Mohammed Abdullah Al-Hagery

Work place: Department of Computer Science, College of Computer, Qassim University, Buraidah, Saudi Arabia



Research Interests: Computational Engineering, Software Construction, Software Development Process, Software Engineering


Mohammed Abdullah Al-Hagery: Associate Professor received his B.Sc in Computer Science from the University of Technology in Baghdad Iraq-1994. He got his MSc. in Computer Science from the University of Science and Technology Yemen-1998. Al-Hagery finished his Ph.D. in Computer Science and Information Technology, (Software Engineering) from the Faculty of Computer Science and IT, University of Putra Malaysia (UPM), November 2004. He was a head of the Computer Science Department at the College of Science and Engineering, USTY, Sana'a from 2004 to 2007. From 2007 to this date, he is a staff member at the College of Computer, Department of Computer Science, Qassim University, KSA. Dr. Al-Hagery was appointed a head of the Research Centre at the Computer College, and a council member of the Scientific Research Deanship Qassim University, KSA from September 2012 to October 2018. Currently, Dr. Al-Hagery is teaching the master degree students and he is the supervisor of a number of master dissertations. He is a jury member and an internal and external examiner for evaluating the PhD, master thesis in his field of a specialist.

Author Articles
Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance

By Mohammed Abdullah Al-Hagery Maryam Abdullah Alzaid Tahani Soud Alharbi Moody Abdulrahman Alhanaya

DOI:, Pub. Date: 8 Oct. 2020

The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data. 

[...] Read more.
Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

By Mohammed Abdullah Al-Hagery Sara Saleh Alfaozan Hajar Abdulrahman Alghofaily Mohammed A. Hadwan

DOI:, Pub. Date: 8 Aug. 2020

Depression is a widespread and serious phenomenon in public health in all societies. In Saudi society, depression is one of the diseases that the community is may refuse to disclose it. There are no studies have analyzed this disease within the Saudi community. The main research objective is to discover the depression level of Saudi People's. In addition to analyzing the age group and the most gender type affected by the depression in this society. The data collected from social media achieved indirectly without any communication with patients as a sample from this society people. It analyzed using Machine Learning algorithms that give accurate results for this disease. Three classification models have been established to diagnose this disease and the findings of this study presented that the depression levels include five ‎classes and ‎the most affected age group in depression was in the ‎age group from 20-26 years. The results show that young Saudi women are more likely to be depressed. The obtained results are very important to the medical field. Researchers and people working in this field can get benefits out of this research. Especially those who want to understand the depression disease in Saudi society and searching for real solutions to overcome this problem.

[...] Read more.
A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

By Mohammed Abdullah Al-Hagery Latifah Abdullah Alreshoodi Maram Abdullah Almutairi Suha Ibrahim Alsharekh Emtenan Saad Alkhowaiter

DOI:, Pub. Date: 8 Jul. 2019

Real-World datasets accumulated over a number of years tend to be incomplete, inconsistent and contain noisy data, this, in turn, will cause an inconsistency of data warehouses. Data owners are having hundred-millions to billions of records written in different languages, hence continuously increases the need for comprehensive, efficient techniques to maintain data consistency and increase its quality. It is known that the data cleaning is a very complex and difficult task, especially for the data written in Arabic as a complex language, where various types of unclean data can occur to the contents. For example, missing values, dummy values, redundant, inconsistent values, misspelling, and noisy data. The ultimate goal of this paper is to improve the data quality by cleaning the contents of Arabic datasets from various types of errors, to produce data for better analysis and highly accurate results. This, in turn, leads to discover correct patterns of knowledge and get an accurate Decision-Making. This approach established based on the merging of different algorithms. It ensures that reliable methods are used for data cleansing. This approach cleans the Arabic datasets based on the multi-level cleaning using Arabic Misspelling Detection, Correction Model (AMDCM), and Decision Tree Induction (DTI). This approach can solve the problems of Arabic language misspelling, cryptic values, dummy values, and unification of naming styles. A sample of data before and after cleaning errors presented.

[...] Read more.
Other Articles