Publications

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Book
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.

Book Chapter
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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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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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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Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Distance similarity as a CBR technique for early detection of breast cancer: An Egyptian case study", Information Systems Design and Intelligent Applications: Springer India, pp. 449–456, 2015. Abstract
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Conference Paper
Banerjee, S., N. Elbendary, A. E. Hassanien, and M. Tolba, "Decision Support System for Customer Churn Reduction Approach", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp.229-234, 2013, Tunisia, , 4-6 Dec, 2013.
Banerjee, S., N. El-Bendary, A. E. Hassanien, and M. F. Tolba, "Decision support system for customer churn reduction approach", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 228–233, 2013. 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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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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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", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 118–127, 2012. Abstract
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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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Ali, M. A. S., G. I. Sayed, T. Gaber, A. E. Hassanien, V. Snasel, and L. F. Silva, "Detection of breast abnormalities of thermograms based on a new segmentation method", Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on: IEEE, pp. 255–261, 2015. Abstract
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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", 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. 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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