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Gaber, T., and A. E. Hassanien, "An Overview of Self-Protection and Self-Healing in Wireless Sensor Networks", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 185–202, 2014. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, and G. Schaefer, "An overview of rough-hybrid approaches in image processing.", IEEE International Conference on Fuzzy Systems (ISBN 978-1-4244-1818-3), Hong Kong, China, pp, 2135 - 2142 , 1-6 June, , 2008. Abstract

Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, Bayesian approaches, swarm optimization, and support vector machines in the image processing domain.

Hassanien, A. E., A. Abraham, J. F. Peters, and G. Schaefer, "An overview of rough-hybrid approaches in image processing", Fuzzy Systems, 2008. FUZZ-IEEE 2008.(IEEE World Congress on Computational Intelligence). IEEE International Conference on: IEEE, pp. 2135–2142, 2008. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, and G. Schaefer, "An overview of rough-hybrid approaches in image processing", Fuzzy Systems, 2008. FUZZ-IEEE 2008.(IEEE World Congress on Computational Intelligence). IEEE International Conference on: IEEE, pp. 2135–2142, 2008. 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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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., Moustafa Zein, M. Atef, A. Adl, K. K. A. Ghany, and A. E. Hassanien, "An Orphan Drug Legislation System", Intelligent Systems' 2014: Springer International Publishing, pp. 389–399, 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, 2014. Abstract

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Ismail, F. H., M. A. Aziz;, and A. E. Hassanien, "Optimizing the parameters of Sugeno based adaptive neuro fuzzy using artificial bee colony: A case study on predicting the wind speed", Federated Conference on Computer Science and Information Systems (FedCSIS),, Poland, , 11-14 Sept. , 2016. Abstract

This paper presents an approach based on Artificial Bee Colony (ABC) to optimize the parameters of membership functions of Sugeno based Adaptive Neuro-Fuzzy Inference System (ANFIS). The optimization is achieved by Artificial Bee Colony (ABC) for the sake of achieving minimum Root Mean Square Error of ANFIS structure. The proposed ANFIS-ABC model is used to build a system for predicting the wind speed. To ensure the accuracy of the model, a different number of membership functions has been used. The experimental results indicates that the best accuracy achieved is 98% with ten membership functions and least value of RMSE which is 0.39.

Ismail, F. H., M. abd elaziz, and A. E. Hassanien, "Optimizing the parameters of Sugeno based adaptive neuro fuzzy using artificial bee colony: A case study on predicting the wind speed", Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on: IEEE, pp. 645–651, 2016. Abstract
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Elhoseny, M., Alaa Tharwat, X. Yuan, and A. E. Hassanien, "Optimizing K-coverage of mobile WSNs", Expert Systems with Applications, vol. 92, 2018. AbstractWebsite

Recently, Wireless Sensor Networks (WSNs) are widely used for monitoring and tracking applications. Sensor mobility adds extra flexibility and greatly expands the application space. Due to the limited energy and battery lifetime for each sensor, it can remain active only for a limited amount of time. To avoid the drawbacks of the classical coverage model, especially if a sensor died, K-coverage model requires at least k sensor nodes monitor any target to consider it covered. This paper proposed a new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. Our model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that our proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted. The improvement that our proposed method achieved regarding the network lifetime was in a range of 26%–41.3% using stationary nodes while it was in a range of 29.3%–45.7% using mobile nodes. In addition, the network throughput is improved in a range of 13%–17.6%. Moreover, the running time to form the network structure and switch between nodes’ modes is reduced by 12%.

El-Said, S. A., Asmaa Osamaa, and A. E. Hassanien, "Optimized hierarchical routing technique for wireless sensors networks", Soft Computing, Springer, vol. July , 2015. Website
El-Said, S. A., Asmaa Osamaa, and A. E. Hassanien, "Optimized hierarchical routing technique for wireless sensors networks", Soft Computing, pp. Ausgabe 11/2016, 2016. AbstractWebsite

Wireless sensor networks are battery-powered ad hoc networks in which sensor nodes that are scattered over a region connect to each other and form multi-hop networks. Since these networks consist of sensors that are battery operated, care has to be taken so that these sensors use energy efficiently. This paper proposes an optimized hierarchical routing technique which aims to reduce the energy consumption and prolong network lifetime. In this technique, the selection of optimal cluster head (CHs) locations is based on artificial fish swarm algorithm that applies various behaviors such as preying, swarming, and following to the formulated clusters and then uses a fitness function to compare the outputs of these behaviors to select the best CHs locations. To prove the efficiency of the proposed technique, its performance is analyzed and compared to two other well-known energy efficient routing techniques: low-energy adaptive clustering hierarchy (LEACH) technique and particle swarm optimized (PSO) routing technique. Simulation results show the stability and efficiency of the proposed technique. Simulation results show that the proposed method outperforms both LEACH and PSO in terms of energy consumption, number of alive nodes, first node die, network lifetime, and total data packets received by the base station. This may be due to considering residual energies of nodes and their distance from base station , and alternating the CH role among cluster’s members. Alternating the CH role balances energy consumption and saves more energy in nodes.

El-Said, S. A., Asmaa Osamaa, and A. E. Hassanien, "Optimized hierarchical routing technique for wireless sensors networks", Soft Computing, vol. 20, no. 11: Springer Berlin Heidelberg, pp. 4549–4564, 2016. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An Optimized Approach for Medical Image Watermarking", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 71–91, 2014. 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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Babers, R., N. I. Ghali, and A. E. Hassanien, "Optimal Community Detection Approach based on Ant Lion Optimization", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
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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Gaber, T., Alaa Tharwat, and A. E. Hassanien, "One-dimensional vs. two-dimensional based features: Plant identification approach", Journal of Applied Logic, 2016. AbstractWebsite

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

Alaa Tharwat, T. Gaber, and A. E. Hassanien, "One-dimensional vs. two-dimensional based features: Plant identification approach", Journal of Applied Logic: Elsevier, 2016. Abstract
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Ali, M. A. S., and A. E. Hassanien, "An observational study to identify the role of online communication in offline social networks ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Ali, M. A. S., and A. E. Hassanien, "An observational study to identify the role of online communication in offline social networks", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 509–522, 2014. Abstract
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Ghali, N. I., W. G. Abd-Elmonim, and A. E. Hassanien, Object-Based Image Retrieval System Using Rough Set Approach, , London, Advances in Reasoning-Based Image Processing Intelligent Systems Intelligent Systems Reference Library, 2012, Volume 29, Part 2, 315-329, 2012. Abstract

In this chapter, we present an object-based image retrieval system using the rough set theory. The system incorporates two major modules: Pre-processing and Object-based image retrieval. In pre processing, an image based object segmentation algorithm in the context of the rough set theory is used to segment the images into meaningful semantic regions. A new object similarity measure is proposed for the image retrieval. Performance is evaluated on an image database and the effectiveness of proposed image retrieval system is demonstrated. Experimental results show that the proposed system performs well in terms of speed and accuracy.

Ghali, N. I., W. G. Abd-Elmonim, and A. E. Hassanien, "Object-based image retrieval system using rough set approach", Advances in Reasoning-Based Image Processing Intelligent Systems: Springer Berlin Heidelberg, pp. 315–329, 2012. Abstract
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