Publications

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Conference Paper
Abder-Rahman Ali, Micael Couceiro, Ahmed M. Anter, A. E. Hassenian, M. F. Tolba, and V. Snasel, "Liver CT Image Segmentation with an Optimum Threshold using Measure of Fuzziness", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications, 22-24 June 2014, , Ostrava, Czech Republic., 22-24 June , 2014.
Abder-Rahman Ali, Micael Couceiro, A. M. Anter, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 83–92, 2014. Abstract
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Metawa, N., M. E.;, K. M. Hassan, and A. E. Hassanien, "Loan portfolio optimization using Genetic Algorithm: A case of credit constraints", 12th International Computer Engineering Conference (ICENCO),, Cairo, 28-29 Dec. , 2016. Abstract

With the increasing impact of capital regulation on banks financial decisions especially in competing environment with credit constraints, it comes the urge to set an optimal mechanism of bank lending decisions that will maximize the bank profit in a timely manner. In this context, we propose a self-organizing method for dynamically organizing bank lending decision using Genetic Algorithm (GA). Our proposed GA based model provides a framework to optimize bank objective when constructing the loan portfolio, which maximize the bank profit and minimize the probability of bank default in a search for an optimal, dynamic lending decision. Multiple factors related to loan characteristics, creditor ratings are integrated to GA chromosomes and validation is performed to ensure the optimal decision. GA uses random search to suggest the best appropriate design. We use this algorithm in order to obtain the most efficient lending decision. The reason for choosing GA is its convergence and its flexibility in solving multi-objective optimization problems such as credit assessment, portfolio optimization and bank lending decision.

Metawa, N., M. Elhoseny, K. M. Hassan, and A. E. Hassanien, "Loan portfolio optimization using genetic algorithm: A case of credit constraints", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 59–64, 2016. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Machine learning based approach for water pollution detection via fish liver microscopic images analysis", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 253–258, 2014. Abstract
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Mohamed Tahoun, Abd El Rahman Shabayek, and A. E. Hassanien, "Matching and Co-Registration of Satellite Images Using Local Features", Proc. International Conference on Space Optical Systems and Applications, (ICSOS), Kobe, Japan, May 7-9, 2014. tahoun_shabayek_abo_icsos_2014_japan.pdf.pdf
Mohamed Tahoun, A. E. R. Shabayayek, and A. E. Hassanien, "Matching and co-registration of satellite images using local features", Proceedings of the International Conference on Space Optical Systems and Applications, Kobe, Japan, vol. 79, 2014. Abstract
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Yamanya, W., A. T. Mohammed Fawzy, and A. E. Hassanien, "Moth-Flame Optimization for Training Multi-layer Perceptrons", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Esraa Elhariri, N. El-Bendary, Mohamed Mostafa 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. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Moustafa Ahmed, A. Hafez, M. Elwak, A. E. Hassanien, and E. Hassanien, "A Multi-Objective Genetic Algorithm for Community Detection in Multidimensional Social Network", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt , Nov. 28-30,, 2015. Abstract

Multidimensionality in social networks is a great issue that
came out into view as a result of that most social media sites such as
Facebook, Twitter, and YouTube allow people to interact with each other
through di erent social activities. The community detection in such mul-
tidimensional social networks has attracted a lot of attention in the recent
years. When dealing with these networks the concept of community de-
tection changes to be, the discovery of the shared group structure across
all network dimensions such that members in the same group interact
with each other more frequently than those outside the group. Most of
the studies presented on the topic of community detection assume that
there is only one kind of relation in the network. In this paper, we propose
a multi-objective approach, named MOGA-MDNet, to discover commu-
nities in multidimensional networks, by applying genetic algorithms. The
method aims to nd community structure that simultaneously maximizes
modularity, as an objective function, in all network dimensions. This
method does not need any prior knowledge about number of communi-
ties. Experiments on synthetic and real life networks show the capability
of the proposed algorithm to successfully detect the structure hidden
within these networks.

M.Moftah, H., A. E. Hassanien, N. Ghali, and M. Shoman, Multi-objective optimization K-mean segmentation approach for MRI Breast Images, , 2012. Abstract

The objective of this paper is to evaluate a new approach intended for reliable MRI breast image segmentation. It is based on the concepts of multi-objective and adaptation to identify target objects through an optimization methodology which keeps the optimum result during its iterations. The proposed approach were used to improve and enhance the traditional k-means clustering algorithm to be more effective and efficient. The clustering and breast cancer segmentation are implemented in the proposed approach at the same time by using the concept of multiobjective, and adaptation continually, in each iteration and then maintaining the best results. To evaluate performance of the presented approach, we run tests over different MRI breast images. The experimental results show that the overall accuracy offered by the multiobjective proposed k-means is high compared with standard K-mean clustering technique.

Mostafa A. Salama, M. M. M. Fouad, N. El-Bendary, and A. E. Hassanien, "Mutagenicity analysis based on Rough Set Theory and Formal Concept Analysis", In Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), , Mysore, India, 23-24 August, 2, 2013. mutagenicity_analysis_based_on_roug_set_FCA.pdf
Babers, R., N. I. Ghali, A. E. Hassanien, and N. M. Madbouly, "Optimal community detection approach based on Ant Lion Optimization", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 284–289, 2015. Abstract
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Abdelaziz, A., A. Adl, Moustafa Zein, M. Atef, K. K. A. Ghany, and A. E. Hassanien, "An Orphan Drug Legislation System", IEEE Conf. on Intelligent Systems (2) 2014: 389-399, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Orphan drugs are a treatment for rare diseases. From that, comes the importance of orphan drug development and discovery. For an orphan drug to be approved by the FDA, it does not have to be similar to any approved orphan drug. So chemists opinions are important to determine the probability of similarity. It is too hard to check all orphan drugs for any rare disease. It takes a long time and big effort, so we introduce in this study a system that classifies the orphan drugs according to their probability of structural similarity. It also compares between them and the unauthorized orphan drug to determine the closest orphan drug to it. That system helps chemists to study a certain orphan database using the five features. That system provides better results. It provides chemists with the clusters of orphan drugs after adding the drug that needs to be authorized to its cluster.

Abdelaziz, A., A. Adl, Moustafa Zein, M. Atef, K. K. A. Ghany, and A. E. Hassanien, "An Orphan Drug Legislation System", IEEE Conf. on Intelligent Systems (2) 2014: 389-399, 2014. Abstract

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Moustafa Zein, Ahmed Abdo, A. Adl, A. E. Hassanien, M. F. Tolba, and V. Snasel, "Orphan drug legislation with data fusion rules using multiple fingerprints measurements", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p32.pdf

The orphan drug certification process from the European committee is
depending on experts opinions that it is not similar to any other drug, this stage is
very complicated and those opinions differ based on the expertise. So, this paper
introduces computational model that gives one accurate probability of similarity,
using multiple fingerprints measurements to similarity, and fuse these measurements
by data fusion rules, that give one probability of similarity helping experts
to determine that drug is similar to existing anyone or not.

Moustafa Zein, Ahmed Abdo, A. Adl, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Orphan Drug Legislation with Data Fusion Rules Using Multiple Fingerprints Measurements", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 261–270, 2014. Abstract
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Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien, "Performance evaluation of computed tomography liver image segmentation approaches", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 109–114, 2012. Abstract
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El-Atta, A. A. H., M. I. Moussa, and A. E. Hassanien, "Predicting Biological Activity of 2, 4, 6-trisubstituted 1, 3, 5-triazines Using Random Forest", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 101–110, 2014. Abstract
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Mohamed Abd. Elfattah, N. El-Bendary, M. A. A. Elsoud, Jan Platoš, and A. E. Hassanien, "Principal Component Analysis Neural Network Hybrid Classification Approach for Galaxies Images.", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Abder-Rahman Ali, Micael S. Couceiro, and A. E. Hassenian, "PSilhOuette: Towards an Optimal Number of Clusters using a Nested Particle Swarm Approach for Liver CT Image Segmentation ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Mahmood, M. A., E. T. Al-Shammari, N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Recommender system for ground-level Ozone predictions in Kuwait", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 107–110, 2013. Abstract
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Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and T. - H. Kim, "Region growing segmentation with iterative K-means for CT liver images", Advanced Information Technology and Sensor Application (AITS), 2015 4th International Conference on: IEEE, pp. 88–91, 2015. Abstract
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Adham Mohamed, H. M. Zawbaa, M. M. M. Fouad, Esraa Elhariri, N. El-Bendary, Mohamed Tahoun, and A. E. Hassanine, "RoadMonitor: An Intelligent Road Surface Condition Monitoring System", IEEE Conf. on Intelligent Systems (2) 2014: 377-387, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Well maintained road network is an essential requirement for the safety and consistency of vehicles moving on that road and the wellbeing of people in those vehicles. On the other hand, guaranteeing an adequate maintenance by road managers can be achieved via having sufficient and accurate information concerning road infrastructure quality that can be as well utilized concurrently by the widespread means of users’ mobile devices both locally and worldwide. This article proposes a road condition monitoring framework that detects the road anomalies such as speed bumps. In the proposed approach, the main indicator for road anomalies is the gyroscope around gravity rotation in addition to the accelerometer sensor as a cross-validation method to confirm the detection results that were gathered from the gyroscope.

Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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