OMAIMA AL-ALLAF

ICIP 2017: 19th International Conference on Image Processing,

Paris-France, 21-22/9/2017

ICIP 2017: 19th International Conference on Image Processing,

Paris-France, 21-22/9/2017

Dr. Omaima N. Ahmad AL-Allaf

Abstract—

Over the communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal 100, number of iterations equal 150 and 3×3 block. According to results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of watermarking image. Better PSO results were obtained when using swarm size equal 100.

Dr. OMAIMA N. A. AL-ALLAF

Journal of Theoretical and Applied Information Technology
15th June 2017. Vol.95. No 11

Abstract

Speech signals are effected by noise generated by various sources of interferences. Removing noise from
speech signals can be regarded as an active research area in signal processing. Thus, we need powerful
methods in this area. Therefore, Function Fitting (FitNet) Artificial Neural Networks model was used in this paper
for enhancing speech signals. Particle Swarm Optimization (PSO) was used during FitNet learning process to optimize
the FitNet learning parameters (such as learning rate, momentum variable and network weights) to achieve best results
of speech signal enhancement. At the same time, different optimization techniques for optimizing the values of learning
parameters were suggested in this work. This is done to improve the performance of FitNet model for signal
enhancement. Better results (320 learning steps, PSNR equal 38 and mean square error (MSE) equal 0.0027) from
experiments were achieved when adopting PSO with FitNet with swarm size equal 40 and PSO number of iterations
equal 100. Good results (312 learning steps, PSNR equal 35.94 and MSE equal 0.00002) were obtained also when
adopting the suggested optimization techniques (learning rate equal 0.00003, 5 hidden units in one hidden layer with
the using of Levenberg-Marquardt (LM) as learning algorithm) for optimizing the learning parameters.

Mohammad A. Alia, Abdelfatah Aref Tamimi and Omaima N. A. AL-Allaf

The International Journal of Multimedia & Its Applications (IJMA) Vol.5, No.6, December 2013

ABSTRACT
In this paper we propose a new student attendance system based on biometric authentication protocol. This
system is basically using the face detection and the recognition protocols to facilitate checking students’
attendance in the classroom. In the proposed system, the classroom’s camera is capturing the students’
photo, directly the face detection and recognition processes will be implemented to produce the instructor
attendance report. Actually, this system is more efficient than others student attendance methods since the
detection and the recognition are considered to be the best and fastest method for biometric attendance
system. Regarding to the students and instructor sides, the system is working without any preparation and
with no more effort.

Abdelfatah Aref Tamimi, Omaima N. A. AL-Allaf  and    Mohammad A. Alia

I.J. Information Technology and Computer Science, 2015, 06, 66-73
Published Online May 2015 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijitcs.2015.06.09

Abstract— The traditional manual attendance system wastes time over students’ responses, but it has worked well for small numbers of students. This research presents a real-time group face-detection system. This system will be used later for student class attendance through automatic student identification. The system architecture and its algorithm will be described in details. The algorithm for the system was based on analyzing facial properties and features in order to perform face detection for checking students’ attendance in real time. The classroom’s camera captures the students’ photo. Then, face detection will be implemented automatically to generate a list of detected student faces. Many experiments were adopted based on real time video captured using digital cameras. The experimental results showed that our approach of face detection offers real-time processing speed with good acceptable detection ratio equal to 94.73%.

Omaima N. Ahmad AL-Allaf,    and     Shahlla A. AbdAlKader

International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-4, Issue-2, May 2015

Abstract— Recently, person identification systems based on
gait recognition had been gained growing large interest from
researchers in the fields of artificial intelligence and image
processing Thus, a gait recognition system based on particle
swarm optimization (PSO) has been suggested in this work to
recognize any person at a distance who performing the movement.
Three feature extraction and dimension reduction algorithms
were used to increase the recognition performance of PSO
algorithm. These algorithms are: Liner Discriminant Analysis
(LDA); Discrete Fourier Transform (DFT); and Discrete Cosine
Transform (DCT). Many experiments were conducted for PSO
with the three algorithms using different: swarm size, block
dimension and number of iterations. Best results obtained when
selecting swarm size equal 40, feature block size 70×70 and 100
number of iterations. At the same time best results of: recognition
rate (97%), MSE (0.0027) and PSNR (38) where obtained when
adopting LDA algorithm in comparison with DFT and DCT. And
also the results obtained from DFT are better than the results
obtained from using DCT. The time required for executing the
LDA is lowest than the time required for executing DFT and DCT.
DCT require more time than the other used feature extraction
algorithms.

Omaima N. A. AL-Allaf

I.J. Information Technology and Computer Science, 2015, 07, 8-18
Published Online June 2015 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijitcs.2015.07.02

Abstract— In this research, four ANN models: Function Fitting (FitNet), Nonlinear AutoRegressive (NARX), Recurrent (RNNs), and Cascaded-ForwardNet were constructed and trained separately to become a filter to remove noise from any speech signal. Each model consists of input, hidden and output layers. Two neurons in the input layer that represent speech signal and its associated noise. The output layer includes one neuron that represent the enhanced signal after removing noise. The four models were trained separately on stereo (noisy and clean) audio signals to produce the clean signal. Experiments were conducted for each model separately with different: architecture; optimization training algorithms; and learning parameters to identify model with best results of removing noise from speech signal. From experiments, best results were obtained from FitNet and NARAX models respectively. TrainLM is the best training algorithm in this case. Finally, the results showed that the suggested architecture of the four models have filtering ability to remove noise form both trained and not trained speech signals samples.

Abdelfatah A. Tamimi, Ayman M. Abdalla, and Omaima Al-Allaf

(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 4, No. 10, 2013

Abstract—A new algorithm is presented for hiding a secret image in the least significant bits of a cover image. The images used may be color or grayscale images. The number of bits used for hiding changes according to pixel neighborhood information of the cover image. The exclusive-or (XOR) of a pixel’s neighbors is used to determine the smoothness of the neighborhood. A higher XOR value indicates less smoothness and leads to using more bits for hiding without causing noticeable degradation to the cover image. Experimental results are presented to show that the algorithm generally hides images without significant changes to the cover image, where the results are sensitive to the smoothness of the cover image.

Mohammad A. Alia, Abdelfatah Aref Tamimi, and Omaima N. A. AL-Allaf

Proceedings of the World Congress on Engineering and Computer Science 2014 Vol I
WCECS 2014, 22-24 October, 2014, San Francisco, USA

Abstract—This paper reviews a comparison study on the
most common used authentication methods. Some of these
methods are actually based on cryptography. In this study, we
show the main cryptographic services. As well as, this study
presents a specific discussion about authentication service.
Since the authentication service is classified into several
categorizes according to their methods. However, this study
gives more about the real life example for each of the
authentication methods. It talks about the simplest
authentication methods as well about the available biometric
authentication methods such as voice, iris, fingerprint, and face
authentication.

Omaima N. Ahmad AL-Allaf

© Springer International Publishing Switzerland 2015
K. Arai et al. (eds.), Intelligent Systems in Science and Information 2014,
Studies in Computational Intelligence 591, DOI 10.1007/978-3-319-14654-6_25

Abstract

This chapter presents how to use parallel computing approaches from
MATLAB Parallel Computing Toolbox to implement genetic algorithm for fractal
image compression. These approaches are: ParFor, CoDistributor and Parallel
Cluster. This is done to decrease processing time as possible as and maintaining
reconstructed image quality. Many experiments were executed with comparisons
between the three approaches. The experimental results showed that decreasing
the GA population size and increasing number of workers used for the three parallel
computing approaches can reduce the compression time. Best results obtained
from implementing parallel approaches with 6 workers and 150 population size.
The execution speed reached 4, CR reached 90.97 % and PSNR reached 34.98 db.
At the same time, best results obtained from Parallel Cluster approach and then
from CoDistributor approach.