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Аватарка пользователя
$30 USD / час
Флаг UNITED KINGDOM
nottingham, united kingdom
$30 USD / час
Сейчас здесь 12:18 AM
На сайте с февраля 2, 2018
2 Рекомендации

Muhammad Uzair Z.

@uzairrzahid

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4,9 (106 отзывов(-а))
6,4
6,4
$30 USD / час
Флаг UNITED KINGDOM
nottingham, united kingdom
$30 USD / час
95 %
Завершенных работ
85 %
В рамках бюджета
84 %
Своевременно
23 %
Рейтинг повторного найма

ML | DL | AI | Python | MATLAB

I am a Machine learning and AI developer and data scientist with 5+ years of experience bringing cutting-edge research in machine learning and others. Especially I have interested in neural networks,biomedical imaging and have studied for more than 3 years. I realize complex technical challenges and deliver the best result to client. My passion is truly tech, I love it. I'm not only good at ML/DL/RL, but also web technologies. And also I've 4+ years of experience of solving problems using different programming languages. Hire me so get desired outcomes because " CLINT SATISFACTION IS MY FIRST PRIORITY "
Freelancer Matlab and Mathematica Engineers United Kingdom

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Авторизуйтесь для обсуждения любых деталей в чате.

Элементы портфолио

Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D
I implemented two techniques for auto-FER.

First, I retrained AlexNet, used transfer learning for classification(Transfer Learning).

Second, I used AlexNet for feature extraction and cascaded it with an SVM for classification.

I achieved 93% accuracy with AlexNet and 95% with AlexNet-SVM cascade which is comparable with the contemporary methods that give 96-98%. Data augmentation and training with larger dataset can improve the accuracy with deep learning

I used JAFFE Data Set to train my both models.
Facial Expression Recognition
The program is made with a GUI (graphical user interface) to be clear and easy to use. The images dataset which the search is made on are stored in a the folder “images”, the main GUI is coded in the two files “CBIR.fig” and”CBIR.m” but the process of features extraction is made by the code “Extract_features.m”. First the query image is loaded at this point all the previously mentioned features are extracted from the image then it is shown in the main GUI platform under title “Loaded image”. Then the extracted features are compares to the already saved and processed database where the distance between the query image and all images in the dataset is calculated. Finally the nearest ten images to the query image are shown in the GUI.

More details can be found at my Github account. 
https://github.com/MUzairZahid
Content Based Image Retrieval (MATLAB)
Train a convolutional neural network (ConvNet) for an image classification task and use the trained model for detecting cars.
CNN for Image Classification
Train a convolutional neural network (ConvNet) for an image classification task and use the trained model for detecting cars.
CNN for Image Classification
Train a convolutional neural network (ConvNet) for an image classification task and use the trained model for detecting cars.
CNN for Image Classification
In this project, I implemented an automated algorithm to classify, from a single short ECG lead recording , whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.
Heart Arrhythmia Classification using ECG Signal
In this project, I implemented an automated algorithm to classify, from a single short ECG lead recording , whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.
Heart Arrhythmia Classification using ECG Signal
In this project, I implemented an automated algorithm to classify, from a single short ECG lead recording , whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.
Heart Arrhythmia Classification using ECG Signal
In this project, I implemented an automated algorithm to classify, from a single short ECG lead recording , whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.
Heart Arrhythmia Classification using ECG Signal
In this project, I implemented an automated algorithm to classify, from a single short ECG lead recording , whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.
Heart Arrhythmia Classification using ECG Signal

Отзывы

Изменения сохранены
Показаны с 1 по 5 из 50+ отзывов
Фильтровать отзывы по: 5,0
€170,00 EUR
excellent work as usual
Matlab and Mathematica Algorithm Electrical Engineering Machine Learning (ML)
+ еще 1
Аватарка пользователя
Флаг Zrax H. @marouenkadri24
19 дней назад
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
Аватарка пользователя
Флаг Jacob A. @mintgreenstrat
27 дней назад
5,0
€100,00 EUR
Excellent work. Delivered exactly what I requested quickly and professionally.
Python Keras Deep Learning
Аватарка пользователя
Флаг Zrax H. @marouenkadri24
1 месяц назад
5,0
$80,00 USD
Good task delivery in time and good quality
Python Software Architecture Audio Processing Audio Editing Micropython
K
Флаг Eric L. @KandemirYILDIZ
1 месяц назад
5,0
$55,00 USD
High quality and complete quickly!
Python Data Processing
H
Флаг Tianshu L. @hahaface
1 месяц назад

Опыт работы

Senior Researcher

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

Research Assistant

CE FAR LAB
янв. 2018 - Настоящее время
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)
апр. 2017 - сент. 2017 (5 месяцев, 1 день)
I was involved in development of a portable, remote respiratory and physical activity monitoring system.

Образование

MS Electrical Engineering (Signal Processing and Machine Learning)

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

BS Telecom

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

Квалификация

Neural Networks and Deep Learning

Coursera
2018

Публикации

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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Верификация

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Связано с Facebook

Сертификаты

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Топ-навыки

Matlab and Mathematica 73 Algorithm 54 Machine Learning (ML) 52 Electrical Engineering 49 Data Science 35

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