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Own, H., and A. E. Hassanien, "Q-shift Complex Wavelet-based Image Registration Algorithm", Proceedings of the 4th International Conference on Computer Recognition Systems, CORES'05, pp. 403-410, Rydzyna Castle, Poland, May 22-25,, 2005. Abstract

This paper presents an efficient image registration technique using the Q-shift complex wavelet transform (Q-shift CWT). It is chosen for its key advantages compared to other wavelet transforms; such as shift invariance, directional selectivity, perfect reconstruction, limited redundancy and efficient computation. The experiments show that the proposed algorithm improves the computational efficiency and yields robust and consistent image registration compared with the classical wavelet transform.

Own, H., and A. Hassanien, "Q-shift Complex Wavelet-based Image Registration Algorithm", Computer Recognition Systems: Springer Berlin/Heidelberg, pp. 403–410, 2005. Abstract
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Own, H., and A. Hassanien, "Q-shift Complex Wavelet-based Image Registration Algorithm", Computer Recognition Systems: Springer Berlin/Heidelberg, pp. 403–410, 2005. Abstract
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P
Oliva, D., M. abd elaziz, and A. E. Hassanien, "Photovoltaic cells design using an improved chaotic whale optimization algorithm", Applied Energy, vol. 200, pp. 141–154, 2017. AbstractWebsite

The using of solar energy has been increased since it is a clean source of energy. In this way, the design of photovoltaic cells has attracted the attention of researchers over the world. There are two main problems in this field: having a useful model to characterize the solar cells and the absence of data about photovoltaic cells. This situation even affects the performance of the photovoltaic modules (panels). The characteristics of the current vs. voltage are used to describe the behavior of solar cells. Considering such values, the design problem involves the solution of the complex non-linear and multi-modal objective functions. Different algorithms have been proposed to identify the parameters of the photovoltaic cells and panels. Most of them commonly fail in finding the optimal solutions. This paper proposes the Chaotic Whale Optimization Algorithm (CWOA) for the parameters estimation of solar cells. The main advantage of the proposed approach is using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm. This situation is beneficial in complex problems, because along the iterative process, the proposed algorithm improves their capabilities to search for the best solution. The modified method is able to optimize complex and multimodal objective functions. For example, the function for the estimation of parameters of solar cells. To illustrate the capabilities of the proposed algorithm in the solar cell design, it is compared with other optimization methods over different datasets. Moreover, the experimental results support the improved performance of the proposed approach regarding accuracy and robustness.

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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An Optimized Approach for Medical Image Watermarking", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 71–91, 2014. Abstract
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Own, H. S., and A. E. Hassanien, "Multiresolution image registration algorithm in wavelet transform domain", Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on, vol. 2: IEEE, pp. 889–892, 2002. Abstract
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Hassanien, A. E., I. El Henawy, and H. Own, "Multiresolution image denosing based on wavelet transform", Machine Graphics and Vision, vol. 10, no. 2, pp. 221–230, 2001. Abstract
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Hassanien, A. E., I. El Henawy, and H. S. Own, "Multiresolution image denoising based on wavelet transform", International Symposium on Optical Science and Technology: International Society for Optics and Photonics, pp. 383–394, 2001. Abstract
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Hassanien, A. E., I. El Henawy, and H. S. Own, "Multiresolution image denoising based on wavelet transform", International Symposium on Optical Science and Technology: International Society for Optics and Photonics, pp. 383–394, 2001. Abstract
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Azar, A. T., A. E. Hassanien, and others, "Hybrid TRS-PSO clustering approach for Web2. 0 social tagging system", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no. 1: IGI Global, pp. 22–37, 2015. Abstract
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Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence: Volume 6: Data Mining, : Springer, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence: Volume 6: Data Mining, : Springer, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, : Springer Science & Business Media, 2009. 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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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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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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