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2018
Abdelhameed Ibrahim, ali ahmed, S. Hussein, and A. E. Hassanien, "Fish Image Segmentation Using Salp Swarm Algorithm", Download book PDF EPUB International Conference on Advanced Machine Learning Technologies and Applications, Cairo, 23 Feb, 2018. Abstract

Fish image segmentation can be considered an essential process in developing a system for fish recognition. This task is challenging as different specimens, rotations, positions, illuminations, and backgrounds exist in fish images. In this research, a segmentation model is proposed for fish images using Salp Swarm Algorithm (SSA). The segmentation is formulated using Simple Linear Iterative Clustering (SLIC) method with initial parameters optimized by the SSA. The SLIC method is used to cluster image pixels to generate compact and nearly uniform superpixels. Finally, a thresholding using Otsu’s method helped to produce satisfactory results of extracted fishes from the original images under different conditions. A fish dataset consisting of real-world images was tested. In experiments, the proposed model shows robustness for different cases compared to conventional work.

Hamad, A., E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "Hybrid Grasshopper Optimization Algorithm and Support Vector Machines for Automatic Seizure Detection in EEG Signals", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 fEB, 2018. Abstract

In this paper, a hybrid classification model using Grasshopper Optimization Algorithm (GOA) and support vector machines (SVMs) for automatic seizure detection in EEG is proposed called GOA-SVM approach. Various parameters were extracted and employed as the features to train the SVM with radial basis function (RBF) kernel function (SVM-RBF) classifiers. GOA was used for selecting the effective feature subset and the optimal settings of SVMs parameters in order to obtain a successful EEG classification. The experimental results confirmed that the proposed GOA-SVM approach, able to detect epileptic and could thus further enhance the diagnosis of epilepsy with accuracy 100% for normal subject data versus epileptic data. Furthermore, the proposed approach has been compared with Particle Swarm Optimization (PSO) with support vector machines (PSO-SVMs) and SVM using RBF kernel function. The computational results reveal that GOA-SVM approach achieved better classification accuracy outperforms both PSO-SVM and typical SVMs.

Sayed, G. I., M. Soliman, and A. E. Hassanien, "Modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine", International Conference on Advanced Machine Learning Technologies and Applications, Cairo, 23 Feb, 2018. Abstract

Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an Optimal Foraging Algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic Algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.

Issa, M., and A. E. Hassanien, "Pairwise Global Sequence Alignment Using Sine-Cosine Optimization Algorithm", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 Feb, 2018. Abstract

Pairwise global sequence alignment is a vital process for finding functional and evolutionary similarity between biological sequences. The main usage of it is searching biological databases for finding the origin of unknown sequence. The standard global alignment based on dynamic programming approach which produces the accurate alignment but with extensive execution time. In this paper, Sine-Cosine optimization algorithm was used for accelerating pairwise global alignment with alignment score near one produced by dynamic programming alignment. The reason for using Sine-Cosine optimization is its excellent exploration of the search space. The developed technique was tested on human and mouse protein sequences and its success for finding alignment similarity 75% of that produced by standard technique.

Dey, A., S. Dey, Siddhartha Bhattacharyya, V. Snasel, and A. E. Hassanien, "Simulated Annealing Based Quantum Inspired Automatic Clustering Technique", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) , Cairo, 23 fEB, 2018. Abstract

Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters. Simulated Annealing (SA) is a popular meta-heuristic inspired by the annealing process used in metallurgy, useful in solving complex optimization problems. In this paper, the use of the Quantum Computing (QC) and SA is explored to design Quantum Inspired Simulated Annealing technique, which can be applied to compute optimum number of clusters for image clustering. Experimental results over a number of images endorse the effectiveness of the proposed technique pertaining to fitness value, convergence time, accuracy, robustness, and standard error. The paper also reports the computation results of a statistical superiority test, known as t-test. An experimental judgement to the classical technique has also be presented, which eventually demonstrates that the proposed technique outperforms the other.

Ahmed, K., A. E. Hassanien, E. Ezzat, and Siddhartha Bhattacharyya, "Swarming Behaviors of Chicken for Predicting Posts on Facebook Branding Pages", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 fEB , 2018. Abstract

The rapid increase in social networks data and users present an urgent need for predicting the performance of posted data over these networks. It helps in many industrial aspects such as election, public opinion detection and advertising or branding over social networks. This paper presents a new posts’ prediction system for Facebook’s branding pages concerning the user’s attention and interaction. CSO is utilized to optimize the ANFIS parameters for accurate prediction. CSO-ANFIS is compared with several methods including ANFIS, particle swarm optimization, genetic algorithm and krill herd optimization.

et.al., A. E. H., "AMLTA (2018): International Conference on Advanced Machine Learning Technologies and Applications", AMLTA (2018): International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Springer, 2018.
Issaa, M., A. E. Hassanien, D. Oliva, A. Helmi, and I. Z. A. and Alzohairy, "ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment", Expert Systems with Applications, vol. 99, issue 1, pp. 56-70, 2018. AbstractWebsite

The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time.

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.

Hassanien, A. E., S. H. Basha, and A. S. Abdalla, "Generalization of Fuzzy C-Means Based on Neutrosophic Logic", Studies in Informatics and Control, vol. 27, issue 1, pp. 43-54, , 2018. Abstract

This article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutrosophic logic (NL), to generalize the Fuzzy C-Means (FCM) clustering system. The NNCMs system assigns objects to clusters using three degrees of membership: a degree of truth, a degree of indeterminacy, and a degree of falsity, rather than only the truth degree used in the FCM. The indeterminacy degree, in the NL, helps in categorizing objects laying in the intersection and the boundary areas. Therefore, the NNCMs reaches more accurate results in clustering. These degrees are initialized randomly without any constraints. That is followed by calculating the clusters’ centers. Then, iteratively, the NNCMs updates the membership values of every object, and the clusters’ centers. Finally, it measures the accuracy and tests the objective function. The performance of the proposed system is tested on the six real-world databases: Iris, Wine, Wisconsin Diagnostic Breast Cancer, Seeds, Pima, and Statlog (Heart). The comparison between the two systems shows that the proposed NNCMs is more accurate.

Inbarani, H. H., S. Udhaya Kumar, A. T. Azar, and A. E. Hassanien, "Hybrid rough-bijective soft set classification system", Neural Computing and Applications, , vol. 29, issue 8, pp. 67–78., 2018. Abstract

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.

Sayed, G. I., and A. E. Hassanien, "A hybrid SA-MFO algorithm for function optimization and engineering design problems", Complex & Intelligent Systems, 2018. Abstract

This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

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.

abd elaziz, M., and A. E. Hassanien, "Modified cuckoo search algorithm with rough sets for feature selection", Download PDF Neural Computing and Applications, vol. 29, issue 4, pp. 925–934, 2018. AbstractWebsite

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

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.

abd elaziz, M., A. A. Ewees, and A. E. Hassanien, "Multi-objective whale optimization algorithm for content-based image retrieval", Download PDF Multimedia Tools and Applications, 2018. AbstractWebsite

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision.

Elhoseny, M., Alaa Tharwat, X. Yuan, and A. E. Hassanien, "Optimizing K-coverage of mobile WSNs", Expert Systems with Applications, vol. 92, 2018. AbstractWebsite

Recently, Wireless Sensor Networks (WSNs) are widely used for monitoring and tracking applications. Sensor mobility adds extra flexibility and greatly expands the application space. Due to the limited energy and battery lifetime for each sensor, it can remain active only for a limited amount of time. To avoid the drawbacks of the classical coverage model, especially if a sensor died, K-coverage model requires at least k sensor nodes monitor any target to consider it covered. This paper proposed a new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. Our model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that our proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted. The improvement that our proposed method achieved regarding the network lifetime was in a range of 26%–41.3% using stationary nodes while it was in a range of 29.3%–45.7% using mobile nodes. In addition, the network throughput is improved in a range of 13%–17.6%. Moreover, the running time to form the network structure and switch between nodes’ modes is reduced by 12%.

abd elaziz, M., Y. S. Moemen, A. E. Hassanien, and S. Xiong, "Quantitative Structure-Activity Relationship Model for HCVNS5B inhibitors based on an Antlion Optimizer-Adaptive Neuro-Fuzzy Inference System, ", Scientific report (Nature) , vol. 1506, 2018. Abstract

The global prevalence of hepatitis C Virus (HCV) is approximately 3% and one-fifth of all HCV carriers live in the Middle East, where Egypt has the highest global incidence of HCV infection. Quantitative structure-activity relationship (QSAR) models were used in many applications for predicting the potential effects of chemicals on human health and environment. The adaptive neuro-fuzzy inference system (ANFIS) is one of the most popular regression methods for building a nonlinear QSAR model. However, the quality of ANFIS is influenced by the size of the descriptors, so descriptor selection methods have been proposed, although these methods are affected by slow convergence and high time complexity. To avoid these limitations, the antlion optimizer was used to select relevant descriptors, before constructing a nonlinear QSAR model based on the PIC50 and these descriptors using ANFIS. In our experiments, 1029 compounds were used, which comprised 579 HCVNS5B inhibitors (PIC50 < ~14) and 450 non-HCVNS5B inhibitors (PIC50 > ~14). The experimental results showed that the proposed QSAR model obtained acceptable accuracy according to different measures, where R2 was 0.952 and 0.923 for the training and testing sets, respectively, using cross-validation, while R2 LOO
was 0.8822 using leave-one-out (LOO).

Hassan, G., and A. E. Hassanien, "Retinal fundus vasculature multilevel segmentation using whale optimization algorithm", Signal, Image and Video Processing, vol. 12, issue 2, pp. 263–270, 2018. AbstractWebsite

The aim was to present a novel automated approach for extracting the vasculature of retinal fundus images. The proposed vasculature extraction method on retinal fundus images consists of two phases: preprocessing phase and segmentation phase. In the first phase, brightness enhancement is applied for the retinal fundus images. For the vessel segmentation phase, a hybrid model of multilevel thresholding along with whale optimization algorithm (WOA) is performed. WOA is used to improve the segmentation accuracy through finding the n−1 optimal n-level threshold on the fundus image. To evaluate the accuracy, sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve analysis measurements are used. The proposed approach achieved an overall accuracy of 97.8%, sensitivity of 88.9%, and specificity of 98.7% for the identification of retinal blood vessels by using a dataset that was collected from Bostan diagnostic center in Fayoum city. The area under the ROC curve reached a value of 0.967. Automated identification of retinal blood vessels based on whale algorithm seems highly successful through a comprehensive optimization process of operational parameters.

2017
Rizk-Allah, R. M., and A. E. Hassanien, " A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

Ismael, G., and A. E. Hassanien, " Moth-flame swarm optimization with Neutrosophic sets for automatic mitosis detection in breast cancer histology images, ", Applied Intelligence, 2017, 2017. AbstractWebsite

This paper presents an automatic mitosis detection approach of histopathology slide imaging based on using neutrosophic sets (NS) and moth-flame optimization (MFO). The proposed approach consists of two main phases, namely candidate’s extraction and candidate’s classification phase. At candidate’s extraction phase, Gaussian filter was applied to the histopathological slide image and the enhanced image was mapped into the NS domain. Then, morphological operations have been implemented to the truth subset image for more enhancements and focus on mitosis cells. At candidate’s classification phase, several features based on statistical, shape, texture and energy features were extracted from each candidate. Then, a principle of the meta-heuristic MFO algorithm was adopted to select the best discriminating features of mitosis cells. Finally, the selected features were used to feed the classification and regression tree (CART). A benchmark dataset consists of 50 histopathological images was adopted to evaluate the performance of the proposed approach. The adopted dataset consists of five distinct breast pathology slides. These slides were stained with H&E acquired by Aperio XT scanners with 40-x magnification. The total number of mitoses in 50 database images is 300, which were annotated by an expert pathologist. Experimental results reveal the capability of the MFO feature selection algorithm for finding the optimal feature subset which maximizing the classification performance compared to well-known and other meta-heuristic feature selection algorithms. Also, the high obtained value of accuracy, recall, precision and f-score for the adopted dataset prove the robustness of the proposed mitosis detection and classification approach. It achieved overall 65.42 % f-score, 66.03 % recall, 65.73 % precision and accuracy 92.99 %. The experimental results show that the proposed approach is fast, robust, efficient and coherent. Moreover, it could be used for further early diagnostic suspicion of breast cancer.

Mostafa, A., A. E. Hassanien, and H. A. Hefny, " Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

In the recent days, a great deal of researches is interested in segmentation of different organs in medical images. Segmentation of liver is as an initial phase in liver diagnosis, it is also a challenging task due to its similarity with other organs intensity values. This paper aims to propose a grey wolf optimization based approach for segmenting liver from the abdomen CT images. The proposed approach combines three parts to achieve this goal. It combines the usage of grey wolf optimization, statistical image of liver, simple region growing and Mean shift clustering technique. The initial cleaned image is passed to Grey Wolf (GW) optimization technique. It calculated the centroids of a predefined number of clusters. According to each pixel intensity value in the image, the pixel is labeled by the number of the nearest cluster. A binary statistical image of liver is used to extract the potential area that liver might exist in. It is multiplied by the clustered image to get an initial segmented liver. Then region growing (RG) is used to enhance the segmented liver. Finally, mean shift clustering technique is applied to extract the regions of interest in the segmented liver. A set of 38 images, taken in pre-contrast phase, was used for liver segmentation and testing the proposed approach. For evaluation, similarity index measure is used to validate the success of the proposed approach. The experimental results of the proposed approach showed that the overall accuracy offered by the proposed approach, results in 94.08% accuracy.

E. Emary, H. M. Zawbaa, A. E. Hassanien, and B. PARV, " Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search, , ", Advances in Data Analysis and Classification, , issue (27 May 2016 on line), , pp. pp 1-17, 2017. AbstractWebsite

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.

Eslam Hassan, A. Hafez, A. E. H. and, and A. Fahmy, " Nature inspired algorithms for solving the community detection problem, ", Logic Journal of the IGPL: Oxford Journals, 2017.
Amira Sayed A. Aziza, S. E. - O. Hanafi, and A. E. Hassanien, " , Comparison of classification techniques applied for network intrusion detection and classification, ", Journal of Applied Logic Available online 14 November 2017, 2017. AbstractWebsite
Babers, R., and A. E. Hassanien, " A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks", ", International Journal of Service Science, Management, Engineering, and Technology, IJSSMET , vol. 8, issue 1, pp. 50-, 2017. AbstractWebsite

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

Alaa Tharwat, and A. E. Hassanien, " Chaotic Antlion Algorithm for Parameter Optimization of Support Vector Machine", Applied Intelligence , vol. in press, 2017. AbstractWebsite

Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel
parameters and penalty parameter (C) significantly influences 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
model (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 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 GA and PSO algorithms

Abdelhameed Ibrahim, T. Horiuchi, S. Tominaga, and A. E. Hassanien, " Color Invariant Representation and Applications", Handbook of Research on Machine Learning Innovations and Trends,, USA, IGI, USA, pp.21, 2017. Abstract

Illumination factors such as shading, shadow, and highlight observed from object surfaces affect the appearance and analysis of natural color images. Invariant representations to these factors were presented in several ways. Most of these methods used the standard dichromatic reflection model that assumed inhomogeneous dielectric material. The standard model cannot describe metallic objects. This chapter introduces an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. The illumination color is estimated from two inhomogeneous surfaces to recover the surface reflectance of object without using a reference white standard. The overall performance of the invariant representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of the representation for effective edge detection is introduced and compared with the state-of-the-art illumination-invariant methods.

El-Said, S. A., H. M. A. Atta, and A. E. Hassanien, " Interactive soft tissue modelling for virtual reality surgery simulation and planning,", Int. J. Computer Aided Engineering and Technology, Inderscience, , vol. 9, issue 1, pp. pp. 38-61, 2017. AbstractWebsite

While most existing virtual reality-based surgical simulators in the literature use linear deformation models, soft-tissues exhibit geometric and material nonlinearities that should be taken into account for realistic modelling of the deformations. In this paper, an interactive soft tissue model (ISTM) which enables flexible, accurate and robust simulation of surgical interventions on virtual patients is proposed. In ISTM, simulating the tool-tissue interactions using nonlinear dynamic analysis is formulated within a total Lagrangian framework, and the energy function is modified by adding a term in order to achieve material incompressibility. The simulation results show that ISTM increases the stability and eliminates integration errors in the dynamic solution, decreases calculation costs by a factor of 5-7, and leads to very stable and sufficiently accurate results. From the simulation results it can be concluded that the proposed model can successfully create acceptable soft tissue models and generate realistically visual effects of surgical simulation.

Issa, M., and A. E. Hassanien, " Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques: ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

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.

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.

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.

el-hoseny, M., Alaa Tharwat, and A. E. Hassanien, "Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm", Journal of Computational Science, 2017. Website
Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

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.

Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Artificial neural network (ANN) models are involved in many applications because of its great computational capabilities. Training of multi-layer perceptron (MLP) is the most challenging problem during the network preparation. Many techniques have been introduced to alleviate this problem. Back-propagation algorithm is a powerful technique to train multilayer feedforward ANN. However, it suffers from the local minima drawback. Recently, meta-heuristic methods have introduced to train MLP like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimizer (ACO), Social Spider Optimization (SSO), Evolutionary Strategy (ES) and Grey Wolf Optimization (GWO). This chapter applied Multi-Verse Optimizer (MVO) for MLP training. Seven datasets are used to show MVO capabilities as a promising trainer for multilayer perceptron. Comparisons with PSO, GA, SSO, ES, ACO and GWO proved that MVO outperforms all these algorithms.

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