Heba F. Eid

Work place: Al-Azhar University, Faculty of Science, Cairo, Egypt

E-mail: heba.fathy@yahoo.com


Research Interests: Network Security, Machine Learning, Computer Vision


Heba Fathy Eid received her B.Sc. with honours in 2005 at Pure Mathematics and Computer Science, from Faculty of Science, Al-Azhar University. She received her MSc degree 2009 in Distributed database systems and her doctoral degree 2013 in Network security, both from Faculty of Science,  Al-Azhar University, Egypt. She is currently Assistant Professor at Al-Azhar University. Her main research interests are in the areas of machine learning, network security, intrusion detection and computer vision.

Author Articles
Variant-Order Statistics based Model for Real Time Plant Species Recognition

By Heba F. Eid Ashraf Darwish

DOI: https://doi.org/10.5815/ijitcs.2017.09.08, Pub. Date: 8 Sep. 2017

There are an urgent need of categorizing plant by its species, to help botanist setting up a plant species database. However, plant recognition model is still very challenging task in computer vision and can be onerous and time consuming because of inefficient representation approaches. This paper, proposes a recognition model for classifying botanical species from leaf images, using combination of variant-order statistics based measures. Hence, the spatial coordinates values of gray pixels defines the qualities of texture, for the proposed model a gray-scale approach is adopted  for analyzing the local patterns of leaves images using second and higher order statistical measures. While, first order statistical measures are used to extract color descriptors from leaves images. Evaluation of the proposed model shows the importance of combining variant-order statistics measures for enhancing the plant leaf recognition accuracy. Several experiments on Flavia dataset and swedish dataset are conducted. Experimental results indicates that; the proposed model yields to improve the recognition rate up to 97.1% and 94.7% for both Flavia and Swedish dataset respectively; while taking less execution time.

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Performance Improvement of Plant Identification Model based on PSO Segmentation

By Heba F. Eid

DOI: https://doi.org/10.5815/ijisa.2016.02.07, Pub. Date: 8 Feb. 2016

Plant identification has been a challenging task for many researchers. Several researches proposed various techniques for plant identification based on leaves shape. However, image segmentation is an essential and critical part of analyzing the leaves images. This paper, proposed an efficient plant species identification model using the digital images of leaves. The proposed identification model adopts the particle swarm optimization for leaves images segmentation. Then, feature selection process using information gain and discritization process are applied to the segmented image’s features. The proposed model was evaluated on the Flavia dataset. Experimental results on different kind of classifiers show an improvement in the identification accuracy up to 98.7%.

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