uzairrzahids profilbilde
@uzairrzahid
United Kingdoms flagg Nottingham, United Kingdom
Medlem siden 2. februar 2018
2 Anbefalinger

uzairrzahid

Pålogget Avlogget
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.
$50 USD/hr
108 omtaler
6.4
  • 95%Jobber fullført
  • 85%På budsjett
  • 84%Punktlighet
  • 23%Gjenta ansettelsespris

Porteføljeartikler

Nylige omtaler

Erfaring

Senior Researcher

Qatar University Dec 2019

Working as a researcher in the field of Biomedical Imaging, Signal Processing, Machine Learning and Deep Learning.

Research Assistant

CE FAR LAB Jan 2018

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)

I was involved in development of a portable, remote respiratory and physical activity monitoring system.

Utdannelse

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)

Kvalifikasjoner

Neural Networks and Deep Learning (2018)

Coursera

Publikasjoner

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.

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.

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.

Sertifikasjoner

  • Preferred Freelancer Program SLA
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  • US English Level 1
    75%

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