ORCID Profile
0000-0002-6346-0106
Current Organisation
University of Jordan
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Publisher: Elsevier BV
Date: 04-2015
DOI: 10.1016/J.COMPMEDIMAG.2014.05.013
Abstract: Tissue texture is known to exhibit a heterogeneous or non-stationary nature therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subbands' textural fractal characteristics for best bases selection of meningioma brain histopathological image classification is proposed. Each subband is analysed using its fractal dimension instead of energy, which has the advantage of being less sensitive to image intensity and abrupt changes in tissue texture. The most significant subband that best identifies texture discontinuities will be chosen for further decomposition, and its fractal characteristics would represent the optimal feature vector for classification. The performance was tested using the support vector machine (SVM), Bayesian and k-nearest neighbour (kNN) classifiers and a leave-one-patient-out method was employed for validation. Our method outperformed the classical energy based selection approaches, achieving for SVM, Bayesian and kNN classifiers an overall classification accuracy of 94.12%, 92.50% and 79.70%, as compared to 86.31%, 83.19% and 51.63% for the co-occurrence matrix, and 76.01%, 73.50% and 50.69% for the energy texture signatures respectively. These results indicate the potential usefulness as a decision support system that could complement radiologists' diagnostic capability to discriminate higher order statistical textural information for which it would be otherwise difficult via ordinary human vision.
Publisher: MDPI AG
Date: 21-03-2022
DOI: 10.3390/MATH10060999
Abstract: Cyber-attacks and unauthorized application usage have increased due to the extensive use of Internet services and applications over computer networks, posing a threat to the service’s availability and consumers’ privacy. A network Intrusion Detection System (IDS) aims to detect aberrant traffic behavior that firewalls cannot detect. In IDSs, dimension reduction using the feature selection strategy has been shown to be more efficient. By reducing the data dimension and eliminating irrelevant and noisy data, several bio-inspired algorithms have been employed to improve the performance of an IDS. This paper discusses a modified bio-inspired algorithm, which is the Grey Wolf Optimization algorithm (GWO), that enhances the efficacy of the IDS in detecting both normal and anomalous traffic in the network. The main improvements cover the smart initialization phase that combines the filter and wrapper approaches to ensure that the informative features will be included in early iterations. In addition, we adopted a high-speed classification method, the Extreme Learning Machine (ELM), and used the modified GWO to tune the ELM’s parameters. The proposed technique was tested against various meta-heuristic algorithms using the UNSWNB-15 dataset. Because the generic attack is the most common attack type in the dataset, the primary goal of this paper was to detect generic attacks in network traffic. The proposed model outperformed other methods in minimizing the crossover error rate and false positive rate to less than 30%. Furthermore, it obtained the best results with 81%, 78%, and 84% for the accuracy, F1-score, and G-mean measures, respectively.
Publisher: Elsevier BV
Date: 06-2010
Publisher: Frontiers Media SA
Date: 16-03-2023
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 10-2017
Publisher: No publisher found
Date: 2007
Publisher: IEEE
Date: 12-2011
Publisher: Springer New York
Date: 2016
Publisher: Springer New York
Date: 2016
Publisher: No publisher found
Date: 2018
Publisher: IEEE
Date: 12-2013
Publisher: British Institute of Radiology
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Publisher: IEEE
Date: 22-05-2023
Publisher: Elsevier BV
Date: 07-2016
DOI: 10.1016/J.ULTRASMEDBIO.2016.02.007
Abstract: Assessment of tumor tissue heterogeneity via ultrasound has recently been suggested as a method for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting the local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine to coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the purpose of ultrasound tissue characterization. The fractal dimension (self-similarity measure) was used to characterize the spatial distribution of scatterers, whereas the lacunarity (sparsity measure) was applied to determine scatterer number density. Performance was assessed based on 608 cross-sectional clinical ultrasound radiofrequency images of liver tumors (230 and 378 representing respondent and non-respondent cases, respectively). Cross-validation via leave-one-tumor-out and with different k-fold methodologies using a Bayesian classifier was employed for validation. The fractal properties of the backscattered echoes based on the Nakagami model (Nkg) and its extend four-parameter Nakagami-generalized inverse Gaussian (NIG) distribution achieved best results-with nearly similar performance-in characterizing liver tumor tissue. The accuracy, sensitivity and specificity of Nkg/NIG were 85.6%/86.3%, 94.0%/96.0% and 73.0%/71.0%, respectively. Other statistical models, such as the Rician, Rayleigh and K-distribution, were found to not be as effective in characterizing subtle changes in tissue texture as an indication of response to treatment. Employing the most relevant and practical statistical model could have potential consequences for the design of an early and effective clinical therapy.
Publisher: SPIE-Intl Soc Optical Eng
Date: 09-12-2019
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/473283
Abstract: Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention c aigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.
Publisher: Elsevier BV
Date: 09-2021
Publisher: SPIE
Date: 23-02-2012
DOI: 10.1117/12.911007
Publisher: Unpublished
Date: 2007
Publisher: Unpublished
Date: 2009
Publisher: NADIA
Date: 30-06-2015
Publisher: Elsevier BV
Date: 2023
Publisher: IEEE
Date: 10-2008
Publisher: Elsevier BV
Date: 04-2015
Publisher: IEEE
Date: 09-2016
Publisher: Springer Science and Business Media LLC
Date: 10-02-2020
DOI: 10.1186/S40537-020-0283-3
Abstract: Online social networks have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, it is vital to have an accurate understanding of the contextual content of social users, thus establishing grounds for measuring their social influence accordingly. In particular, there is the need for a better understanding of domain-based social trust to improve and expand the analysis process and determining the credibility of Social Big Data. The aim of this paper is to determine domain-based social influencers by means of a framework that incorporates semantic analysis and machine learning modules to measure and predict users’ credibility in numerous domains at different time periods. The evaluation of the experiment conducted herein validates the applicability of semantic analysis and machine learning techniques in detecting highly trustworthy domain-based influencers.
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 11-2009
Publisher: Elsevier
Date: 2014
Publisher: Springer International Publishing
Date: 2016
Publisher: Association for Computing Machinery (ACM)
Date: 22-03-2021
DOI: 10.1145/3447746
Publisher: MDPI AG
Date: 04-06-2021
DOI: 10.3390/APP11115228
Abstract: Deep learning has emerged as a new area of machine learning research. It is an approach that can learn features and hierarchical representation purely from data and has been successfully applied to several fields such as images, sounds, text and motion. The techniques developed from deep learning research have already been impacting the research on Natural Language Processing (NLP). Arabic diacritics are vital components of Arabic text that remove ambiguity from words and reinforce the meaning of the text. In this paper, a Deep Belief Network (DBN) is used as a diacritizer for Arabic text. DBN is an algorithm among deep learning that has recently proved to be very effective for a variety of machine learning problems. We evaluate the use of DBNs as classifiers in automatic Arabic text diacritization. The DBN was trained to in idually classify each input letter with the corresponding diacritized version. Experiments were conducted using two benchmark datasets, the LDC ATB3 and Tashkeela. Our best settings achieve a DER and WER of 2.21% and 6.73%, receptively, on the ATB3 benchmark with an improvement of 26% over the best published results. On the Tashkeela benchmark, our system continues to achieve high accuracy with a DER of 1.79% and 14% improvement.
Publisher: Elsevier BV
Date: 09-2020
Publisher: IEE
Date: 2008
DOI: 10.1049/CP:20080455
Publisher: Elsevier BV
Date: 09-2010
DOI: 10.1016/J.COMPMEDIMAG.2009.12.011
Abstract: Noise is one of the major problems that hinder an effective texture analysis of disease in medical images, which may cause variability in the reported diagnosis. In this paper seven texture measurement methods (two wavelet, two model and three statistical based) were applied to investigate their susceptibility to subtle noise caused by acquisition and reconstruction deficiencies in computed tomography (CT) images. Features of lung tumours were extracted from two different conventional and contrast enhanced CT image data-sets under filtered and noisy conditions. When measuring the noise in the background open-air region of the analysed CT images, noise of Gaussian and Rayleigh distributions with varying mean and variance was encountered, and Fishers' distance was used to differentiate between an original extracted lung tumour region of interest (ROI) with the filtered and noisy reconstructed versions. It was determined that the wavelet packet (WP) and fractal dimension measures were the least affected, while the Gaussian Markov random field, run-length and co-occurrence matrices were the most affected by noise. Depending on the selected ROI size, it was concluded that texture measures with fewer extracted features can decrease susceptibility to noise, with the WP and the Gabor filter having a stable performance in both filtered and noisy CT versions and for both data-sets. Knowing how robust each texture measure under noise presence is can assist physicians using an automated lung texture classification system in choosing the appropriate feature extraction algorithm for a more accurate diagnosis.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 26-05-2014
Publisher: Elsevier
Date: 2017
Publisher: Elsevier BV
Date: 10-2022
Publisher: Springer Science and Business Media LLC
Date: 27-02-2023
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2017
End Date: 2018
Funder: Council for International Exchange of Scholars
View Funded ActivityStart Date: 2012
End Date: 2014
Funder: Deanship of Scientific Research, University of Jordan
View Funded Activity