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T
Terzopoulos, D., C. McIntosh, T. McInerney, and G. Hamarneh, "Deformable Organisms", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 433–474, 2009. Abstract
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TarasKotyk, N. D., A. S. Ashour, A. D. C. Victoria, T. Gaber, A. E. Hassanien, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), 2015, , Beni Suef, Egypt, November 28-30, , 2015. Abstract

Apoptosis is an imperative constituent of various processes including proper progression and functioning of the immune system, embryonic development as well as chemical-induced cell death. Improper apoptosis is a reason in numerous human/animal’s conditions involving ischemic damage, neurodegenerative diseases, autoimmune disorders and various types of cancer. An outstanding feature of neurodegenerative diseases is the loss of specific neuronal populations. Thus, the detection of the dead cells is a necessity. This paper proposes a novel algorithm to achieve the dead cells detection based on color intensity and contrast changes and aims for fully automatic apoptosis detection based on image analysis method. A stained cultures images using Caspase stain of albino rats hippocampus specimens using light microscope (total 21 images) were used to evaluate the system performance. The results proved that the proposed system is efficient as it achieved high accuracy (98.89 ± 0.76 %) and specificity (99.36 ± 0.63 %) and good mean sensitivity level of (72.34 ± 19.85 %).

Taher, A., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft Computing, 2014. Abstract
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Taher, A., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft Computing, vol. June 2014, 2014. AbstractWebsite

Massive and complex data are generated every day in many fields. Complex data refer to data sets that are so large that conventional database management and data analysis tools are insufficient to deal with them. Managing and analysis of medical big data involve many different issues regarding their structure, storage and analysis. In this paper, linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) is presented for dimensionality reduction, feature selection and classification. Four real-world data sets are provided to demonstrate the performance of the proposed neuro-fuzzy classifier. The new classifier is compared with the other classifiers for different classification problems. The results indicated that applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only help reducing the dimensionality of large data sets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.

S
Salama, M., Data Mining for Medical Informatics, , Cairo, Cairo Unv, 2012. AbstractThesis.pdfPresentation.pdf

The work presented in this thesis investigates the nature of real-life data, mainly in the medical field, and the problems in handling such nature by the conventional data mining techniques. Accordingly, a set of alternative techniques are proposed in this thesis to handle the medical data in the three stages of data mining process. In the first stage which is preprocessing, a proposed technique named as interval-based feature evaluation technique that depends on a hypothesis that the decrease of the overlapped interval of values for every class label leads to increase the importance of such attribute. Such technique handles the difficulty of dealing with continuous data attributes without the need of applying discretization of the input and it is proved by comparing the results of the proposed technique to other attribute evaluation and selection techniques. Also in the preprocessing stage, the negative effect of normalization algorithm before applying the conventional PCA has been investigated and how the avoidance of such algorithm enhances the resulted classification accuracy. Finally in the preprocessing stage, an experimental analysis introduces the ability of rough set methodology to successfully classify data without the need of applying feature reduction technique. It shows that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including Support Vector Machine, Hidden Naive Bayesian network, Bayesian network and other techniques.
In the machine learning stage, frequent pattern-based classification technique is proposed; it depends on the detection of variation of attributes among objects of the same class. The preprocessing of the data like standardization, normalization, discretization or feature reduction is not required in this technique which enhances the performance in time and keeps the original data without being distorted. Another contribution has been proposed in the machine learning stage including the support vector machine and fuzzy c-mean clustering techniques; this contribution is about the enhancement of the Euclidean space calculations through applying the fuzzy logic in such calculations. This enhancement has used chimerge feature evaluation techniques in applying fuzzification on the level of features. A comparison is applied on these enhanced techniques to the other classical data mining techniques and the results shows that classical models suffers from low classification accuracy due to the dependence of un-existed presumption.
Finally, in the visualization stage, a proposed technique is presented to visualize the continuous data using Formal Concept Analysis that is better than the complications resulted from the scaling algorithms.

Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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O
Oliva, D., and A. E. Hassanien, "Digital Images Segmentation Using a Physical-Inspired Algorithm", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Segmentation is one of the most important tasks in image processing. It classifies the pixels into two or more groups depending on their intensity levels and a threshold value. The classical methods exhaustively search the best thresholds for a spec image. This process requires a high computational effort, to avoid this situation has been incremented the use of evolutionary algorithms. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. This chapter introduces a multilevel thresholding (MT) algorithm based on the EMO and the Otsu's method as objective function. The combination of those techniques generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image reducing the number of iterations.

Oliva, D., and A. E. Hassanien, "Digital Images Segmentation Using a Physical-Inspired Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 975–996, 2017. Abstract
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M
Mukherjee, A., N. Dey, N. Kausar, A. S. Ashour, R. Taiar, and A. E. Hassanien, "A disaster management specific mobility model for flying ad-hoc network", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, no. 3: IGI Global, pp. 72–103, 2016. Abstract
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Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density based fuzzy thresholding for image segmentation", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 118–127, 2012. Abstract
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Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density Based Fuzzy Thresholding for Image Segmentation", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, pp. 118--127, 2012. Abstract3220118.pdf

In this paper, we introduce an image segmentation framework which
applies automatic threshoding selection using fuzzy set theory and fuzzy
density model. With the use of different types of fuzzy membership function,
the proposed segmentation method in the framework is applicable for images of
unimodal, bimodal and multimodal histograms. The advantages of the method
are as follows: (1) the threshoding value is automatically retrieved thus requires
no prior knowledge of the image; (2) it is not based on the minimization of a
criterion function therefore is suitable for image intensity values distributed
gradually, for example, medical images; (3) it overcomes the problem of local
minima in the conventional methods. The experimental results have
demonstrated desired performance and effectiveness of the proposed approach.

Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density based fuzzy thresholding for image segmentation", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 118–127, 2012. Abstract
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K
Kotyk, T., N. Dey, A. S. Ashour, C. V. A. Drugarin, T. Gaber, A. E. Hassanien, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 57–68, 2016. Abstract
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J
Jagatheesan, K., B. Anand, N. Dey, T. Gaber, A. E. Hassanien, and T. - H. Kim, "A Design of PI Controller using Stochastic Particle Swarm Optimization in Load Frequency Control of Thermal Power Systems", Information Science and Industrial Applications (ISI), 2015 Fourth International Conference on: IEEE, pp. 25–32, 2015. Abstract
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H
Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Artificial neural network (ANN) models are involved in many applications because of its great computational capabilities. Training of multi-layer perceptron (MLP) is the most challenging problem during the network preparation. Many techniques have been introduced to alleviate this problem. Back-propagation algorithm is a powerful technique to train multilayer feedforward ANN. However, it suffers from the local minima drawback. Recently, meta-heuristic methods have introduced to train MLP like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimizer (ACO), Social Spider Optimization (SSO), Evolutionary Strategy (ES) and Grey Wolf Optimization (GWO). This chapter applied Multi-Verse Optimizer (MVO) for MLP training. Seven datasets are used to show MVO capabilities as a promising trainer for multilayer perceptron. Comparisons with PSO, GA, SSO, ES, ACO and GWO proved that MVO outperforms all these algorithms.

Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1076–1093, 2017. Abstract
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Hassanien, A. E., and J. M. Ali, "Digital mammogram segmentation algorithm using pulse coupled neural networks", Image and Graphics (ICIG'04), Third International Conference on: IEEE, pp. 92–95, 2004. Abstract
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Hassanien, A. E., and J. M. Ali, "Digital mammogram segmentation algorithm using pulse coupled neural networks", Image and Graphics (ICIG'04), Third International Conference on: IEEE, pp. 92–95, 2004. Abstract
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Hassanien, A. E., J. M. H. Ali, and H. Nobuhara, "Detection of Spiculated Masses in Mammograms Based on Fuzzy Image Processing.", Artificial Intelligence and Soft Computing - ICAISC 2004, 7th International Conference, , Zakopane, Poland, Volume 3070/2004, 1002-1007, June 7-11, 2004. Abstract

This paper presents an efficient technique for the detection of spiculated massesin the digitized mammogram to assist the attending radiologist in making his decisions. The presented technique consists of two stages, enhancement of spiculation masses followed by the segmentation process. Fuzzy Histogram Hyperbolization (FHH) algorithm is first used to improve the quality of the digitized mammogram images. The Fuzzy C-Mean (FCM) algorithm is then applied to the preprocessed image to initialize the segmentation. Four measures of quantifying enhancement have been developed in this work. Each measure is based on the statistical information obtained from the labelled region of interest and a border area surrounding it. The methodology is based on the assumption that target and background areas are accurately specified. We have tested the algorithms on digitized mammograms obtained from the Digital Databases for Mammographic Image Analysis Society (MIAS).

Hassanien, A. E., Developing Advanced Web Services Through P2P Computing And Autonomous Agents: Trends And Innovations, , USA, IGI-Global USA, 2010. AbstractWebsite

In recent years, the development of distributed systems, in particular the Internet, has been influenced heavily by three paradigms: peer-to-peer, autonomous agents, and service orientation. Developing Advanced Web Services through P2P Computing and Autonomous Agents: Trends and Innovations establishes an understanding of autonomous peer-to-peer Web Service models and developments as well as extends growing literature on emerging technologies. This scholarly publication is an important reference for researchers and academics working in the fields of peer-to-peer computing, Web and grid services, and agent technologies.

Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Hassan, E. A., A. I. Hafez, A. E. Hassanien, and A. A. Fahmy, "A discrete bat algorithm for the community detection problem", International Conference on Hybrid Artificial Intelligence Systems: Springer International Publishing, pp. 188–199, 2015. Abstract
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