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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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Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A pso-inspired multi-robot map exploration algorithm using frontier-based strategy", International Journal of System Dynamics Applications (IJSDA), vol. 2, no. 2: IGI Global, pp. 1–13, 2013. Abstract
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Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A pso-inspired multi-robot map exploration algorithm using frontier-based strategy", International Journal of System Dynamics Applications (IJSDA), vol. 2, no. 2: IGI Global, pp. 1–13, 2013. Abstract
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Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A PSO-inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy", International Journal of System Dynamics Applications,, vol. 2, issue 2, pp. 1-13, 2013. AbstractWebsite

Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm that coordinates a team of autonomous mobile robots to explore an unknown environment. The proposed strategy is based on frontiers which are the regions on the boundary between open and unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to reduce the size of unknown region. Based on the PSO model incorporated in the algorithm, robots are navigated towards remote frontier after exploring the local area. The exploration completes when there is no frontier cell in the environment. The experiments implemented on both simulated and real robot scenarios show that the proposed algorithm is capable of completing the exploration task. Compared to the conventional method of randomly selecting frontier, the proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional experimental results show the decreased coverage time when the number of robots increases, which further suggests the validity, efficiency and scalability.

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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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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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.

Tarek Gaber, Alaa Tharwat, V. S. A. E. H.:, "Plant Identification: Two Dimensional-Based Vs. One Dimensional-Based Feature Extraction Methods", 10th International Conference on Soft Computing Models in Industrial and Environmental Applications, Spain, july, 2015. Abstract

In this paper, a plant identification approach using 2D digital leaves images is proposed. The approach made use of two methods of features extraction (one-dimensional (1D) and two-dimensional (2D) techniques) and the Bagging classifier. For the 1D-based method, PCA and LDA techniques were applied, while 2D-PCA and 2D-LDA 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 proposed approach, with its four feature extraction techniques, was tested using Flavia dataset which consists of 1907 colored leaves images. The experimental results showed that the accuracy and the performance of our approach, with the 2D-PCA and 2D-LDA, was much better than using the PCA and LDA. Furthermore, it was proven that the 2D-LDA-based method gave the best plant identification accuracy and increasing the weak learners of the Bagging classifier leaded to a better accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

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Salama, M., A. E. Hassanien, and A. A. Fahmy, "Pattern-based Subspace Classification Approach", The Second IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2010), Kitakyushu- Japan, 15 Dec, 2010. Abstract

The use of patterns in predictive models has received a lot of attention in recent years. This paper presents a pattern-based classification model which extracts the patterns that have similarity among all objects in a specific class. This introduced model handles the problem of the dependence on a user-defined threshold that appears in the pattern-based subspace clustering. The experimental results obtained, show that the overall pattern-based classification accuracy is high compared with other machine learning techniques including Support vector machine, Bayesian Network, multi-layer perception and decision trees.

Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Pattern-based subspace classification model", Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on: IEEE, pp. 357–362, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Pattern-based subspace classification model", Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on: IEEE, pp. 357–362, 2010. Abstract
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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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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.

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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Sahlol, A., M. A. Fattah, C. Y. Suen, and A. E. Hassanien, "Particle Swarm Optimization with Random Forests for Handwritten Arabic Recognition System", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 437–446, 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.
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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.

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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.
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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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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.

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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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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., Pervasive Computing, : Springer Science & Business Media, 2009. Abstract
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Tourism