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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.
Sayed, G. I., M. A. Ali, T. Gaber, A. E. Hassanien, and V. Snasel, "A hybrid segmentation approach based on neutrosophic sets and modified watershed: a case of abdominal CT Liver parenchyma", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 144–149, 2015. Abstract
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Sayed, G. I., M. Soliman, and A. E. Hassanien, "Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 487–506, 2016. Abstract
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Sayed, G. I., A. E. Hassanien, T. M. Nassef, and J. - S. Pan, "Alzheimer’s Disease Diagnosis Based on Moth Flame Optimization", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 298–305, 2016. Abstract
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Sayed, G. I., and A. E. Hassanien, "Adaptive particle swarm optimization approach for CT Liver Parenchyma segmentation", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt , Nov. 28-30, 2015. Abstract

Image segmentation is an important task in the image processing
field. Efficient segmentation of images considered important for further object
recognition and classification. This paper presents a novel segmentation
approach based on Particle Swarm Optimization (PSO) and an adaptive
Watershed algorithm. An application of liver CT imaging has been chosen and
PSO approach has been applied to segment abdominal CT images. The
experimental results show the efficiency of the proposed approach and it
obtains overall accuracy 94% of good liver extraction.

Sayed, G. I., A. E. Hassanien, and G. Schaefer, "An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images ", Procedia Computer Science, , vol. 90 , pp. Pages 68-73, 2016. AbstractWebsite

In this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification.

Sayed, G. I., M. Soliman, and A. E. Hassanien, "Modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine", International Conference on Advanced Machine Learning Technologies and Applications, Cairo, 23 Feb, 2018. Abstract

Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an Optimal Foraging Algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic Algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.

Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic Grey Wolf Optimizer", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 261–266, 2015. 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, "Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images", Applied Intelligence: Springer US, pp. 1–12, 2017. Abstract
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Sayed, G. I., A. E. Hassanien, and G. Schaefer, "An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images", Procedia Computer Science, vol. 90: Elsevier, pp. 68–73, 2016. Abstract
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Sayed, G. I., and A. E. Hassanien, "Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 219–228, 2016. Abstract
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Sayed, G. I., A. Darwish, A. E. Hassanien, and J. - S. Pan, "Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 306–313, 2016. Abstract
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Sayed, G. I., A. E. Hassanien, M. A. Ali, and T. Gaber, "A Hybrid segmentation approach based on Neutrosophic sets and modified watershed: A case of abdominal CT liver parenchyma", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Sayed, G. I., and A. E. Hassanien, "A hybrid SA-MFO algorithm for function optimization and engineering design problems", Complex & Intelligent Systems, 2018. Abstract

This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf optimizer", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
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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Sato, M., A. E. Hassanien, and M. Nakajima, "Nonlinear registration of medical images using Cauchy-Navier spline transformation", Medical Imaging'99: International Society for Optics and Photonics, pp. 774–781, 1999. Abstract
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Sato, M., A. E. Hassanien, and M. Nakajima, "Non-Linear Image Registration: Combining Viscous Fluid Deformations and Elastic Body Splines", 映像情報メディア学会技術報告, vol. 22, no. 45: 一般社団法人映像情報メディア学会, pp. 1–6, 1998. Abstract
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Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members", Social Networking, London, Intelligent Systems Reference Library Springer, 2014.
Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Degree of Trust Under Context Sensitive Relational Database Hierarchy Using Hybrid Intelligent Approach", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no. 1: IGI Global, pp. 1–21, 2015. Abstract
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Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members", Social Networking: Springer International Publishing, pp. 201–213, 2014. Abstract
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Sara Yassen, T. Gaber, and A. E. Hassanien, "Integer Wavelet Transform for Thermal Image Authentication", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , November 13 - 15, 2015. Abstract

Thermal imaging is a technology with property of
seeing objects in the darkness. Such property makes this technology
very important tool for security and surveillance applications.
In this paper, a thermal image authentication technique using
hash function is proposed. In this technique, the thermal images
are used as cover images and bits from secret data (i.e. messages
or images) are then hidden in the cover images. This is achieved
by using the hash function and IntegerWavelet Transform (IWT).
1, 2 and 3 bits per bytes have been hidden in both horizontal
and vertical components of wavelet transform. The proposed
technique has been evaluated based on mean square error (MSE),
peak signal to noise ratio (PSNR), image fidelity (IF) and standard
deviation (SD). The results have shown better performance of the
proposed technique comparing with the most related work.

Sara Ahmed, T. Gaber, and A. E. Hassanien, "Telemetry Data Mining Techniques, Applications, and Challenges", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

The most recent rise of telemetry is around the use of Radio-telemetry technology for tracking the traces of moving objects. Initially, the radio telemetry was first used in the 1960s for studying the behavior and ecology of wild animals. Nowadays, there's a wide spectrum application of can benefits from radio telemetry technology with tracking methods, such as path discovery, location prediction, movement behavior analysis, and so on. Accordingly, rapid advance of telemetry tracking system boosts the generation of large-scale trajectory data of tracking traces of moving objects. In this study, we survey various applications of trajectory data mining and review an extensive collection of existing trajectory data mining techniques to be used as a guideline for designing future trajectory data mining solutions.

Sami, M., N. El-Bendary, T. - H. Kim, and A. E. Hassanien, "Using particle swarm optimization for image regions annotation", International Conference on Future Generation Information Technology: Springer Berlin Heidelberg, pp. 241–250, 2012. Abstract
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