
Kifaa hadi thanoon
Research InterestsDigital image processing &Digital signal processing &neural network&Artifical intelligence algorithems
Gender | MALE |
---|---|
Place of Work | Technical Engineering College for Computer and AI / Mosul |
Position | Lecturer |
Qualification | Master |
Speciality | Digital Image Processing & Signal |
kh.thanoon@ntu.edu.iq | |
Phone | 07740890306 |
Address | Second Competencies, single, Mosul, Iraq |
1. PERSONAL DETAILS
2. EDUCATIONAL QUALIFICATIONS
3. ACADEMIC EXPERIANCE
4. ACADEMIC ADMINSTRATION EXPERIENCE
5. PROFESSIONAL ACTIVITIES
6. Publications
Skills
neural network (75%)
digital signal processing (75%)
Digital image processing (80%)
Working Experience
teaching , research [Lecturer]
Jan 5, 2003 - Present• Delivering lectures, seminars and tutorials using modern teaching & learning pedagogies.
• Undertaking personal research projects and actively contributing to the institution’s research profile.
• Undertaking administrative tasks related to the department, such as student admissions, induction programmes, invigilation, mark entry, maintain student attendance records and involvement in committees and boards
Publications
Measure the Software Quality based on Grasshopper Optimization Algorithm
Jun 1, 2020Journal International Journal of Computing and Digital Systems
publisher International Journal of…
Software quality is very essential function from development during the early life of software engineering. Software Quality helps to detect errors and potential errors during initial stage of design and software development process. In this paper, Grasshopper Optimization Algorithm (GOA) is used to improve software quality. Where multiple quality measures were used to calculate the quality standards that were used in testing the approved software. As software testing focuses on software defect. In addition this paper presents GOA to extract the best features of extraction to testing and evaluation of a set of software applications. The paper depended on NASA standards data. The result and experiment show that improved performance quality for all classification methods applied in the research grasshopper optimization algorithm based on feature selection and bagging for Software defect prediction.
Conferences
High Frequency Coefficient Effect on Image Based on Contourlet Transformation
Mar 3, 2019 - Mar 5, 2019Publisher IEEE
DOI 10.1109/ICCISTA.2019.8830649
Country Iraq
Location Kirkuk