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Ghali, N. I., W. G. Abd-Elmonim, and A. E. Hassanien, "Object-based image retrieval system using rough set approach", Advances in Reasoning-Based Image Processing Intelligent Systems: Springer Berlin Heidelberg, pp. 315–329, 2012. Abstract
Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: reviews and recent advances", Advances in Robotics and Virtual Reality: Springer Berlin Heidelberg, pp. 363–385, 2012. Abstract
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 %).

Gaber, T., N. Zhang, and A. E. Hassanien, "A novel approach to allow multiple resales of DRM-protected contents", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 86–91, 2013. Abstract
Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier", Computers and Electronics in Agriculture, vol. 122: Elsevier, pp. 55–66, 2016. Abstract
Gaber, T., Alaa Tharwat, Abdelhameed Ibrahim, V. Snáel, and A. E. Hassanien, "Human thermal face recognition based on random linear oracle (RLO) ensembles", Intelligent Networking and Collaborative Systems (INCOS), 2015 International Conference on: IEEE, pp. 91–98, 2015. Abstract
Gaber, T., A. E. Hassanien, and M. F. Tolba, "Repeated reselling permission multi-reselling approach for a license in DRM environment", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 197–202, 2013. Abstract
Gaber, T., and A. E. Hassanien, "An Overview of Self-Protection and Self-Healing in Wireless Sensor Networks", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 185–202, 2014. Abstract
Gaber, T., G. Ismail, A. Anter, M. Soliman, M. Ali, N. Semary, A. E. Hassanien, and V. Snasel, "Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm", Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE: IEEE, pp. 4254–4257, 2015. Abstract
Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier", Computers and Electronics in Agriculture, vol. 122 , issue March 2016 , pp. 55–66, 2016. Website
Gaber, T., Alaa Tharwat, Abdelhameed Ibrahim, V. Snasel, and A. E. Hassanien, "Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles,", IEEE International Conference on Intelligent Networking and Collaborative Systems, ,015, pp. 91-98 . , Taipei, Taiwan, 2-4 September , 2015. Abstractabo2.pdf

This paper proposes a human thermal face recognition approach with two variants based on Random linear
Oracle (RLO) ensembles. For the two approaches, the Segmentation-based Fractal Texture Analysis (SFTA) algorithm was used for extracting features and the RLO ensemble classifier was used for recognizing the face from its thermal image. For the dimensionality reduction, one variant (SFTALDA-RLO) was used the technique of Linear Discriminant Analysis (LDA) while the other variant (SFTA-PCA-RLO) was used the Principal Component Analysis (PCA). The classifier’s model was built using the RLO classifier during the training phase and in the testing phase then this model was used to identify the unknown sample images. The two variants were evaluated using the Terravic Facial IR Database and the experimental results showed that the two variants achieved a good recognition rate at 94.12% which is better than related work.

Gaber, T., Alaa Tharwat, and A. E. Hassanien, "One-dimensional vs. two-dimensional based features: Plant identification approach", Journal of Applied Logic, 2016. 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.

Gaber, T., Alaa Tharwat, V. Snasel, and A. E. Hassanien, "Plant identification: Two dimensional-based vs. one dimensional-based feature extraction methods", 10th international conference on soft computing models in industrial and environmental applications: Springer International Publishing, pp. 375–385, 2015. Abstract