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D
Gaber, T., T. Kotyk, N. Dey, A. D. C. V. Amira Ashour, A. E. Hassanienan, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) , Springer. , Beni Suef University, Beni Suef, Egypt, Nov. 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 %).

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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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 %).

Elbedwehy, M. N., H. M. Zawbaa, N. Ghali, and A. E. Hassanien, "Detection of Heart Disease using Binary Particle Swarm Optimization", IEEE Federated Conference on Computer Science and Information Systems, Wroclaw - Poland, pp. 199–204, 2012. Abstractdetection_of_heart_disease_using_binary_particle.pdf

This article introduces a computer-aided diagnosis
system of the heart valve disease using binary particle swarm
optimization and support vector machine, in conjunction with
K-nearest neighbor and with leave-one-out cross-validation. The
system was applied in a representative heart dataset of 198
heart sound signals, which come both from healthy medical cases
and from cases suffering from the four most usual heart valve
diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral
stenosis (MS) and mitral regurgitation (MR). The introduced
approach starts with an algorithm based on binary particle
swarm optimization to select the most weighted features. This
is followed by performing support vector machine to classify
the heart signals into two outcome: healthy or having a heart
valve disease, then its classified the having a heart valve disease
into four outcomes: aortic stenosis (AS), aortic regurgitation
(AR), mitral stenosis (MS) and mitral regurgitation (MR). The
experimental results obtained, show that the overall accuracy
offered by the employed approach is high compared with other
techniques.

Elbedwehy, M. N., H. M. Zawbaa, N. Ghali, and A. E. Hassanien, "Detection of heart disease using binary particle swarm optimization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 177–182, 2012. Abstract
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Elbedwehy, M. N., H. M. Zawbaa, N. Ghali, and A. E. Hassanien, "Detection of heart disease using binary particle swarm optimization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 177–182, 2012. 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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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. 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).

Aziz, A. S. A., M. Salama, A. E. Hassanien, and E. L. Sanaa, "Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 597–602, 2012. Abstract
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Aziz, A. S. A., M. Salama, A. E. Hassanien, and E. L. Sanaa, "Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 597–602, 2012. Abstract
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Aziz, A. S. A., M. Salama, A. E. Hassanien, and E. L. Sanaa, "Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 597–602, 2012. Abstract
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Ahmed, S. A., T. M. Nassef, N. I. Ghali, G. Schaefer, and A. E. Hassanien, "Determining protrusion cephalometric readings from panoramic radiographic images", Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on: IEEE, pp. 321–324, 2012. Abstract
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Ahmed, S. A., T. M. Nassef, N. I. Ghali, G. Schaefer, and A. E. Hassanien, "Determining protrusion cephalometric readings from panoramic radiographic images", Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on: IEEE, pp. 321–324, 2012. Abstract
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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.

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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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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Adl, A., I. B. Shaheed, M. I. Shaalan, A. K. Al-Mokaddem, and A. E. Hassanien, "Digital Pathological Services Capability Framework", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 109–118, 2014. Abstract
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Azar, A. T., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft computing, vol. 19, no. 4: Springer Berlin Heidelberg, pp. 1115–1127, 2015. Abstract
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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.

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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Tourism