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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
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
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
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
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
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
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
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.

Alaa Tharwat, T. Gaber, A. E. Hassanien, and B. E. Elnaghi, "Particle Swarm Optimization: A Tutorial", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 614–635, 2017. Abstract
Farouk, A., M. Elhoseny, J. Batle, M. Naseri, and A. E. Hassanien, "A Proposed Architecture for Key Management Schema in Centralized Quantum Network", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 997–1021, 2017. Abstract
Tharwt, A., and A. E. Hassanien, "Particle Swarm Optimization: A Tutorial", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

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.

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.

Farouk, A., M. Elhoseny, and A. E. Hassanien, "A Proposed Architecture for Key Management Schema in Centralized Quantum Network", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Most existing realizations of quantum key distribution (QKD) are point-to-point systems with one source transferring to only one destination. Growth of these single-receiver systems has now achieved a reasonably sophisticated point. However, many communication systems operate in a point-to-multi-point (Multicast) configuration rather than in point-to-point mode, so it is crucial to demonstrate compatibility with this type of network in order to maximize the application range for QKD. Therefore, this chapter proposed architecture for implementing a multicast quantum key distribution Schema. The proposed architecture is designed as a Multicast Centralized Key Management Scheme Using Quantum Key Distribution and Classical Symmetric Encryption. In this architecture, a secured key generation and distribution solution has been proposed for a single host sending to two or more (N) receivers using centralized Quantum Multicast Key Distribution Centre and classical symmetric encryption.

Issa, M., and A. E. Hassanien, "Pairwise Global Sequence Alignment Using Sine-Cosine Optimization Algorithm", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 Feb, 2018. Abstract

Pairwise global sequence alignment is a vital process for finding functional and evolutionary similarity between biological sequences. The main usage of it is searching biological databases for finding the origin of unknown sequence. The standard global alignment based on dynamic programming approach which produces the accurate alignment but with extensive execution time. In this paper, Sine-Cosine optimization algorithm was used for accelerating pairwise global alignment with alignment score near one produced by dynamic programming alignment. The reason for using Sine-Cosine optimization is its excellent exploration of the search space. The developed technique was tested on human and mouse protein sequences and its success for finding alignment similarity 75% of that produced by standard technique.