy treatment for head-and-neck cancer. We find that the Fractional-Order Darwinian PSO algorithm outperforms other PSO algorithms in terms of accuracy, stability and speed which makes it the favourite choice when the accuracy and time-of-execution are a concern.
ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010, 2010
The financial industry is becoming more and more dependent on advanced computer technologies in o... more The financial industry is becoming more and more dependent on advanced computer technologies in order to maintain competitiveness in a global economy. Fuzzy logic represents an exciting technology with a wide scope for po tential applications. There is a growing interest both in the field of fuzzy logic computing and in the financial world in ex plaining the use of fuzzy logic to forecast the future changes in prices of stocks, exchange rates, commodities, and other finan cial time series. Fuzzy algorithms are intensively used for the identification of dynamic models, combining both numerical and heuristic knowledge. Fuzzy logic provides a remarkably simple way to draw definite conclusions from vague, ambigu ous or imprecise information. In this paper, we are investi gating the ability of Fuzzy logic (FL) to tackle the financial time series forecasting problems. Experimental results on set of applications indicated that fuzzy logic can effectively solve these types of problems. In order to examine the effectiveness of fuzzy logic applied to forecasting, the comparison with Ar tificial Neural Networks (ANNs) is performed.
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Papers by Zainab Rahamneh