Publications

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2018
Alaa Tharwat, and A. E. Hassanien, "Chaotic antlion algorithm for parameter optimization of support vector machine", Applied Intelligence, vol. 48, issue 3, pp. 670–686, 2018. AbstractWebsite

Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel parameters and penalty parameter (C) significantly influence the classification accuracy. In this paper, a novel Chaotic Antlion Optimization (CALO) algorithm has been proposed to optimize the parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed algorithm (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the CALO-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, standard Ant Lion Optimization (ALO) SVM, and three well-known optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Social Emotional Optimization Algorithm (SEOA). The experimental results proved that the proposed algorithm is capable of finding the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with GA, PSO, and SEOA algorithms.

Rizk Masoud, A. E. Hassanien, and Siddhartha Bhattacharyya, "Chaotic Crow Search Algorithm for Fractional Optimization Problems", Applied soft computing , 2018. Abstract

This paper presents a chaotic crow search algorithm (CCSA) for solving fractional optimization problems (FOPs). To refine the global convergence speed and enhance the exploration/exploitation tendencies, the proposed CCSA integrates chaos theory (CT) into the CSA. CT is introduced to tune the parameters of the standard CSA, yielding four variants, with the best chaotic variant being investigated. The performance of the proposed CCSA is validated on twenty well-known fractional benchmark problems. Moreover, it is validated on a fractional economic environmental power dispatch problem by attempting to minimize the ratio of total emissions to total fuel cost. Finally, the proposed CCSA is compared with the standard CSA, particle swarm optimization (PSO), firefly algorithm (FFA), dragonfly algorithm (DA) and grey wolf algorithm (GWA). Additionally, the efficiency of the proposed CCSA is justified using the non parametric Wilcoxon signed-rank test. The experimental results prove that the proposed CCSA outperforms other algorithms in terms of quality and reliability.

Ahmed M. Anter, and A. E. Hassenian, "Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation", Journal of Computational Science, 2018. Abstract

In this paper, an improved segmentation approach for abdominal CT liver tumor based on neutrosophic sets (NS), particle swarm optimization (PSO), and fast fuzzy C-mean algorithm (FFCM) is proposed. To increase the contrast of the CT liver image, the intensity values and high frequencies of the original images were removed and adjusted firstly using median filter approach. It is followed by transforming the abdominal CT image to NS domain, which is described using three subsets namely; percentage of truth T, percentage of falsity F, and percentage of indeterminacy I. The entropy is used to evaluate indeterminacy in NS domain. Then, the NS image is passed to optimized FFCM using PSO to enhance, optimize clusters results and segment liver from abdominal CT. Then, these segmented livers passed to PSOFCM technique to cluster and segment tumors. The experimental results obtained based on the analysis of variance (ANOVA) technique, Jaccard Index and Dice Coefficient measures show that, the overall accuracy offered by neutrosophic sets is accurate, less time consuming and less sensitive to noise and performs well on non-uniform CT images.

2017
Alaa Tharwat, Yasmine S. Moemen, and A. E. Hassanien, "Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines", Journal of Biomedical Informatics, vol. 68, pp. 132-149 , 2017. AbstractWebsite

Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug toxicity in the early stage of drug development. Hence, there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drugs that biotransformed in liver. The toxic effects were calculated for the current data, namely, mutagenic, tumorigenic, irritant and reproductive effect. Each drug is represented by 31 chemical descriptors (features). The proposed model consists of three phases. In the first phase, the most discriminative subset of features is selected using rough set-based methods to reduce the classification time while improving the classification performance. In the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique (SMOTE), BorderLine SMOTE and Safe Level SMOTE are used to solve the problem of imbalanced dataset. In the third phase, the Support Vector Machines (SVM) classifier is used to classify an unknown drug into toxic or non-toxic. SVM parameters such as the penalty parameter and kernel parameter have a great impact on the classification accuracy of the model. In this paper, Whale Optimization Algorithm (WOA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. The experimental results proved that the proposed model achieved high sensitivity to all toxic effects. Overall, the high sensitivity of the WOA + SVM model indicates that it could be used for the prediction of drug toxicity in the early stage of drug development.

Alaa Tharwat, Y. S. Moemen, and A. E. Hassanien, "Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines", Journal of Biomedical Informatics, vol. 68: Academic Press, pp. 132–149, 2017. Abstract
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Aziz, A. S. A., M. M. Fouad, and A. E. Hassanien, "Cloud Computing Forensic Analysis: Trends and Challenges", Multimedia Forensics and Security: Springer International Publishing, pp. 3–23, 2017. Abstract
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Abdelhameed Ibrahim, T. Horiuchi, S. Tominaga, and A. E. Hassanien, "Color Invariant Representation and Applications", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1041–1061, 2017. Abstract
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Chakraborty, S., S. Chatterjee, N. Dey, A. S. Ashour, and A. E. Hassanien, "Comparative Approach Between Singular Value Decomposition and Randomized Singular Value Decomposition-based Watermarking", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 133–149, 2017. Abstract
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2016
El-Bendary, N., Esraa Elhariri, M. Hazman, S. M. Saleh, and A. E. Hassanien, "Cultivation-time Recommender System Based on Climatic Conditions for Newly Reclaimed Lands in Egypt", Procedia Computer Science, vol. Volume 96, , pp. Pages 110-119, 2016. AbstractWebsite

This research proposes cultivation-time recommender system for predicting the best sowing dates for winter cereal crops in the newly reclaimed lands in Farafra Oasis, The Egyptian Western Desert. The main goal of the proposed system is to support the best utilization of farm resources. In this research, predicting the best sowing dates for the aimed crops is based on weather conditions prediction along with calculating the seasonal accumulative growing degree days (GDD) fulfillment duration for each crop. Various Machine Learning (ML) regression algorithms have been used for predicting the daily minimum and maximum air temperature based on historical weather conditions data for twenty-five growing seasons (1990/91 to 2014/15). Experimental results showed that using the M5P and IBk ML regression algorithms have outperformed the other implemented regression algorithms for predicting the daily minimum and maximum air temperature based on historical weather conditions data. That has been measured based on the calculated mean absolute error (MAE). Also, obtained experimental results obviously indicated that the best cultivation-time prediction by the proposed recommender system has been achieved by the M5P algorithm, based on the seasonal accumulative GDD fulfillment duration, for the coming five growing seasons (2016/17 to 2019/20).

Aziz, A. S. A., E. L. Sanaa, and A. E. Hassanien, "Comparison of classification techniques applied for network intrusion detection and classification", Journal of Applied Logic: Elsevier, 2016. Abstract
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Ibrahim, R. A., H. A. Hefny, and A. E. Hassanien, "Controlling Rumor Cascade over Social Networks", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 456–466, 2016. Abstract
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Torky, M., R. Baberse, R. Ibrahim, A. E. Hassanien, G. Schaefer, I. Korovin, and S. Y. Zhu, "Credibility investigation of newsworthy tweets using a visualising Petri net model", Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on: IEEE, pp. 003894–003898, 2016. Abstract
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El-Bendary, N., Esraa Elhariri, M. Hazman, S. M. Saleh, and A. E. Hassanien, "Cultivation-time recommender system based on climatic conditions for newly reclaimed lands in Egypt", Procedia Computer Science, vol. 96: Elsevier, pp. 110–119, 2016. Abstract
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2015
Mostafa, A., M. A. Fattah, A. E. Hassanien, H. Hefny, and G. S. Shao Ying Zhu, "CT Liver Segmentation Using Artificial Bee Colony Optimisation", 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, Procedia Computer Science , Singapore, September, 2015. Abstract

The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

Ayeldeen, H., O. Hegazy, and A. E. Hassanien, "Case selection strategy based on K-means clustering,", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Case-based reasoning: A knowledge extraction tool to use", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
Soliman, H., M. A. Fattah, and A. E. Hassanien, "Cloud Computing Framework for Solving Virtiual College Educations", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
Ashour, A. S., S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, and A. E. Hassanien, "Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach", Journal of Signal and Information Processing, vol. 6, pp. 244-257, 2015. Abstractjsip_2015083113193757_1.pdfWebsite

Medical image enhancement is an essential process for superior disease diagnosis as well as for
detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical
imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However,
speckle noise corrupts the CT images and makes the clinical data analysis ambiguous.
Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and
sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using
log transform in an optimization framework. In order to achieve optimization, a well-known
meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal
parameter settings for log transform. The performance of the proposed technique is studied on a
low contrast CT image dataset. Besides this, the results clearly show that the CS based approach
has superior convergence and fitness values compared to PSO as the CS converge faster that
proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness >
of the proposed enhancement technique.

Ayeldeen, H., O. Hegazy, and A. E. Hassanien, "Case selection strategy based on K-means clustering", Information Systems Design and Intelligent Applications: Springer India, pp. 385–394, 2015. Abstract
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Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Case-Based Reasoning: A Knowledge Extraction Tool to Use", Information systems design and intelligent applications: Springer India, pp. 369–378, 2015. Abstract
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Ayeldeen, H., M. A. Fattah, O. Shaker, A. E. Hassanien, and T. - H. Kim, "Case-Based Retrieval Approach of Clinical Breast Cancer Patients", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 38–41, 2015. Abstract
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Mahmoud, H. A., H. M. El Hadad, F. A. Mousa, and A. E. Hassanien, "Cattle classifications system using Fuzzy K-Nearest Neighbor Classifier", Informatics, Electronics & Vision (ICIEV), 2015 International Conference on: IEEE, pp. 1–5, 2015. Abstract
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Alnashar, H. S., M. A. Fattah, M. M. Mosbah, and A. E. Hassanien, "Cloud computing framework for solving virtual college educations: A case of egyptian virtual university", Information Systems Design and Intelligent Applications: Springer India, pp. 395–407, 2015. Abstract
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Mohamed Tahoun, Abd El Rahman Shabayek, R. Reulke, and A. E. Hassanien, "Co-registration of Satellite Images Based on Invariant Local Features", Intelligent Systems' 2014: Springer International Publishing, pp. 653–660, 2015. Abstract
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Hassan, E. A., A. I. Hafez, A. E. Hassanien, and A. A. Fahmy, "Community detection algorithm based on artificial fish swarm optimization", Intelligent Systems' 2014: Springer International Publishing, pp. 509–521, 2015. Abstract
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