Publications

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Abraham, A., and A. - E. Hassanien, Computational social networks: Tools, perspectives and applications, : Springer Science & Business Media, 2012. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, : Springer Science & Business Media, 2009. Abstract
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Acharjee, S., S. Chakraborty, S. Samanta, A. T. Azar, A. E. Hassanien, and N. Dey, "Highly secured multilayered motion vector watermarking", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, 28-30 Nov. 2014.
Acharjee, S., S. Chakraborty, S. Samanta, A. T. Azar, A. E. Hassanien, and N. Dey, "Highly secured multilayered motion vector watermarking", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 121–134, 2014. 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.

Adham Mohamed, M. M. M. Fouad, Esraa Elhariri, N. El-Bendary, H. M. Zawbaa, Mohamed Tahoun, and A. E. Hassanien, "RoadMonitor: an intelligent road surface condition monitoring system", Intelligent Systems' 2014: Springer International Publishing, pp. 377–387, 2015. Abstract
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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
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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.

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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Ahmad. Taher Azar, A. E. Hassanien, and T. - H. Kim, "Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis.", International Conference on Bio-Science and Bio-Technology (BSBT2012), , Kangwondo, Korea. pp. 94--105, December 16-19, 2012. Abstract3530094.pdf

The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.

Ahmed, S., T. Gaber, Alaa Tharwat, and A. E. Hassanien, "Muzzle-based Cattle Identification using Speed up Robust Feature Approach", IEEE International Conference on Intelligent Networking and Collaborative Systems, ,015, pp. 99-104, Taipei, Taiwan, 2-4 September , 2015. Abstractabo1.pdf

Starting from the last century, animals identification
became important for several purposes, e.g. tracking,
controlling livestock transaction, and illness control. Invasive and
traditional ways used to achieve such animal identification in
farms or laboratories. To avoid such invasiveness and to get more
accurate identification results, biometric identification methods
have appeared. This paper presents an invariant biometric-based
identification system to identify cattle based on their muzzle
print images. This system makes use of Speeded Up Robust
Feature (SURF) features extraction technique along with with
minimum distance and Support Vector Machine (SVM) classifiers.
The proposed system targets to get best accuracy using minimum
number of SURF interest points, which minimizes the time
needed for the system to complete an accurate identification.
It also compares between the accuracy gained from SURF
features through different classifiers. The experiments run 217
muzzle print images and the experimental results showed that
our proposed approach achieved an excellent identification rate
compared with other previous works.

Ahmed, M. M., A. I. Hafez, M. M. Elwakil, 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 System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 129–139, 2016. Abstract
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Ahmed, K., A. I. Hafez, and A. E. Hassanien, "A discrete krill herd optimization algorithm for community detection", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 297–302, 2015. Abstract
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Ahmed, K., A. A. Ewees, M. abd elaziz, A. E. Hassanien, T. Gaber, P. - W. Tsai, and J. - S. Pan, "A Hybrid Krill-ANFIS Model for Wind Speed Forecasting", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 365–372, 2016. Abstract
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Ahmed, K., A. E. Hassanien, E. Ezzat, and Siddhartha Bhattacharyya, "Swarming Behaviors of Chicken for Predicting Posts on Facebook Branding Pages", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 fEB , 2018. Abstract

The rapid increase in social networks data and users present an urgent need for predicting the performance of posted data over these networks. It helps in many industrial aspects such as election, public opinion detection and advertising or branding over social networks. This paper presents a new posts’ prediction system for Facebook’s branding pages concerning the user’s attention and interaction. CSO is utilized to optimize the ANFIS parameters for accurate prediction. CSO-ANFIS is compared with several methods including ANFIS, particle swarm optimization, genetic algorithm and krill herd optimization.

Ahmed, M. M., M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "Discrete Group Search Optimizer for Community Detection in Social Networks", International Joint Conference on Rough Sets: Springer International Publishing, pp. 439–448, 2016. Abstract
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Ahmed, Z., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets.", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, 8-10 Dec. , 2012. Abstract3220052.pdf

The risk of hepatitis-C virus is considered as a challenge in
the field of medicine. Applying feature reduction technique and generating
rules based on the selected features were considered as an important
step in data mining. It is needed by medical experts to analyze the generated
rules to find out if these rules are important in real life cases.
This paper presents an application of a rough set analysis to discover
the dependency between the attributes, and to generate a set of reducts
consisting of a minimal number of attributes. The experimental results
obtained, show that the overall accuracy offered by the rough sets is high.

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, K., and A. E. Hassanien, "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Complex social networks analysis is an important research trend, which basically based on community detection. Community detection is the process of dividing the complex social network into a dynamic number of clusters based on their edges connectivity. This paper presents an efficient Elephant Swarm Optimization Algorithm for community detection problem (EESO) as an optimization approach. EESO can define dynamically the number of communities within complex social network. Experimental results are proved that EESO can handle the community detection problem and define the structure of complex networks with high accuracy and quality measures of NMI and modularity over four popular benchmarks such as Zachary Karate Club, Bottlenose Dolphin, American college football and Facebook. EESO presents high promised results against eight community detection algorithms such as discrete krill herd algorithm, discrete Bat algorithm, artificial fish swarm algorithm, fast greedy, label propagation, walktrap, Multilevel and InfoMap.

Ahmed, M. M., M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "Discrete Group Search Optimizer for community detection in multidimensional social network", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec. , 2016. Abstract

Multidimensionality is a distinctive aspect of real world social networks. Multidimensional social networks appeared as a result of that most social media sites such as Facebook, Twitter, and YouTube enable people to interact with each other through different social activities, reflecting different kinds of relationships between them. Recently, studying community structures hidden in multidimensional social networks has attracted a lot of attention. When dealing with these networks, the concept of community detection problem changes to be the discovery of the shared group structure across all network dimensions, such that members in the same group are tightly connected with each other, but are loosely connected with others outside the group. Studies in community detection topic have traditionally focused on networks that represent one type of interactions or one type of relationships between network entities. In this paper, we propose Discrete Group Search Optimizer (DGSO-MDNet) to solve the community detection problem in Multidimensional social networks, without any prior knowledge about the number of communities. The method aims to find community structure that maximizes multi-slice modularity, as an objective function. The proposed DGSO-MDNet algorithm adopts the locus-based adjacency representation and several discrete operators. Experiments on synthetic and real life networks show the capability of the proposed algorithm to successfully detect the structure hidden within these networks compared with other high performance algorithms in the literature.

Ahmed, K., A. E. Hassanien, E. Ezzat, and P. - W. Tsai, "An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 281–288, 2016. Abstract
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Ahmed, S. A., N. I. Ghali, and A. E. Hassanien, "Optimize the correspondence using particle swarm optimization for medical image registration", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 80–84, 2012. Abstract
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Ahmed, K., A. E. Hassanien, and E. Ezzat, "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1062–1075, 2017. Abstract
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Ahmed, M. M., M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "Discrete Group Search Optimizer for community detection in multidimensional social network", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 47–52, 2016. Abstract
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Ahmed, S., T. Gaber, Alaa Tharwat, A. E. Hassanien, and V. Snáel, "Muzzle-based cattle identification using speed up robust feature approach", Intelligent Networking and Collaborative Systems (INCOS), 2015 International Conference on: IEEE, pp. 99–104, 2015. Abstract
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