Publications

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Miscellaneous
Hassanien, A. E., Pervasive Computing, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. - E., J. H. Abawajy, A. Abraham, and H. Hagras, Pervasive computing: innovations in intelligent multimedia and applications, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. - E., J. H. Abawajy, A. Abraham, and H. Hagras, Pervasive computing: innovations in intelligent multimedia and applications, : Springer Science & Business Media, 2009. Abstract
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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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Journal Article
Elghamrawy, S., and Aboul Ella Hassa, "A Partitioning Framework for Cassandra NoSQL Database using Rendezvous Hashing", Journal of Supercomputing (SUPE), Springer , vol. pp 1–22, 2017. AbstractWebsite

Due to the gradual expansion in data volume used in social networks and cloud computing, the term “Big data” has appeared with its challenges to store the immense datasets. Many tools and algorithms appeared to handle the challenges of storing big data. NoSQL databases, such as Cassandra and MongoDB, are designed with a novel data management system that can handle and process huge volumes of data. Partitioning data in NoSQL databases is considered one of the critical challenges in database design. In this paper, a MapReduce Rendezvous Hashing-Based Virtual Hierarchies (MR-RHVH) framework is proposed for scalable partitioning of Cassandra NoSQL database. The MapReduce framework is used to implement MR-RHVH on Cassandra to enhance its performance in highly distributed environments. MR-RHVH distributes the nodes to rendezvous regions based on a proposed Adopted Virtual Hierarchies strategy. Each region is responsible for a set of nodes. In addition, a proposed bloom filter evaluator is used to ensure the accurate allocation of keys to nodes in each region. Moreover, a number of experiments were performed to evaluate the performance of MR-RHVH framework, using YCSB for database benchmarking. The results show high scalability rate and less time consuming for MR-RHVH framework over different recent systems.

Toews, M., and T. Arbel, "Parts-Based Appearance Modeling of Medical Imagery", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 291, 2009. Abstract
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El-Bendary, N., H. M. Zawbaa, A. E. Hassanien, and V. Snasel, "PCA-based home videos annotation system", International Journal of Reasoning-based Intelligent Systems, vol. 3, no. 2: Inderscience Publishers, pp. 71–79, 2011. Abstract
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El-Bendary, N., H. M. Zawbaa, A. E. Hassanien, and V. Snasel, "PCA-based home videos annotation system", International Journal of Reasoning-based Intelligent Systems, vol. 3, no. 2: Inderscience Publishers, pp. 71–79, 2011. Abstract
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Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
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Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
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Azar, A. T., S. S. Kumar, H. H. Inbarani, and A. E. Hassanien, "Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosis", International Journal of Modelling, Identification and Control, vol. 26, no. 1: Inderscience Publishers (IEL), pp. 42–51, 2016. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Petri Net Modeling Methods For Generating Self-Similar Fractal Images", 映像情報メディア学会技術報告, vol. 22, no. 45: 一般社団法人映像情報メディア学会, pp. 13–18, 1998. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Petri Net Modeling Methods for Generating Self-Similar Fractal Images (マルチメディア情報処理研究会)", 映像情報メディア学会誌: 映像情報メディア, vol. 52, no. 12: 一般社団法人映像情報メディア学会, pp. 1807, 1998. Abstract

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Oliva, D., M. abd elaziz, and A. E. Hassanien, "Photovoltaic cells design using an improved chaotic whale optimization algorithm", Applied Energy, vol. 200, pp. 141–154, 2017. AbstractWebsite

The using of solar energy has been increased since it is a clean source of energy. In this way, the design of photovoltaic cells has attracted the attention of researchers over the world. There are two main problems in this field: having a useful model to characterize the solar cells and the absence of data about photovoltaic cells. This situation even affects the performance of the photovoltaic modules (panels). The characteristics of the current vs. voltage are used to describe the behavior of solar cells. Considering such values, the design problem involves the solution of the complex non-linear and multi-modal objective functions. Different algorithms have been proposed to identify the parameters of the photovoltaic cells and panels. Most of them commonly fail in finding the optimal solutions. This paper proposes the Chaotic Whale Optimization Algorithm (CWOA) for the parameters estimation of solar cells. The main advantage of the proposed approach is using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm. This situation is beneficial in complex problems, because along the iterative process, the proposed algorithm improves their capabilities to search for the best solution. The modified method is able to optimize complex and multimodal objective functions. For example, the function for the estimation of parameters of solar cells. To illustrate the capabilities of the proposed algorithm in the solar cell design, it is compared with other optimization methods over different datasets. Moreover, the experimental results support the improved performance of the proposed approach regarding accuracy and robustness.

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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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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Hassanien, A. E., H. Al-Qaheri, and E. - S. A. El-Dahshan, "Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network", Applied Soft Computing, vol. 11, no. 2: Elsevier, pp. 2035–2041, 2011. Abstract
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