Teaching Preferences
Online Teaching
One-on-One at $20-30 per hour
Group Teaching at $10-20 per hour
Offers free trial classes
Teaches following Subjects/Exams
C++ (Computer: Programming)
Language of Instruction:
English, Arabic
C (Computer: Programming)
Language of Instruction:
English, Arabic
HTML (Computer: Programming)
Language of Instruction:
English, Arabic
Java (Computer: Programming)
Language of Instruction:
English, Arabic
Mark up Languages (Computer: Web Development)
Language of Instruction:
English, Arabic
PHP (Computer: Web Development)
Language of Instruction:
English, Arabic
HTML (Computer: Programming)
Language of Instruction:
English, Arabic
Pascal (Computer: Programming)
Language of Instruction:
English, Arabic
Teaching Experience
Teaching Assistant
Al-Baath University, Syria
Sep 2003 - Sep 2005
Software Engineering, Programming Languages (Basic, Pascal, C, C++, Java), Web development (HTML, Java script, PHP, MySQL), Algorithms
Undergraduate projects supervision: Mobile programming with J2ME, Bio-informatics applications,
Building GIS System.
Education
PhD.
Cardiff University / Prifysgol Caerdydd, United Kingdom
Sep 2005 - Dec 2009
Thesis title: “Data clustering using the Bees algorithm and the Kd-tree structure”.
Bachelor
Jami't Al-Ba'ath, Syria
Sep 1998 - Sep 2003
Five years program, IT Faculty.
Final year project: “Designing and developing a virtual faculty”.
Publications and Research
Data clustering using the Bees Algorithm
D. T. Pham, S. Otri, A. A. Afify, M. Mahmuddin, and H. Al-Jabbouli
Clustering is concerned with partitioning a data set into homogeneous groups. One
of the most popular clustering methods is k-means clustering because of its
simplicity and computational efficiency. K-means clustering involves search and
optimisation. The main problem with this clustering method is its tendency to
converge to local optima. The authors’ team have developed a new populationbased
search algorithm called the Bees Algorithm that is capable of locating nearoptimal
solutions efficiently. This paper proposes a clustering method that
integrates the simplicity of the k-means algorithm with the capability of the Bees
Algorithm to avoid local optima. The paper presents test results to demonstrate the
efficacy of the proposed algorithm.
Application of the Bees Algorithm to the Selection Features for Manufacturing Data
D. T. Pham, M. Mahmuddin, S. Otri and H. Al-Jabbouli
Data with a large number of features tend to be deficient in accuracy and precision. Some of the
features may contain irrelevant information caused by data redundancy or by noise. A “wrapper”
feature selection method using the Bees Algorithm and Multilayer Perceptron (MLP) networks is
described in this paper. The Bees Algorithm is employed to select an optimal set of features for a
particular pattern classification task. Each “bee” represents a possible set of features. The MLP
classification error is computed for a data set with those features. This information is supplied to the
Bees Algorithm to enable it to select the combination of features producing the lowest classification
error. The proposed method has been tested on data collected in semiconductor manufacturing. The
results presented in the paper clearly demonstrate the effectiveness of the method.
Application of the Bees Algorithm to the Fuzzy Clustering
D. T. Pham, H. Al-Jabbouli, M. Mahmuddin, S. Otri and Ahmed Haj Darwish
This paper discusses the application of the Bees Algorithm to fuzzy clustering. The Bees Algorithm is used to
optimise the performance of the fuzzy C-Means (FCM) algorithm and improve its clustering results. Computational
experiments show that the Bees Algorithm gives a significant improvement over the FCM algorithm on its own and
better results compared to the FCM algorithm combined with a Genetic Algorithm.