Publications

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2018
Alaa Tharwat, M. Elhoseny, A. E. Hassanien, and T. G. A. and Kumar, "Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm", Cluster Computing, 2018. Abstract

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bézier curve-based model for path planning. The control points of the Bézier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.

2017
Inbarani, H., U. S. Kum, A. T. Azar, and A. E. Hassanien, "Hybrid Rough-Bijective Soft Set Classification system,", Neural Computing and Applications (NCAA) , pp. , pp, 1-21, 2017 , 2017. AbstractWebsite

In today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.

Hassanien, A. E., M. M. Fouad, A. A. Manaf, M. Zamani, R. Ahmad, and J. Kacprzyk, Multimedia Forensics and Security: Foundations, Innovations, and Applications, , Germany , Springer, 2017. AbstractWebsite

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Khairy, M., Alaa Tharwat, T. Gaber, and A. E. Hassanien, "A wheelchair control system using the human machine interaction: Single-modal and Multi-modal approaches", ournal of Intelligent Systems (JISYS), vol. In press, 2017.
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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2016
Torky, M., R. Babers, R. A. Ibrahim, A. E. Hassanien, G. Schaefer, I. Korovin, and S. Y. Zhu, " Credibility investigation of newsworthy tweets using a visualising Petri net model", 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), , USA, 9-12 Oct. 2016. Abstract

Investigating information credibility is an important problem in online social networks such as Twitter. Since misleading information can get easily propagated in Twitter, ranking tweets according to their credibility can help to detect rumors and identify misinformation. In this paper, we propose a Petri net model to visualise tweet credibility in Twitter. We consider the uniform resource locator (URL) as an effective feature in evaluating tweet credibility since it is used to identify the source of tweets, especially for newsworthy tweets. We perform an experimental evaluation on about 1000 tweets, and show that the proposed model is effective for assigning tweets to two classes: credible and incredible tweets, which each class being further divided into two sub-classes (“credible” and “seem credible” and “doubtful” and “incredible” tweets, respectively) based on appropriate features.

Hassanien, A. E., M. A. Fattah, S. Aboulenin, G. Schaefer, S. Y. Zhu, and I. Korovin, " Historic handwritten manuscript binarisation using whale optimization, Systems", IEEE International Conference on Systems, Man, and Cybernetics (SMC), 9, 9-12 Oct. 2016. Abstract

Preserving the content of historic handwritten manuscripts is important for a variety of reasons. On the other hand, digital libraries are rapidly expanding and thus facilitate to store this information directly in digital form. For digitising text documents, a crucial step is to binarise the captured images to separate the text from the background. In this paper, we propose an effective approach for binarisation of handwritten Arabic manuscripts which employs a whale optimisation algorithm, incorporating a fuzzy c-means objective function, to obtain optimal thresholds. Experimental results confirm the effectiveness of the proposed approach compared to earlier methods.

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, issue 3, 2016. AbstractWebsite

The extended Mobile Ad-hoc Network architecture is a paramount research domain due to a wide enhancement of smart phone and open source Unmanned Aerial Vehicle (UAV) technology. The novelty of the current work is to design a disaster aware mobility modeling for a Flying Ad-hoc network infrastructure, where the UAV group is considered as nodes of such ecosystem. This can perform a collaborative task of a message relay, where the mobility modeling under a “Post Disaster” is the main subject of interest, which is proposed with a multi-UAV prototype test bed. The impact of various parameters like UAV node attitude, geometric dilution precision of satellite, Global Positioning System visibility, and real life atmospheric upon the mobility model is analyzed. The results are mapped with the realistic disaster situation. A cluster based mobility model using the map oriented navigation of nodes is emulated with the prototype test bed.

Kilany, M., A. E. Hassanien, A. Badr, P. - W. Tsai, and J. - S. Pan, "A Behavioral Action Sequences Process Design", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 502–512, 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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Kotyk, T., N. Dey, A. S. Ashour, C. V. A. Drugarin, 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), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 57–68, 2016. Abstract
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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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Hassanien, A. E., M. A. Fattah, S. Aboulenin, G. Schaefer, S. Y. Zhu, and I. Korovin, "Historic handwritten manuscript binarisation using whale optimisation", Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on: IEEE, pp. 003842–003846, 2016. Abstract
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Hassanien, A. E., M. M. Fouad, A. A. Manaf, M. Zamani, R. Ahmad, and J. Kacprzyk, Multimedia Forensics and Security: Foundations, Innovations, and Applications, : Springer, 2016. Abstract
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Azar, A. T., S. S. Kumar, H. H. Inbarani, and A. E. Hassanien, "Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosis", International Journal of Modelling, Identification and Control, vol. 26, no. 1: Inderscience Publishers (IEL), pp. 42–51, 2016. Abstract
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Kotyk, T., S. Chakraborty, N. Dey, T. Gaber, A. E. Hassanien, and V. Snasel, "Semi-automated System for Cup to Disc Measurement for Diagnosing Glaucoma Using Classification Paradigm", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 653–663, 2016. Abstract
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2015
Gaber, T., T. Kotyk, N. Dey, A. D. C. V. Amira Ashour, A. E. Hassanienan, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) , Springer. , Beni Suef University, Beni Suef, Egypt, Nov. 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 %).

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.

Radhwan, A., M. Kamel, M. Y. Dahab, and A. E. Hassanien, "Forecasting Exchange Rates: A Chaos-Based Regression Approach. Intelligent Approach.", International Journal of Rough Sets and Data Analysis (IJRSDA) , vol. 2, issue 1, 2015. AbstractWebsite

Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market (FOREX) is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression (SVR) for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution (DE) geneticalgorithm(as a global optimization technique) was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.

Inbarani, H., S. Kumar, A. E. Hassanien, and A. T. Azar, "Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System. ", International Journal of Rough Sets and Data Analysis (IJRSDA) , vol. 2, issue 1, 2015. AbstractWebsite

Social tagging is one of the important characteristics of WEB2.0. The challenge of Web 2.0 is a huge amount of data generated over a short period. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of Social Bookmarking systems. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Particle Swarm optimization (TRS-PSO) clustering algorithm for clustering tags in social systems. Then the proposed method is compared to the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis illustrates the effectiveness of the proposed approach.

Kilany, M., A. E. Hassanien, and A. Badr, "Accelerometer-based human activity classification using Water Wave Optimization approach", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 175–180, 2015. Abstract
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Hassanien, A. - E., A. T. Azar, V. Snasel, J. Kacprzyk, and J. H. Abawajy, Big data in complex systems: challenges and opportunities, : Springer, 2015. Abstract
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Amin, I. I., A. E. Hassanien, S. K. Kassim, and H. A. Hefny, "Big DNA Methylation data analysis and visualizing in a common form of breast cancer", Big Data in Complex Systems: Springer International Publishing, pp. 375–392, 2015. Abstract
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Ismail, F. H., E. A. Hassan, A. E. Hassanien, and T. - H. Kim, "Blog Clustering with Committee Approach", 2015 Fourth International Conference on Information Science and Industrial Applications (ISI): IEEE, pp. 61–64, 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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Tourism