Publications

Export 268 results:
Sort by: [ Author  (Asc)] Title Type Year
[A] B C D E F G H I J K L M N O P Q R S T U V W X Y Z   [Show ALL]
A
Alaa Tharwat, Abdelhameed Ibrahim, A. E. Hassanien, and G. Schaefer, "Ear recognition using block-based principal component analysis and decision fusion", International Conference on Pattern Recognition and Machine Intelligence: Springer International Publishing, pp. 246–254, 2015. Abstract
n/a
Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face Sketch Recognition Using Local Invariant", 7th IEEE International Conference of Soft Computing and Pattern Recognition, Kyushu University, Fukuoka, Japan, , 2015, November 13 - 15, 2015. Abstract

n/a

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

Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face sketch recognition using local invariant features", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 117–122, 2015. Abstract
n/a
Alaa Tharwat, T. Gaber, M. K. Shahin, B. Refaat, and A. E. H. Ali, "SIFT-based Arabic Sign Language Recognition System", The 1st Afro-European Conference for Industrial Advancement, , Addis Ababa, Ethiopia, November 17-19, , 2014.
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Cattle identification based on muzzle images using gabor features and SVM classifier", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 236–247, 2014. Abstract
n/a
Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face sketch synthesis and recognition based on linear regression transformation and multi-classifier technique", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 183–193, 2016. Abstract
n/a
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "One-dimensional vs. two-dimensional based features: Plant identification approach", Journal of Applied Logic: Elsevier, 2016. Abstract
n/a
Alaa Tharwat, T. Gaber, M. M. Fouad, V. Snasel, and A. E. Hassanien, "Towards an automated zebrafish-based toxicity test model using machine learning", Procedia Computer Science, vol. 65: Elsevier, pp. 643–651, 2015. Abstract
n/a
Alaa Tharwat, T. Gaber, Abdelhameed Ibrahim, and A. E. Hassanien, "Linear Discriminant Analysis: A Detailed Tutorial", AI Communications, IOS press, 2017. linear_discreminate_analysisp_detailed_tutorails.pdf
Alaa Tharwat, Mahir M. Sharif, A. E. Hassanien, and H. A. Hefny, "Improving Enzyme Function Classification Performance Based on Score Fusion Method.", 10th International Conference Hybrid Artificial Intelligent System, Bilbao, Spain, 23 June, 2015.
Alaa Tharwat, A. E. Hassanien, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of Support Vector Machine", Pattern Recognition Letters: North-Holland, 2016. Abstract
n/a
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.

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
n/a
Alaa Tharwat, A. M. Ghanem, and A. E. Hassanien, "Three different classifiers for facial age estimation based on k-nearest neighbor", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 55–60, 2013. Abstract
n/a
Alaa Tharwat, Y. Abdelmonem, and A. E. Hassanien, " A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method, ", Nature Scientific Report,, vol. 6, Article number: 38660 , 2016. AbstractWebsite

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

Alaa Tharwat, H. M. Zawbaa, T. Gaber, A. E. Hassanien, and V. Snasel, "Automated zebrafish-based toxicity test using bat optimization and adaboost classifier", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 169–174, 2015. Abstract
n/a
Alaa Tharwat, Hani Mahdi, and A. E. Hassanien, "Plant Recommender System Based on Multi-label Classification", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 825–835, 2016. Abstract
n/a
Alaa Tharwat, Ahmed M. Ghanem, and A. E. Hassanien, "Three different classifiers for facial age estimation based on K-nearest neighbor", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) - pp. 55 - 60 , 2013, Cairo, EGYPT -, December 29-30, , 2013.
Alaa Tharwat, Yasmine S. Moemen, and A. E. Hassanien, "Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines", Journal of Biomedical Informatics, vol. 68, pp. 132-149 , 2017. AbstractWebsite

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

Alaa Tharwat, B. E. Elnaghi, and A. E. Hassanien, "Meta-Heuristic Algorithm Inspired by Grey Wolves for Solving Function Optimization Problems", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 480–490, 2016. Abstract
n/a
Alaa Tharwat, Abdelhameed Ibrahim, A. E. Hassanien, and G. Schaefer, "Ear Recognition Using Block-Based Principal Component Analysis and Decision Fusion", 6th International Conference Pattern Recognition and Machine Intelligence (PReMI 2015:), Warsaw, Poland, 2 July, 2015.
Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face Sketch Synthesis and Recognition Based on Linear Regression Transformation and Multi-Classifier Technique", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015. Abstract

Fish detection and identi cation are important steps towards
monitoring sh behavior. The importance of such monitoring step comes
from the need for better understanding of the sh ecology and issuing
conservative actions for keeping the safety of this vital food resource.
The recent advances in machine learning approaches allow many appli-
cations to easily analyze and detect a number of sh species. The main
competence between these approaches is based on two main detection
parameters: the time and the accuracy measurements. Therefore, this
paper proposes a sh detection approach based on BAT optimization
algorithm (BA). This approach aims to reduce the classi cation time
within the sh detection process. The performance of this system was
evaluated by a number of well-known machine learning classi ers, KNN,
ANN, and SVM. The approach was tested with 151 images to detect the
Nile Tilapia sh species and the results showed that k-NN can achieve
high accuracy 90%, with feature reduction ratio close to 61% along with
a noticeable decrease in the classi cation time.

Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Two biometric approaches for cattle identification based on features and classifiers fusion", International Journal of Image Mining, vol. 1, no. 4: Inderscience Publishers (IEL), pp. 342–365, 2015. Abstract
n/a
Alaa Tharwat, B. E. Elnaghi, A. M. Ghanem, and A. E. Hassanien, "Automatically Human Age Estimation Approach via Two-Dimensional Facial Image Analysis", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 491–501, 2016. Abstract
n/a