Publications

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Conference Paper
Ayeldeen, H., A. E. Hassanien, and A. A. Fahmy, "Evaluation of semantic similarity across MeSH ontology: a Cairo University thesis mining case study", Artificial Intelligence (MICAI), 2013 12th Mexican International Conference on: IEEE, pp. 139–144, 2013. Abstract
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Ahmad. Taher Azar, A. E. Hassanien, and T. - H. Kim, "Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis.", International Conference on Bio-Science and Bio-Technology (BSBT2012), , Kangwondo, Korea. pp. 94--105, December 16-19, 2012. Abstract3530094.pdf

The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.

Book Chapter
El-Bendary, N., V. Snasel, G. Adam, F. Mansour, N. I. Ghali, O. S. Soliman, and A. E. Hassanien, "E-Contract Securing System Using Digital Signature Approach", Advanced Communication and Networking: Springer Berlin Heidelberg, pp. 183–189, 2011. Abstract
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El-Bendary, N., V. Snasel, G. Adam, F. Mansour, N. I. Ghali, O. S. Soliman, and A. E. Hassanien, "E-Contract Securing System Using Digital Signature Approach", Advanced Communication and Networking: Springer Berlin Heidelberg, pp. 183–189, 2011. 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: Springer International Publishing, pp. 755–765, 2015. Abstract
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Belem, B., and P. Plassmann, "Early Detection of Wound Inflammation by Color Analysis", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 89–111, 2009. Abstract
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Ayeldeen, H., M. A. Mahmood, and A. E. Hassanien, "Effective Classification and Categorization for Categorical Sets: Distance Similarity Measures", Information Systems Design and Intelligent Applications: Springer India, pp. 359–368, 2015. Abstract
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Ahmed, K., and A. E. Hassanien, "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Complex social networks analysis is an important research trend, which basically based on community detection. Community detection is the process of dividing the complex social network into a dynamic number of clusters based on their edges connectivity. This paper presents an efficient Elephant Swarm Optimization Algorithm for community detection problem (EESO) as an optimization approach. EESO can define dynamically the number of communities within complex social network. Experimental results are proved that EESO can handle the community detection problem and define the structure of complex networks with high accuracy and quality measures of NMI and modularity over four popular benchmarks such as Zachary Karate Club, Bottlenose Dolphin, American college football and Facebook. EESO presents high promised results against eight community detection algorithms such as discrete krill herd algorithm, discrete Bat algorithm, artificial fish swarm algorithm, fast greedy, label propagation, walktrap, Multilevel and InfoMap.

Ahmed, K., A. E. Hassanien, and E. Ezzat, "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1062–1075, 2017. 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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Asmaa Osamaa, S. A. El-Said, and A. E. Hassanien, "Energy-Efficient Routing Techniques for Wireless Sensors Networks", Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization: IGI Global, pp. 37–62, 2016. 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.

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.

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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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. 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.

Awad, A. I., and A. E. Hassanien, "Erratum: Impact of Some Biometric Modalities on Forensic Science", Computational Intelligence in Digital Forensics: Forensic Investigation and Applications: Springer International Publishing, pp. E1–E1, 2014. Abstract
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Abder-Rahman Ali, Micael Couceiro, A. M. Anter, and A. E. Hassanien, "Evaluating an Evolutionary Particle Swarm Optimization for Fast Fuzzy C-Means Clustering on Liver CT Images", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, 2014. Abstract

An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for
optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance
of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept
of fractional calculus is used to control the convergence rate of particles, wherein each one of them
represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on
liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and
Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities
during the clustering process instead of the raw image data. The experimental results based on the
Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed
algorithm is fast, accurate, and less time consuming.

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 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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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. Abstract
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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. Abstract
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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. Abstract
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