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ULAB Co-Curricular App
Research AssitantJan 2017
Currently I am working as a Deep Learning research assistant under Dr. Nabeel Mohammed. Currently I am working with following technologies: 1. Keras, Tensorflow, Theano as Deep Learning Library 2. Scrapping data from websites 3. Python to write scripts 4. Django Framework with Restful API 5. Java and Ionic Framework for Android and iOS App
Web Application DeveloperSep 2014 - Dec 2015 (1 year)
I worked about one and a half year in STS as a Web Application Developer. While I was in STS, I worked with these following technologies: 1. Wordpress Theme, Plugin Development and Customisation 2. UI/UX Design 3. PHP Symfony and Laravel Framework with Restful API 4. Testing and Debugging Web and Mobile Applications 5. Iionic Framework for Android and iOS Applications I also learned to manage CPanel and configuring linux based VPS while working on my projects.
BSc2013 - 2017 (4 years)
National Hackathon (2016)ICT Division, Bangladesh
Participants will work for 36 hours at a stretch to find out new solutions to the challenges and drawbacks through technology to achieve the 10 major SDGs. The goals are agricultural productivity, newborns and children, violence against women, sustainable tourism, qualified teachers in education, road accidents, marine resources, environment in the city, energy efficiency and corruption. I worked for the solution to energy efficiency and created an intelligent home automation system.
Champion, Project Showcase in CSE FEST 2016 (2016)University of Liberal Arts Bangladesh
Project: Asset Tracking System Architecture: CQRS Tech Spec: NodeJS, MongoDB, Parse Server For more:
2nd Runner-up, Project Showcase in CSE FEST 2016 (2016)University of Liberal Arts Bangladesh
Project: Smart Home Automate Device: Raspberry Pi2 (Using GPIO) Language: Python (Flask framework) Database: Sqlite3 For more:
Localizing, Detecting and Extracting areas of interest from full-page document
In this paper, I present a system to locate, detect and extract the area of interest from full-page document using Deep Learning. The proposed system builds a pipeline of following three models: (1) a CRN(Convolutional Regression Network) to localize the interested area, (2) a Binary Classification layer to detect if the localized area contains our interested content or not, (3) finally an CNN-LSTM combined OCR(Optical Character Recognition) model to extract the content from localized area.