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Fozia Hameed

  • Ambitious Candidate with more than 11 years experience in academia.

  • Passionate to secure a PhD position in Computer Science with the aim to become an independent scientist.

  • Experience of supervising various Graduate projects, working with different tools.

  • Excellent Interpersonal skills to maintain collaborations with ability to work in groups and independently.

2006-2008

Master of Science: Computer Science

81.20%, CGPA - 3.52/4

Sep 2011- Ongoing

Lecturer

King Khalid University, Saudi Arabia

CS subjects -Graduate Students

Feb 2010-April 2011

Lecturer

Islamabad Model College for Girls, F-6/2, Pakistan

Taught C, C++, Data Bases, Software Engineering, Networks, Object oriented Programming Languages To undergraduate Students

Lecturer

Global College of Sciences

Taught Computer Scince to Intermediate level

Aug -Dec 2008

EXPERIENCE

Intern

Elixir Technologies,Islamabad

Developed several scripts on Rational Robot

Jul-Sep 2006

EXPERIENCE

2021

•State of the Art in Neural Networks and their Applications, Academic Press, 2021

Nazia Hameed, Antesar Shabut, Fozia Hameed, Silvia Cirstea, Alamgir Hossain, Chapter 7 - Achievements of neural network in skin lesions classification, Editor(s): Ayman S. El-Baz, Jasjit S. Suri, , Pages 133-151, ISBN 9780128197400.

2019

•International Conference on Computing, Electronics & Communications Engineering (iCCECE), London, United Kingdom, 2019

N. Hameed, A. Shabut, F. Hameed, S. Cirestea and A. Hossain, "An Intelligent Inflammatory Skin Lesions Classification Scheme for Mobile Devices," , pp. 83-88

PUBLICATIONS

2019

•Computers 2019

Hameed, N.; Hameed, F.; Shabut, A.; Khan, S.; Cirstea, S.; Hossain, A. An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. , 8, 62.

2010

IEEE Second International Conference on Computational Intelligence, Modelling and Simulation,2010

Gulam Kassem, Imran Ahmed, Fozia Hameed, Asif Zakariya,” TCP Variants: An Overview”,

PUBLICATIONS

IMPROVING POSE ESTIMATION FOR THE DETECTION OF STROKE

Objective 1: Develop a novel pose-estimation model to approximate the location of obscured landmarks.

RESEARCH

Objective 2: Refine the developed model to find a minimal solution that maintains sufficient model performance whilst minimising the computational cost.

Objective 3: Use the model to identify symptoms of stroke during standard neurological examinations; namely impaired upper limb coordination and unilateral weakness in the upper limbs.

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