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$50 USD / hour
Flag of UNITED KINGDOM
nottingham, united kingdom
$50 USD / hour
It's currently 12:56 AM here
Joined February 2, 2018
2 Recommendations

Muhammad Uzair Z.

@uzairrzahid

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4.9 (108 reviews)
6.4
6.4
82%
82%
$50 USD / hour
Flag of UNITED KINGDOM
nottingham, united kingdom
$50 USD / hour
95%
Jobs Completed
85%
On Budget
84%
On Time
23%
Repeat Hire Rate

ML | DL | AI | Python | MATLAB

As a Machine Learning and AI developer, I have a strong passion for technology and 5+ years of experience in delivering cutting-edge solutions to clients. My areas of expertise include neural networks, biomedical imaging, and other advanced machine learning techniques. In addition to my technical skills, I am also well-versed in web technologies and have a proven track record of problem-solving using various programming languages. My goal is always to exceed client expectations and ensure that their satisfaction is my top priority. If you are looking for a dedicated and skilled developer who can deliver desired outcomes, please do not hesitate to hire me.

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Reviews

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Showing 1 - 5 out of 50+ reviews
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5.0
€100.00 EUR
He handled that challenge like a true professional.
Python
Software Architecture
Machine Learning (ML)
Deep Learning
User Avatar
Flag of Zrax H.
@marouenkadri24
1 year ago
5.0
€200.00 EUR
Nice work , on the time . in one word it's the best one .
Matlab and Mathematica
Algorithm
Electrical Engineering
Machine Learning (ML)
+1 more
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Flag of Zrax H.
@marouenkadri24
1 year ago
5.0
€170.00 EUR
excellent work as usual
Matlab and Mathematica
Algorithm
Electrical Engineering
Machine Learning (ML)
+1 more
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Flag of Zrax H.
@marouenkadri24
1 year ago
5.0
$300.00 AUD
Yet more repeat business with Muhammah. I'll be going straight back for more.
Matlab and Mathematica
Algorithm
Electrical Engineering
Machine Learning (ML)
Data Science
User Avatar
Flag of Jacob A.
@mintgreenstrat
1 year ago
5.0
€100.00 EUR
Excellent work. Delivered exactly what I requested quickly and professionally.
User Avatar
Flag of Zrax H.
@marouenkadri24
1 year ago

Experience

Senior Researcher

Qatar University
Dec 2019 - Present
Working as a researcher in the field of Biomedical Imaging, Signal Processing, Machine Learning and Deep Learning.

Research Assistant

CE FAR LAB
Jan 2018 - Present
I am working a project which involves object tracking and localization using live video feed from camera which will be used to help visually blind people.

Research Assistant

SIGMA LABS NUST (Research Lab for Signal Processing And Machine Learning)
Apr 2017 - Sep 2017 (5 months, 1 day)
I was involved in development of a portable, remote respiratory and physical activity monitoring system.

Education

MS Electrical Engineering (Signal Processing and Machine Learning)

National University of Science and Technology, Pakistan 2016 - 2018
(2 years)

BS Telecom

University of Engineering and Technology, Taxila, Pakistan 2012 - 2016
(4 years)

Qualifications

Neural Networks and Deep Learning

Coursera
2018

Publications

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

IEEE Transactions on Biomedical Engineering
Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles.

Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks

IEEE Transactions on Neural Networks and Learning Systems
In this study, to further boost the peak detection performance along with an elegant computational efficiency, we propose 1D Self-Organized Operational Neural Networks (Self-ONNs) with generative neurons. The experimental results over the China Physiological Signal Challenge-2020 (CPSC) dataset show that the proposed 1D Self-ONNs can significantly surpass the state-of-the-art deep CNN with less computational complexity.

Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network

IEEE Transactions on Biomedical Engineering
In this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model. Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision.

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