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| 2. |
- Awad, Ali Ismail
(författare)
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Fingerprint local invariant feature extraction on GPU with CUDA
- 2013
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Ingår i: Informatica. - 0350-5596 .- 1854-3871. ; 37:3, s. 279-284
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Tidskriftsartikel (refereegranskat)abstract
- Driven from its uniqueness, immutability, acceptability, and low cost, fingerprint is in a forefront between biometric traits. Recently, the GPU has been considered as a promising parallel processing technology due to its high performance computing, commodity, and availability. Fingerprint authentication is keep growing, and includes the deployment of many image processing and computer vision algorithms. This paper introduces the fingerprint local invariant feature extraction using two dominant detectors, namely SIFT and SURF, which are running on the CPU and the GPU. The paper focuses on the consumed time as an important factor for fingerprint identification. The experimental results show that the GPU implementations produce promising behaviors for both SIFT and SURF compared to the CPU one. Moreover, the SURF feature detector provides shorter processing time compared to the SIFT CPU and GPU implementations.
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| 3. |
- Awad, Ali Ismail, et al.
(författare)
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Impact of Some Biometric Modalities on Forensic Science
- 2014
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Ingår i: Computational Intelligence in Digital Forensics: Forensic Investigation and Applications. - Cham : Springer International Publishing. - 9783319058849 - 9783319058856 ; , s. 47-62
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Bokkapitel (refereegranskat)abstract
- Recently, forensic science has had many challenges in many different types of crimes and crime scenes, vary from physical crimes to cyber or computer crimes. Accurate and efficient human identification or recognition have become crucial for forensic applications due to the large diversity of crime scenes, and because of the increasing need to accurately identify criminals from the available crime evidences. Biometrics is an emerging technology that provides accurate and highly secure personal identification and verification systems for civilian and forensic applications. The positive impact of biometric modalities on forensic science began with the rapid developments in computer science, computational intelligence, and computing approaches. These advancements have been reflected in the biometric modality capturing process, feature extraction, feature robustness, and features matching. A complete and automatic biometric identification or recognition systems have been built accordingly. This chapter presents a study of the impacts of using some biometric modalities in forensic applications. Although biometrics identification replaces human work with computerized and automatic systems in order to achieve better performance, new challenges have arisen. These challenges lie in biometric system reliability and accuracy, system response time, data mining and classification, and protecting user privacy. This chapter sheds light on the positive and the negative impacts of using some biometric modalities in forensic science. In particular, the impacts of fingerprint image, facial image, and iris patterns are considered. The selected modalities are covered preliminarily before tackling their impact on forensic applications. Furthermore, an extensive look at the future of biometric modalities deployment in forensic applications is covered as the last part of the chapter.
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| 4. |
- Charif, Bilal, et al.
(författare)
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Business and Government Organizations' Adoption of Cloud Computing
- 2014
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Ingår i: Intelligent Data Engineering and Automated Learning – IDEAL 2014. - Switzerland : Springer International Publishing. - 9783319108391 - 9783319108407 ; , s. 492-501
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Konferensbidrag (refereegranskat)abstract
- Cloud computing is very much accepted and acknowledged worldwide as a promising computing paradigm. On-demand provisioning based on a pay-per-use business model helps reduce costs through sharing computing and storage resources. Although cloud computing has become popular, some business and government organizations are still lagging behind in adopting cloud computing. This study reports the status of cloud utilization among business and government organizations, and the concerns of organizations regarding the adoption of cloud computing. The study shows that some government agencies are lagging behind in cloud computing use, while others are leading the way. Security is identified as the major reason for delay in adopting cloud computing. The outcomes of the data analysis process prove that some security measures such as encryption, access authentication, antivirus protection, firewall, and service availability are required by clients for adoption of cloud computing in the future.
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| 5. |
- King, James, et al.
(författare)
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A Distributed Security Mechanism for Resource-Constrained IoT Devices
- 2016
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Ingår i: Informatica. - 0350-5596 .- 1854-3871. ; 40:1, s. 133-143
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Things (IoT) devices have developed to comprise embedded systems and sensors with the ability to connect, collect, and transmit data over the Internet. Although solutions to secure IoT systems exist, Class-0 IoT devices with insufficient resources to support such solutions are considered a resource constrained in terms of secure communication. This paper provides a distributed security mechanism that targets Class-0 IoT devices. The research goal is to secure the entire data path in two segments, device-to-gateway and gateway-to-server data communications. The main concern in the provided solution is that lighter security operations with minimal resource requirements are performed in the IoT device, while heavier tasks are performed in the gateway side. The proposed mechanism utilizes a symmetric encryption for data objects combined with the native wireless security to offer a layered security technique between the device and the gateway. In the offered solution, the IoT gateways provide additional protection by securing data using Transport Layer Security (TLS). Real-time experimental evaluations have demonstrated the applicability of the proposed mechanism pertaining to the security assurance and the consumed resources of the target Class-0 IoT devices.
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| 6. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned
- 2019
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Ingår i: Magnetic Resonance Imaging. - : Elsevier. - 0730-725X .- 1873-5894. ; 61, s. 300-318
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Tidskriftsartikel (refereegranskat)abstract
- The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.
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| 7. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
- 2016
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Ingår i: Building Sustainable Health Ecosystems. - Cham : Springer International Publishing. - 9783319446714 - 9783319446721 ; , s. 151-160
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Konferensbidrag (refereegranskat)abstract
- The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.
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| 8. |
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| 9. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Design and implementation of a computer-aided diagnosis system for brain tumor classification
- 2017
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Ingår i: 2016 28th International Conference on Microelectronics (ICM). - 9781509057214 ; , s. 73-76
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Konferensbidrag (refereegranskat)abstract
- Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.
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| 10. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images
- 2019
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Ingår i: IEEE-ICM 2019 CAIRO-EGYPT. - : IEEE. ; , s. 304-307
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.
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