| 1. |
- 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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| 2. |
- Elrawy, Mohammed Faisal, et al.
(författare)
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Flow-based Features for a Robust Intrusion Detection System Targeting Mobile Traffic
- 2016
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Ingår i: 23rd International Conference on Telecommunications (ICT). - Piscataway, NJ : IEEE Communications Society. - 9781509019908
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Konferensbidrag (refereegranskat)abstract
- The security risks and threats that impact wired and wireless networks are now applicable to mobile telecommunication networks. Threat detection systems should be more intelligent because threats are becoming more dangerous. An intrusion detection system (IDS) is a potential network security solution for protecting the confidentiality, integrity, and availability of user data and information resources. A fast and effective IDS for mobile networks that does not violate the user's privacy or the network's QoS is required. This paper offers a set of flow-based features that can be utilized for mobile network traffic as a prerequisite for a privacy-aware and QoS-robust IDS. The principal component analysis (PCA) method was used for reduction of the features. Twelve features in six groups, which represent the user data in mobile traffic, were extracted and evaluated for IDSs. The evaluation process achieved a F-measure weighted average equal to 0.834, and the experimental time was equal to 12.9 seconds. The accomplished measurements have demonstrated the applicability of the proposed set of features.
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