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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 %.

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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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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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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Hassanien, A. E., G. Schaefer, and H. AlQaheri, "Prostate Boundary Detection in Ultrasound Images Based on Type-II Fuzzy Sets and Modified Fuzzy C-Means", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 187–195, 2010. 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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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., "Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network.", Appl. Soft Computing, vol. 11, issue 2, pp. 2035-2041, 2011. AbstractCU-PDF.pdfWebsite

Pulse-coupled neural networks (PCNNs) are a biologically inspired type of neural networks. It is a simplified model of the cat's visual cortex with local connections to other neurons. PCNN has the ability to extract edges, segments and texture information from images. Only a few changes to the PCNN parameters are necessary for effective operation on different types of data. This is an advantage over published image processing algorithms that generally require information about the target before they are effective. The main aim of this paper is to provide an accurate boundary detection algorithm of the prostate ultrasound images to assist radiologists in making their decisions. To increase the contrast of the ultrasound prostate image, the intensity values of the original images were adjusted firstly using the PCNN with median filter. It is followed by the PCNN segmentation algorithm to detect the boundary of the image. Combining adjusting and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. The experimental results obtained show that the overall boundary detection overlap accuracy offered by the employed PCNN approach is high compared with other machine learning techniques including Fuzzy C-mean and Fuzzy Type-II.

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., Pervasive Computing : Innovations in Intelligent Multimedia and Applications, , London, Computer Communications and Networks - Springer , 2010. AbstractWebsite

Pervasive computing (also referred to as ubiquitous computing or ambient intelligence) aims to create environments where computers are invisibly and seamlessly integrated and connected into our everyday environment. Pervasive computing and intelligent multimedia technologies are becoming increasingly important, although many potential applications have not yet been fully realized. These key technologies are creating a multimedia revolution that will have significant impact across a wide spectrum of consumer, business, healthcare, and governmental domains.

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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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., Pervasive Computing, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. E., G. Schaefer, and H. AlQaheri, "Prostate Boundary Detection in Ultrasound Images Based on Type-II Fuzzy Sets and Modified Fuzzy C-Means", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 187–195, 2010. Abstract
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Hassanien, A. E., "Pulse coupled neural network for detection of masses in digital mammogram", Neural network world journal, vol. 2, no. 6, pp. 129–141, 2006. Abstract
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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., "Pulse coupled neural network for detection of masses in digital mammogram", Neural network world journal, vol. 2, no. 6, pp. 129–141, 2006. 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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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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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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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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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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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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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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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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