Publications

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Gaber, T., Alaa Tharwat, V. Snasel, and A. E. Hassanien, "Plant identification: Two dimensional-based vs. one dimensional-based feature extraction methods", 10th international conference on soft computing models in industrial and environmental applications: Springer International Publishing, pp. 375–385, 2015. Abstract
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Alaa Tharwat, Hani Mahdi, and A. E. Hassanien, "Plant Recommender System Based on Multi-label Classification", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 825–835, 2016. Abstract
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Alaa Tharwat, T. Gaber, Y. M. Awad, N. Dey, and A. E. Hassanien, "Plants identification using feature fusion technique and bagging classifier", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 461–471, 2016. Abstract
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Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Possibilistic fuzzy c-means clustering optimized with Cuckoo search for retinal vessel segmentation", The annual IEEE International Joint Conference on Neural Networks (IJCNN) –, Beijing, China, July 6-11, , 2014. 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., 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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El-Atta, A. A. H., M. I. Moussa, and A. E. Hassanien, "Predicting activity approach based on new atoms similarity kernel function", Journal of Molecular Graphics and Modelling, vol. 60, pp. 55–62, 2015. Website
El-Atta, A. A. H., M. I. Moussa, and A. E. Hassanien, "Predicting activity approach based on new atoms similarity kernel function", Journal of Molecular Graphics and Modelling, vol. 60: Elsevier, pp. 55–62, 2015. 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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Fatma Yakoub, Moustafa Zein, K. Y. A. A. A. E. H., "Predicting Personality Traits and Social Context Based on Mining the Smartphones SMS Data", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, , Ostrava, Czech Republic, , June 29 - July , 2015. Abstract

Reality Mining is one of the first efforts that have been exerted to utilize smartphone’s data; to analyze human behavior. The smartphone data are used to identify human behavior and discover more attributes about smartphone users, such as their personality traits and their relationship status. Text messages and SMS logs are two of the main data resources from the smartphones. In this paper, The proposed system define the user personality by observing behavioral characteristics derived from smartphone logs and the language used in text messages. Hence, The supervised machine learning methods (K-nearest nighbor (KNN), support vector machine, and Naive Bayes) and text mining techniques are used in studying the textual matter messages. From this study, The correlation between text messages and predicate users personality traits is broken down. The results provided an overview on how text messages and smartphone logs represent the user behavior; as they chew over the user personality traits with accuracy up to 70 %.

Yakoub, F., Moustafa Zein, K. Yasser, A. Adl, and A. E. Hassanien, "Predicting personality traits and social context based on mining the smartphones SMS data", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 511–521, 2015. Abstract
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Sahlol, A., A. M. Hemdan, and A. E. Hassanien, "Prediction of Antioxidant Status in Fish Farmed on Selenium Nanoparticles using Neural Network Regression Algorithm", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 353–364, 2016. Abstract
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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.

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
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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
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Reham Gharbia, Ali Hassan El Baz, A. T. Azar, and A. E. Hassanien, "Principal component analysis and fuzzy-based rules approach for satellite image fusion", The annual IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 6 July, 2014.
Fattah, M. A., 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: Springer International Publishing, pp. 225–237, 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.
Heba, E. F., A. Darwish, A. E. Hassanien, and A. Abraham, "Principle components analysis and support vector machine based intrusion detection system", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 363–367, 2010. Abstract
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Heba, E. F., A. Darwish, A. E. Hassanien, and A. Abraham, "Principle components analysis and support vector machine based intrusion detection system", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 363–367, 2010. Abstract
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Hassanien, A. E., "Proceeding of the 6th International Conference on Soft Computing Models in Industrial and Environmental Applications", The 6th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2011 , Spain, Advances in Intelligent and Soft Computing, Vol. 87 , 2011.
Hassanien, A. E., "Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing", Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Berlin, Heidelberg, Springer-Verlag , 2009.
Hassanien, A. E., K. Shaalan, T. Gaber, A. T. Azar, and F. Tolba, Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, : Springer, 2016. Abstract
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Abraham, A., K. Wegrzyn-Wolska, A. E. Hassanien, Václav Snášel, and A. M. Alimi, Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, : Springer, 2016. Abstract
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Youssef, A., A. Nitaj, and A. E. Hassanien, Progress in Cryptology-AFRICACRYPT 2013, : Springer Berlin Heidelberg, 2013. Abstract
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