Publications

Export 106 results:
Sort by: Author [ Title  (Desc)] 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]
C
Abraham, A., and A. - E. Hassanien, Computational social networks: Tools, perspectives and applications, : Springer Science & Business Media, 2012. Abstract
n/a
Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
n/a
Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
n/a
Hassanien, A. - E., Computational Social Networks Analysis, , London, Computer Communications and Networks Series - Springer, 2010. AbstractWebsite

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities. This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems.

Hassanien, A. - E., Computational Social Networks Analysis, : Computer Communications and Networks Series-Springer, 2010. Abstract
n/a
Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
n/a
Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
n/a
Elbedwehy, M. N., M. E. Ghoneim, and A. E. Hassanien, "Computational model for artificial learning using fonnal concept analysis", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 9–14, 2013. Abstract
n/a
Elbedwehy, M. N., M. E. Ghoneim, and A. E. Hassanien, "Computational model for artificial learning using fonnal concept analysis", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 9–14, 2013. Abstract
n/a
Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Application (Springer), vol. In press, 2014.
Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Application , vol. June 2014, 2014. AbstractWebsite

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.

Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Applications, vol. 25, no. 7-8: Springer London, pp. 1517–1534, 2014. Abstract
n/a
Hassanien, A. E., E. T. Al-Shammari, and N. I. Ghali, "Computational intelligence techniques in bioinformatics", Computational biology and chemistry, vol. 47: Elsevier, pp. 37–47, 2013. Abstract
n/a
Hassanien, A. E., E. T. Al-Shammari, and N. I. Ghali, "Computational intelligence techniques in bioinformatics", Computational biology and chemistry, vol. 47: Elsevier, pp. 37–47, 2013. Abstract
n/a
Ahmed M. Anter, and A. E. Hassenian, "Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation", Journal of Computational Science, 2018. Abstract

In this paper, an improved segmentation approach for abdominal CT liver tumor based on neutrosophic sets (NS), particle swarm optimization (PSO), and fast fuzzy C-mean algorithm (FFCM) is proposed. To increase the contrast of the CT liver image, the intensity values and high frequencies of the original images were removed and adjusted firstly using median filter approach. It is followed by transforming the abdominal CT image to NS domain, which is described using three subsets namely; percentage of truth T, percentage of falsity F, and percentage of indeterminacy I. The entropy is used to evaluate indeterminacy in NS domain. Then, the NS image is passed to optimized FFCM using PSO to enhance, optimize clusters results and segment liver from abdominal CT. Then, these segmented livers passed to PSOFCM technique to cluster and segment tumors. The experimental results obtained based on the analysis of variance (ANOVA) technique, Jaccard Index and Dice Coefficient measures show that, the overall accuracy offered by neutrosophic sets is accurate, less time consuming and less sensitive to noise and performs well on non-uniform CT images.

Wu, Z. Q., J. Jiang, and Y. H. Peng, "Computational Intelligence on Medical Imaging with Artificial Neural Networks in", Computational intelligence in medical imaging techniques and applications, 2009. Abstract
n/a
Hassanien, A. E., G. Schaefer, and A. Darwish, "Computational intelligence in speech and audio processing: recent advances", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 303–311, 2010. Abstract
n/a
Hassanien, A. E., G. Schaefer, and A. Darwish, "Computational intelligence in speech and audio processing: recent advances", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 303–311, 2010. Abstract
n/a
Hassanien, A. - E., M. G. Milanova, T. G. Smolinski, and A. Abraham, "Computational intelligence in solving bioinformatics problems: Reviews, perspectives, and challenges", Computational Intelligence in Biomedicine and Bioinformatics: Springer Berlin Heidelberg, pp. 3–47, 2008. Abstract
n/a
Hassanien, A. - E., M. G. Milanova, T. G. Smolinski, and A. Abraham, "Computational intelligence in solving bioinformatics problems: Reviews, perspectives, and challenges", Computational Intelligence in Biomedicine and Bioinformatics: Springer Berlin Heidelberg, pp. 3–47, 2008. Abstract
n/a
Hassanien, A. E., "Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges", Computational Intelligence in Biomedicine and Bioinformatics , London, Studies in Computational Intelligence,Springer, Volume 151/2008, 3-47, 2008. Abstract

This chapter presents a broad overview of Computational Intelligence (CI) techniques including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Fuzzy Sets (FS), and Rough Sets (RS). We review a number of applications of computational intelligence to problems in bioinformatics and computational biology, including gene expression, gene selection, cancer classification, protein function prediction, multiple sequence alignment, and DNA fragment assembly. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to solve bioinformatic problems and how bioinformatics could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are presented. An extensive bibliography is also included.

Hassanien, A. E., Computational Intelligence in Multimedia Processing: Recent Advances, , USA, Studies in Computational Intelligence, Springer Vol. 96 , 2008. AbstractWebsite

For the last decades Multimedia processing has emerged as an important technology to generate content based on images, video, audio, graphics, and text. Furthermore, the recent new development represented by High Definition Multimedia content and Interactive television will generate a huge volume of data and important computing problems connected with the creation, processing and management of Multimedia content. "Computational Intelligence in Multimedia Processing: Recent Advances" is a compilation of the latest trends and developments in the field of computational intelligence in multimedia processing. This edited book presents a large number of interesting applications to intelligent multimedia processing of various Computational Intelligence techniques, such as rough sets, Neural Networks; Fuzzy Logic; Evolutionary Computing; Artificial Immune Systems; Swarm Intelligence; Reinforcement Learning and evolutionary computation.

Hassanien, A. - E., and A. Abraham, Computational intelligence in multimedia processing: recent advances, : Springer, 2008. Abstract
n/a
Hassanien, A. - E., and A. Abraham, Computational intelligence in multimedia processing: recent advances, : Springer, 2008. Abstract
n/a
Hassanien, A. - E., A. Abraham, J. Kacprzyk, and J. F. Peters, "Computational intelligence in multimedia processing: foundation and trends", Computational Intelligence in Multimedia Processing: Recent Advances: Springer Berlin Heidelberg, pp. 3–49, 2008. Abstract
n/a