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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., 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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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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Salama, M. A., A. E. Hassanien, A. A. Fahmy, and T. - H. Kim, "Heart Sound Feature Reduction Approach for Improving the Heart Valve Diseases Identification", Signal Processing, Image Processing and Pattern Recognition: Springer Berlin Heidelberg, pp. 280–290, 2011. Abstract
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Salama, M. A., A. E. Hassanien, A. A. Fahmy, and T. - H. Kim, "Heart Sound Feature Reduction Approach for Improving the Heart Valve Diseases Identification", Signal Processing, Image Processing and Pattern Recognition: Springer Berlin Heidelberg, pp. 280–290, 2011. Abstract
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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
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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 (IJSBBT), vol. 1, no. 4: IGI Global, pp. 16–27, 2012. Abstract
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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., N. El-Bendary, A. E. Hassanien, K. Revett, and A. A. Fahmy, "Interval-based attribute evaluation algorithm", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 153–156, 2011. Abstract
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Salama, M. A., N. El-Bendary, A. E. Hassanien, K. Revett, and A. A. Fahmy, "Interval-based attribute evaluation algorithm", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 153–156, 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., 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., A. Mostafa, and A. E. Hassanien, "The prediction of virus mutation using neural networks and rough set techniques", . EURASIP J. Bioinformatics and Systems Biology , vol. 10, 2016. AbstractWebsite

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

Salama, M., Data Mining for Medical Informatics, , Cairo, Cairo Unv, 2012. AbstractThesis.pdfPresentation.pdf

The work presented in this thesis investigates the nature of real-life data, mainly in the medical field, and the problems in handling such nature by the conventional data mining techniques. Accordingly, a set of alternative techniques are proposed in this thesis to handle the medical data in the three stages of data mining process. In the first stage which is preprocessing, a proposed technique named as interval-based feature evaluation technique that depends on a hypothesis that the decrease of the overlapped interval of values for every class label leads to increase the importance of such attribute. Such technique handles the difficulty of dealing with continuous data attributes without the need of applying discretization of the input and it is proved by comparing the results of the proposed technique to other attribute evaluation and selection techniques. Also in the preprocessing stage, the negative effect of normalization algorithm before applying the conventional PCA has been investigated and how the avoidance of such algorithm enhances the resulted classification accuracy. Finally in the preprocessing stage, an experimental analysis introduces the ability of rough set methodology to successfully classify data without the need of applying feature reduction technique. It shows that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including Support Vector Machine, Hidden Naive Bayesian network, Bayesian network and other techniques.
In the machine learning stage, frequent pattern-based classification technique is proposed; it depends on the detection of variation of attributes among objects of the same class. The preprocessing of the data like standardization, normalization, discretization or feature reduction is not required in this technique which enhances the performance in time and keeps the original data without being distorted. Another contribution has been proposed in the machine learning stage including the support vector machine and fuzzy c-mean clustering techniques; this contribution is about the enhancement of the Euclidean space calculations through applying the fuzzy logic in such calculations. This enhancement has used chimerge feature evaluation techniques in applying fuzzification on the level of features. A comparison is applied on these enhanced techniques to the other classical data mining techniques and the results shows that classical models suffers from low classification accuracy due to the dependence of un-existed presumption.
Finally, in the visualization stage, a proposed technique is presented to visualize the continuous data using Formal Concept Analysis that is better than the complications resulted from the scaling algorithms.

Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Feature evaluation based fuzzy C-mean classification", Fuzzy Systems (FUZZ), 2011 IEEE International Conference on: IEEE, pp. 2534–2539, 2011. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Feature evaluation based fuzzy C-mean classification", Fuzzy Systems (FUZZ), 2011 IEEE International Conference on: IEEE, pp. 2534–2539, 2011. Abstract
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