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Hey šŸ‘‹, I'm Md Abrar Jahin, goes by Abrar!

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Hi, I'm Md Abrar Jahin, an AI Researcher and Multidisciplinary Scientist šŸš€ from Bangladesh who obtained a B.Sc. Eng. in Industrial & Production Engineering from the Department of Industrial Engineering and Management at Khulna University of Engineering and Technology (KUET). I'm currently working as a Visiting Researcher (2024-Present)) at Okinawa Institute of Science and Technology (OIST) Graduate University in the Physics and Biology Unit. I'm a former Research Intern at OIST (2021-2022) and UiT-The Arctic University of Norway (2021). I also got the opportunity as a Visiting Research Student (2023-2024) at OIST.

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Talking about Personal Stuff:

  • šŸŒ± Iā€™m currently learning Physics-Informed Neural Networks and Conformal Prediction Models;
  • šŸ‘Æ Iā€™m looking forward to collaborating on Machine Learning and Deep Learning-based research projects šŸ¤;
  • šŸ¤” Iā€™m currently a potential candidate for the USA doctoral programs (Fall'25);
  • šŸ’¬ Ask me about anything; I am happy to help;
  • āš”ļø Fun-Fact: Besides programming, I'm a lead guitarist & music composer. I also love painting. This is My YouTube Channel - Abrar Jahin where I upload my guitar covers;
  • šŸ“« How to reach me: Gmail Badge Gmail Badge Gmail Badge

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ā­ļø From Abrar2652

Md Abrar Jahin's Projects

abrar2652.github.io icon abrar2652.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

basics-of-python icon basics-of-python

It contains all important notebooks which will help to learn the basics of Python

cash-flow-forecast-challenge-solution-2020-topcoder icon cash-flow-forecast-challenge-solution-2020-topcoder

Cash Flow Forecasting Challenge held in 2020 on the popular TopCoder hackathon platform. I created my ML forecasting model using Python SciPy libraries. The step-by-step guide from data exploration to analysis has been shown in the notebook.

countdown-timer icon countdown-timer

Google Android Development Challenge 2021 is back again. This is my first-time project submission for the Android Dev Challenge 2nd week task of creating a CountDown App using JetPack Compose with Android Studio Canary Build.

ieee-cis-fraud-detection-project icon ieee-cis-fraud-detection-project

This is the first project to be completed in Upskill ISA Intelligent Machines. The project was done after the end of the competition. The XGBClassifier used in this model obtained 0.950844 public scores on Kaggle.

im-assignments icon im-assignments

Upskill ISA (Income Sharing Agreement) collaborated with Intelligent Machines offering a "Machine Learning Course" where only 39 shortlisted candidates are provided with the opportunity to take this course for free. The assignments of this course completed by me are uploaded in this repository.

male-domestic-abuse icon male-domestic-abuse

This repository contains the EDA and model development codes used in the male domestic violence research

map-of-bangladesh icon map-of-bangladesh

On 16th December, the Bangladeshis celebrate Victory Day as they achieved victory in the war of independence of 1971 on this date. As a tribute, I complied a code visualizing the map of Bangladesh.

msd-nlp icon msd-nlp

This repository contains codes related to natural language processing-based classification of musculoskeletal disorder (MSD) risk factors

naive-bees-classifier icon naive-bees-classifier

A machine learning model after detailed image processing applications that classifies a bee as either honey bee or bumble bee

opencv-cctv-camera-software icon opencv-cctv-camera-software

I've made a project on Python using OpenCV that is CCTV Camera Software. To run this source code on the compiler, firstly make sure that the OpenCV is installed using !pip install OpenCV-python. Then importing cv2, time, win32gui, win32con the codes can be run successfully. After running this code, it'll ask for the intention. Press '1' to check the CCTV camera running background. Then just clicking the 'Esc' button can stop it or 'm' to minimize the window.

pet-adoption---hackerearth-hackathon icon pet-adoption---hackerearth-hackathon

My Machine Learning Model got the accuracy of 87.38717% in the "HackerEarth Machine Learning challenge: Pet Adoption Hackathon" and my position was 401st among 24375 accepted submissions.

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