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Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
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Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
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Shehab, A., M. Elhoseny, and A. E. Hassanien, "A hybrid scheme for Automated Essay Grading based on LVQ and NLP techniques", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 65–70, 2016. Abstract
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Shehab, A., M. Elhoseny, and A. E. Hassanien, "A hybrid scheme for Automated Essay Grading based on LVQ and NLP techniques", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec, 2016. Abstract

This paper presents a hybrid approach to an Automated Essay Grading System (AEGS) that provides automated grading and evaluation of student essays. The proposed system has two complementary components: Writing Features Analysis tools, which rely on natural language processing (NLP) techniques and neural network grading engine, which rely on a set of pre-graded essays to judge the student answer and assign a grade. By this way, students essays could be evaluated with a feedback that would improve their writing skills. The proposed system is evaluated using datasets from computer and information sciences college students' essays in Mansoura University. These datasets was written as part of mid-term exams in introduction to information systems course and Systems analysis and design course. The obtained results shows an agreement with teachers' grades in between 70% and nearly 90% with teachers' grades. This indicates that the proposed might be useful as a tool for automatic assessment of students' essays, thus leading to a considerable reduction in essay grading costs.

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. Abstract

Enzymes 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.

Sharif, M. M., Alaa Tharwat, A. E. Hassanien, H. A. Hefny, and G. Schaefer, "Enzyme function classification based on borda count ranking aggregation method", Machine Intelligence and Big Data in Industry: Springer International Publishing, pp. 75–85, 2016. Abstract
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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: IGI Global, pp. 161–186, 2017. Abstract
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Sharif, M. M., Alaa Tharwat, A. E. Hassanien, and H. A. Hefeny, "Automated Enzyme Function Classification Based on Pairwise Sequence Alignment Technique", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 499–510, 2015. Abstract
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Sharif, M. M., Alaa Tharwat, A. E. Hassanien, and H. A. Hefny, "Enzyme vs. non-enzyme classification based on principal component analysis and AdaBoost classifier", Computing, Communication and Automation (ICCCA), 2016 International Conference on: IEEE, pp. 288–293, 2016. Abstract
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Shang-Ling, S. Z. Jui, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, pp. 66-76, 2016. AbstractWebsite

Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.

Semary, N. A., Alaa Tharwat, Esraa Elhariri, and A. E. Hassanien, "Fruit-Based Tomato Grading System Using Features Fusion and Support Vector Machine", IEEE Conf. on Intelligent Systems (2) 2014: 401-410, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Machine learning and computer vision techniques have applied for evaluating food quality as well as crops grading. In this paper, a new classification system has been proposed to classify infected/uninfected tomato fruits according to its external surface. The system is based on feature fusion method with color and texture features. Color moments, GLCM, and Wavelets energy and entropy have been used in the proposed system. Principle Component Analysis (PCA) technique has been used to reduce the feature vector obtained after fusion to avoid dimensionality problem and save time and cost. Support vector machine (SVM) was used to classify tomato images into 2 classes; infected/uninfected using Min-Max and Z-Score normalization methods. The dataset used in this research contains 177 tomato fruits each was captured from four faces (Top, Side1, Side2, and End). Using 70% of the total images for training phase and 30% for testing, our proposed system achieved accuracy 92%.

Semary, N. A., Alaa Tharwat, Esraa Elhariri, and A. E. Hassanien, "Fruit-based tomato grading system using features fusion and support vector machine", Intelligent Systems' 2014: Springer International Publishing, pp. 401–410, 2015. Abstract
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Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma Classification based on Ensemble Classification of Dermoscopy Image Features", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma classification based on ensemble classification of dermoscopy image features", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 291–298, 2014. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Niraj P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., A. Hassanien, and J. Jiang, Computational Intelligence in Medical Imaging: Techniques and Applications, : CRC press, 2009. Abstract
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Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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