Publications

Export 162 results:
Sort by: Author Title Type [ Year  (Asc)]
2016
Mostafa, A., M. Houseni, N. Allam, A. E. Hassanien, H. Hefny, and P. - W. Tsai, "Antlion Optimization Based Segmentation for MRI Liver Images", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 265–272, 2016. Abstract
n/a
Mostafa, A., A. Fouad, M. A. Fattah, A. E. Hassanien, and H. Hefny, "Artificial Bee Colony Based Segmentation for CT Liver Images", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 409–430, 2016. Abstract
n/a
Elhoseny, M., N. Metawa, and A. E. Hassanien, "An automated information system to ensure quality in higher education institutions", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 196–201, 2016. Abstract
n/a
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
n/a
Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and H. Hefny, "Enhanced region growing segmentation for CT liver images", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 115–127, 2016. Abstract
n/a
Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. - K. Mohamed, "Grey Wolf Optimizer and Case-Based Reasoning Model for Water Quality Assessment", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 229–239, 2016. Abstract
n/a
Mostafa, A., A. Fouad, M. Houseni, N. Allam, A. E. Hassanien, H. Hefny, and I. Aslanishvili, "A Hybrid Grey Wolf Based Segmentation with Statistical Image for CT Liver Images", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 846–855, 2016. Abstract
n/a
Hassanien, A. E., N. El-Bendary, Asmaa Hashem Sweidan, A. E. -karim Mohamed, and O. M. Hegazy, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46: Elsevier, pp. 1043–1055, 2016. Abstract
n/a
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
n/a
Hassanien, A. E., M. M. Fouad, A. A. Manaf, M. Zamani, R. Ahmad, and J. Kacprzyk, Multimedia Forensics and Security: Foundations, Innovations, and Applications, : Springer, 2016. Abstract
n/a
Ali, A. F., A. Mostafa, G. I. Sayed, M. A. Fattah, and A. E. Hassanien, "Nature Inspired Optimization Algorithms for CT Liver Segmentation", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 431–460, 2016. Abstract
n/a
Abder-Rahman Ali, Micael Couceiro, A. Anter, and A. - E. Hassanien, "Particle swarm optimization based fast fuzzy C-means clustering for liver CT segmentation", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 233–250, 2016. Abstract
n/a
Adl, A., Moustafa Zein, and A. E. Hassanien, "PQSAR: The membrane quantitative structure-activity relationships in cheminformatics", Expert Systems with Applications, vol. 54: Pergamon, pp. 219–227, 2016. Abstract
n/a
Salama, M. A., A. E. Hassanien, and A. Mostafa, "The prediction of virus mutation using neural networks and rough set techniques", EURASIP Journal on Bioinformatics and Systems Biology, vol. 2016, no. 1: Springer International Publishing, pp. 1–11, 2016. Abstract
n/a
Alaa Tharwat, Y. S. Moemen, and A. E. Hassanien, "A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method", Scientific Reports, vol. 6: Nature Publishing Group, 2016. Abstract
n/a
Mohamed Tahoun, Abd El Rahman Shabayek, H. Nassar, M. M. Giovenco, R. Reulke, Eid Emary, and A. E. Hassanien, "Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 135–171, 2016. Abstract
n/a
Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "Similarity Measures Based Recommender System for Rehabilitation of People with Disabilities", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 523–533, 2016. Abstract
n/a
Elshazly, H. I., A. F. Ali, H. Mahmoud, A. M. Elkorany, and A. E. Hassanien, "Weighted reduct selection metaheuristic based approach for rules reduction and visualization", Computing, Communication and Automation (ICCCA), 2016 International Conference on: IEEE, pp. 274–280, 2016. Abstract
n/a
Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and H. Hefny, "Wolf local thresholding approach for liver image segmentation in CT images", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 641–651, 2016. Abstract
n/a
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, issue 3, 2016. AbstractWebsite

The extended Mobile Ad-hoc Network architecture is a paramount research domain due to a wide enhancement of smart phone and open source Unmanned Aerial Vehicle (UAV) technology. The novelty of the current work is to design a disaster aware mobility modeling for a Flying Ad-hoc network infrastructure, where the UAV group is considered as nodes of such ecosystem. This can perform a collaborative task of a message relay, where the mobility modeling under a “Post Disaster” is the main subject of interest, which is proposed with a multi-UAV prototype test bed. The impact of various parameters like UAV node attitude, geometric dilution precision of satellite, Global Positioning System visibility, and real life atmospheric upon the mobility model is analyzed. The results are mapped with the realistic disaster situation. A cluster based mobility model using the map oriented navigation of nodes is emulated with the prototype test bed.

Hassaniena, A. E., N. El-Bendary, Asmaa Hashem Sweidan, and A. E. -karim Mohamed, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46, issue 1, pp. 1043–1055, 2016. AbstractWebsite

Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia “Oreochromis niloticus” fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate – Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.

Adl, A., Moustafa Zein, and A. E. Hassanien, "PQSAR: The membrane quantitative structure-activity relationships in cheminformatics", Expert Systems with Applications, vol. 54, issue 1, pp. 219–227, 2016. AbstractWebsite

The applications of quantitative structure activity relationships (QSAR) are used to establish a correlation between structure and biological response. Similarity searching is one of QSAR major phases. Innovating new strategies for similarity searching is an urgent task in cheminformatics research for three reasons: (i) the increasing size of chemical search space of compound databases; (ii) the importance of similarity measurements to (2D) and (3D) QSAR models; and (iii) similarity searching is a time consuming process in drug discovery. In this study, we introduce theoretical similarity searching strategy based on membrane computing. It solves time consumption problem. We adopt a ranking sorting algorithm with P System to rank probabilities of similarity according to a predefined similarity threshold. That bio-inspired model, simulating biological living cell, presents a high performance parallel processing system, we called it PQSAR. It relies on a set of rules to apply ranking algorithm on probabilities of similarity. The simulated experiments show how the effectiveness of PQSAR method enhanced the performance of similarity searching significantly; and introduced a standard ranking algorithm for similarity searching.

Salama, M. A., A. Mostafa, and A. E. Hassanien, "The prediction of virus mutation using neural networks and rough set techniques", . EURASIP J. Bioinformatics and Systems Biology , vol. 10, 2016. AbstractWebsite

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

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.

Elhoseny, M., N. Metawa, and A. E. Hassanien, "An automated information system to ensure quality in higher education institutions,", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec, 2016. Abstract

Despite the great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an automated information system. The present work introduces a framework for an automated information system that manages the quality assurance in higher educations institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance center in a higher education institution to apply its qualitys standards, and to make sure that they are being maintained and enhanced. This information system contains a core module and 17 sub-modules, which are described in this paper.

Tourism