Publications

Export 66 results:
Sort by: Author Title [ Type  (Asc)] Year
Book
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.

Book Chapter
Abder-Rahman Ali, Micael Couceiro, A. Anter, and A. - E. Hassanien, "Particle swarm optimization based fast fuzzy C-means clustering for liver CT segmentation", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 233–250, 2016. Abstract
n/a
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.

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
n/a
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
n/a
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
n/a
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.

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
n/a
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
n/a
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
n/a
Conference Paper
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.

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
n/a
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
n/a
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
n/a
Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien, "Performance evaluation of computed tomography liver image segmentation approaches", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 109–114, 2012. Abstract
n/a
Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "Plant classification system based on leaf features", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 271–276, 2014. Abstract
n/a
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.

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
n/a
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
n/a
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
n/a
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

n/a

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
n/a
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 %.

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
n/a