Publications

Export 255 results:
Sort by: [ Author  (Desc)] Title Type Year
A B C D E F G [H] I J K L M N O P Q R S T U V W X Y Z   [Show ALL]
H
Hussein, H. K., A. - E. Hassanien, and M. Nakajima, "Escape-Time Modified Algorithm for Generating Fractal Images Based on Petri Net Reachability", IEICE TRANSACTIONS on Information and Systems, vol. 82, no. 7: The Institute of Electronics, Information and Communication Engineers, pp. 1101–1108, 1999. Abstract
n/a
Hussein, H. K., A. - E. Hassanien, and M. Nakajima, "Regular Section-PAPERS-Image Processing, Computer Graphics and Pattern Recognition-Escape-Time Modified Algorithm for Generating Fractal Images Based on Petri Net Reachability", IEICE Transactions on Information and Systems, vol. 82, no. 7: Tokyo, Japan: Institute of Electronics, Information and Communication Engineers, c1992-, pp. 1101–1108, 1999. Abstract
n/a
Houssein, E. H., M. A. S. Ali, and A. E. Hassanien, "An image steganography algorithm using Haar Discrete Wavelet Transform with Advanced Encryption System", Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on: IEEE, pp. 641–644, 2016. Abstract
n/a
Hossam Zawbaee, Eid Emary, A. E. Hassanien, and M. Tolba, "Hajj Human Event Classification System using Machine Learning Techniques", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 192-197, 2013, Tunisia, , 4-6 Dec, 2013.
Hossam Moftah, Walaa Elmasry, M. Ibrahim, A. E. Hassanien, and G. Schaefer, "Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation", Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on: IEEE, pp. 942–945, 2013. Abstract
n/a
Hossam Moftah, Walaa Elmasry, A. E. Hassanien, Adel Alimi, H. Karray, and M. Tolba, "Ant-based clustering algorithm for magnetic resonance breast image segmentation", 13th IEEE International Conference on Hybrid Intelligent Systems | (HIS13) . pp. 162-167, Tunisia, , 4-6 Dec, 2013.
Hossam Moftah, Walaa Elmasry, N. Ghali, A. E. Hassanien, and M. Showman, "Volume Identification and Estimation of MRI Brain Tumor.", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012)., Pune. India, 4-7 Dec. 2012, , pp. 120 - 124, 2012. Abstract

This paper deals with two dimensional magnetic resonance imaging (MRI) sequence of brain slices which include many objects to identify and estimate the volume of the brain tumors. More than twenty five features based on shape, color and texture was extracted to obtain feature vector for each object to characterize the tumor and identify it. Experimental results show that the accuracy of the estimation of tissue volumes is very high.

Hossam Moftah, Walaa Elmasry, M. Ibrahiem, A. E. Hassanien, and G. Schaefer, "Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation", 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp.942- 945, Okinawa, Japan. , 5 Nov., 2013.
Hossam M. Zawbaa, E. Emary, A. E. Hassanien, and B. PARV, "A wrapper approach for feature selection based on swarm optimization algorithm inspired from the behavior of social-spiders", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan,, November 13 - 1, 2015. Abstract

In this paper, a proposed system for feature selection
based on social spider optimization (SSO) is proposed. SSO is
used in the proposed system as searching method to find optimal
feature set maximizing classification performance and mimics
the cooperative behavior mechanism of social spiders in nature.
The proposed SSO algorithm considers two different search
agents (social members) male and female spiders, that simulate
a group of spiders with interaction to each other based on the
biological laws of the cooperative colony. Depending on spider
gender, each spider (individual) is simulating a set of different
evolutionary operators of different cooperative behaviors that are
typically found in the colony. The proposed system is evaluated
using different evaluation criteria on 18 different datasets, which
compared with two common search methods namely particle
swarm optimization (PSO), and genetic algorithm (GA). SSO
algorithm proves an advance in classification performance using
different evaluation indicators

Hossam, M. M., A. E. Hassanien, and M. Shoman, "3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 320–324, 2010. Abstract
n/a
Hossam, M. M., A. E. Hassanien, and M. Shoman, "3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 320–324, 2010. Abstract
n/a
Hore, S., T. Bhattacharya, N. Dey, A. E. Hassanien, A. Banerjee, and S. R. B. Chaudhuri, "A Real Time Dactylology Based Feature Extractrion for Selective Image Encryption and Artificial Neural Network", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 203–226, 2016. Abstract
n/a
Horàk, Z., Václav Snášel, A. Abraham, and A. E. Hassanien, "Fuzzified Aho-Corasick Search Automata", Information Assurance and Security (IAS), 2010 Sixth International Conference on: IEEE, pp. 338–342, 2010. Abstract
n/a
Horàk, Z., Václav Snášel, A. Abraham, and A. E. Hassanien, "Fuzzified Aho-Corasick Search Automata", Information Assurance and Security (IAS), 2010 Sixth International Conference on: IEEE, pp. 338–342, 2010. Abstract
n/a
Ho, S. H., A. E. Hassanien, N. Van Du, Q. Salih, and H. Sooi, "FUZZY C-MEANS CLUSTERING WITH ADJUSTABLE FEATURE WEIGHTING DISTRIBUTION FOR BRAIN MRI VENTRICLES SEGMENTATION Kai Xiao1", Update, vol. 15, pp. 1, 2001. Abstract
n/a
Helal, M. A., T. El-Arief, A. E. Hassanien, and N. El-Haggar, "An Efficient Texture Segmentation Algorithm for Isolating Iris Patterns Based on Wavelet Theory", PATTERN RECOGNITION AND IMAGE ANALYSIS C/C OF RASPOZNAVANIYE OBRAZOV I ANALIZ IZOBRAZHENII, vol. 14, no. 1: NAUKA/INTERPERIODICA PUBLISHING, pp. 97–103, 2004. Abstract
n/a
Heba M. Taha, N. El-Bendary, A. E. Hassanien, Y. Badr, and V. Snasel, "Retinal Feature-Based Registration Schema", Informatics Engineering and Information Science, Berlin Heidelberg, pp. 26-36, Communications in Computer and Information Science - Springer , 2011. Abstract

This paper presents a feature-based retinal image registration schema. A structural feature, namely, bifurcation structure, has been used for the proposed feature-based registration schema. The bifurcation structure is composed of a master bifurcation point and its three connected neighbors. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. The proposed schema is composed of five fundamental phases, namely, input retinal images pre-processing, vascular network detection, noise removal, bifurcation points detection in vascular networks, and bifurcation points matching in pairs of retinal images. The effectiveness of the proposed schema is demonstrated by the experiments with 12 pairs retinal images collected from clinical patients. The registration is carried out through optimizing a certain similarity function, namely, normalized correlation of images. It has been observed that the proposed schema has achieved good performance accuracy.

Heba, T., E. - B. Nashwa, H. AboulElla, B. Yehia, and S. Vaclav, "Retinal Feature-Based Registration Schema", Informatics Engineering and Information Science Communications in Computer and Information Science Volume 252, 2011, pp 26-36 , Ostrava, Czech Republic, 7-9 July, 2011. Abstract

This paper presents a feature-based retinal image registration schema. A structural feature, namely, bifurcation structure, has been used for the proposed feature-based registration schema. The bifurcation structure is composed of a master bifurcation point and its three connected neighbors. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. The proposed schema is composed of five fundamental phases, namely, input retinal images pre-processing, vascular network detection, noise removal, bifurcation points detection in vascular networks, and bifurcation points matching in pairs of retinal images. The effectiveness of the proposed schema is demonstrated by the experiments with 12 pairs retinal images collected from clinical patients. The registration is carried out through optimizing a certain similarity function, namely, normalized correlation of images. It has been observed that the proposed schema has achieved good performance accuracy.

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
n/a
Heba, E., M. Salama, A. E. Hassanien, and T. - H. Kim, "Bi-Layer Behavioral-Based Feature Selection Approach for Network Intrusion Classification", Security Technology - International Conference, SecTech 2011, pp.195-203, Jeju Island, Korea, December 8-10,, 2011. Abstract

To satisfy the ever growing need for effective screening and diagnostic tests, medical practitioners have turned their attention to high resolution, high throughput methods. One approach is to use mass spectrometry based methods for disease diagnosis. Effective diagnosis is achieved by classifying the mass spectra as belonging to healthy or diseased individuals. Unfortunately, the high resolution mass spectrometry data contains a large degree of noisy, redundant and irrelevant information, making accurate classification difficult. To overcome these obstacles, feature extraction methods are used to select or create small sets of relevant features. This paper compares existing feature selection methods to a novel wrapper-based feature selection and centroid-based classification method. A key contribution is the exposition of different feature extraction techniques, which encompass dimensionality reduction and feature selection methods. The experiments, on two cancer data sets, indicate that feature selection algorithms tend to both reduce data dimensionality and increase classification accuracy, while the dimensionality reduction techniques sacrifice performance as a result of lowering the number of features. In order to evaluate the dimensionality reduction and feature selection techniques, we use a simple classifier, thereby making the approach tractable. In relation to previous research, the proposed algorithm is very competitive in terms of (i) classification accuracy, (ii) size of feature sets, (iii) usage of computational resources during both training and classification phases.

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
n/a
Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1076–1093, 2017. Abstract
n/a
Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Grey wolf optimizer-based back-propagation neural network algorithm", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec, 2016. Abstract

For many decades, artificial neural network (ANN) proves successful results in thousands of problems in many disciplines. Back-propagation (BP) is one of the candidate algorithms to train ANN. Due to the way of BP to find the solution for the underlying problem, there is an important drawback of it, namely the stuck in local minima rather than the global one. Recent studies introduce meta-heuristic techniques to train ANN. The current work proposes a framework in which grey wolf optimizer (GWO) provides the initial solution to a BP ANN. Five datasets are used to benchmark GWO BP performance with other competitors. The first competitor is an optimized BP ANN based on genetic algorithm. The second is a BP ANN powered by particle swarm optimizer. The third is the BP algorithm itself and lastly a feedforward ANN enhanced by GWO. The carried experiments show that GWOBP outperforms the compared algorithms.

Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Artificial neural network (ANN) models are involved in many applications because of its great computational capabilities. Training of multi-layer perceptron (MLP) is the most challenging problem during the network preparation. Many techniques have been introduced to alleviate this problem. Back-propagation algorithm is a powerful technique to train multilayer feedforward ANN. However, it suffers from the local minima drawback. Recently, meta-heuristic methods have introduced to train MLP like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimizer (ACO), Social Spider Optimization (SSO), Evolutionary Strategy (ES) and Grey Wolf Optimization (GWO). This chapter applied Multi-Verse Optimizer (MVO) for MLP training. Seven datasets are used to show MVO capabilities as a promising trainer for multilayer perceptron. Comparisons with PSO, GA, SSO, ES, ACO and GWO proved that MVO outperforms all these algorithms.

Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Grey wolf optimizer-based back-propagation neural network algorithm", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 213–218, 2016. Abstract
n/a
Tourism