Twine
Twine

Building brains for machines, beauty for screens, and websites that don’t crash (usually).

Rawshan Etika

Building brains for machines, beauty for screens, and websites that don’t crash (usually).

Available to hire

Building brains for machines, beauty for screens, and websites that don’t crash (usually).

Skills

Experience Level

Adobe Illustrator
Expert
Adobe Photoshop
Expert
CSS
Expert
Flyer
Expert
Web Design
Expert
WordPress
Expert
Product Design
Expert
Microsoft Excel
Expert
AI Data Labelling
Expert
Adobe XD
Expert
React JS
Expert
App Design
Expert
Java
Expert
Flutter
Intermediate
JavaScript
Intermediate
AngularJS
Intermediate
See more

Language

English
Advanced
Bengali
Fluent

Education

Bsc at North south university
January 1, 2020 - December 31, 2024

Qualifications

Add your qualifications or awards here.

Industry Experience

Computers & Electronics, Education, Media & Entertainment, Non-Profit Organization, Software & Internet
    uniE621 Poster for an event
    photo graphicdesigner illustrator branding designer
    uniE613 Corporate Website for Amir Group
    • Developed a custom WordPress theme with animations using Vanilla JavaScript and CSS. • Integrated Google Maps API to display multiple location pins.
    uniE613 IAN Impaction Detection Using Pre-Trained ViT Model
    • Utilized the PVIT model to classify impacted teeth in proximity to the IAN nerve, achieving 95% accuracy (compared to ResNet’s 89%). • Applied Dino V2 for precise segmentation of the IAN nerve and impacted regions. • Fine-tuned pre-trained models for enhanced detection performance on dental imaging datasets.
    uniE613 Web-Based Mental Health Illness Diagnosis with NLP Chatbot
    • Developed a web-based diagnostic system for detecting mental health conditions (depression, suicide, stress) using machine learning models (AdaBoost, SGD, Naive Bayes) achieving over 96% accuracy. • Integrated models using Streamlit and deployed a basic NLP chatbot to provide mental health tips and guidance.
    uniE613 Cataract Eye Disease Classification with Instance Segmentation
    • Processed and annotated a custom cataract dataset with categories for various cataract types (normal, cortical, nuclear, immature, mature) using Roboflow. • Leveraged instance segmentation models (Yolov11, SAM2, Mask R-CNN) to classify cataract types and determine the infected area as a percentage of the total region. • Designed a pipeline for classification and segmentation, integrating novel dataset preparation techniques to improve detection accuracy and performance.