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$7 USD / hour
Flag of PAKISTAN
sheikhupura, pakistan
$7 USD / hour
It's currently 1:45 PM here
Joined June 6, 2016
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Momna D.

@Momnadar1

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$7 USD / hour
Flag of PAKISTAN
sheikhupura, pakistan
$7 USD / hour
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AI & Machine Learning | Amazon Web Services

With a master's degree in computer science from Information Technology University, I am a software engineer who specializes in cloud computing and machine learning. I have multiple publications and certifications in these domains, showcasing my knowledge and expertise. My most recent work experience was at SHARE Mobility, a company that provides smart and sustainable mobility services. As part of the backend development team, I used Python, AWS, and various frameworks and tools to create and improve the software systems that power the company's products and services. I also collaborated with other engineers to ensure the quality and performance of the software solutions, following the best practices and standards. I enjoy working in a fast-paced and dynamic environment, where I can apply my skills and learn new technologies.

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Experience

Data and Backend Engineer

Apperative Solutions
Nov 2020 - Oct 2021 (11 months, 2 days)
I worked on many mini-projects, but the most challenging was to deploy structure data after scraping from multiple websites using NoSQL-MongoDB. The second most challenging was to write Flask API CRUD operations from scratch for NoSQL data to SQL-DB (MySQL).

Education

MSCS

Information Technology University, Pakistan 2017 - 2020
(3 years)

BSCS

Government College University, Lahore, Pakistan 2013 - 2017
(4 years)

Qualifications

BSCS

GCU, Lahore, Pakistan
2013
Bachelor of Science Computer Science

MSCS

ITU, Lahore Pakistan
2017
Master of Science Computer Science

Publications

Authorship Attribution in Urdu Poetry

ResearchGate
This paper has done its work on the Urdu language dataset. The dataset is collected from five nonsocial websites. Dataset is collected by web scraping. Pre-processing is done by removing characters like punctuation mark. After data pre-processing is completed, machine learning algorithms and neural networks are trained and tested on this dataset. Algorithms used are SVM, NB, (MLP) and pre-trained model word2vec. After comparison of these algorithms, we got the highest accuracy of 82.85% of SVM.

Hatred content Detection in Wikipedia-Talk Corpus using IBM-Cloud

ResearchGate
With the help of big data analytics and machine learning algorithms we developed a system on IBM Cloud to check whether a text in comment, tweet or post posted on social media contains positive or negative content. We used Wikipedia-Talk corpus dataset in our project. We created a data warehouse we can efficiently store this data and we also preformed sentiment analysis to see if a comment contain positivity or negativity. We also trained an SVC model to detect hated comments with 94% precision

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