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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Machine learning based approach for water pollution detection via fish liver microscopic images analysis", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 253–258, 2014. Abstract
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Bakrawy, L. M. E., A. - E. " "Hassanien, and N. I. Ghali, Machine Learning in Image Authentication, , Cairo, Al-Azhar University, 2012. Abstract

In recent years image authentication has gained substantial attraction by the research community. It promises the solution to many problems such as content piracy, illicit manipulation of medical/legal documents, content security and so on. As watermark-based image authentication approaches are efficient and attractive, some types of watermarks such as logos, labels, trademark, or random sequence representing the author’s ownership, are mbedded into the desired digital image. Generally, a registration to the authentication center is necessary, which helps to solve ownership disputes by identifying the owner of the disputed media. If necessary, the embedded watermark in the digital image can be used to verify ownership Due to the open environment of Internet downloading, copyright protection introduces a new set of challenging problems regarding security and illegal distribution of privately owned images. One solution to these problems is digital watermarking, i.e., the insertion of information into the image data in such a way that the added information is not visible and yet resistant to image alterations. A watermarking technique is to prevent digital images that belong to rightful owners from being illegally commercialized or used, and it can verify the intellectual property right. The watermark should be robust and transparent, but the ways of pursuing transparency and robustness are conflict. For instance, if we would like to concentrate on the transparency issue, it is natural to embed the smallest modulation into images whenever possible. However, due to such small values in the embedded watermark, attacks can easily destroy the problems The first proposed solution is based on the associative watermarking and vector quantization. It achieves more effective against several images processing such as blurring, sharpening adding in Gaussian noise, cropping, and JPEG lossy compression especially in case of Gaussian noise and blurring. Also this technique is implemented to hide biometric data, fingerprint image, over three different types of medical images: CT, MRI and interventional images. It also achieves an effective resistance against several images processing such as JPEG lossy compression, sharpening, blurring and adding in Gaussian noise The second contribution in this thesis is strict authentication of multimodal biometric images using an improved secure hash function (ISHA-1) and near sets. It indicates that the proposed hash function is collision resistant and assures a good compression and preimage resistance. Also it reduces the time of implementation comparing to standard secure hash function. Moreover, the difference in time between SHA-1 and ISHA-1 increases by increasing the number of letters in message since the running time of implementation of ISHA-1 is limited compared to the running time of implementation of SHA-1. Also the proposed approach guarantees the security assurance and reduces the time of implementation. The third proposed contribution is fragile watermarking approach for image authentication based on rough k-means only and hybridization of rough k-means and particle swarm optimization. It can embed watermark without causing noticeable visual artifacts, and does not only achieve superior tamper detection in images accurately, it also recovers tampered regions effectively. In addition, it shows that the proposed approach can effectively thwart different attacks, such as the cut-and paste attack and collage attack, while sustaining superior tamper detection and localization accuracy. Moreover, the running time of implemented hybrid system is limited compared to the running time of the implemented rough k-means only. Especially, when we used exponential particle swarm optimization to optimize the parameters of rough k-means.

Abdel-Aziz, A. S., A. E. Hassanien, A. T. Azar, and S. E. - O. Hanafi, "Machine learning techniques for anomalies detection and classification", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 219–229, 2013. Abstract
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Hassanien, A. E., Machine Learning Techniques for Prostate Ultrasound Image Diagnosis, , German, Studies in Computational Intelligence - Springer, 2010. Abstract

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this chapter, we present a machine learning scheme, employing a combination of fuzzy sets, wavelets and rough sets, for analyzing prostrate ultrasound images in order diagnose prostate cancer. To address the image noise problem we first utilize an algorithm based on type-II fuzzy sets to enhance the contrast of the ultrasound image. This is followed by performing a modified fuzzy c-mean clustering algorithm in order to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalized, followed by application of a rough set analysis for discrimination of different regions of interest to determine whether they represent cancer or not. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including decision trees, discriminant analysis, rough neural networks, and neural networks.

Hassanien, A. E., H. Al-Qaheri, Václav Snášel, and J. F. Peters, "Machine learning techniques for prostate ultrasound image diagnosis", Advances in Machine Learning I: Springer Berlin Heidelberg, pp. 385–403, 2010. Abstract
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Hassanien, A. E., H. Al-Qaheri, Václav Snášel, and J. F. Peters, "Machine learning techniques for prostate ultrasound image diagnosis", Advances in Machine Learning I: Springer Berlin Heidelberg, pp. 385–403, 2010. Abstract
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Rehab Mahmoud, Nashwa El-Bendary, H. M. A. E. H. H. S. M. O. A., "Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, , Ostrava, Czech Republic, , June 29 - July , 2015. Abstract

Disabilities, specially Spinal Cord Injuries (SCI), affect people behaviors, their response, and the participation in daily activities. People with SCI need long care, cost, and time to improve their heath status. So, the rehabilitation of people with SCI on different period of times is required. In this paper, we proposed an automated system to estimate the rehabilitation length of stay of patients with SCI. The proposed system is divided into three phases; (1) pre-processing phase, (2) classification phase, and (3) rehabilitation length of stay measurement phase. The proposed system is automating International Classification of Functioning, Disability and Health classification (ICF) coding process, monitoring progress in patient status, and measuring the rehabilitation time based on support vector machines algorithm. The proposed system used linear and radial basis (RBF) kernel functions of support vector machines (SVMs) classification algorithm to classify data. The accuracy obtained was full match on training and testing data for linear kernel function and 93.3 % match for RBF kernel function.

Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, A. E. Hassanien, and H. A. Shaheen, "Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 523–534, 2015. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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Hassanien, A. E., "Machine Learning-Based Soccer Video Summarization System.", Multimedia, Computer Graphics and Broadcasting - International Conference, MulGraB 2011,, Jeju Island, Korea, December 8-10, 2011. Abstract

This paper presents a machine learning (ML) based event detection and summarization system for soccer matches. The proposed system is composed of six phases. Firstly, in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing phase, it applies two types of classification to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the system applies two machine learning algorithms, namely; support vector machine (SVM) and neural network (NN), for emphasizing important segments with logo appearance. Also, in the score board detection phase, the system uses both ML algorithms for detecting the caption region providing information about the score of the game. Subsequently, in the excitement event detection phase, the system uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor filter for detecting goal net. Finally, in the logo-based event detection and summarization phase, the system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. Compared to the performance results obtained using SVM classifier, the proposed system attained good NN-based performance results concerning recall ratio, however it attained poor NN-based performance results concerning precision ratio.

Hassanien, A. E., "Machine Learning-Based Soccer Video Summarization System.", Multimedia, Computer Graphics and Broadcasting-International Conference, MulGraB 2011,, 2017. Abstract
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Hossam Moftah, Walaa Elmasry, M. Ibrahiem, A. E. Hassanien, and G. Schaefer, "Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation", 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp.942- 945, Okinawa, Japan. , 5 Nov., 2013.
Hossam Moftah, Walaa Elmasry, M. Ibrahim, A. E. Hassanien, and G. Schaefer, "Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation", Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on: IEEE, pp. 942–945, 2013. Abstract
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Mohamed Tahoun, Abd El Rahman Shabayek, and A. E. Hassanien, "Matching and Co-Registration of Satellite Images Using Local Features", Proc. International Conference on Space Optical Systems and Applications, (ICSOS), Kobe, Japan, May 7-9, 2014. tahoun_shabayek_abo_icsos_2014_japan.pdf.pdf
Mohamed Tahoun, A. E. R. Shabayayek, and A. E. Hassanien, "Matching and co-registration of satellite images using local features", Proceedings of the International Conference on Space Optical Systems and Applications, Kobe, Japan, vol. 79, 2014. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Kompatsiaris, Y., S. Nikolopoulos, T. Lidy, and A. Rauber, "Media Search Cluster White Paper on" Search Computing".", ERCIM News, vol. 2012, no. 88, 2012. Abstract
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Xiao, K., A. E. Hassanien, and N. I. Ghali, "Medical image segmentation using information extracted from deformation", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 157–163, 2011. Abstract
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Xiao, K., A. E. Hassanien, and N. I. Ghali, "Medical image segmentation using information extracted from deformation", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 157–163, 2011. Abstract
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Dey, N., V. Bhateja, and A. E. Hassanien, Medical Imaging in Clinical Applications: Algorithmic and Computer-Based Approaches, , Germany , Springer, 2016. images_1.jpgWebsite
Dey, N., V. Bhateja, and A. E. Hassanien, Medical Imaging in Clinical Applications: Algorithmic and Computer-Based Approaches, : Springer, 2016. Abstract
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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma Classification based on Ensemble Classification of Dermoscopy Image Features", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.