Publications

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Journal Article
Mukherjee, A., N. Dey, N. Kausar, A. S. Ashour, R. Taiar, and A. E. Hassanien, "A disaster management specific mobility model for flying ad-hoc network", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, no. 3: IGI Global, pp. 72–103, 2016. Abstract
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Dadkhah, S., A. A. Manaf, Y. Hori, A. E. Hassanien, and S. Sadeghi, "An effective SVD-based image tampering detection and self-recovery using active watermarking", Signal Processing: Image Communication, vol. 29, no. 10: Elsevier, pp. 1197–1210, 2014. Abstract
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Asmaa Hashem Sweidan, E. - B. Nashwa Mamdouh, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Hybrid-Biomarker Case-Based Reasoning System for Water Pollution Assessment in Abou Hammad Sharkia, Egypt", Applied Soft computing , pp. Accepted, 2015. AbstractWebsite

Water pollution by organic materials or metals is one of the
problems that threaten humanity, both nowadays and over the next decades.
Morphological changes in Nile Tilapia "Oreochromis niloticus'' fish liver
and gills can also represent the adaptation strategies to maintain some
physiological functions or to assess acute and chronic exposure to
chemicals found in water and sediments. This paper presents an automatic
system for assessing water quality; in Sharkia Governorate - Egypt, based
on microscopic images of fish gills and liver. The proposed system used
fish gills and liver as hybrid-biomarker in order to detect water
pollution. It utilized case-based reasoning (CBR) for indicating the
degree of water quality based on the different histopathological changes
in fish gills and liver microscopic images. Various performance
evaluation metrics; namely, retrieval accuracy, Receiver Operating
Characteristic (ROC) curves, F-measure, and G-mean, have been used in
order to objectively indicate the true performance of the system
considering the unbalanced data. Experimental results showed that the
proposed hybrid-biomarker CBR based system achieved water quality
prediction accuracy of 97.9% using cosine distance similarity measure.
Also, it outperformed both SVMs and LDA classifiers for the tested
microscopic images dataset.

Hassaniena, A. E., N. El-Bendary, Asmaa Hashem Sweidan, and A. E. -karim Mohamed, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46, issue 1, pp. 1043–1055, 2016. AbstractWebsite

Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia “Oreochromis niloticus” fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate – Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.

Hassanien, A. E., N. El-Bendary, Asmaa Hashem Sweidan, A. E. -karim Mohamed, and O. M. Hegazy, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46: Elsevier, pp. 1043–1055, 2016. Abstract
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Moustafa Zein, F. Yakoub, A. Adl, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", Procedia Computer Science, vol. 65: Elsevier, pp. 582–591, 2015. Abstract
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Hassanien, A. E., T. Gaber, U. Mokhtar, and H. Hefny, "An Improved Moth Flame Optimization Algorithm based on Rough Sets for Tomato Diseases Detection", Journal of Computers and Electronics in Agriculture, vol. 136, issue 15, pp. 86-96 , 2017. AbstractWebsite

Plant diseases is one of the major bottlenecks in agricultural production that have bad effects on the economic of any country. Automatic detection of such disease could minimize these effects. Features selection is a usual pre-processing step used for automatic disease detection systems. It is an important process for detecting and eliminating noisy, irrelevant, and redundant data. Thus, it could lead to improve the detection performance. In this paper, an improved moth-flame approach to automatically detect tomato diseases was proposed. The moth-flame fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. The proposed algorithm used both of the power of exploration of the moth flame and the high performance of rough sets for the feature selection task to find the set of features maximizing the classification accuracy which was evaluated using the support vector machine (SVM). The performance of the MFORSFS algorithm was evaluated using many benchmark datasets taken from UCI machine learning data repository and then compared with feature selection approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with rough sets. The proposed algorithm was then used in a real-life problem, detecting tomato diseases (Powdery mildew and early blight) where a real dataset of tomato disease were manually built and a tomato disease detection approach was proposed and evaluated using this dataset. The experimental results showed that the proposed algorithm was efficient in terms of Recall, Precision, Accuracy and F-Score, as long as feature size reduction and execution time.

Hassanien, A. E., T. Gaber, U. Mokhtar, and H. Hefny, "An improved moth flame optimization algorithm based on rough sets for tomato diseases detection", Computers and Electronics in Agriculture, vol. 136: Elsevier, pp. 86–96, 2017. Abstract
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Mostafa, A., A. E. Hassanien, and H. Hefney, "Liver segmentation in MRI images based on whale optimization algorithm,", Multimedia Tools and Applications, Springer, 2017.
M.Moftah, A. E. Hassanien, A. Taher, and M. Shoman, "MRI Breast cancer diagnosis approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier", Applied Soft Computing, Elsiever, vol. 14, issue Part A, pp. 62-71, 2014. Website
Hassanien, A. E., H. M. Moftah, A. T. Azar, and M. Shoman, "MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier", Applied Soft Computing, vol. 14: Elsevier, pp. 62–71, 2014. Abstract
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Moftah, H. M., A. E. Hassanien, N. Ghali, and M. Shoman, Multi-objective optimization K-mean segmentation approach for MRI Breast Images, , 2012. Abstract
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M.Rizk-Allaha, R., A. E. Hassanien, and M. Elhoseny, "A multi-objective transportation model under neutrosophic environment", Computers & Electrical Engineering, 2018. AbstractWebsite

In this paper, a new compromise algorithm for multi-objective transportation problem (MO-TP) is developed, which is inspired by Zimmermann's fuzzy programming and the neutrosophic set terminology. The proposed NCPA is characterized by assigning three membership functions for each objective namely, truth membership, indeterminacy membership and falsity membership. With the membership functions for all objectives, a neutrosophic compromise programming model is constructed with the aim to find best compromise solution (BCS). This model can cover a wide spectrum of BCSs by controlling the membership functions interactively. The performance of the NCPA is validated by measuring the ranking degree using TOPSIS approach. Illustrative examples are reported and compared with exists models in the literature. Based on the provided comparisons, NCPA is superior to fuzzy and different approaches.

M.Rizk-Allaha, R., A. E. Hassanien, and M. Elhoseny, "A multi-objective transportation model under neutrosophic environment", Computers & Electrical Engineering, 2018. AbstractWebsite

In this paper, a new compromise algorithm for multi-objective transportation problem (MO-TP) is developed, which is inspired by Zimmermann's fuzzy programming and the neutrosophic set terminology. The proposed NCPA is characterized by assigning three membership functions for each objective namely, truth membership, indeterminacy membership and falsity membership. With the membership functions for all objectives, a neutrosophic compromise programming model is constructed with the aim to find best compromise solution (BCS). This model can cover a wide spectrum of BCSs by controlling the membership functions interactively. The performance of the NCPA is validated by measuring the ranking degree using TOPSIS approach. Illustrative examples are reported and compared with exists models in the literature. Based on the provided comparisons, NCPA is superior to fuzzy and different approaches.

El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem", Studies in Informatics and Control, vol. 22, no. 2, pp. 113–122, 2013. Abstract
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El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem", Studies in Informatics and Control, vol. 22, no. 2, pp. 113–122, 2013. Abstract
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Hassanien, A. E., M. A. Fattah, K. M. AMIN, and S. MOHAMED, "A Novel Hybrid Binarization Technique for Images of Historical Arabic Manuscripts", Studies in Informatics and Control, , vol. 24, issue 3, pp. 271-282, 2015. AbstractWebsite

In this paper, a novel binarization approach based on neutrosophic sets and sauvola’s approach is presented.
This approach is used for historical Arabic manuscript images which have problems with types of noise. The input RGB image is changed into the NS domain, which is shown using three subsets, namely, the percentage of indeterminacy in a subset, the percentage of falsity in a subset and the percentage of truth in a subset. The entropy in NS is used for evaluating the indeterminacy with the most important operation ”λ mean” operation in order to minimize indeterminacy which can be used to reduce noise. Finally, the manuscript is binarized using an adaptive thresholding technique. The main advantage of the proposed approach is that it preserves weak connections and provides smooth and continuous strokes. The performance of the proposed approach is evaluated both objectively and subjectively against standard databases and manually collected data base. The proposed method gives high results compared with other famous binarization approaches

Hassanien, A. E., M. A. Fattah, K. M. AMIN, and S. MOHAMED, "A novel hybrid binarization technique for images of historical Arabic manuscripts", Studies in Informatics and Control, vol. 24, no. 3, pp. 271–282, 2015. Abstract
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Adl, A., Moustafa Zein, and A. E. Hassanien, "PQSAR: The membrane quantitative structure-activity relationships in cheminformatics", Expert Systems with Applications, vol. 54, issue 1, pp. 219–227, 2016. AbstractWebsite

The applications of quantitative structure activity relationships (QSAR) are used to establish a correlation between structure and biological response. Similarity searching is one of QSAR major phases. Innovating new strategies for similarity searching is an urgent task in cheminformatics research for three reasons: (i) the increasing size of chemical search space of compound databases; (ii) the importance of similarity measurements to (2D) and (3D) QSAR models; and (iii) similarity searching is a time consuming process in drug discovery. In this study, we introduce theoretical similarity searching strategy based on membrane computing. It solves time consumption problem. We adopt a ranking sorting algorithm with P System to rank probabilities of similarity according to a predefined similarity threshold. That bio-inspired model, simulating biological living cell, presents a high performance parallel processing system, we called it PQSAR. It relies on a set of rules to apply ranking algorithm on probabilities of similarity. The simulated experiments show how the effectiveness of PQSAR method enhanced the performance of similarity searching significantly; and introduced a standard ranking algorithm for similarity searching.

Adl, A., Moustafa Zein, and A. E. Hassanien, "PQSAR: The membrane quantitative structure-activity relationships in cheminformatics", Expert Systems with Applications, vol. 54: Pergamon, pp. 219–227, 2016. Abstract
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El-Atta, A. A. H., M. I. Moussa, and A. E. Hassanien, "Predicting activity approach based on new atoms similarity kernel function", Journal of Molecular Graphics and Modelling, vol. 60, pp. 55–62, 2015. Website
El-Atta, A. A. H., M. I. Moussa, and A. E. Hassanien, "Predicting activity approach based on new atoms similarity kernel function", Journal of Molecular Graphics and Modelling, vol. 60: Elsevier, pp. 55–62, 2015. Abstract
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Salama, M. A., A. Mostafa, and A. E. Hassanien, "The prediction of virus mutation using neural networks and rough set techniques", . EURASIP J. Bioinformatics and Systems Biology , vol. 10, 2016. AbstractWebsite

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

Salama, M. A., A. E. Hassanien, and A. Mostafa, "The prediction of virus mutation using neural networks and rough set techniques", EURASIP Journal on Bioinformatics and Systems Biology, vol. 2016, no. 1: Springer International Publishing, pp. 1–11, 2016. Abstract
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Alaa Tharwat, Y. S. Moemen, and A. E. Hassanien, "A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method", Scientific Reports, vol. 6: Nature Publishing Group, 2016. Abstract
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Tourism