Publications

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Book Chapter
Noman, S., S. M. Shamsuddin, and A. E. Hassanien, "Hybrid learning enhancement of RBF network with particle swarm optimization", Foundations of Computational, Intelligence Volume 1: Springer Berlin Heidelberg, pp. 381–397, 2009. Abstract
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abd elaziz, M., A. A. Ewees, and A. E. Hassanien, "Hybrid Swarms Optimization Based Image Segmentation", Hybrid Soft Computing for Image Segmentation: Springer International Publishing, pp. 1–21, 2016. Abstract
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El-Baz, A. H., A. E. Hassanien, and G. Schaefer, "Identification of Diabetes Disease Using Committees of Neural Network-Based Classifiers", Machine Intelligence and Big Data in Industry: Springer International Publishing, pp. 65–74, 2016. Abstract
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Mokhtar, U., M. A. S. Ali, A. E. Hassanien, and H. Hefny, "Identifying two of tomatoes leaf viruses using support vector machine", Information Systems Design and Intelligent Applications: Springer India, pp. 771–782, 2015. Abstract
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Awad, A. I., and A. E. Hassanien, "Impact of some biometric modalities on forensic science", Computational Intelligence in Digital Forensics: Forensic Investigation and Applications: Springer International Publishing, pp. 47–62, 2014. Abstract
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Mahmood, M. A., N. El-Bendary, Jan Platoš, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-agent Recommender System", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 201–213, 2014. Abstract
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Fouad, M. M. M., and A. E. Hassanien, "Key pre-distribution techniques for WSN security services", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 265–283, 2014. Abstract
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Eid, H. F., A. E. Hassanien, T. - H. Kim, and S. Banerjee, "Linear correlation-based feature selection for network intrusion detection model", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 240–248, 2013. Abstract
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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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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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Rouibah, K., "Mobile-commerce intention to use via SMS: The case of Kuwait", Emerging markets and e-commerce in developing economies: IGI Global, pp. 230–253, 2009. Abstract
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Esraa Elhariri, N. El-Bendary, M. M. M. Fouad, Jan Platoš, A. E. Hassanien, and A. M. M. Hussein, "Multi-class SVM based classification approach for tomato ripeness", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 175–186, 2014. Abstract
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Aziz, A. S. A., and A. E. Hassanien, "Multilayer Machine Learning-Based Intrusion Detection System", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 225–247, 2014. Abstract
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Issa, M., and A. E. Hassanien, "Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 409–423, 2017. Abstract
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Salama, M. A., M. M. M. Fouad, N. El-Bendary, and A. E. O. Hassanien, "Mutagenicity Analysis Based on Rough Set Theory and Formal Concept Analysis", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 265–273, 2014. Abstract
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Ali, A. F., A. Mostafa, G. I. Sayed, M. A. Fattah, and A. E. Hassanien, "Nature Inspired Optimization Algorithms for CT Liver Segmentation", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 431–460, 2016. Abstract
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Ali, A. F., A. E. Hassanien, and Václav Snášel, "The nelder-mead simplex method with variables partitioning for solving large scale optimization problems", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 271–284, 2014. Abstract
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Sayed, G. I., and A. E. Hassanien, "Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Alzheimer's disease (AD) is considered one of the most common dementia's forms affecting senior's age staring from 65 and over. The standard method for identifying AD are usually based on behavioral, neuropsychological and cognitive tests and sometimes followed by a brain scan. Advanced medical imagining modalities such as MRI and pattern recognition techniques are became good tools for predicting AD. In this chapter, an automatic AD diagnosis system from MRI images based on using machine learning tools is proposed. A bench mark dataset is used to evaluate the performance of the proposed system. The adopted dataset consists of 20 patients for each diagnosis case including cognitive impairment, Alzheimer's disease and normal. Several evaluation measurements are used to evaluate the robustness of the proposed diagnosis system. The experimental results reveal the good performance of the proposed system.

Sayed, G. I., and A. E. Hassanien, "Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 522–540, 2017. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "A new heuristic function of ant colony system for retinal vessel segmentation", Medical Imaging: Concepts, Methodologies, Tools, and Applications: IGI Global, pp. 2063–2081, 2017. Abstract
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Darwish, A., M. M. El-Gendy, and A. E. Hassanien, "A New Hybrid Cryptosystem for Internet of Things Applications", Multimedia Forensics and Security: Springer International Publishing, pp. 365–380, 2017. Abstract
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Ismail, F. H., A. F. Ali, S. Esmat, and A. E. Hassanien, "Newcastle Disease Virus Clustering Based on Swarm Rapid Centroid Estimation", Advances in Nature and Biologically Inspired Computing: Springer International Publishing, pp. 359–367, 2016. Abstract
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