I teach undergraduate courses in Computer Science, including Database for the second stage. This course introduces students to the fundamental concepts of database systems, including data modeling, entity–relationship diagrams, relational algebra, normalization, and SQL programming. It also covers the use of database management tools such as MySQL and Microsoft Access. The course focuses on both theoretical understanding and practical implementation through designing, building, and managing relational databases for real-world applications. I also teach Web Design courses for the forth stage, This course introduces students to the fundamentals of website design and development. It covers Internet structure, client–server architecture, web hosting, and cloud computing basics. Students learn how to design and build static and dynamic websites using HTML, CSS, PHP, and MySQL. The course also focuses on publishing websites and understanding key factors that affect website performance and usability.
I have supervised fourth-year undergraduate students in the Department of Computer Science for their graduation projects during previous academic years. The most recent project was about designing a smart system for organizing students’ entry to class using Arduino technology.
FG Mohammed, HM Al-Dabbas, Science International, 2018 - Cited by 2
The use of multimedia technology is growing every day, and it is difficult and time-consuming to provide allowed data while preventing secret information from being used without authorization. The material that has been watermarked can only be accessed by authorized users. Digital watermarking is a popular method for protecting digital data. The embedding of secret data into actual information is the subject of digital watermarking. This paper examines watermarking techniques, methodologies, and attacks, as well as the development of watermarking digital images stored in both the spatial and frequency domains.
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HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023
HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
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Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s
... Show MoreFG Mohammed, HM Al-Dabbas, Iraqi journal of science, 2018 - Cited by 6
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Due to advancements in computer science and technology, impersonation has become more common. Today, biometrics technology is widely used in various aspects of people's lives. Iris recognition, known for its high accuracy and speed, is a significant and challenging field of study. As a result, iris recognition technology and biometric systems are utilized for security in numerous applications, including human-computer interaction and surveillance systems. It is crucial to develop advanced models to combat impersonation crimes. This study proposes sophisticated artificial intelligence models with high accuracy and speed to eliminate these crimes. The models use linear discriminant analysis (LDA) for feature extraction and mutual info
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In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen
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Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security
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