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and ella S. Udhaya Kumar, H. Hannah Inbarani, A. T. A. A. H., "Identification of Heart Valve Disease using Bijective, Soft sets Theory ", International Journal of Rough Sets and Data Analysis, vol. 1, issue 2, pp. , 1(2), 1-13, 2014. Abstract

Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naïve Bayes (NB) and J48.

Saad, O., A. Darwish, and R. Faraj, "A survey of machine learning techniques for Spam filtering", International Journal of Computer Science and Network Security (IJCSNS), vol. 12, no. 2: International Journal of Computer Science and Network Security, pp. 66, 2012. Abstract
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Sahba, F., H. R. Tizhoosh, and M. M. A. Salama, "Reinforced Medical Image Segmentation", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 327–345, 2009. Abstract
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Sahlol, A. T., A. A. Ewees, A. M. H.;, and A. E. Hassanien, "Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite", 12th International Computer Engineering Conference (ICENCO),, Cairo, 28-29 Dec, 2016. Abstract

Analytical prediction of oxidative stress biomarkers in ecosystem provides an expressive result for many stressors. These oxidative stress biomarkers including superoxide dismutase, glutathione peroxidase and catalase activity in fish liver tissue were analyzed within feeding different levels of selenium nanoparticles. Se-nanoparticles represent a salient defense mechanism in oxidative stress within certain limits; however, stress can be engendered from toxic levels of these nanoparticles. For instance, prediction of the level of pollution and/or stressors was elucidated to be improved with different levels of selenium nanoparticles using the bio-inspired Sine-Cosine algorithm (SCA). In this paper, we improved the prediction accuracy of liver enzymes of fish fed by nano-selenite by developing a neural network model based on SCA, that can train and update the weights and the biases of the network until reaching the optimum value. The performance of the proposed model is better and achieved more efficient than other models.

Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

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
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Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 897–914, 2017. Abstract
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Sahlol, A. T., A. A. Ewees, A. M. Hemdan, and A. E. Hassanien, "Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 35–40, 2016. Abstract
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Sahlol, A. T., C. Y. Suen, H. M. Zawbaa, A. E. Hassanien, and M. A. Fattah, "Bio-inspired BAT optimization algorithm for handwritten Arabic characters recognition", Evolutionary Computation (CEC), 2016 IEEE Congress on: IEEE, pp. 1749–1756, 2016. Abstract
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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
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing Springer , 2013. AbstractWebsite

The selection of the optimal ensembles of classifiers in multiple-classifier selection technique is un-decidable in many cases and it is potentially subjected to a trial-and-error search. This paper introduces a quantitative meta-learning approach based on neural network and rough set theory in the selection of the best predictive model. This approach depends directly on the characteristic, meta-features of the input data sets. The employed meta-features are the degree of discreteness and the distribution of the features in the input data set, the fuzziness of these features related to the target class labels and finally the correlation and covariance between the different features. The experimental work that consider these criteria are applied on twenty nine data sets using different classification techniques including support vector machine, decision tables and Bayesian believe model. The measures of these criteria and the best result classification technique are used to build a meta data set. The role of the neural network is to perform a black-box prediction of the optimal, best fitting, classification technique. The role of the rough set theory is the generation of the decision rules that controls this prediction approach. Finally, formal concept analysis is applied for the visualization of the generated rules.

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., N. El-Bendary, and A. E. Hassanien, "Towards secure mobile agent based e-cash system", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 1–6, 2011. Abstract
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 165–177, 2013. Abstract
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Salama, M. A., and A. E. Hassanien, "Fuzzification of Euclidean Space Approach in Machine Learning Techniques", International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), vol. 5, no. 4: IGI Global, pp. 29–43, 2014. Abstract
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Salama, M. A., and A. E. Hassanien, "Binarization and Validation in Formal Concept Analysis", International Journal of Systems Biology and Biomedical Technologies, vol. 1, issue 4, pp. 17-28, 2012. AbstractWebsite

Representation and visualization of continuous data using the Formal Concept Analysis (FCA) became an
important requirement in real-life fields. Application of formal concept analysis (FCA) model on numerical
data, a scaling or Discretization / binarization procedures should be applied as preprocessing stage. The
Scaling procedure increases the complexity of computation of the FCA, while the binarization process leads to a distortion in the internal structure of the input data set. The proposed approach uses a binarization procedure prior to applying FCA model, and then applies a validation process to the generated lattice to measure or ensure its degree of accuracy. The introduced approach is based on the evaluation of each attribute according to the objects of its extent set. To prove the validity of the introduced approach, the technique is applied on two data sets in the medical field which are the Indian Diabetes and the Breast Cancer data sets. Both data sets show the generation of a valid lattice.

Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Uni-class pattern-based classification model", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 1293–1297, 2010. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and A. M. Alimi, "Formal concept analysis approach for comparison between Mutagenicity and Carcinogenicity in Cheminformatics", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 267–272, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and A. Mostafa, "The prediction of virus mutation using neural networks and rough set techniques", EURASIP Journal on Bioinformatics and Systems Biology, vol. 2016, no. 1: Springer International Publishing, pp. 1–11, 2016. Abstract
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Salama, M., A. E. Hassanien, and Adel Alimi, "Formal concept analysis approach for comparison between mutagenicity and carcinogenicity in Cheminformatics. ", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 268-273, 2013, Tunisia, , 4-6 Dec, 2013.
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