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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Event detection based approach for soccer video summarization using machine learning", Int J Multimed Ubiquitous Eng, vol. 7, no. 2, pp. 63–80, 2012. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Event detection based approach for soccer video summarization using machine learning", Int J Multimed Ubiquitous Eng, vol. 7, no. 2, pp. 63–80, 2012. Abstract
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Yasser Mahmoud Awad, A. A. Abdullah, T. Y. Bayoumi, K. Abd-Elsalam, and A. E. Hassanien, "Early Detection of Powdery Mildew Disease in Wheat (Triticum aestivum L.) Using Thermal Imaging Technique", Intelligent Systems'2014 Advances in Intelligent Systems and Computing Volume 323, 2015, pp 755-765, Poland , 2014. Abstract

Powdery mildew caused by Erysiphe graminis f. sp. tritici is one of the most harmful disease causing great losses in wheat yield. Currently, thermal spectral sensing of plant disease under different environmental conditions in field is a cutting-edge research. Objectives of this study were to assess thermal imaging of normal and infected leaves for early detection of powdery mildew in wheat after the artificial infection with Erysiphe graminis fungus in a pot experiment under greenhouse conditions. Pot experiment lasting for 30 days was conducted. Additionally, wheat seedlings were artificially infected with pathogen at 10 days from sowing. This is the first study in Egypt to use thermal imaging technique for early detection of powdery mildew disease on leaf using thermal signatures of artificial infected leaves as a reference images. Particularly, the variations in temperature between infected and healthy leaves of wheat and the variation between air and leaf-surface temperatures under greenhouse conditions were sensed for early detection of disease. Results revealed that infection with powdery mildew pathogen induced changes in leaf temperature (from 0.37 °C after one hour from the infection to 0.78 °C at 21 days after infection with the pathogen) and metabolism, contributing to a distinct thermal signature characterizing the early and late phases of the infection.

Yasser Mahmoud Awad, A. A. Abdullah, T. Y. Bayoumi, K. Abd-Elsalam, and A. E. Hassanien, "Early Detection of Powdery Mildew Disease in Wheat (Triticum aestivum L.) Using Thermal Imaging Technique", Intelligent Systems' 2014: Springer International Publishing, pp. 755–765, 2015. Abstract
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Xiao, K., L. A. Liang, Haibing Guan, and A. E. Hassanien, "Extraction and application of deformation-based feature in medical images", Neurocomputing, vol. 120: Elsevier, pp. 177–184, 2013. Abstract
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Xiao, K., L. A. Liang, Haibing Guan, and A. E. Hassanien, "Extraction and application of deformation-based feature in medical images", Neurocomputing, vol. 120: Elsevier, pp. 177–184, 2013. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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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 , 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, 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. 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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Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members", Social Networking, London, Intelligent Systems Reference Library Springer, 2014.
Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Degree of Trust Under Context Sensitive Relational Database Hierarchy Using Hybrid Intelligent Approach", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no. 1: IGI Global, pp. 1–21, 2015. Abstract
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Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members", Social Networking: Springer International Publishing, pp. 201–213, 2014. 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 K. Revett, "Employment of neural network and rough set in meta-learning.", Memetic Computing- Springer, vol. 5, issue 3, pp. 165-177, 2013. Website
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., 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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Osman, M. A., A. Darwish, A. E. Khedr, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 925–941, 2017. Abstract
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Osman, M. A., A. Darwish, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

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