Doctor's name here
Osama Moh’d Radi Alia, Ph.D.
Computer Science Department
Faculty of Computers and Information Technology
University of Tabuk
Tabuk, Kingdom of Saudi Arabia
Email: email@example.com , firstname.lastname@example.org
PhD. Computer Science, Universiti Sains Malaysia, Malaysia, January 2011.
Harmony Search-based Fuzzy Clustering Algorithms for Image Segmentation
M.Sc. Computer Science, Amman Arab University for Graduate Studies, Jordan, 2005.
Video Segmentation Via Dual Shot Boundary Detection
Rating: Excellent (3.84/4)
B.Sc. Computer Science, Al-Isra Private University, Jordan, 2002.
Internet Banking System
Rating: Very Good (80.3/100)
Diploma Computer Science-Programming, The Intermediate University College, Jordan, 1992.
Rating: Very Good (82.4/100)
Research Fields of Interest
Artificial Intelligence application such as Image Processing & Segmentation, Medical Image Analysis, Crowd Dynamics, Wireless sensor networks, Pattern Recognition, Data Clustering, Evolutionary Computing.
List of Publications
Shuaib M. M., Alia O. M., Zainuddin Z.,“Incorporating Prediction Factor into the Investigation Capability
in the Social Force Model: Application on Avoiding Grouped Pedestrians“, Appl. Math. Info.
Sci. Volume 7, No. 1 (Jan. 2013), PP:323-331
Alia O. M., Mandava R., Aziz M. E., “A Hybrid Harmony Search Algorithm for MRI Brain Segmentation”,
DOI: 10.1007/s12065-011-0048-1, Evolutionary Intelligence, Springer.
Alia O. M., Mandava R., “The Variants of the Harmony Search Algorithm - An Overview”, DOI :
10.1007/s10462-010-9201-y, Artificial Intelligence Review, Springer.
Osama Moh’d Radi Alia, Ph.D. 2
Osama Alia, M. A. Al-Betar, R. Mandava, A. T. Khader, (2011). Data Clustering using Harmony Search
Algorithm. SEMCCO 2011 : Swarm Evolutionary and Memetic Computing Conference. B.K. Panigrahi
et al. (Eds.): SEMCCO 2011, Part II, LNCS 7077, pp. 55-65, Springer-Verlag, Berlin, Heidelberg.
Springer publisher, 2011
Mandava R., Alia O. M., Wei B. C., Ramachandram D., Aziz M. E., Shuaib I. L., “Osteosarcoma Segmentation
in MRI using Dynamic Harmony Search Based Clustering”, International Conference of Soft
Computing and Pattern Recognition, SOCPAR10, Cergy-Pontoise, France, 2010.
Alia O. M., Mandava R., Aziz M. E., “A Hybrid Harmony Search Algorithm to MRI Brain Segmentation”,
The 9th IEEE International Conference on Cognitive Informatics, ICCI2010, pp. 712-719, Beijing, China,
Alia O. M., Mandava R., Ramachandram D., Aziz M. E., “Dynamic fuzzy clustering using Harmony
Search with application to image segmentation”, IEEE International Symposium on Signal Processing and
Information Technology, ISSPIT09, pp. 538-543, Ajman, UAE, 2009.
Alia O. M., Mandava R., Ramachandram D., Aziz M. E., “A Novel Image Segmentation Algorithm
Based on Harmony Fuzzy Search Algorithm”, International Conference of Soft Computing and Pattern
Recognition, SOCPAR09, pp. 335-340, Malacca, Malaysia, 2009.
Alia O. M., Mandava R., Ramachandram D., Aziz M. E., “Harmony search-based cluster initialization
for fuzzy c-means segmentation of MR images”, International Technical Conference of IEEE Region 10,
TENCON, pp. 1-6, Singapore, 2009.
Kaabneh K., Alia O., Suleiman A., Abuirbaleh A., “Video Segmentation Via Dual Shot Boundary
Detection (DSBD)”, 2nd Information and Communication Technologies Conference, ICTTA2006, pp. 1530-
1533, Syria, 2006.
Under-Graduate Level (at University of Tabuk):
Computer Programming 1 (C#.Net),
Computer Programming 2 (Advance C#.Net)„
Visual Programming (VB.Net),
Digital Image Processing,
Post-Graduate Level (at USM):
Computer Vision and Image Analysis.
Under-Graduate Level (at Al-Isra University):
Introduction to Computer Science,
Computer Skills (1),
Computer Skills (2) - C Language for eng.& IT students,
Osama Moh’d Radi Alia, Ph.D. 3
JAVA Programming Language,
May,2011 - till now : Assistant Prof. at Faculty of Computers and Information Technology University
of Tabuk - KSA
Sept,2005 - June,2007: Lecturer at Faculty of Science and Information Technology, Al-Isra Private
University, Amman, Jordan
April,2004 - Sept,2005: Internet Lab Supervisor, Computer Center, Al-Isra Private University, Amman,
Aug,1993 - Feb,2004: Assistant unit manager & Check books printing machines operator, The Housing
Bank for Trade & Finance, Amman, Jordan
Aug,1992 - July,1993: Training center supervisor & Training courses teacher, Sakha&Sawalha est.
for computer systems, Amman, Jordan
Participations, Committees and workshops
Member of T.A. committee at University of Tabuk.
Head of quality assurance committee at - IT Department - University of Tabuk.
Head of exam committee for the applications of computer jobs at University of Tabuk.
Projects & Grants
Harmony Search-based Energy-efficient Routing Algorithm for Wireless Sensor Networks - Sensor
Networks & Cellular Systems Research Center - University of Tabuk (In Progress)
Grant: 185000 SR
This research will investigate the Harmony Search Algorithm to propose a new routing algorithm that
can improve the lifetime of Wireless Sensor Networks and their performance.
Delineation and Visualization of Tumor and Risk Structures (DVTRS): USM
Researcher, Software Developer
Grant: 1000000 RM
This research project is intended to develop solutions in medical image analysis and retrieval. This
project is implemented in Java.
Osama Moh’d Radi Alia, Ph.D. 4
Musculoskeletal Tumour Analysis
Main Researcher, Software Developer
Grant: 8000 RM
This research project is intended to develop a solution for automatic segmentation of Osteosarcoma
(the second most prevalent type of malignant bone tumours) in MRI images. This approach uses
multi-spectral information from STIR and T2-weighted MRI sequences. This project is implemented in
C, C++, C#, Java, Matlab, Visual Basic, LATEX.
Last updated: March 11, 2013
Computer Programming 1
Computer Programming 2
Computer Programming 1
Computer Programming 2
|Office Hour|| |
Incorporating Prediction Factor into the Investigation Capability in the Social Force Model: Application on Avoiding Grouped Pedestrians
Incorporating decision making capability into a microscopic crowd dynamic model is an essential factor for introducing a well-representative model for the pedestrian flow. In normal situations, this capability was presented by providing the simulated independent pedestrians in the Social Force Model an investigation ability of their walkways. However, predicting semi-blocked situations by the simulated intelligent pedestrians has not been treated in their investigation. In this paper, the authors describe the investigation capability of independent pedestrians in normal situations. In addition, the prediction aspect is introduced for the simulated independent pedestrians as an extension for the investigation capability to let the model appear more representative of what actually happens in reality. Simulations are performed to validate the work qualitatively by tracing the behavior of the simulated pedestrians and studying the impact of this behavior on their interactions with the grouped pedestrians. Finally, a comparison between the extended model and the original model with regards to the quantitative measurement (the efficiency of motion) is made.
Data Clustering Using Harmony Search Algorithm
Being one of the main challenges to clustering algorithms, the sensitivity of fuzzy c-means (FCM) and hard c-means (HCM) to tune the initial clusters centers has captured the attention of the clustering communities for quite a long time. In this study, the new evolutionary algorithm, Harmony Search (HS), is proposed as a new method aimed at addressing this problem. The proposed approach consists of two stages. In the first stage, the HS explores the search space of the given dataset to find out the near-optimal cluster centers. The cluster centers found by the HS are then evaluated using reformulated c-means objective function. In the second stage, the best cluster centers found are used as the initial cluster centers for the c-means algorithms. Our experiments show that an HS can minimize the difficulty of choosing an initialization for the c-means clustering algorithms. For purposes of evaluation, standard benchmark data are experimented with, including the Iris, BUPA liver disorders, Glass, Diabetes, etc. along with two generated data that have several local extrema.
The variants of the harmony search algorithm: an overview
The harmony search (HS) algorithm is a relatively new population-based metaheuristic optimization algorithm. It imitates the music improvisation process where musicians improvise their instruments' pitch by searching for a perfect state of harmony. Since the emergence of this algorithm in 2001, it attracted many researchers from various fields especially those working on solving optimization problems. Consequently, this algorithm guided researchers to improve on its performance to be in line with the requirements of the applications being developed. These improvements primarily cover two aspects: (1) improvements in terms of parameters setting, and (2) improvements in terms of hybridizing HS components with other metaheuristic algorithms. This paper presents an overview of these aspects, with a goal of providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.
A Hybrid Harmony Search Algorithm for MRI Brain Segmentation
Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner. In our algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length encoding in each harmony memory vector, this algorithm is able to represent variable numbers of candidate cluster centers at each iteration. A new HS operator, called the empty operator. It has been introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. Evaluation of the proposed algorithm has been performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results show the ability of this algorithm to find the appropriate number of naturally occurring regions in brain images. Furthermore, the superiority of the proposed algorithm over various state-of-the-art segmentation algorithms is demonstrated quantitatively.