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Tharwt, A., and A. E. Hassanien, "Particle Swarm Optimization: A Tutorial", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

Tharwatd, A., T. Gaber, and A. E. Hassanien, " One-dimensional vs. two-dimensional based features: Plant identification approach, ", Journal of Applied Logic , vol. Available online 15 November 2017 , 2017. AbstractWebsite

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

Tharwat;, A., A. E. Hassanien;, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of support vector machine", Pattern recognition letter, 2017. AbstractWebsite

Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.

Terzopoulos, D., C. McIntosh, T. McInerney, and G. Hamarneh, "Deformable Organisms", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 433–474, 2009. Abstract
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Tarek Gaber, Alaa Tharwat, V. S. A. E. H.:, "Plant Identification: Two Dimensional-Based Vs. One Dimensional-Based Feature Extraction Methods", 10th International Conference on Soft Computing Models in Industrial and Environmental Applications, Spain, july, 2015. Abstract

In this paper, a plant identification approach using 2D digital leaves images is proposed. The approach made use of two methods of features extraction (one-dimensional (1D) and two-dimensional (2D) techniques) and the Bagging classifier. For the 1D-based method, PCA and LDA techniques were applied, while 2D-PCA and 2D-LDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner, was used. The proposed approach, with its four feature extraction techniques, was tested using Flavia dataset which consists of 1907 colored leaves images. The experimental results showed that the accuracy and the performance of our approach, with the 2D-PCA and 2D-LDA, was much better than using the PCA and LDA. Furthermore, it was proven that the 2D-LDA-based method gave the best plant identification accuracy and increasing the weak learners of the Bagging classifier leaded to a better accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

TarasKotyk, N. D., A. S. Ashour, A. D. C. Victoria, T. Gaber, A. E. Hassanien, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), 2015, , Beni Suef, Egypt, November 28-30, , 2015. Abstract

Apoptosis is an imperative constituent of various processes including proper progression and functioning of the immune system, embryonic development as well as chemical-induced cell death. Improper apoptosis is a reason in numerous human/animal’s conditions involving ischemic damage, neurodegenerative diseases, autoimmune disorders and various types of cancer. An outstanding feature of neurodegenerative diseases is the loss of specific neuronal populations. Thus, the detection of the dead cells is a necessity. This paper proposes a novel algorithm to achieve the dead cells detection based on color intensity and contrast changes and aims for fully automatic apoptosis detection based on image analysis method. A stained cultures images using Caspase stain of albino rats hippocampus specimens using light microscope (total 21 images) were used to evaluate the system performance. The results proved that the proposed system is efficient as it achieved high accuracy (98.89 ± 0.76 %) and specificity (99.36 ± 0.63 %) and good mean sensitivity level of (72.34 ± 19.85 %).

Taher, A., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft Computing, vol. June 2014, 2014. AbstractWebsite

Massive and complex data are generated every day in many fields. Complex data refer to data sets that are so large that conventional database management and data analysis tools are insufficient to deal with them. Managing and analysis of medical big data involve many different issues regarding their structure, storage and analysis. In this paper, linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) is presented for dimensionality reduction, feature selection and classification. Four real-world data sets are provided to demonstrate the performance of the proposed neuro-fuzzy classifier. The new classifier is compared with the other classifiers for different classification problems. The results indicated that applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only help reducing the dimensionality of large data sets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.

Taher, A., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft Computing, 2014. Abstract
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Taha, H. M., N. El-Bendary, A. E. Hassanien, Y. Badr, and V. Snasel, "Retinal feature-based registration schema", Informatics engineering and information science: Springer Berlin Heidelberg, pp. 26–36, 2011. Abstract
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Taha, H. M., N. El-Bendary, A. E. Hassanien, Y. Badr, and V. Snasel, "Retinal feature-based registration schema", Informatics engineering and information science: Springer Berlin Heidelberg, pp. 26–36, 2011. Abstract
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Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Robust watermarking approach for 3D triangular mesh using self organization map", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 99–104, 2013. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 2: IGI Global, pp. 43–53, 2013. Abstract
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Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. Abstract
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Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive Watermarking Approach for Medical Imaging Using Swarm Intelligent", International Journal of Smart Home, (ISSN: 1975-4094), vol. 6, issue 1, pp. 37-45, 2012. AbstractIJSH_ 2012.pdfWebsite

In this paper we present a secure patient medical images and authentication scheme which enhances the security, confidentiality and integrity of medical images transmitted through the Internet. This paper proposes a watermarking by invoking particle swarm optimization (PSO) technique in adaptive quantization index modulation and singular value decomposition in conjunction with discrete wavelet transform (DWT) and discrete cosine transform (DCT). The proposed approach promotes the robustness and watermarked image quality. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes, robust against a wide variety of common attacks and reliable enough for tracing colluders.

Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive watermarking approach for medical imaging using swarm intelligent", International Journal of Smart Home, vol. 6, no. 1, pp. 37–50, 2012. Abstract
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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. Abstract
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Soliman, M. M., and A. E. Hassanien, "3D Watermarking Approach Using Particle Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

This work proposes a watermarking approach by utilizing the use of Bio-Inspired techniques such as swarm intelligent in optimizing watermarking algorithms for 3D models. In this proposed work we present an approach of 3D mesh model watermarking by introducing a new robust 3D mesh watermarking authentication methods by ensuring a minimal surface distortion at the same time ensuring a high robustness of extracted watermark. In order to achieve these requirements this work proposes the use of Particle Swarm Optimization (PSO) as Bio-Inspired optimization techniques. The experiments were executed using different sets of 3D models. In all experimental results we consider two important factors: imperceptibility and robustness. The experimental results show that the proposed approach yields a watermarked object with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks.

Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Robust 3D Mesh Watermarking Approach Using Genetic Algorithms", Intelligent Systems' 2014: Springer International Publishing, pp. 731–741, 2015. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive medical images watermarking using quantum particle swarm optimization", Telecommunications and Signal Processing (TSP), 2012 35th International Conference on: IEEE, pp. 735–739, 2012. Abstract
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