Publications

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2016
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.

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.

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.

Sayed, G. I., and A. E. Hassanien, "Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 219–228, 2016. Abstract
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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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Reham Gharbia, Ali Hassan El Baz, and A. E. Hassanien, "An adaptive image fusion rule for remote sensing images based on the particle swarm optimization", Computing, Communication and Automation (ICCCA), 2016 International Conference on: IEEE, pp. 1080–1085, 2016. Abstract
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Fouad, M. M., V. Snasel, and A. E. Hassanien, "An Adaptive PSO-Based Sink Node Localization Approach for Wireless Sensor Networks", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 679–688, 2016. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive watermarking approach based on weighted quantum particle swarm optimization", Neural Computing and Applications, vol. 27, no. 2: Springer London, pp. 469–481, 2016. Abstract
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Sayed, G. I., A. E. Hassanien, T. M. Nassef, and J. - S. Pan, "Alzheimer’s Disease Diagnosis Based on Moth Flame Optimization", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 298–305, 2016. Abstract
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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
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Hassanien, A. E., H. Hefny, and P. - W. Tsai, "Antlion Optimization Based Segmentation for MRI Liver Images", Genetic and Evolutionary Computing: Proceedings of the Tenth International Conference on Genetic and Evolutionary Computing, November 7-9, 2016 Fuzhou City, Fujian Province, China, vol. 536: Springer, pp. 265, 2016. Abstract
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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
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Sayed, G. I., A. E. Hassanien, and G. Schaefer, "An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images", Procedia Computer Science, vol. 90: Elsevier, pp. 68–73, 2016. Abstract
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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
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Alaa Tharwat, B. E. Elnaghi, A. M. Ghanem, and A. E. Hassanien, "Automatically Human Age Estimation Approach via Two-Dimensional Facial Image Analysis", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 491–501, 2016. Abstract
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Alaa Tharwat, A. E. Hassanien, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of Support Vector Machine", Pattern Recognition Letters: North-Holland, 2016. Abstract
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Kilany, M., A. E. Hassanien, A. Badr, P. - W. Tsai, and J. - S. Pan, "A Behavioral Action Sequences Process Design", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 502–512, 2016. Abstract
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Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Binary ant lion approaches for feature selection", Neurocomputing, vol. 213: Elsevier, pp. 54–65, 2016. Abstract
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Eid Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection", Neurocomputing, vol. 172: Elsevier, pp. 371–381, 2016. Abstract
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Sahlol, A. T., C. Y. Suen, H. M. Zawbaa, A. E. Hassanien, and M. A. Fattah, "Bio-inspired BAT optimization algorithm for handwritten Arabic characters recognition", Evolutionary Computation (CEC), 2016 IEEE Congress on: IEEE, pp. 1749–1756, 2016. Abstract
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Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "Bio-inspired optimization for feature set dimensionality reduction", Advances in Computational Tools for Engineering Applications (ACTEA), 2016 3rd International Conference on: IEEE, pp. 184–189, 2016. Abstract
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Sayed, G. I., M. Soliman, and A. E. Hassanien, "Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 487–506, 2016. Abstract
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Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier", Computers and Electronics in Agriculture, vol. 122: Elsevier, pp. 55–66, 2016. Abstract
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Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, no. 2: IEEE, pp. 66–76, 2016. Abstract
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Tourism