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Asad, A. H., Eid Elamry, A. E. Hassanien, and M. F. Tolba, "New global update mechanism of ant colony system for retinal vessel segmentation", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 221–227, 2013. Abstract
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Asad, A. H., Eid Elamry, A. E. Hassanien, and M. Tolba, "New Global Update Mechanism of Ant Colony System for Retinal Vessel Segmentation,", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 222-228, 2013, Tunisia, , 4-6 Dec, 2013.
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.

Ashour, A. S., S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, A. E. Hassanien, and others, "Computed tomography image enhancement using cuckoo search: a log transform based approach", Journal of Signal and Information Processing, vol. 6, no. 03: Scientific Research Publishing, pp. 244, 2015. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, A. E. Hassanien, and V. Snasel, "Water Pollution Detection System Based on Fish Gills as a Biomarker", International Conference on Communications, management, and Information technology (ICCMIT'2015), 2015. Abstract

This article presents an automatic system for assessing water quality based on fish gills microscopic images. As fish gills are a good biomarker for assessing water quality, the proposed system uses fish gills microscopic images in order to detect water pollution. The proposed system consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the shape is the main characteristic of fish gills microscopic images, the proposed system uses shape feature based on edge detection and wavelets transform for classifying the water-quality degree. Furthermore, it implemented Principal Components Analysis (PCA) along with Support Vector Machines (SVMs) algorithms for feature extraction and water quality degree classification. The datasets used for experiments were constructed based on real sample images for fish gills. Training dataset is divided into four classes representing the different histopathological changes and the corresponding water quality degrees. Experimental results showed that the proposed classification system has obtained water quality classification accuracy of 95.41%, using the SVMs linear kernel function and 10-fold cross validation with 37 images per class for training.

Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, and O. M. H. A. E. -karim Mohamed, "Water Quality Classification Approach based on Bio-inspired Gray Wolf Optimization, ", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , , November 13 - 15, 2015. Abstract

Abstract—This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water
quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed,
along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of
water pollutants.

Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Machine learning based approach for water pollution detection via fish liver microscopic images analysis", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 253–258, 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.

Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. - K. Mohamed, "Grey Wolf Optimizer and Case-Based Reasoning Model for Water Quality Assessment", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 229–239, 2016. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, A. E. -karim Mohamed, and O. Hegazy, "Grey wolf optimizer and case-based reasoning model for water quality assessment", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt, Nov. 28-30, 2015. Abstract

This paper presents a bio-inspired optimized classification model for
assessing water quality. As fish gills histopathology is a good biomarker for indicating
water pollution, the proposed classification model uses fish gills microscopic
images in order to asses water pollution and determine water quality.
The proposed model comprises five phases; namely, case representation for
defining case attributes via pre-processing and feature extraction steps, retrieve,
reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms
have been utilized for feature extraction stage. Case-based reasoning
(CBR) has been employed, along with the bio-inspired Gray Wolf Optimization
(GWO) algorithm, for optimizing feature selection and the k case retrieval parameters
in order to asses water pollution. The datasets used for conducted experiments
in this research contain real sample microscopic images for fish gills
exposed to copper and water pH in different histopathlogical stages. Experimental
results showed that the average accuracy achieved by the proposed GWO-CBR
classification model exceeded 97.2% considering variety of water pollutants.

Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, A. E. Hassanien, and V. Snasel, "Water pollution detection system based on fish gills as a biomarker", Procedia Computer Science, vol. 65: Elsevier, pp. 601–611, 2015. Abstract
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Asmaa Hashem Sweidan, Nashwa El-Bendary, O. M. H. A. E. H.:, "Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, pp. 547-557,, Ostrava, Czech Republic, June 29 - July , 2015. Abstract

This paper presents Case-Based Reasoning (CBR) system to asses water pollution based on fish liver histopathology as biomarker. The proposed approach utilizes fish liver microscopic images in order to asses water pollution based on knowledge stored in the case-based database and stores likelihood description of the previous solutions in order to make the knowledge stored more flexible. The proposed case-based reasoning system consists of 5 phases; namely case representation (pre-processing and feature extraction), retrieve, reuse/adapt, revise, and retain phases. After applying pre-processing and feature extraction algorithms on the input images, similarity between the input and case base database is being calculated in order to retrieve similarity. Experimental results show that the performance of CBR systems increases according to the number of retrieved cases in each scenario against each strategy. The proposed system achieved 95.9

Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, and A. E. Hassanien, "Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 547–557, 2015. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Water quality classification approach based on bio-inspired Gray Wolf Optimization", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 1–6, 2015. 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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Atteya, W. A., L. E. M. Bakrawy, R. Batista, S. Bazzi, M. Beheshti, A. P. Bennett, A. Boulmakoul, S. Bureerat, L. Caggiani, N. S. Choubey, et al., "Akbarzadeh-T, Mohammad-R. 175 Antunes, Carlos Henggeler 13", Advances in Intelligent and Soft Computing 96, vol. 6, pp. 437, 2011. Abstract
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Attia, K. A., A. I. Hafez, and A. ella hasanien, "A Discrete Krill Herd Optimization Algorithm For Community Detection", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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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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Awad, A. I., and A. E. Hassanien, "Impact of some biometric modalities on forensic science", Computational Intelligence in Digital Forensics: Forensic Investigation and Applications: Springer International Publishing, pp. 47–62, 2014. Abstract
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Awad, A. I., H. zawbaa, and A. E. Hassanien, "A Cattle Identification of Approach Using Live Captured Muzzle Print Images", International conference on Advances in Security of Information and Communication Networks, (SecNet 2013) , Springer , Egypt, 3-5 Sept, , 2013. a_cattle_identification.pdf
Awad, A. I., A. E. Hassanien, and K. Baba, "Advances in Security of Information and Communication Networks", Communications in Computer and Information Science, vol. 381, 2013. Abstract
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Awad, A. I., A. E. Hassanien, and H. M. Zawbaa, "A cattle identification approach using live captured muzzle print images", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 143–152, 2013. Abstract
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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.
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