Basic information
Name :
NEVEEN IBRAHIM MOHAMED GHALI
Title:
Professors
Education
Certificate
Major
University
Year
PhD
Computer Science
Helwan University - Faculty of Computers and Information
2003
Masters
Computer Science
Helwan University - Faculty of Computers and Information
1999
Bachelor
.
Ain Shams University - Faculty of Science
1996
Researches /Publications
An Adaptive Context Modeling Approach Using Genetic Algorithm in IoTs Environments - 01/0
Author :
NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors :
Asaad Ahmed,Shereen A. El-aal,Afaf A. S. Zaghrout
Date of Publication :
01/02/2020
Abstract :
Internet of Things (loTs) is the future of ubiquitous and personalized intelligent service delivery. It depends on installing intelligent sensors to sense and control physical environment to generate enormous amount of data with various data types. Context aware computing is employed for transforming these sensor data into knowledge through three stages: collection, modeling and reasoning. In context modeling, raw data represents in according meaningful manner statically. Furthermore, with growth of IoTs live applications, static modeling is not convenient because of changing context data structure overtime. The work in this paper is dedicated to propose a new dynamic approach for context modeling based on genetic algorithm and satisfaction factor. In addition, flexibility indicator property and context based are defined to measure the performance of the proposed approach
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A Dynamic Genetic-Based Context Modeling Approach in Internet of Things Environments - 01/1
Author :
NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors :
Ahmed. A. A. Gad-Elrab , Shereen A. El-aal , Afaf A. S. Zaghrout
Date of Publication :
01/12/2019
Abstract :
Internet of Things (IoTs) enables entities every day to
communicate and collaborate with each other for providing
information, data and services to inhabitants and users. IoTs
consists of a large number of smart devices that can generate
immense amount of data with different types. These sensors
raw data needs to be modeled in a certain structure before
filtering and processing to provision context information.
This process is called context modeling. Context modeling
provides definition of how context data are structured and
maintained through context aware system. However,
employing model for every context type through context
aware application is static and is specified by the application
developer. The main problem in IoTs is that the structure of
context data changes overtime, therefore static modeling
cannot be adaptable for modeling these changes. In this
paper, a new dynamic approach for context modeling based
on genetic algorithm and satisfaction factor is proposed.
Firstly, the proposed approach uses genetic algorithm to find
the best matching between a set of contexts and a set of
available context models. Secondly, it uses a satisfaction
factor to calculate the satisfaction degree for each context
with each available context model and select the context
model with high satisfaction degree as the structure model
of this context, dynamically. In addition, flexibility
indicator property and context based are defined to measure
the performance of the proposed approach. The results of
conducted simulations show that the proposed approach
achieves higher performance than static approach for context
modeling.
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Optimizing community detection in social networks using antlion and K-median - 01/1
Author :
NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors :
Amany A. Naem
Date of Publication :
01/12/2019
Abstract :
Antlion Optimization (ALO) is one of the latest population based
optimization methods that proved its good performance in a variety of
applications. The ALO algorithm copies the hunting mechanism of antlions
to ants in nature. Community detection in social networks is conclusive to
understanding the concepts of the networks. Identifying network
communities can be viewed as a problem of clustering a set of nodes into
communities. k-median clustering is one of the popular techniques that has
been applied in clustering. The problem of clustering network can be
formalized as an optimization problem where a qualitatively objective
function that captures the intuition of a cluster as a set of nodes with better in
ternal connectivity than external connectivity is selected to be optimized. In
this paper, a mixture antlion optimization and k-median for solving the
community detection problem is proposed and named as K-median
Modularity ALO. Experimental results which are applied on real life
networks show the ability of the mixture antlion optimization and k-median
to detect successfully an optimized community structure based on putting the
modularity as an objective function.
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Facial Expressions Recognition in Thermal Images based on Deep Learning Techniques - 01/1
Author :
NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors :
Yomna M. Elbarawy,Rania Salah El-Sayed
Date of Publication :
01/10/2019
Abstract :
Facial expressions are undoubtedly the best
way to express human attitude which is crucial in social
communications. This paper gives attention for exploring
the human sentimental state in thermal images through
Facial Expression Recognition (FER) by utilizing
Convolutional Neural Network (CNN). Most traditional
approaches largely depend on feature extraction and
classification methods with a big pre-processing level but
CNN as a type of deep learning methods, can
automatically learn and distinguish influential features
from the raw data of images through its own multiple
layers. Obtained experimental results over the IRIS
database show that the use of CNN architecture has a
96.7% recognition rate which is high compared with
Neural Networks (NN), Autoencoder (AE) and other
traditional recognition methods as Local Standard
Deviation (LSD), Principle Component Analysis (PCA)
and K-Nearest Neighbor (KNN)
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Local Entropy and Standard Deviation for Facial Expressions Recognition in Thermal Imaging - 01/1
Author :
NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors :
Yomna M. Elbarawy, Rania Salah El-Sayed
Date of Publication :
01/12/2018
Abstract :
Emotional reactions are the best way to express human attitude and thermal
imaging mainly used to utilize detection of temperature variations as in
detecting spatial and temporal variation in the water status of grapevine. By
merging the two facts this paper presents the Discrete Cosine Transform
(DCT) with Local Entropy (LE) and Local Standard Deviation (LSD)
features as an efficient filters for investigating human emotional state in
thermal images. Two well known classifiers, K-Nearest Neighbor (KNN) and
Support Vector Machine (SVM) were combined with the earlier features and
applied over a database with variant illumination, as well as occlusion by
glasses and poses to generate a recognition model of facial expressions in
thermal images. KNN based on DCT and LE gives the best accuracy
compared with other classifier and features results.
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Antlion optimization and boosting classifier for spam email detection - 01/1
Author :
NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors :
Amany A. Naem,Afaf A. Saleh
Date of Publication :
01/12/2018
Abstract :
Spam emails are not necessary, though they are harmful as they include viruses and spyware, so there is an emerging need for detecting spam emails. Several methods for detecting spam emails were suggested based on the methods of machine learning, which were submitted to reduce non-relevant emails and get results of high precision for spam email classification. In this work, a new predictive method is submitted based on antlion optimization (ALO) and boosting termed as ALO-Boosting for solving spam emails problem. ALO is a computational model imitates the preying technicality of antlions to ants in the life cycle. Where ALO was utilized to modify the actual place of the population in the separate seeking area, thus obtaining the optimum feature subset for the better classification submit based on boosting classifier. Boosting classifier is a classification algorithm that points to a group of algorithms which modifies soft learners into powerful learners. The proposed procedure is compared against support vector machine (SVM), k-nearest neighbours algorithm (KNN), and bootstrap aggregating (Bagging) on spam email datasets in a set of implementation measures. The experimental outcomes show the ability of the proposed method to successfully detect optimum features with the smallest value of selected features and a high precision of measures for spam email classification based on boosting classifier.
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