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Kifaa hadi thanoon

Research Interests

Digital 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
Email kh.thanoon@ntu.edu.iq
Phone 07740890306
Address Second Competencies, single, Mosul, Iraq
About Image
CURRICULUM VITAE Assistance Professor kifaa Hadi Thanoon

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

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, 2020

Journal International Journal of Computing and Digital Systems

publisher International Journal of…

DOI 10.12785/ijcds/100186

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.

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Conferences

Conferences

High Frequency Coefficient Effect on Image Based on Contourlet Transformation
Mar 3, 2019 - Mar 5, 2019

Publisher IEEE

DOI 10.1109/ICCISTA.2019.8830649

Country Iraq

Location Kirkuk

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