I find pleasure working in technological fields of Computer Science where solving problem with the ongoing needs is the main concern. I would like to work with an organization that will allow me to apply all my skills and experience.
National level competition named MUJIB BORSHO IT CARNIVAL 2020 organized by Dhaka International University.
Intra university programming contest of BRAC University organized by BUCC.
Software Engineer
PIPELINE Bangladesh Ltd.
Primary responsibility is to write clean and maintainable code in Golang, Python, JavaScript, TypeScript etc and write efficient SQL and NoSQL queries for software systems and big data manipulation.
Junior Software Engineer
Tirzok Private Limited
Worked with Odoo, opensource ERP. Maintained odoo with other microservises to prepare a software pipline to provide effective ERP solutions with different use cases.
Junior Software Engineer
Field Information Solutions Ltd(Field Buzz)
Smartphone-based software company for tracking operations in the last mile in emerging markets, connecting low-income and marginalized communities. My role was to write suitable codes to make these taks easy and user friendly.
BSc. Computer Science Engineering
BRAC University
Higher Secondary School Certificate
Shyampur Model School and College
Secondary School Certificate
Shyampur Model School and College
Junior School Certificate
Shyampur Model School and College
• Online food ordering system.
• Build Web version of this system with API.
• Worked with Django, SQLite and REST API.
• Sign up app for Brac University Computer Club.
• It was used in club fair.
• Worked with Tkinter, Pillow, Openpyxl, and Pyinstaller.
• A bot that can identify programming related problems from conversation and suggest related stackoverflow links. • Worked with ChatterBot, Numpy, Pandas, Sci-kit Learn and NLP.
Abstract: Crime rate is increasing proportionally with the increasing rate of the population. The most prominent approach was to introduce Closed-Circuit Television (CCTV) camera- based surveillance to tackle the issue. Video surveillance cameras have added a new dimension to detect crime. Several research works on autonomous security camera surveillance are currently ongoing, where the fundamental goal is to discover violent activity from video feeds. From the technical viewpoint, this is a challenging problem because analyzing a set of frames, i.e., videos in temporal dimension to detect violence might need careful machine learning model training to reduce false results. This research focuses on this problem by integrating state-of- the-art Deep Learning methods to ensure a robust pipeline for autonomous surveillance for detecting violent activities, e.g., kicking, punching, and slapping. Initially, we designed a dataset of this specific interest, which contains 600 videos (200 for each action). Later, we have utilized existing pre-trained model archi- tectures to extract features, and later used deep learning network for classification. Also, We have classified our models’ accuracy, and confusion matrix on different pre-trained architectures like VGG16, InceptionV3, ResNet50, Xception and MobileNet V2 among which VGG16 and MobileNet V2 performed better.
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