Sharif, M. M., Alaa Tharwat, A. E. Hassanien, and H. A. Hefny,
"Enzyme Function Classification: Reviews, Approaches, and Trends: ",
Handbook of Research on Machine Learning Innovations and Trends , USA, IGI, USA pp. 26 , 2017.
AbstractEnzymes are important in our life and it plays a vital role in the most biological processes in the living organisms and such as metabolic pathways. The classification of enzyme functionality from a sequence, structure data or the extracted features remains a challenging task. Traditional experiments consume more time, efforts, and cost. On the other hand, an automated classification of the enzymes saves efforts, money and time. The aim of this chapter is to cover and reviews the different approaches, which developed and conducted to classify and predict the functions of the enzyme proteins in addition to the new trends and challenges that could be considered now and in the future. The chapter addresses the main three approaches which are used in the classification the function of enzymatic proteins and illustrated the mechanism, pros, cons, and examples for each one.
Azar, A. T., A. E. Hassanien, T. - H. Kim, and others,
"Expert system based on neural-fuzzy rules for thyroid diseases diagnosis",
Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012.
Abstractn/a
Azar, A. T., A. E. Hassanien, T. - H. Kim, and others,
"Expert system based on neural-fuzzy rules for thyroid diseases diagnosis",
Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012.
Abstractn/a
Azar, A. T., A. E. Hassanien, T. - H. Kim, and others,
"Expert system based on neural-fuzzy rules for thyroid diseases diagnosis",
Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012.
Abstractn/a
Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien,
"A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms",
Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012.
Abstractn/a
Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien,
"A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms",
Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012.
Abstractn/a
Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy:,
"Genetic Algorithms for Multi-Objective Community Detection in Complex Networks.",
Social Networks: A Framework of Computational Intelligence , London, Volume 526, pp 145-171, Springer, 2014.
Panda, M., A. E. Hassanien, and A. Abraham,
"Hybrid Data Mining Approach for Image Segmentation Based Classification",
Biometrics: Concepts, Methodologies, Tools, and Applications: IGI Global, pp. 1543–1561, 2017.
Abstractn/a
Hassanien, A. E.,
"Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization",
Foundations of Computational Intelligence, Volume 1: Learning and Approximation, Volume 201/2009, 381-397, London, Springer-Verlag , 2009.
AbstractThis study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.
]